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'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
_UpperCamelC... | 9 |
'''simple docstring'''
from manim import *
class UpperCamelCase__ (a ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = Rectangle(height=0.5 ,width=0.5 )
lowerCamelCase__ = Rectangle(height=0.46 ,... | 9 | 1 |
'''simple docstring'''
UpperCamelCase : Dict = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import ... | 9 |
'''simple docstring'''
UpperCamelCase : Tuple = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits... | 9 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pi... | 9 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : List[str] = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resol... | 9 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : List[Any] = logging.get_logger(__name__)... | 9 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase : Optional[Any] = ['small', 'medium', 'large']
UpperCamelCase : Dict = 'lm_head.decoder.weight'
UpperCamelCase : int = 'lm_head.weight'
def ... | 9 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax... | 9 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_... | 9 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : str = logging.get_logger(__name__)
UpperCamelCase : int = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config... | 9 |
'''simple docstring'''
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 9 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
UpperCamelCase : List[str] = logging.get_logger(__name__)
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,*_lower... | 9 |
'''simple docstring'''
import numpy
# List of input, output pairs
UpperCamelCase : List[Any] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCamelCase : Optional[int] = (((5_15, 22, 13), 5_55), ((61, 35, 49... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int ):
if exponent == 1:
return base
if exponent % 2 == 0:
lowerCamelCase__ = _modexpt(__lowerCAmelCase , exponent // ... | 9 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[str] ):
lowerCamelCase__ = ... | 9 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Tuple = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 9 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase : list[int] = [ord(l... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = [[0 for _ in range(__lowerCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCamelCase__ = 1
for n in range(m + 1 ):
... | 9 |
'''simple docstring'''
import argparse
import struct
import unittest
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ):
lowerCamelCase__ = data
# Initialize hash values
lowerCamelCase__ = [
... | 9 | 1 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
... | 9 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A__ ( __lowerCAmelCase : Union[str, Any] ):
lowerCamelCase__ = [
"""encoder.version""",
"""decoder.vers... | 9 | 1 |
'''simple docstring'''
UpperCamelCase : Tuple = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits... | 9 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoPro... | 9 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
def A__ ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Tuple ):
... | 9 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def A__ ( ... | 9 | 1 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from... | 9 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.proces... | 9 | 1 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, Truncatio... | 9 |
'''simple docstring'''
from __future__ import annotations
import math
def A__ ( __lowerCAmelCase : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all e... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
if number > 0:
raise ValueError("""input must be a negative integer""" )
lowerCamelCase__ = len(bin(__lowerCAmelCase )[3:] )
lowerCamelCase__ = bin(abs(__lowerCAmelC... | 9 |
'''simple docstring'''
def A__ ( ):
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(__lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F... | 9 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProces... | 9 |
'''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, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : int = logging.get_logger(__name__)
... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__lowerCAmelCase , n - 1 , __low... | 9 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : int ... | 9 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import Config... | 9 |
'''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, Ten... | 9 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoMod... | 9 |
'''simple docstring'''
from math import factorial
UpperCamelCase : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def A__ ( __lowerCAmelCase : int ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeErro... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
def A__ ( __lowerCAmelCase : list[list[int]] ):
lowerCamelCase__ = len(__lowerCAmelCase )
