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 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a : str = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try... | 397 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : List[str] = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-tran... | 36 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( A : int ) -> list[int]:
UpperCAmelCase_ : Dict = 2
UpperCAmelCase_ : int = []
while i * i <= n:
if n % i:
i += 1
else:
... | 541 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_obje... | 36 | 0 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 442 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str] ) -> Any:
'''simple docstring'''
snake_case : Tuple = {
"""en""": """Machine learning is gre... | 36 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ... | 147 |
__lowercase : List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowercase : str ... | 36 | 0 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import ... | 43 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowercase : str = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ):
'''simpl... | 36 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
class lowercase__ :
'''simple docstring'''
def __init__( self , __snake_case ):
_SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment... | 533 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import ... | 36 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCAmelCase : Union[str, Any] = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
_lowerCAme... | 242 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowercase : Optional[Any] = pytest.mark.integration
@pytest.mark.parametrize("""path""" ,... | 36 | 0 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCAmelCase : Union[str, Any] = {'''UserAgent''': UserAgent().random}
def _A ( SCREAMING_SNAKE_CASE : Optional[Any] ):
"""... | 563 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase : Optional[Any] = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''':... | 36 | 0 |
'''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 (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 44 |
from __future__ import annotations
def lowercase ( __A : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__A ) / len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 0 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase = 400_0000 ):
"""simple docstring"""
lowercase_ : int = []
lowercase_ : Any = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__A )
lowercase_ ... | 620 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pr... | 36 | 0 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : str = []
A_ : Optional[int] = []
A_ : str = []
... | 590 |
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, Bi... | 36 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a : str = logging.get_logger(__name__)
__a : Dict = {
'''faceb... | 397 |
import os
import pytest
from attr import dataclass
__lowercase : Optional[int] = '''us-east-1''' # defaults region
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : Dict = '''arn:aws:iam::558105141721:role/sagemaker... | 36 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipe... | 541 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 36 | 0 |
'''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_sqlalche... | 442 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobert... | 36 | 0 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__lowerCAmelCase = False
class lowerCamelCase_ ( unittest.TestCase ):
pass
... | 147 |
from __future__ import annotations
def lowercase ( __A : int ) -> list[int]:
'''simple docstring'''
snake_case : Dict = 2
snake_case : int = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 36 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ... | 43 |
import numpy as np
def lowercase ( __A : np.array ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 0 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from... | 533 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params... | 36 | 0 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 242 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _A ( pl.LightningModule ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstri... | 36 | 0 |
def _A ( SCREAMING_SNAKE_CASE : int = 100 ):
"""simple docstring"""
a__ : Tuple =0
a__ : Tuple =0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__... | 563 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowercase : Optional[Any] = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
__lowercase : Optional[int] = None
def lowercase ( ) -> Optional[Any]:
... | 36 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAme... | 44 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimensio... | 36 | 0 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
if len(__A ) != len(__A ):
raise ValueError("String lengths must match!" )
lowercase_ : List[Any] = 0
for chara, chara in zip(__A ... | 620 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowercase ( __A : str , __A : str , **__A : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = AutoConfig.from_pretrained(__A , ... | 36 | 0 |
from __future__ import annotations
UpperCamelCase = [True] * 100_0001
UpperCamelCase = 2
while i * i <= 100_0000:
if seive[i]:
for j in range(i * i, 100_0001, i):
UpperCamelCase = False
i += 1
def _SCREAMING_SNAKE_CASE ... | 590 |
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
__lowercase : Any = logging.get_logger(__name__)
__lowercase : str = {
'''... | 36 | 0 |
from ... import PretrainedConfig
__a : Union[str, Any] = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class UpperCAmelCase( snake_case_ ):
"""simple docstring"""
a : ... | 397 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : List[str] = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-tran... | 36 | 0 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_UpperCamelCase : int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies ... | 541 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_obje... | 36 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase__ : int ):
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
a_ : Tuple = 1
a_ : Union[str, Any] = 1
... | 442 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str] ) -> Any:
'''simple docstring'''
snake_case : Tuple = {
"""en""": """Machine learning is gre... | 36 | 0 |
import os
from datetime import datetime as dt
from github import Github
__lowerCAmelCase = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _lowercase ( ) -> Any:
"""simple docstring"""
