code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_... | 11 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase ... | 11 | 1 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSaving... | 11 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Optional[Any] = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
]
}
t... | 11 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa... | 11 | 1 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A_ ( _lowerCAmelCase : ... | 11 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
... | 11 | 1 |
'''simple docstring'''
from __future__ import annotations
UpperCAmelCase_ : Optional[Any] = tuple[int, int, int]
UpperCAmelCase_ : str = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
UpperCAmelCase_ : List[str] = '... | 11 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : int,__A : Any=None,**__A : O... | 11 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
return 1 if input_a == input_a else 0
def A_ ( ):
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert xnor... | 11 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 11 | 1 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgume... | 11 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : float ):
"""simple docstring"""
return 10 - x * x
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
if equation(_lowerCAmelCase ) *... | 11 | 1 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class UpperCAmelCase__ ( nn.Module ):
def __init__( self : Tuple,__A : int = 1_6,__A : int = 8_8,__A : ... | 11 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fro... | 11 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stabl... | 11 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPIN... | 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : Optional[int] = {
'configuration_efficientformer': [
'EFFICI... | 11 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tok... | 11 | 1 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_att... | 11 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_av... | 11 | 1 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
UpperCAmelCase_ : List[Any] = [
# tf -> hf
('/', '.'),
('layer_', 'layers... | 11 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : str = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase__ ... | 11 | 1 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Thre... | 11 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ : Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 11 | 1 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase_ : List[Any] = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burn... | 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 11 | 1 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
UpperCAmelCase_ : Optional[Any] = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thiri... | 11 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_t... | 11 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTo... | 11 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int = 1000000 ):
"""simple docstring"""
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 11 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : int = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[int] = {
'ut/deta': 'https://huggingface.co... | 11 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCAmelCase , _lowe... | 11 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
U... | 11 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import H... | 11 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : str = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase__ ... | 11 |
'''simple docstring'''
import random
from typing import Any
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
for _ in range(len(_lowerCAmelCase ) ):
_lowerCamelCase : Any = random.randint(0 , len(_lowerCAmelCase ... | 11 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not..... | 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 11 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : int ):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(_lowerCAmelCase ) , _lowerCAmelCase )
return number - int(_lowerCAmelCase )... | 11 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Union[str, Any] = ... | 11 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class UpperCAmelCase__ :
lowerCAmelCase_ = None
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ =... | 11 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A_ ( _lowerCAmelCase : ... | 11 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : Any = logging.ge... | 11 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase ... | 11 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 11 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 11 | 1 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase__ ( A ):
lowerC... | 11 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa... | 11 | 1 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase_ : Any = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membershi... | 11 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
... | 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : List[Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'token... | 11 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : int,__A : Any=None,**__A : O... | 11 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCAmelCase_ : int = ... | 11 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 11 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 11 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : float ):
"""simple docstring"""
return 10 - x * x
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
if equation(_lowerCAmelCase ) *... | 11 | 1 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ... | 11 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fro... | 11 | 1 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _lowerCAm... | 11 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPIN... | 11 | 1 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
for i in range(0 , _lowerCAmelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
... | 11 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tok... | 11 | 1 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1337 , num_examples=42 , dataset_... | 11 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_av... | 11 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCAmelCase_ : int = TypeVar('T')
UpperCAmelCase_ : Any = TypeVar('U')
class UpperCAmelCase__ ( Generic[T, U] ):
