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 |
|---|---|---|---|---|
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __magic_name__ ( A : List[Any] ):
'''simple docstring'''
a = {}
a = job["started_at"]
a = job["completed_at"]
a = date_parser.p... | 662 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 662 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : List[Any] = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:
if no... | 662 |
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 .tokenization_big_bird impor... | 662 | 1 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
c... | 662 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__lowerCAmelCase : List[Any] = logging.getLogger(__name__)
def __magic_name__ ( ):
'''simple docstring'''
a = argparse.ArgumentParser(
... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : str = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class snake_case__ (_UpperCamelCase ):
... | 662 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __magic_name__ ( A : List[str] ):
'''simple docstring'''
a = {}
a = tokeni... | 662 | 1 |
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 version.parse(fairseq.__... | 662 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 662 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def __magic_name__ ( A : List[str], A : Tuple ):
'''simple docstring'''
a = Mock()
a = conn, Mock()
... | 662 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase : Any = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTok... | 662 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
__lowerCAmelCase : Dict = logging.get_logger(__name__)
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
def __init__( self : Dict , *__lowerCa... | 662 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_c... | 662 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_uti... | 662 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__lowerCAmelCase : int = {'tokenization_tapex': ['TapexTokenizer']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()['__fi... | 662 | 1 |
from heapq import heappop, heappush
import numpy as np
def __magic_name__ ( A : np.ndarray, A : tuple[int, int], A : tuple[int, int], A : bool, ):
'''simple docstring'''
a , a = grid.shape
a = [-1, 1, 0, 0]
a =... | 662 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : Dict = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlipConfig... | 662 | 1 |
from __future__ import annotations
def __magic_name__ ( A : list, A : int, A : int, A : int ):
'''simple docstring'''
a = []
a , a = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.... | 662 |
import math
import flax.linen as nn
import jax.numpy as jnp
def __magic_name__ ( A : jnp.ndarray, A : int, A : float = 1, A : float = 1, A : float = 1.0E4, A : bool = False, A : float = 1.0, ):
'''simple docstring'''
... | 662 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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_torch_available():
i... | 662 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils imp... | 662 | 1 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__lowerCAmelCase : Optional[Any] = '\\n\n'
__lowerCAmelCase : Optional[Any] = '\nPerplexity (PPL) is one of the mos... | 662 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acce... | 662 | 1 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__lowerCAmelCase : Optional[int] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__lowerCAmelCase : Optional[int] = typing.Union[np.floataa, int, float] # noqa:... | 662 |
# 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 git won't be considered
# since... | 662 | 1 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import B... | 662 |
def __magic_name__ ( A : int, A : int, A : int ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
a = _modexpt(A, exponent // 2, A ) % modulo_value
return (x * x) % modulo_value
else... | 662 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
impor... | 662 |
def __magic_name__ ( A : str, A : str ):
'''simple docstring'''
def get_matched_characters(A : str, A : str ) -> str:
a = []
a = min(len(_stra ), len(_stra ) ) // 2
for i, l in enumerate(_stra ... | 662 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__lowerCAmelCase : Tuple = datasets.utils.logging.get_logger(__name__)
@dataclass
class snake_case__ ... | 662 |
__lowerCAmelCase : List[Any] = {str(digit): digit**5 for digit in range(10)}
def __magic_name__ ( A : int ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A ) )
def __magic_name__ ( ):
'''simple ... | 662 | 1 |
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 AutoProcessor, BertTokenizer, BlipImageProcessor,... | 662 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 662 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Dict = logging.get_logger(__name__)
__lowerCAmelCase : Union[... | 662 |
def __magic_name__ ( A : list ):
'''simple docstring'''
for i in range(len(A ) - 1, 0, -1 ):
a = False
for j in range(A, 0, -1 ):
if unsorted[j] < unsorted[j - 1]:
a , a = unsorted[j - 1], u... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : Dict = logging.get_logger(__name__)
