code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_... | 111 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transfo... | 111 | 1 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=loggi... | 111 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is... | 111 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/dec... | 111 |
'''simple docstring'''
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 re... | 111 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"""andrea... | 111 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCam... | 111 | 1 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transfo... | 111 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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_modelin... | 111 | 1 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : str = current_set.copy()
for row_index, row in enumerate(SCREAMING_SNAKE_CASE ):
__A : Optional[int] = row[0]
f... | 111 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printin... | 111 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or ... | 111 |
'''simple docstring'''
import unittest
import numpy as np
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , ):
'''simple docstring'''
__A : List[Any] = ... | 111 | 1 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A ,__A : Optional[Any] = img.shape[0], img.shape[1]
# converting each pixel's color to its ne... | 111 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = " " ):
'''simple docstring'''
__A : List[str] = []
__A : Tuple = 0
for index, char in enumerate(SCREAMING_SNAKE_CASE ):
... | 111 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
... | 111 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimensio... | 111 | 1 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutpu... | 111 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 111 | 1 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blan... | 111 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or ... | 111 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 111 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 111 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProce... | 111 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import To... | 111 | 1 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common im... | 111 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase = {"""configuration_vit""": ["""VIT_PRETRAI... | 111 | 1 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return 10 - x * x
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if equation(SCREAMING_SNAKE_... | 111 |
'''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... | 111 | 1 |
'''simple docstring'''
import baseaa
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return baseaa.baaencode(string.encode("utf-8" ) )
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
... | 111 |
'''simple docstring'''
import functools
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : int = len(SCREAMING_SNAKE_CASE )
__A : int = len(SCREAMING_SNAKE_CASE )
... | 111 | 1 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=5 ):
'''simple docstring'''
... | 111 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : List[str] = 1
__A : Union[str, Any] = 2
while i * i <= n:
__A : List[Any] = 0
while n % i == 0... | 111 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available()... | 111 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalD... | 111 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatt... | 111 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.me... | 111 | 1 |
'''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 = logging.get_logger(__name__)
_... | 111 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_C... | 111 | 1 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHT... | 111 |
'''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 ...... | 111 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 100 , ):
'''simple docstring'... | 111 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_... | 111 | 1 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_UpperCamelCase = datasets.logging.get_logger(__name__)
_UpperCamelCase = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics f... | 111 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transfo... | 111 | 1 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_C... | 111 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is... | 111 | 1 |
'''simple docstring'''
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
... | 111 |
'''simple docstring'''
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 re... | 111 | 1 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.me... | 111 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCam... | 111 | 1 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_UpperCamelCase = HUGGINGFACE_HUB_CACHE
_UpperCamelCase = """config.json"""
_UpperCamelCase = """diffusion_pytorch_model.bin"""
_UpperCamelCase = """diffu... | 111 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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_modelin... | 111 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if not postfix_notation:
return 0
__A : Tuple = {"+", "-", "*", "/"}
__A : ... | 111 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printin... | 111 | 1 |
'''simple docstring'''
import math
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return math.pow(SCREAMING_SNAKE_CASE , 2 ) - a
def _lowercase (SCREAMING_SNAKE_CASE ):
'''si... | 111 |
'''simple docstring'''
import unittest
import numpy as np
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , ):
'''simple docstring'''
__A : List[Any] = ... | 111 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase = {"""... | 111 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = " " ):
'''simple docstring'''
__A : List[str] = []
__A : Tuple = 0
for index, char in enumerate(SCREAMING_SNAKE_CASE ):
... | 111 | 1 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10... | 111 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimensio... | 111 | 1 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__A : List[str] = f"Input value of [number={number}] must be an integer"
... | 111 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 111 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
_UpperCamelCase = "... | 111 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or ... | 111 | 1 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1024 ):
... | 111 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 111 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_UpperCamelCase = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase ):
"""simple docstring"""
... | 111 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import To... | 111 | 1 |
'''simple docstring'''
import numpy as np