# We need to create solution object to save path.
lowerCamelCase__ = [[0 for _ in range(__lowerCAm... | 9 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCamelCase : Optional[Any] = 'src/diffusers'
# Matches is_xxx_available()
UpperCamelCase ... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
if length <= 0 or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("""Length must be a positive integer.""" )
return [n * (2 * n - 1) for n in range(__lowerCAmelCase ... | 9 |
'''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 : List[Any] = get_tests_dir('fi... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
UpperCamelCase : int = 'https://api.github.com'
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCamelCase : Optional[An... | 9 |
'''simple docstring'''
from manim import *
class UpperCamelCase__ (a ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = Rectangle(height=0.5 ,width=0.5 )
lowerCamelCase__ = Rectangle(height=0.46 ,... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
def A__ ( __lowerCAmelCase : list[int] , __lowerCAmelCase : int ):
lowerCamelCase__ = 0
lowerCamelCase__ = len(__lowerCAmelCase ) - 1
while i < j:
if nums[i] + nums[j]... | 9 |
'''simple docstring'''
UpperCamelCase : Tuple = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits... | 9 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformer... | 9 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : List[str] = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resol... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = abs(__lowerCAmelCase )
lowerCamelCase__ = 0
while n > 0:
res += n % 10
n //= 10
return res
def A__ ( __lowerCAmelCase : int ... | 9 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase : Optional[Any] = ['small', 'medium', 'large']
UpperCamelCase : Dict = 'lm_head.decoder.weight'
UpperCamelCase : int = 'lm_head.weight'
def ... | 9 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diff... | 9 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision... | 9 |
'''simple docstring'''
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 9 | 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 : ... | 9 |
'''simple docstring'''
import numpy
# List of input, output pairs
UpperCamelCase : List[Any] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCamelCase : Optional[int] = (((5_15, 22, 13), 5_55), ((61, 35, 49... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 9 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[str] ):
lowerCamelCase__ = ... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_... | 9 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase : list[int] = [ord(l... | 9 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase : Union[str, Any] = {
'configuration_convnext': ['CONVNEXT_PRETRAIN... | 9 |
'''simple docstring'''
import argparse
import struct
import unittest
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ):
lowerCamelCase__ = data
# Initialize hash values
lowerCamelCase__ = [
... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
lowerCamelCase__ = 0
while number:
... | 9 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A__ ( __lowerCAmelCase : Union[str, Any] ):
lowerCamelCase__ = [
"""encoder.version""",
"""decoder.vers... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Dict ):
lowerCamelCase__ = 0
lowerCamelCase__ = len(__lowerCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
... | 9 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoPro... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
_UpperCamelCase = 42
_UpperCamelCase = 42
... | 9 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def A__ ( ... | 9 | 1 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_av... | 9 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.proces... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def A__ ( __lowerCAmelCase : list ):
if not postfix_notation:
return 0
lowerCamelCase__ = {"""+""", """-""", """*""", """/"""}
lowerCamelCase__ = []
... | 9 |
'''simple docstring'''
from __future__ import annotations
import math
def A__ ( __lowerCAmelCase : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all e... | 9 | 1 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
UpperCamelCase : str = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def A__ ( ):
lowerCamelCase__ = os.path.dirname(os.path.realpath(__lowerCAmelCase ) )
lo... | 9 |
'''simple docstring'''
def A__ ( ):
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(__lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F... | 9 | 1 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class UpperCamelCase__ :
'''simple docstring'''
... | 9 |
'''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, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : int = logging.get_logger(__name__)
... | 9 | 1 |
'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import... | 9 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : int ... | 9 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
... | 9 |
'''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, Ten... | 9 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase : int = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
... | 9 |
'''simple docstring'''
from math import factorial
UpperCamelCase : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def A__ ( __lowerCAmelCase : int ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeErro... | 9 | 1 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
UpperCamelCase : Dict = ''
UpperCamelCase : Optional[Any] = ''
UpperCamelCase : int = ''
UpperCamelCase : Optional[int] = ''
def A__ ( __lowerCAmelCase : str )... | 9 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCamelCase : Optional[Any] = 'src/diffusers'
# Matches is_xxx_available()
UpperCamelCase ... | 9 | 1 |
'''simple docstring'''
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase : List[Any] = {
'facebook/mask2former-swin-small-coco-instance': (
'https... | 9 |
'''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 : List[Any] = get_tests_dir('fi... | 9 | 1 |
'''simple docstring'''
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transforme... | 9 |
'''simple docstring'''
from manim import *
class UpperCamelCase__ (a ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = Rectangle(height=0.5 ,width=0.5 )
lowerCamelCase__ = Rectangle(height=0.46 ,... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def A__ ( __lowerCAmelCase : float , __lowerCAmelCase : float , __lowerCAmelCase : float ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError... | 9 |
'''simple docstring'''
UpperCamelCase : Tuple = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits... | 9 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : List[str] = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resol... | 9 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : List[str] = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resol... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