__lowerCAmelCase : List[str] = {
'google/bit-50': 'h... | 662 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowerCAmelCase : Optional[Any] = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n au... | 662 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
__lowerCAmelCase : Optional[int] = '\nimport os\n'
__lowerCAmelCase : Any = '\ndef foo():\n import os\n return False\n'
__lowerCAmelCase : int = '\ndef foo():\n def bar():\n if True... | 662 |
import argparse
import os
import re
__lowerCAmelCase : Union[str, Any] = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCAmelCase : Dict = re.compile(r'[A-Z_]+_MAPPING(\... | 662 | 1 |
import warnings
from functools import wraps
from typing import Callable
def __magic_name__ ( A : Callable ):
'''simple docstring'''
@wraps(A )
def _inner_fn(*A : Any, **A : Optional[Any] ):
warnings.warn(
(F"""'{fn.__name__}' is... | 662 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : int = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] =... | 662 | 1 |
from ...processing_utils import ProcessorMixin
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = """SpeechT5FeatureExtractor"""
SCREAMING_SNAKE_CASE_ : int = """SpeechT5Tokenizer"""
def __... | 662 |
from __future__ import annotations
import time
import numpy as np
__lowerCAmelCase : List[str] = [8, 5, 9, 7]
__lowerCAmelCase : str = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__lowerCAmelCase : Optional[Any] = [
[3, 2, 1, 4]... | 662 | 1 |
import re
from filelock import FileLock
try:
import nltk
__lowerCAmelCase : List[str] = True
except (ImportError, ModuleNotFoundError):
__lowerCAmelCase : str = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def __magic_name_... | 662 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 662 | 1 |
import warnings
from .generation import TFGenerationMixin
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
warnings.warn(
"""Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
"""be removed in Transform... | 662 |
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 .tokenization_big_bird impor... | 662 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig,... | 662 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__lowerCAmelCase : List[Any] = logging.getLogger(__name__)
def __magic_name__ ( ):
'''simple docstring'''
a = argparse.ArgumentParser(
... | 662 | 1 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
fr... | 662 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __magic_name__ ( A : List[str] ):
'''simple docstring'''
a = {}
a = tokeni... | 662 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...... | 662 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 662 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __magic_name__ ( ):
'''simple docstring'''
a = [randint(-1000, 1000 ) for i in range(10 )]
a = randint(-5000, 5000 ... | 662 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase : Any = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTok... | 662 | 1 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__lowerCAmelCase : List[str] = object()
# For specifying empty leaf dict `{}`
__lowerCAmelCase : str = object()
def ... | 662 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_c... | 662 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__lowerCAmelCase : Dict = 'src/transformers'
__lowerCAmelCase : Option... | 662 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__lowerCAmelCase : int = {'tokenization_tapex': ['TapexTokenizer']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()['__fi... | 662 | 1 |
from __future__ import annotations
from collections import deque
class snake_case__ :
"""simple docstring"""
def __init__( self : Any , __lowerCamelCase : list[str] ) -> Optional[Any]:
a = []
self.adlist.append(
{"value":... | 662 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : Dict = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlipConfig... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[str] = logging.get_logger(__name__)
__lowerCAmelCase : List[Any] = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See al... | 662 |
import math
import flax.linen as nn
import jax.numpy as jnp
def __magic_name__ ( A : jnp.ndarray, A : int, A : float = 1, A : float = 1, A : float = 1.0E4, A : bool = False, A : float = 1.0, ):
'''simple docstring'''
... | 662 | 1 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...... | 662 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils imp... | 662 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 662 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acce... | 662 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lower... | 662 |
# 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 git won't be considered
# since... | 662 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__... | 662 |
def __magic_name__ ( A : int, A : int, A : int ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
a = _modexpt(A, exponent // 2, A ) % modulo_value
return (x * x) % modulo_value
else... | 662 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRet... | 662 |
def __magic_name__ ( A : str, A : str ):
'''simple docstring'''
def get_matched_characters(A : str, A : str ) -> str:
a = []
a = min(len(_stra ), len(_stra ) ) // 2
for i, l in enumerate(_stra ... | 662 | 1 |
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():
from transformers.mod... | 662 |