_UpperCamelCase = Github(os.environ["GITHUB_TOKEN"] ... | 147 |
__lowercase : List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowercase : str ... | 36 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return "".join(sorted(__A ) )
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return word_by_sig... | 43 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowercase : str = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ):
'''simpl... | 36 | 0 |
'''simple docstring'''
from collections.abc import Callable
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : float = a
_SCREAMING_SNAKE_CASE : float = b
... | 533 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import ... | 36 | 0 |
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... | 242 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowercase : Optional[Any] = pytest.mark.integration
@pytest.mark.parametrize("""path""" ,... | 36 | 0 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from di... | 563 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase : Optional[Any] = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''':... | 36 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Dict = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig'''... | 44 |
from __future__ import annotations
def lowercase ( __A : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__A ) / len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 0 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase = True , _UpperCamelCase = math.inf , _UpperCamelCase = -math.inf , _UpperCamelCase = math.inf , _UpperC... | 620 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pr... | 36 | 0 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
return str(__A ) == str(__A )[::-1]
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
return int(__A ) + int(str(__A )[::-1] )
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE = 10_000 ):
A_ : ... | 590 |
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, Bi... | 36 | 0 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
__a : int = '''
import os
'''
__a : Optional[Any] = '''
def foo():
import os
return False
'''
__a : Optional[int] = '''
def foo(... | 397 |
import os
import pytest
from attr import dataclass
__lowercase : Optional[int] = '''us-east-1''' # defaults region
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : Dict = '''arn:aws:iam::558105141721:role/sagemaker... | 36 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( A : int , A : int ) -> int:
return int((input_a, input_a).count(0 ) == 0 )
def __UpperCAmelCase ( ) -> None:
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 ... | 541 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 36 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
... | 442 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobert... | 36 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase = {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MA... | 147 |
from __future__ import annotations
def lowercase ( __A : int ) -> list[int]:
'''simple docstring'''
snake_case : Dict = 2
snake_case : int = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 36 | 0 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from... | 43 |
import numpy as np
def lowercase ( __A : np.array ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 0 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
if len(__A ) < 2:
return collection
def circle_sort_util(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> bool:
_SCREAMING_SNAKE_CASE : ... | 533 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params... | 36 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
_lowerCAmelCase : Dict = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/m... | 242 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _A ( pl.LightningModule ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstri... | 36 | 0 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __l... | 563 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowercase : Optional[Any] = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
__lowercase : Optional[int] = None
def lowercase ( ) -> Optional[Any]:
... | 36 | 0 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accele... | 44 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimensio... | 36 | 0 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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-2.0
#
#... | 620 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowercase ( __A : str , __A : str , **__A : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = AutoConfig.from_pretrained(__A , ... | 36 | 0 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = CustomTokenizer
pass
| 590 |
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
__lowercase : Any = logging.get_logger(__name__)
__lowercase : str = {
'''... | 36 | 0 |
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,) -> float:
lowercase__ : Any = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0... | 397 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : List[str] = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-tran... | 36 | 0 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils impor... | 541 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_obje... | 36 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : str = logging.get_logger(__name__)
lowerCAmelCase_ : Union[str, Any] = {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abej... | 442 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str] ) -> Any:
'''simple docstring'''
snake_case : Tuple = {
"""en""": """Machine learning is gre... | 36 | 0 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillation"""
... | 147 |
__lowercase : List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowercase : str ... | 36 | 0 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMI... | 43 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowercase : str = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ):
'''simpl... | 36 | 0 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ = 400_0000 ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : str = [0, 1]
_SCREAMING_SNAKE_CASE : List[str] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i +... | 533 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import ... | 36 | 0 |
from manim import *
class __magic_name__ ( lowerCAmelCase_ ):
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =Rectangle(height=0.5 , width=0.5 )