def A__ ( __lowerCAmelCase : list[int] , __lowerCAmelCase : list[int] , __lowerCAmelCase : int ):
lowerCamelCase__ = list(range(len(__lowerCAmelCase ) ) )
lowerCamelCase__ ... | 9 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase : Optional[Any] = ['small', 'medium', 'large']
UpperCamelCase : Dict = 'lm_head.decoder.weight'
UpperCamelCase : int = 'lm_head.weight'
def ... | 9 | 1 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
UpperCamelCase : Optional[Any] = 'src/transformers'
# Matches is_xxx_available()
UpperCamelCase : int = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
... | 9 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_... | 9 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def A__ ( __lowerCAmelCase : dict , __lowerCAmelCase : str , __lowerCAmelCase : set , __lowerCAmelCase : set , __lowerCAmelCase : dict , __lowerCAmelCase : ... | 9 |
'''simple docstring'''
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 9 | 1 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transforme... | 9 |
'''simple docstring'''
import numpy
# List of input, output pairs
UpperCamelCase : List[Any] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCamelCase : Optional[int] = (((5_15, 22, 13), 5_55), ((61, 35, 49... | 9 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxC... | 9 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[str] ):
lowerCamelCase__ = ... | 9 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing... | 9 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase : list[int] = [ord(l... | 9 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnal... | 9 |
'''simple docstring'''
import argparse
import struct
import unittest
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ):
lowerCamelCase__ = data
# Initialize hash values
lowerCamelCase__ = [
... | 9 | 1 |
'''simple docstring'''
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 9 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A__ ( __lowerCAmelCase : Union[str, Any] ):
lowerCamelCase__ = [
"""encoder.version""",
"""decoder.vers... | 9 | 1 |
'''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, Ten... | 9 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoPro... | 9 | 1 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCamelCase : Dict = '__DUMMY_TRANSFORMERS_USER__'
UpperCamelCase : Tuple = 'Dummy User'
UpperC... | 9 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def A__ ( ... | 9 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__ (metaclass=a ):
'''simple docstring'''
_UpperCamelCase = ['flax']
def __init__( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(se... | 9 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.proces... | 9 | 1 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.proces... | 9 |
'''simple docstring'''
from __future__ import annotations
import math
def A__ ( __lowerCAmelCase : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all e... | 9 | 1 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCa... | 9 |
'''simple docstring'''
def A__ ( ):
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(__lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F... | 9 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
UpperCamelCase : Dict = True
except (ImportError, ModuleNotFoundError):
UpperCamelCase : Dict = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
... | 9 |
'''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, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : int = logging.get_logger(__name__)
... | 9 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_v... | 9 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : int ... | 9 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
... | 9 |
'''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, Ten... | 9 | 1 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
def... | 9 |
'''simple docstring'''
from math import factorial
UpperCamelCase : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def A__ ( __lowerCAmelCase : int ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeErro... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 9 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCamelCase : Optional[Any] = 'src/diffusers'
# Matches is_xxx_available()
UpperCamelCase ... | 9 | 1 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.conf... | 9 |
'''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 : List[Any] = get_tests_dir('fi... | 9 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under... | 9 |
'''simple docstring'''
from manim import *
class UpperCamelCase__ (a ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = Rectangle(height=0.5 ,width=0.5 )
lowerCamelCase__ = Rectangle(height=0.46 ,... | 9 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
UpperCamelCase : Optional[int] = 'docs/source/en/_toctree.yml'
def A__ ( __lowerCAmelCase : Union[str, Any] ):
lowerCamelCase__ = defaultdict(__lowerCAmelCase )
... | 9 |
'''simple docstring'''
UpperCamelCase : Tuple = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits... | 9 | 1 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from... | 9 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : List[str] = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resol... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCAmelCase ... | 9 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase : Optional[Any] = ['small', 'medium', 'large']
UpperCamelCase : Dict = 'lm_head.decoder.weight'
UpperCamelCase : int = 'lm_head.weight'
def ... | 9 | 1 |
'''simple docstring'''
import numpy
# List of input, output pairs
UpperCamelCase : List[Any] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCamelCase : Optional[int] = (((5_15, 22, 13), 5_55), ((61, 35, 49... | 9 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_... | 9 | 1 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | 9 |
'''simple docstring'''
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 9 | 1 |
'''simple docstring'''
from math import factorial
UpperCamelCase : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def A__ ( __lowerCAmelCase : int ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeErro... | 9 |
'''simple docstring'''
import numpy
# List of input, output pairs
UpperCamelCase : List[Any] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCamelCase : Optional[int] = (((5_15, 22, 13), 5_55), ((61, 35, 49... | 9 | 1 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def A__ ( *__lowerCAmelCase : int ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowerCamelCase__ = l... | 9 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[str] ):
