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 Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
fr... | 36 |
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 | 1 |
def lowercase ( __A : Tuple , __A : Optional[int] ) -> Optional[int]:
'''simple docstring'''
snake_case : List[Any] = [1]
for i in range(2 , __A ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bound... | 36 |
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 | 1 |
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,
resize,
to_channel_dime... | 36 |
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 | 1 |
def lowercase ( __A : list[int] ) -> float:
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
snake_case : Optional[int] = sum(__A ) / len(__A ) # Calculate the average
return sum... | 36 |
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 | 1 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available(... | 36 |
# 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 | 1 |
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 |
__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 | 1 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__lowercase : Any ... | 36 |
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 | 1 |
import requests
from bsa import BeautifulSoup
def lowercase ( __A : str = "AAPL" ) -> str:
'''simple docstring'''
snake_case : List[Any] = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
snake_case : Dict = BeautifulSoup(reque... | 36 |
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 | 1 |
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 GenerationTesterMixin
from ...test_modeling_common i... | 36 |
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 | 1 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__lowercase : List[str] = logging.get_logger(__name__)
def ... | 36 |
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 | 1 |
def lowercase ( __A : int = 3 , __A : int = 7 , __A : int = 100_0000 ) -> int:
'''simple docstring'''
snake_case : Tuple = 0
snake_case : Union[str, Any] = 1
for current_denominator in range(1 , limit + 1 ):
snake_ca... | 36 |
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 | 1 |
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 |
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 | 1 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class _A :
'''simple docstring'''
__lowerCamelCase : Optional[str] = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path of model to be trained.'''} ... | 36 |
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 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__lowercase : Any = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', acti... | 36 |
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 | 1 |
from ... import PretrainedConfig
__lowercase : Union[str, Any] = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] ... | 36 |
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 | 1 |
from PIL import Image
def lowercase ( __A : Image ) -> Image:
'''simple docstring'''
snake_case , snake_case : Any = image.size
snake_case : Optional[int] = 0
snake_case : Optional[int] = image.load()
for i in r... | 36 |
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 | 1 |
import os
from datetime import datetime as dt
from github import Github
__lowercase : List[str] = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def lowercase ( ) -> Any:
'''simple docstring'''
snake_case : List[str] = ... | 36 |
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 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 36 |
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 | 1 |
from math import pi
def lowercase ( __A : int , __A : int ) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 36 |
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 | 1 |
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
from ...test_c... | 36 |
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 | 1 |
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 |
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 | 1 |
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 Path
from urllib.p... | 36 |
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 | 1 |
from __future__ import annotations
def lowercase ( __A : dict , __A : str ) -> set[str]:
'''simple docstring'''
snake_case , snake_case : str = set(__A ), [start]
while stack:
snake_case : str = stack.pop()
ex... | 36 |
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 | 1 |
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 |
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 | 1 |
# 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 import BaseOutput, randn_tensor
from .... | 36 |
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 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _A ( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ):
'''simple docstring'''
snake_case ... | 36 |
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 | 1 |
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
@dataclass
class _A ( snake_case ... | 36 |
# 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 | 1 |
from __future__ import annotations
def lowercase ( __A : int , __A : int ) -> tuple[int, int]:
'''simple docstring'''
if b == 0:
return (1, 0)
((snake_case) , (snake_case)) : List[str] = extended_euclid(__A , a % b )
snake_cas... | 36 |
__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 | 1 |
import sys
__lowercase : Union[str, Any] = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''668966... | 36 |
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 | 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 ... | 36 |
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 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase : List[Any] = logging.get_logger(__name__)
__lowercase : Tuple = {
'''xlm-mlm-en-2048''': '''... | 36 |
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 | 1 |
def lowercase ( __A : int , __A : int ) -> Optional[int]:
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__A , int(b / 2 ) ) * actual_power(__A , int(b / 2 ) )
else:
return a * actual_power(__A , ... | 36 |
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 | 1 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowercase ( __A : Any , __A : bool = True , __A : float = math.inf , __A : float = -math.inf , __A : float = math.inf , __A : float = -math.inf , __A : bool = F... | 36 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : Dict = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFor... | 36 |
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 | 1 |
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 |
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 | 1 |
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 ... | 36 |
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 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
B... | 36 |
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 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowercase : Optional[int] = logging.get_logger(__name__)
__lowercase : Any = {''... | 36 |
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 | 1 |
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 |
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 | 1 |
import gc
import threading
import time
import psutil
import torch
class _A :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
snake_case : Union[str, Any] = psutil.Process()
snake_case : Union[str, Any] = False
de... | 36 |
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 | 1 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__lowercase : str = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
'''... | 36 |
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 | 1 |
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,
resize,