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return vector *... | 111 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase = {"""configuration_vit""": ["""VIT_PRETRAI... | 111 | 1 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
assert column_title.isupper()
__A : Optional[int] = 0
__A : Any = len(SCREAMING_SNAKE_CASE ) - 1
__A : Optional[Any] ... | 111 |
'''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... | 111 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase__ = ['speech']
def __init__( self , *lowerCamelCase , ... | 111 |
'''simple docstring'''
import functools
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : int = len(SCREAMING_SNAKE_CASE )
__A : int = len(SCREAMING_SNAKE_CASE )
... | 111 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common i... | 111 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : List[str] = 1
__A : Union[str, Any] = 2
while i * i <= n:
__A : List[Any] = 0
while n % i == 0... | 111 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 111 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalD... | 111 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCToken... | 111 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.me... | 111 | 1 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock i... | 111 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_C... | 111 | 1 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_UpperCamelCase = TypeVar("""T""")
class __magic_name__ ( Generic[T] ):
"""simple docstring"""
lowerCame... | 111 |
'''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 ...... | 111 | 1 |
'''simple docstring'''
import re
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]" , str_ )]
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple doc... | 111 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_... | 111 | 1 |
'''simple docstring'''
from math import pi
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 111 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transfo... | 111 | 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 = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://... | 111 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is... | 111 | 1 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return np.dot(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ... | 111 |
'''simple docstring'''
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 re... | 111 | 1 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_UpperCamelCase = logging.getLogger(__name__)
class __magic_name__ :
... | 111 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCam... | 111 | 1 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : str = len(SCREAMING_SNAKE_CASE )
for _ in range(SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 , 2 ... | 111 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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_modelin... | 111 | 1 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm m... | 111 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printin... | 111 | 1 |
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
... | 111 |
'''simple docstring'''
import unittest
import numpy as np
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , ):
'''simple docstring'''
__A : List[Any] = ... | 111 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCamelCase = {
"""configuration_vision_text_dual_encoder""": ["""VisionText... | 111 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = " " ):
'''simple docstring'''
__A : List[str] = []
__A : Tuple = 0
for index, char in enumerate(SCREAMING_SNAKE_CASE ):
... | 111 | 1 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_UpperCamelCase = get_logger(__name__)
class __magic_name__ ( enum.Enum ):
"""simple docstring"""
... | 111 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimensio... | 111 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CO... | 111 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 111 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_asyn... | 111 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or ... | 111 | 1 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def is_in_circle(SCREAMING_SNAKE_CASE , SCREAMING... | 111 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 111 | 1 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
... | 111 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import To... | 111 | 1 |
'''simple docstring'''
_UpperCamelCase = {str(digit): digit**5 for digit in range(10)}
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(SCREAMING_SNAKE_CASE ) )
def _lower... | 111 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase = {"""configuration_vit""": ["""VIT_PRETRAI... | 111 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A ,__A : Tuple = np.shape(SCREAMING_SNAKE_CASE )
if rows != columns:
__A : ... | 111 |
'''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... | 111 | 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... | 111 |
'''simple docstring'''
import functools
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : int = len(SCREAMING_SNAKE_CASE )
__A : int = len(SCREAMING_SNAKE_CASE )
... | 111 | 1 |
import fire
from utils import calculate_rouge, save_json
def __lowercase ( snake_case, snake_case, snake_case=None, **snake_case ):
"""simple docstring"""
__magic_name__ :Dict = [x.strip() for x in open(snake_case ).readlines()]
__magic_name__ :Tuple = [x.strip(... | 0 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : List[str] = 1
__A : Union[str, Any] = 2
while i * i <= n:
__A : List[Any] = 0
while n % i == 0... | 111 | 0 |
import torch
from diffusers import StableDiffusionPipeline
__snake_case = '''path-to-your-trained-model'''
__snake_case = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
__snake_case = '''A photo of sks dog in a buck... | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalD... | 111 | 0 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set... | 2 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.me... | 111 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerCAmelCase : List[str] ... | 3 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_C... | 111 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
'''configuration_... | 4 |
'''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 ...... | 111 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configurati... | 5 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_... | 111 | 0 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'google... | 6 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transfo... | 111 | 0 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict ):
_A = [2, 1, 2, -1]
_A = [1, 2, 3, 4]
def lowerCAmelCase_ ( self : Dict ):... | 7 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is... | 111 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import req... | 8 |
'''simple docstring'''
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 re... | 111 | 0 |