def __init__... | 11 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : str = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase__ ... | 11 | 1 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_ : str = get_tests_dir('fixtures/te... | 11 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ : Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 11 | 1 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def A_ ( _lowerCAmelCase... | 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 11 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if... | 11 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_t... | 11 | 1 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ : Optional[int] = 'src/transformers'
# Matches is_xxx_available()
UpperCAmelCase_ : Optional[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_stru... | 11 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int = 1000000 ):
"""simple docstring"""
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 11 | 1 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_... | 11 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCAmelCase , _lowe... | 11 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def A_ ( _lowerCAmelCase : Sequence[int] | None = None ):
"""simple docstring"""
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
_lowerCamelCase ... | 11 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import H... | 11 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
UpperCAmelCase_ : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # no... | 11 |
'''simple docstring'''
import random
from typing import Any
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
for _ in range(len(_lowerCAmelCase ) ):
_lowerCamelCase : Any = random.randint(0 , len(_lowerCAmelCase ... | 11 | 1 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCAmelCase__ ( A ):
def lowerCamelCase_ ( self : List[Any],__A : str ):
with open(__A,encoding="utf-8" ) as inp... | 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 11 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(A ) , 'Tat... | 11 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Union[str, Any] = ... | 11 | 1 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Union[str, Any] = ... | 11 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A_ ( _lowerCAmelCase : ... | 11 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 11 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase ... | 11 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCAmelCase , _lowe... | 11 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 11 | 1 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
... | 11 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa... | 11 | 1 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'vocab_file'... | 11 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
... | 11 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = (DDPMScheduler,)
def lowerCamelCase_ ( self : str,**__A : List[str] ):
... | 11 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : int,__A : Any=None,**__A : O... | 11 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
UpperCAmelC... | 11 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 11 | 1 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == n... | 11 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : float ):
"""simple docstring"""
return 10 - x * x
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
if equation(_lowerCAmelCase ) *... | 11 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 1000 ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = 1
_lowerCamelCase : Dict = 0
for divide_by_number in range(_lowerCAmelCase ... | 11 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fro... | 11 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCAmelCase__ :
def __init__( self : Tuple,__A : int ):
_lowerCamelCase : Union[str, Any] = num_of_nodes
_lowerCamelCase : list[list[int]] ... | 11 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPIN... | 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ : Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 11 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tok... | 11 | 1 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int = 1000000 ):
"""simple docstring"""
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 11 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_av... | 11 | 1 |
'''simple docstring'''
import random
def A_ ( _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : bool = False ):
"""simple docstring"""
_lowerCamelCase : dict = {i: [] for i in range(_lowerCAmelCase )}
# if p... | 11 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : str = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase__ ... | 11 | 1 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
if len(_lowerCAmelCase ) == 0:
return []
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = min(_lowerCAm... | 11 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ : Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 11 | 1 |
'''simple docstring'''
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
fro... | 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : Tuple = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 11 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_t... | 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : str = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenizatio... | 700 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int = 1000000 ):
"""simple docstring"""
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 11 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction... | 701 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCAmelCase , _lowe... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
UpperCAmelCase_ : List[Any] = 100
UpperCAmelCase_ : Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase_ : int
for prime in range(3, ceil(NUM_PRI... | 702 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import H... | 11 | 0 |
'''simple docstring'''
import numpy as np
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_lowerCamelCase : in... | 703 |
'''simple docstring'''
import random
from typing import Any
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
for _ in range(len(_lowerCAmelCase ) ):
_lowerCamelCase : Any = random.randint(0 , len(_lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : List[Any] = min(_lowercase ) # min() finds the minimum value
_lowerCamelCase : List[str] = max(_lowercase ) # max() finds the... | 704 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 11 | 0 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
) | 705 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Union[str, Any] = ... | 11 | 0 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_av... | 706 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A_ ( _lowerCAmelCase : ... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def A_ ( _lowerCAmelCa... | 707 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
... | 708 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 11 | 0 |
import os
import string
import sys
UpperCAmelCase_ : Optional[int] = 1 << 8