__lowerCAmelCase : List[Any] = {str(digit): digit**5 for digit in range(10)}
def __magic_name__ ( A : int ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A ) )
def __magic_name__ ( ):
'''simple ... | 662 | 1 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, i... | 662 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 662 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtrac... | 662 |
def __magic_name__ ( A : list ):
'''simple docstring'''
for i in range(len(A ) - 1, 0, -1 ):
a = False
for j in range(A, 0, -1 ):
if unsorted[j] < unsorted[j - 1]:
a , a = unsorted[j - 1], u... | 662 | 1 |
import copy
import re
class snake_case__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = """hp"""
SCREAMING_SNAKE_CASE_ : str = {}
SCREAMING_SNAKE_CASE_ : Dict = None
@classmethod
def _... | 662 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowerCAmelCase : Optional[Any] = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n au... | 662 | 1 |
import math
import flax.linen as nn
import jax.numpy as jnp
def __magic_name__ ( A : jnp.ndarray, A : int, A : float = 1, A : float = 1, A : float = 1.0E4, A : bool = False, A : float = 1.0, ):
'''simple docstring'''
... | 662 |
import argparse
import os
import re
__lowerCAmelCase : Union[str, Any] = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCAmelCase : Dict = re.compile(r'[A-Z_]+_MAPPING(\... | 662 | 1 |
def __magic_name__ ( A : int, A : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
'''simple docstring'''
assert and_gate(0, 0 ) == 0
assert and_gate(0, ... | 662 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : int = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] =... | 662 | 1 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pip... | 662 |
from __future__ import annotations
import time
import numpy as np
__lowerCAmelCase : List[str] = [8, 5, 9, 7]
__lowerCAmelCase : str = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__lowerCAmelCase : Optional[Any] = [
[3, 2, 1, 4]... | 662 | 1 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
def __lt__( self : str , __lowerCamelCase : int ) ... | 662 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 662 | 1 |
class snake_case__ :
"""simple docstring"""
def __init__( self : Dict , __lowerCamelCase : int , __lowerCamelCase : Tuple=None , __lowerCamelCase : Optional[int]=None ) -> List[str]:
a = data
a = previo... | 662 |
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 .tokenization_big_bird impor... | 662 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : List[Any] = {
'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
'pro... | 662 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__lowerCAmelCase : List[Any] = logging.getLogger(__name__)
def __magic_name__ ( ):
'''simple docstring'''
a = argparse.ArgumentParser(
... | 662 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __magic_name__ ( A : str ):
'''simple docstring'''
return "".join(sorted(A ) )
def __magic_name__ ( A : str ):
'''simple docstrin... | 662 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __magic_name__ ( A : List[str] ):
'''simple docstring'''
a = {}
a = tokeni... | 662 | 1 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTes... | 662 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 662 | 1 |
import math
import sys
def __magic_name__ ( A : int ):
'''simple docstring'''
if number != int(A ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number"... | 662 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase : Any = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTok... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
__lowerCAmelCase : List[str] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-tra... | 662 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_c... | 662 | 1 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__lowerCAmelCase : str = datasets.utils.logging.get_logger(__name__)
class snake_case__ (folder_based_builder.FolderBasedBuilderConfig ):
... | 662 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__lowerCAmelCase : int = {'tokenization_tapex': ['TapexTokenizer']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()['__fi... | 662 | 1 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.... | 662 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : Dict = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlipConfig... | 662 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import s... | 662 |
import math
import flax.linen as nn
import jax.numpy as jnp
def __magic_name__ ( A : jnp.ndarray, A : int, A : float = 1, A : float = 1, A : float = 1.0E4, A : bool = False, A : float = 1.0, ):
'''simple docstring'''
... | 662 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Any = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise Optiona... | 662 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils imp... | 662 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : str = {
'configuration_table_transformer': [
'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TableTransformerConfig',
'TableTransform... | 662 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acce... | 662 | 1 |
from statistics import mean
import numpy as np
def __magic_name__ ( A : list, A : list, A : list, A : int ):
'''simple docstring'''