from __future__ import annotations
def A ( __UpperCamelCase , __UpperCamelCase ) -> set[str]:
A__ , A__ = set(__UpperCamelCase ), [start]
while stack:
A__ = stack.pop()
explored.add(__UpperCamelCase )
# Differences from BFS:
# 1)... | 9 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCam... | 111 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _snake_case ( __snake_case , __snake_case , ... | 10 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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_modelin... | 111 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase_ = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
lowercase_ ... | 11 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printin... | 111 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase__ : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase__ : str = {
"""micr... | 12 |
'''simple docstring'''
import unittest
import numpy as np
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , ):
'''simple docstring'''
__A : List[Any] = ... | 111 | 0 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 13 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = " " ):
'''simple docstring'''
__A : List[str] = []
__A : Tuple = 0
for index, char in enumerate(SCREAMING_SNAKE_CASE ):
... | 111 | 0 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torc... | 14 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimensio... | 111 | 0 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
A : Union[str, Any] = lo... | 15 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 111 | 0 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassif... | 16 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or ... | 111 | 0 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase_ ( _lowercase ):
_lowercase : Dict = '''M-CLIP'''
def __init__( self : List[str] , __A : str=1024 , __A : Optional[Any]=76... | 17 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 111 | 0 |
'''simple docstring'''
from __future__ import annotations
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
_lowerCAmelCase = []
create_all_state(1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CA... | 18 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import To... | 111 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
_a = logging.getLogger(__name__)
def lowerCamelCase__ ( ) -> str:
"""simple docstring"""
... | 19 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase = {"""configuration_vit""": ["""VIT_PRETRAI... | 111 | 0 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class lowercase_ :
def __init__( self , lowercase_ , lowercase_=sys.maxsize)... | 20 |
'''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... | 111 | 0 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __A ( UpperCamelCase__ ):
Upp... | 21 |
'''simple docstring'''
import functools
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : int = len(SCREAMING_SNAKE_CASE )
__A : int = len(SCREAMING_SNAKE_CASE )
... | 111 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import Gene... | 22 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__A : List[str] = 1
__A : Union[str, Any] = 2
while i * i <= n:
__A : List[Any] = 0
while n % i == 0... | 111 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Tuple = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransfo... | 23 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalD... | 111 | 0 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : list[list[int]] , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : set )-> int:
'''simple docstring'''
__snake_case , __snake_case = len(_lowerCamelC... | 24 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.me... | 111 | 0 |
from string import ascii_uppercase
a_ = {str(ord(c) - 55): c for c in ascii_uppercase}
def lowerCamelCase__ ( _a , _a):
if isinstance(_a , _a):
raise TypeError("int() can't convert non-string with explicit base")
if num < 0:
raise ValueError("parameter must be positive int"... | 25 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_C... | 111 | 0 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass... | 26 |
'''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 ...... | 111 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Dict = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvN... | 27 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_... | 111 | 0 |
'''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_video_input... | 28 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {name: getattr(transfo... | 111 | 0 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
... | 29 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is... | 111 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import A... | 30 |
'''simple docstring'''
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 re... | 111 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from... | 31 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCam... | 111 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.ut... | 32 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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_modelin... | 111 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowerCamelCase__ : Union[str, Any] = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", ... | 33 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printin... | 111 | 0 |
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __snake_case ( _l... | 34 |
'''simple docstring'''
import unittest
import numpy as np
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , ):
'''simple docstring'''
__A : List[Any] = ... | 111 | 0 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
a_ :Optional[int] = [
'kernels/rwkv/wkv_cuda.cu',
'kernels/rwkv/wkv_op.cpp',
'kernels/deformable_detr/ms_deform_attn.h',
'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
'mo... | 35 |
'''simple docstring'''
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = " " ):
'''simple docstring'''
__A : List[str] = []
__A : Tuple = 0
for index, char in enumerate(SCREAMING_SNAKE_CASE ):
... | 111 | 0 |
class _A :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
snake_case : dict[str, TrieNode] = {} # Mapping from char to TrieNode
snake_case : Optional[int] = False
def snake_case_ ( self ,SCREAMING_SNAKE... | 36 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimensio... | 111 | 0 |
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, ids... | 37 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 111 | 0 |
'''simple docstring'''
# Copyright 2021 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-2... | 38 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or ... | 111 | 0 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast... | 39 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 111 | 0 |
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