UpperCAmelCase_ : Any = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'left': 68 + ARRO... | 709 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(lowerCamelCase__ ):
for j in... | 710 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
if not nums:
raise ValueError("List is empty" )
return sum(__lowerCAmelCase ) / len(__lowerCAmelCase )
if __name__... | 711 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : int,__A : Any=None,**__A : O... | 11 | 0 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCA... | 712 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 11 | 0 |
'''simple docstring'''
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
UpperCAmelCase_ : List[Any] = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'atten... | 713 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : float ):
"""simple docstring"""
return 10 - x * x
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
if equation(_lowerCAmelCase ) *... | 11 | 0 |
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = [0, 2, 4, 6, 8]
UpperCAmelCase_ : Tuple = [1, 3, 5, 7, 9]
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAm... | 714 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fro... | 11 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] ... | 715 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPIN... | 11 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
fr... | 716 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tok... | 11 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
UpperCAmelCase_ : Optional[... | 717 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_av... | 11 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : List[Any] = logging.get_... | 718 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : str = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase__ ... | 11 | 0 |
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def A_ (... | 719 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ : Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 11 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArg... | 720 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 11 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class UpperCAmelCase__ ( unittest.TestCase ):
def lowerCamelCas... | 721 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_t... | 11 | 0 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECK... | 700 |
'''simple docstring'''
from math import sqrt
def A_ ( _lowerCAmelCase : int = 1000000 ):
"""simple docstring"""
_lowerCamelCase : int = 0
_lowerCamelCase : int = 0
_lowerCamelCase : int
while num_cuboids <= limit:
... | 11 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
UpperCAmelCase_ ... | 701 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCAmelCase , _lowe... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : list[list[str]] = [[] for _ in range(snake_case_ )]
_lowerCamelCase : Optional[int] = key - 1
if k... | 702 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import H... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
UpperCAmelCase_ : Tuple = 100
UpperCAmelCase_ : Optional[int] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase_ : int
for prime in range(3, ceil(N... | 703 |
'''simple docstring'''
import random
from typing import Any
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
for _ in range(len(_lowerCAmelCase ) ):
_lowerCamelCase : Any = random.randint(0 , len(_lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCAmelCase_ : int = logging.get_logger(__name__)
class UpperCAmelCase__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self : Tu... | 704 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 11 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( __a ):
def __init__( self : int,*__A : Dict,**... | 705 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : Union[str, Any] = ... | 11 | 0 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMult... | 706 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A_ ( _lowerCAmelCase : ... | 11 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int = 10 , _lowerCAmelCase : int = 22 ):
"""simple docstring"""
_lowerCamelCase : List[Any] = range(1 , UpperCAmelCase__ )
_lowerCamelCase : Tuple = range(1 , UpperCA... | 707 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def A_ ( _lowerCAmelCase ... | 11 | 0 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def A_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[str] , _... | 708 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A_ ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
... | 11 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 709 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa... | 11 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__UpperCAmelCase )
class UpperCAmelCase__ ( __UpperCAmelCase ):
lowerCAmelCase_ ... | 710 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
... | 11 | 0 |
'''simple docstring'''
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathl... | 711 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : int,__A : Any=None,**__A : O... | 11 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=A )
class UpperCAmelCase__ ( A ):
# `task` is not a ClassVar since we want it to be p... | 712 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 11 | 0 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class UpperCAmelCase__ ... | 713 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : float ):
"""simple docstring"""
return 10 - x * x
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
if equation(_lowerCAmelCase ) *... | 11 | 0 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base im... | 714 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fro... | 11 | 0 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
UpperCAmelCase_ : Dict = logging.getLogger(__name__)
if i... | 715 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPIN... | 11 | 0 |
'''simple docstring'''
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class UpperCAmelCase__ ( UpperCamelCase__ ):
lowerCAmelCase_ = """facebook/bart-large-mnli"""
lowerCAmelCase_ = (
"""This is ... | 716 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tok... | 11 | 0 |
'''simple docstring'''
from itertools import count
def A_ ( _lowerCAmelCase : Optional[int] = 50 ):
"""simple docstring"""
_lowerCamelCase : int = [1] * min_block_length
for n in count(__lowerCAmelCase ):
fill_count_functions... | 717 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_av... | 11 | 0 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
UpperCAmelCase_ : Dict = get_logger(__name__)
class UpperCAmelCase__ :
def __init__( self : Tuple,__A : Optional[Any],__A : Dict=None ):
_lowe... | 718 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : str = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCAmelCase__ ... | 11 | 0 |
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