a = 0
# Number of processes finished
a = 0
# Displays the finished process.
# If ... | 662 |
# 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 git won't be considered
# since... | 662 | 1 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __magic_name__ ( A : Optional[int] ):
'''simple docstring'''
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings", set() )
@pyt... | 662 |
def __magic_name__ ( A : int, A : int, A : int ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
a = _modexpt(A, exponent // 2, A ) % modulo_value
return (x * x) % modulo_value
else... | 662 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : List[str] = logging.get_logger(__name__)
__... | 662 |
def __magic_name__ ( A : str, A : str ):
'''simple docstring'''
def get_matched_characters(A : str, A : str ) -> str:
a = []
a = min(len(_stra ), len(_stra ) ) // 2
for i, l in enumerate(_stra ... | 662 | 1 |
from ..utils import DummyObject, requires_backends
class snake_case__ (metaclass=_UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["""torch""", """transformers""", """onnx"""]
def __init__( self : Optional[Any] ... | 662 |
__lowerCAmelCase : List[Any] = {str(digit): digit**5 for digit in range(10)}
def __magic_name__ ( A : int ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A ) )
def __magic_name__ ( ):
'''simple ... | 662 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __magic_name__ ( A : int, A : int, A : float = 1 / sqrt(2 ) ):
'''simple docstring'''
a = tau * frequency / samplerate
a = sin(A )
a ... | 662 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 662 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : str = {
'configuration_clip': [
'CLIP_... | 662 |
def __magic_name__ ( A : list ):
'''simple docstring'''
for i in range(len(A ) - 1, 0, -1 ):
a = False
for j in range(A, 0, -1 ):
if unsorted[j] < unsorted[j - 1]:
a , a = unsorted[j - 1], u... | 662 | 1 |
# 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 git won't be considered
# since... | 662 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowerCAmelCase : Optional[Any] = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n au... | 662 | 1 |
def __magic_name__ ( A : list[int], A : int ):
'''simple docstring'''
a = len(A )
a = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence... | 662 |
import argparse
import os
import re
__lowerCAmelCase : Union[str, Any] = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCAmelCase : Dict = re.compile(r'[A-Z_]+_MAPPING(\... | 662 | 1 |
import itertools
import math
def __magic_name__ ( A : int ):
'''simple docstring'''
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 even numbers, all multiples of 3 ar... | 662 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : int = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] =... | 662 | 1 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 662 |
from __future__ import annotations
import time
import numpy as np
__lowerCAmelCase : List[str] = [8, 5, 9, 7]
__lowerCAmelCase : str = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__lowerCAmelCase : Optional[Any] = [
[3, 2, 1, 4]... | 662 | 1 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
fro... | 662 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 662 | 1 |
from __future__ import annotations
import time
import numpy as np
__lowerCAmelCase : List[str] = [8, 5, 9, 7]
__lowerCAmelCase : str = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__lowerCAmelCase : Optional[Any] = [
[3, 2, 1, 4]... | 662 |
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 .tokenization_big_bird impor... | 662 | 1 |
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, TensorFlowBenchmarkArguments
@require_tf
... | 662 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__lowerCAmelCase : List[Any] = logging.getLogger(__name__)
def __magic_name__ ( ):
'''simple docstring'''
a = argparse.ArgumentParser(
... | 662 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampli... | 662 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __magic_name__ ( A : List[str] ):
'''simple docstring'''
a = {}
a = tokeni... | 662 | 1 |
def __magic_name__ ( A : list[int], A : str ):
'''simple docstring'''
a = int(A )
# Initialize Result
a = []
# Traverse through all denomination
for denomination in reversed(A ):
# Find denominations
while int(A ... | 662 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 662 | 1 |
__lowerCAmelCase : List[Any] = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_av... | 662 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase : Any = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTok... | 662 | 1 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning th... | 662 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_c... | 662 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__lowerCAmelCase : Optional[Any] = logging.getLogger()
@unittest.skip("""Temporarily disable th... | 662 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__lowerCAmelCase : int = {'tokenization_tapex': ['TapexTokenizer']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()['__fi... | 662 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__lowerCAmelCase : List[str] = logging.get_logger(__name__)
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
def __init__( self : Dict , ... | 662 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : Dict = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlipConfig... | 662 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase : List[Any] = {
'configuration_distilbert': [
'DISTILBERT_PRETRAI... | 662 |
import math
import flax.linen as nn
import jax.numpy as jnp
def __magic_name__ ( A : jnp.ndarray, A : int, A : float = 1, A : float = 1, A : float = 1.0E4, A : bool = False, A : float = 1.0, ):
'''simple docstring'''
... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
__lowerCAmelCase : Tuple = {
'shi-labs/dina... | 662 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils imp... | 662 | 1 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase : Any = loggin... | 662 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acce... | 662 | 1 |
import operator