__a =Rectangle(height=0.46 , width=0.46... | 242 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowercase : Optional[Any] = pytest.mark.integration
@pytest.mark.parametrize("""path""" ,... | 36 | 0 |
UpperCAmelCase : dict[tuple[int, int, int], int] = {}
def _A ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if late == 3 or absent == 2:
return 0
# if we have no days left... | 563 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase : Optional[Any] = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''':... | 36 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_att... | 44 |
from __future__ import annotations
def lowercase ( __A : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__A ) / len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 0 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ = pytest.mark.integration
@pytest.mark.parametrize("path"... | 620 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pr... | 36 | 0 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesser... | 590 |
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, Bi... | 36 | 0 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def snake_cas... | 397 |
import os
import pytest
from attr import dataclass
__lowercase : Optional[int] = '''us-east-1''' # defaults region
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : Dict = '''arn:aws:iam::558105141721:role/sagemaker... | 36 | 0 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.... | 541 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 36 | 0 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
lowerCAmelCase_ : Any = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 ... | 442 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobert... | 36 | 0 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from tr... | 147 |
from __future__ import annotations
def lowercase ( __A : int ) -> list[int]:
'''simple docstring'''
snake_case : Dict = 2
snake_case : int = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 36 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_di... | 43 |
import numpy as np
def lowercase ( __A : np.array ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 0 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ ): # noqa: E741
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Any = len(__A )
_SCREAMING_SNAKE_CASE : Dict = 0
_SCREAMING_SNAKE_CASE : Optional[Any] = [0] * n
_SCREAMING_SN... | 533 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params... | 36 | 0 |
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,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtr... | 242 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _A ( pl.LightningModule ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstri... | 36 | 0 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __lowerCAmelCase ( unittest.TestCase):
_lowercase : int = JukeboxTokenizer
_lowercase : int = {
... | 563 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowercase : Optional[Any] = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
__lowercase : Optional[int] = None
def lowercase ( ) -> Optional[Any]:
... | 36 | 0 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase_ : Tuple = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2... | 44 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimensio... | 36 | 0 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : str = len(__A )
lowercase_ : Union[str, Any] = len(__A )
lowercase_ : Union[str, Any] =... | 620 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowercase ( __A : str , __A : str , **__A : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = AutoConfig.from_pretrained(__A , ... | 36 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
log... | 590 |
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
__lowercase : Any = logging.get_logger(__name__)
__lowercase : str = {
'''... | 36 | 0 |
from collections import defaultdict
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> int:
lowercase__ : Union[str, Any] = 1
lowercase__ : str = True
for v in tree[start]:
if v not in visited:
ret += dfs(__A... | 397 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : List[str] = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-tran... | 36 | 0 |
'''simple docstring'''
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_sub... | 541 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_obje... | 36 | 0 |
'''simple docstring'''
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
lowerCAmelCase_ : Tuple = logging.get_logger(__name__)
lowerCAmelCase_ : Uni... | 442 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowercase ( __A : Dict , __A : Union[str, Any] , __A : List[str] ) -> Any:
'''simple docstring'''
snake_case : Tuple = {
"""en""": """Machine learning is gre... | 36 | 0 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__lowerCAmelCase = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action=... | 147 |
__lowercase : List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowercase : str ... | 36 | 0 |
import math
import random
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False ):
"""simple docstring"""
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowerCAmelCase = 0.0_2
def _a ( SCREAMING_SNAKE_... | 43 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowercase : str = logging.get_logger(__name__)
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_=None ,**SCREAMING_SNAKE_CASE_ ):
'''simpl... | 36 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase__ ( _snake_case ):
'''simple docstring'''
A_ : Optional[Any] = ['''image_processor''', '''tokenizer''']
... | 533 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import ... | 36 | 0 |
def UpperCamelCase_( _snake_case : Tuple , _snake_case : Optional[int] ):
"""simple docstring"""
__a =[1]
for i in range(2 , __A ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
__a ... | 242 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowercase : Optional[Any] = pytest.mark.integration
@pytest.mark.parametrize("""path""" ,... | 36 | 0 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __lowerC... | 563 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase : Optional[Any] = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''':... | 36 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tenso... | 44 |
from __future__ import annotations
def lowercase ( __A : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__A ) / len(__A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 0 |
'''simple docstring'''
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
lowercase_ : Dict = len(__A )