lowerCamelCase__ = ... | 9 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : str ):
if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
raise ValueError("""String lengths must match!""" )
lowerCamelCase__ = 0
for chara, ch... | 9 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase : list[int] = [ord(l... | 9 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 9 |
'''simple docstring'''
import argparse
import struct
import unittest
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ):
lowerCamelCase__ = data
# Initialize hash values
lowerCamelCase__ = [
... | 9 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test... | 9 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A__ ( __lowerCAmelCase : Union[str, Any] ):
lowerCamelCase__ = [
"""encoder.version""",
"""decoder.vers... | 9 | 1 |
'''simple docstring'''
import unittest
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 ImageProcessingSavingTestMixin
if is_torch_available():
... | 9 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoPro... | 9 | 1 |
'''simple docstring'''
import cva
import numpy as np
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase ):
if k in (0.04, 0.06):
lowerCamelCase__ = k
lowerCamelCase__ ... | 9 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def A__ ( ... | 9 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase : Any = logging.getLogger(__name__)
class UpperCamelCase__ (_lowerCamelCase ):
'''simple docstring'''
... | 700 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.proces... | 9 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ... | 701 |
'''simple docstring'''
from __future__ import annotations
import math
def A__ ( __lowerCAmelCase : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all e... | 9 | 0 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@... | 702 |
'''simple docstring'''
def A__ ( ):
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(__lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F... | 9 | 0 |
'''simple docstring'''
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformer... | 703 |
'''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, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : int = logging.get_logger(__name__)
... | 9 | 0 |
'''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_... | 704 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : int ... | 9 | 0 |
'''simple docstring'''
from math import factorial
UpperCamelCase : Tuple = {str(d): factorial(d) for d in range(10)}
def A__ ( __lowerCAmelCase : int ):
return sum(DIGIT_FACTORIAL[d] for d in str(_A ) )
def A__ ( ):
lowerCamelCase__ ... | 705 |
'''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, Ten... | 9 | 0 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNe... | 706 |
'''simple docstring'''
from math import factorial
UpperCamelCase : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def A__ ( __lowerCAmelCase : int ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeErro... | 9 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Optional[int] = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise Op... | 707 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCamelCase : Optional[Any] = 'src/diffusers'
# Matches is_xxx_available()
UpperCamelCase ... | 9 | 0 |
'''simple docstring'''
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : List[Any] =... | 708 |
'''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 : List[Any] = get_tests_dir('fi... | 9 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProces... | 709 |
'''simple docstring'''
from manim import *
class UpperCamelCase__ (a ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = Rectangle(height=0.5 ,width=0.5 )
lowerCamelCase__ = Rectangle(height=0.46 ,... | 9 | 0 |
'''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
Uppe... | 710 |
'''simple docstring'''
UpperCamelCase : Tuple = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits... | 9 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCamelCase__ (UpperCamelCase_ ):
def UpperCamelCase_ ( self ):
return [
{"col_1": 3, "col_2": "a"},
... | 711 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : List[str] = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resol... | 9 | 0 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCamelCase__ ... | 712 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase : Optional[Any] = ['small', 'medium', 'large']
UpperCamelCase : Dict = 'lm_head.decoder.weight'
UpperCamelCase : int = 'lm_head.weight'
def ... | 9 | 0 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : Any ):
lowerCamelCase__ = [0] * len(lowerCAmelCase__ )
lowerCamelCase__ = []
lowerCamelCase__ = [1] * len(lowerCAmelCase__ )
for values in graph.values():
for i ... | 713 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_... | 9 | 0 |
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_transformers.convert_swi... | 714 |
'''simple docstring'''
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 9 | 0 |
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def A__ ( __lowerCAmelCase : Optional[Any] ):
lowerCamelCase__ = 0
while number > 0:
... | 715 |
'''simple docstring'''
import numpy
# List of input, output pairs
UpperCamelCase : List[Any] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCamelCase : Optional[int] = (((5_15, 22, 13), 5_55), ((61, 35, 49... | 9 | 0 |
'''simple docstring'''
import datasets
UpperCamelCase : Any = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n ... | 716 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[str] ):
lowerCamelCase__ = ... | 9 | 0 |
def A__ ( __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 1000 ):
lowerCamelCase__ = 1
lowerCamelCase__ = 0
for divide_by_number in range(a_ , digit + 1 ):
lowerCamelCase__ = []
lowerCamelCase__ ... | 717 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase : list[int] = [ord(l... | 9 | 0 |
'''simple docstring'''
UpperCamelCase : List[Any] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transform... | 718 |
'''simple docstring'''
import argparse
import struct
import unittest
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ):
lowerCamelCase__ = data
# Initialize hash values
lowerCamelCase__ = [
... | 9 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision,... | 719 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A__ ( __lowerCAmelCase : Union[str, Any] ):
lowerCamelCase__ = [
"""encoder.version""",
"""decoder.vers... | 9 | 0 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : float , __lowerCAmelCase : float , __lowerCAmelCase : float , __lowerCAmelCase : float , __lowerCAmelCase : float , ):
lowerCamelCase__ = [redshift, radiation_density, matter_density, dark... | 720 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoPro... | 9 | 0 |
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