def __magic_name__ ( A : list, A : bool = False, A : list | None = None ):
'''simple docstring'''
a = operator.lt if reverse else operator.gt
a = solution or []
if not arr:
return solution
a = [ar... | 662 |
# 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 git won't be considered
# since... | 662 | 1 |
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 .tokenization_big_bird impor... | 662 |
def __magic_name__ ( A : int, A : int, A : int ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
a = _modexpt(A, exponent // 2, A ) % modulo_value
return (x * x) % modulo_value
else... | 662 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...fe... | 662 |
def __magic_name__ ( A : str, A : str ):
'''simple docstring'''
def get_matched_characters(A : str, A : str ) -> str:
a = []
a = min(len(_stra ), len(_stra ) ) // 2
for i, l in enumerate(_stra ... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCAmelCase : Tuple = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class snake_case__ (_UpperCa... | 662 |
__lowerCAmelCase : List[Any] = {str(digit): digit**5 for digit in range(10)}
def __magic_name__ ( A : int ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A ) )
def __magic_name__ ( ):
'''simple ... | 662 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class snake_case__ :
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCamelCase : int ) -> None:
a = value
a = None
a = N... | 662 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 662 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = """Speech2TextFeatureExtractor"""
SCREAMING_SNAKE_CASE_ ... | 662 |
def __magic_name__ ( A : list ):
'''simple docstring'''
for i in range(len(A ) - 1, 0, -1 ):
a = False
for j in range(A, 0, -1 ):
if unsorted[j] < unsorted[j - 1]:
a , a = unsorted[j - 1], u... | 662 | 1 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( A : Tuple, A : List[str], A : int ):
'''simple docs... | 662 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowerCAmelCase : Optional[Any] = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n au... | 662 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : int = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] =... | 662 |
import argparse
import os
import re
__lowerCAmelCase : Union[str, Any] = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCAmelCase : Dict = re.compile(r'[A-Z_]+_MAPPING(\... | 662 | 1 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
def __init__... | 662 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : int = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] =... | 662 | 1 |
from __future__ import annotations
import numpy as np
def __magic_name__ ( A : np.ndarray ):
'''simple docstring'''
a , a = np.shape(A )
if rows != columns:
a = (
"'table' has to be of square shaped array but got a "
F... | 662 |
from __future__ import annotations
import time
import numpy as np
__lowerCAmelCase : List[str] = [8, 5, 9, 7]
__lowerCAmelCase : str = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__lowerCAmelCase : Optional[Any] = [
[3, 2, 1, 4]... | 662 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case__ (_... | 662 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 662 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ... | 662 |
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 .tokenization_big_bird impor... | 662 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCAmelCase : int = {
'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deform... | 662 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__lowerCAmelCase : List[Any] = logging.getLogger(__name__)
def __magic_name__ ( ):
'''simple docstring'''
a = argparse.ArgumentParser(
... | 662 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
fr... | 662 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __magic_name__ ( A : List[str] ):
'''simple docstring'''
a = {}
a = tokeni... | 662 | 1 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def __magic_name__ ( ... | 662 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 662 | 1 |
def __magic_name__ ( A : list, A : list ):
'''simple docstring'''
_validate_point(A )
_validate_point(A )
if len(A ) != len(A ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b... | 662 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase : Any = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTok... | 662 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
__lowerCAmelCase : Any = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/mai... | 662 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_c... | 662 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase : Union[str, Any] = {
'configuration_blenderbot': [
'BLENDERBOT_P... | 662 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__lowerCAmelCase : int = {'tokenization_tapex': ['TapexTokenizer']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()['__fi... | 662 | 1 |
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
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCAmelCase : Option... | 662 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : Dict = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlipConfig... | 662 | 1 |
from typing import Any
import numpy as np
def __magic_name__ ( A : np.ndarray ):
'''simple docstring'''
return np.array_equal(A, matrix.conjugate().T )
def __magic_name__ ( A : np.ndarray, A : np.ndarray ):
'''simpl... | 662 |
import math
import flax.linen as nn
import jax.numpy as jnp
def __magic_name__ ( A : jnp.ndarray, A : int, A : float = 1, A : float = 1, A : float = 1.0E4, A : bool = False, A : float = 1.0, ):
'''simple docstring'''
... | 662 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class snake_case__ (nn.Module ):
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCamelCase : int = 16 , __lowe... | 662 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils imp... | 662 | 1 |
from typing import Any
class snake_case__ :
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCamelCase : Any ) -> Optional[int]:
a = data
a = None
class snake_case__ :
"""simple docstring""... | 662 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acce... | 662 | 1 |
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