# We need to create solution object to save path.
lowercase_ : Union[str, Any] ... | 620 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pr... | 36 | 0 |
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 import FileLock
from .logging import get_logge... | 590 |
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, Bi... | 36 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class UpperCAmelCase:
"""simple docstring"""
def __init__( self , lowerCamelCase=2 , lowerCamelCase=3 , lowerCamelCase=64 , lowerC... | 397 |
import os
import pytest
from attr import dataclass
__lowercase : Optional[int] = '''us-east-1''' # defaults region
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : Dict = '''arn:aws:iam::558105141721:role/sagemaker... | 36 | 0 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class snake_case__ ( ctypes.Structure):
a_ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_by... | 541 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 36 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from d... | 442 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobert... | 36 | 0 |
from __future__ import annotations
class A__ :
"""simple docstring"""
def __init__( self : int , lowerCamelCase__ : int ):
a__ : List[str] = order
# a_{0} ... a_{k}
a__ : Optional[int] = [1.0] + [0.0] * order
# b_{0} ... b_{k}
... | 37 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_t... | 37 | 1 |
import unittest
from transformers import SqueezeBertConfig, 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 import ModelTesterMixin, ids_tensor, ... | 37 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
UpperCamelCase : Dict = """<<<<<<< This should probably be modified because it mentions: """
UpperCamelCase :... | 37 | 1 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import... | 37 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class A__ ( A__ ):
"""simple docstring"""
_lowercase = ''
_lowercase = (
None # protocol passed in prefix to the url. ex: ... | 37 | 1 |
def UpperCamelCase_ ( __a , __a , __a , __a ) -> int:
a__, a__ : Union[str, Any] = len(__a ), len(grid[0] )
if (
min(__a , __a ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or... | 37 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCamelCase_ ( __a , __a , __a ) -> Optional[Any]:
a__ : Union[str, Any] = ("dense.weight", "attention.self.query", "attention.self.key", "attention.sel... | 37 | 1 |
from math import sqrt
def UpperCamelCase_ ( __a ) -> int:
a__ : Union[str, Any] = 0
for i in range(1 , int(sqrt(__a ) + 1 ) ):
if n % i == 0 and i != sqrt(__a ):
total += i + n // i
elif i == sqrt(__a ):
... | 37 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_config... | 37 | 1 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
UpperCamelCase : Any = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": operator.ge,
""">""": o... | 37 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 37 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_util... | 37 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niel... | 37 | 1 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCamelCase_ ( __a , __a , __a ) -> Optional[Any]:
a__ : Union[str, Any] = ("dense.weight", "attention.self.query", "attention.self.key", "attention.sel... | 37 |
def UpperCamelCase_ ( __a , __a ) -> Tuple:
a__ : Optional[int] = [0 for i in range(r + 1 )]
# nc0 = 1
a__ : Union[str, Any] = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
a__ : Any = mi... | 37 | 1 |
from __future__ import annotations
import numpy as np
def UpperCamelCase_ ( __a ) -> Any:
return np.maximum(0 , __a )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 37 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .token... | 37 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase : Dict = logging.get_logger(__name__)
def UpperCamelCase_ ( __a ) -> Union[str, Any]:
a__ : Tuple ... | 37 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
Up... | 37 | 1 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configura... | 37 |
from statistics import mean, stdev
def UpperCamelCase_ ( __a , __a = 3 ) -> list:
a__ : List[str] = min(__a )
a__ : str = max(__a )
# normalize data
return [round((x - x_min) / (x_max - x_min) , __a ) for x in data]
def UpperCamelCase_... | 37 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : List[Any] = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {
"""microsoft/unispeech-sat-base-100h-libri-ft""": (
... | 37 |
def UpperCamelCase_ ( __a = 50 ) -> int:
a__ : Tuple = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 37 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transform... | 37 |
class A__ :
"""simple docstring"""
def __init__( self : List[Any] , lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[str] ):
a__ : str = name
a__ : Optional[int] = value
a__ : Dict = we... | 37 | 1 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
UpperCamelCase : Dict = """<<<<<<< This should probably be modified because it mentions: """
UpperCamelCase :... | 37 |
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... | 37 | 1 |
import gc
import threading
import time
import psutil
import torch
class A__ :
"""simple docstring"""
def __init__( self : int ):
a__ : Optional[int] = psutil.Process()
a__ : Union[str, Any] = False
def _UpperCamelCase( self : Any ):
a__ ... | 37 |
import math
from datetime import datetime, timedelta
def UpperCamelCase_ ( __a ) -> datetime:
a__ : Union[str, Any] = year % 19
a__ : List[str] = year % 4
a__ : str = year % 7
a__ : Any = math.floor(year / 100 )
a__ : List[str] = m... | 37 | 1 |
def UpperCamelCase_ ( __a = 50 ) -> int:
a__ : Tuple = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 37 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testi... | 37 | 1 |
from __future__ import annotations
from collections.abc import Callable
def UpperCamelCase_ ( __a , __a , __a , __a = 100 , ) -> float:
a__ : Dict = x_start
a__ : Optional[int] = fnc(__a )
a__ : Union[str, Any] = 0.0
for _ in range(__a... | 37 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.util... | 37 | 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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set... | 37 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase : Dict = logging.get_logger(__name__)
def UpperCamelCase_ ( __a ) -> Union[str, Any]:
a__ : Tuple ... | 37 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class A__ ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def _UpperCamelCase( self : List[str] ... | 37 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase_ ( ) -> int:
a__ : Any = HfArgumentParser(__a )
a__ : Any = parser.parse_args_into_dataclasses()[0]
a__ : Optional[int] = TensorFlowBenchmark(args=__a... | 37 | 1 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils i... | 37 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.pa... | 37 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.