to_channel_dime... | 36 |
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 | 1 |
def lowercase ( __A : list ) -> list:
'''simple docstring'''
if len(__A ) < 2:
return collection
def circle_sort_util(__A : list , __A : int , __A : int ) -> bool:
snake_case : int = False
if low == high:
... | 36 |
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 | 1 |
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 Acce... | 36 |
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 | 1 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import requir... | 36 |
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 | 1 |
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 |
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 | 1 |
from collections.abc import Callable
def lowercase ( __A : Callable[[float], float] , __A : float , __A : float ) -> float:
'''simple docstring'''
snake_case : float = a
snake_case : float = b
if function(__A ) == 0: # o... | 36 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__lowercase : List[str] = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:
... | 36 |
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 | 1 |
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_attention_mask
from ...test_pip... | 36 |
# 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 | 1 |
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
logg... | 36 |
__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 | 1 |
def lowercase ( __A : int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case : Tuple = 1
snake_case : Union[str, Any] = 1
while repunit:
snake_case : Tuple = (10 * r... | 36 |
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 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__lowercase : str =... | 36 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase : str = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['''X... | 36 |
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 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
ge... | 36 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnn... | 36 |
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 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowercase ( ) -> List[str]:
'''simple docstring'''
with offline(OfflineSimulationMode... | 36 |
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 | 1 |
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,
WavaVecaFeatureExtractor,
WavaVec... | 36 |
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 | 1 |
def lowercase ( __A : str , __A : list[str] ) -> str:
'''simple docstring'''
snake_case : Optional[int] = """"""
for word_or_phrase in separated:
if not isinstance(__A , __A ):
raise Exception("""join() accepts only strings to be j... | 36 |
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 | 1 |
import math
def lowercase ( __A : float , __A : float ) -> float:
'''simple docstring'''
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 ... | 36 |
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 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : str = logging.get_logger(__name__)
__lowercase : Optional[int] = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
'''mi... | 36 |
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 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase_ ( lowerCamelCase ):
a__ ... | 0 |
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 __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCa... | 1 |
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 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin... | 2 |
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 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = ["""image_processor""", """tokenizer"""]
lowerCAmelCase_ = ... | 3 |
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 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-k... | 4 |
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 datetime import datetime as dt
from github import Github
_lowercase = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def... | 5 |
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 |
from ...configuration_utils import PretrainedConfig
_lowerCamelCase = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://huggingface.co/google/tapas-base-f... | 6 |
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 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
a = logging.get_logger(__name__)
class l... | 7 |
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 |
'''simple docstring'''
import argparse
import os
import re
lowercase__ : Optional[int] = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
lowercase__ : Dict = re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and pu... | 8 |
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 |
from __future__ import annotations
import math
def A ( __UpperCamelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 9 |
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 |
from __future__ import annotations
import math
def _snake_case ( __snake_case ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 10 |
# 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 |
'''simple docstring'''
def lowerCAmelCase (__A = 600_851_475_143):
"""simple docstring"""
try:
_a = int(__A)
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''')
if n <= 0:
raise ValueError('''... | 11 |
__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 numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCamelCase__ : Any = version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
from nltk import word_tokenize... | 12 |
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 UpperCAmelCase__ ( UpperCAmelCase_ : str ) -> bool:
__lowerCamelCase : Dict = [int(UpperCAmelCase_ ) for i in ip_va_address.split('.' ) if i.isdigit()]
return len(UpperCAmelCase_ ) == 4 and all(0 <= int(UpperCAme... | 13 |
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 argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
a__ = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''attention.self''',
... | 14 |
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 logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
A : Tuple = logging.getLogger(__name__)
class A ( UpperCAmelCase__ ):
'''simple do... | 15 |
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 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ..... | 16 |
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 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : list[float] ,a__ : list[float] ) -> float:
__A : str = sorted(numsa + numsa )
__A , __A : Any = divmod(len(a__ ) ,2 )
if mod == 1:
return all_numbers[div]
else:... | 17 |
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 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_SCREAMING... | 18 |
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 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_a = """\
@misc{chen2021evaluating,
tit... | 19 |
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 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require... | 20 |
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 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Optional[Any] = {
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TableTransformerConfi... | 21 |
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 |
'''simple docstring'''
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
fr... | 22 |
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 Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ : str = logging.get_logger(__name__)
snake_case__ : List[str] = ... | 23 |
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'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def ... | 24 |
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 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ =... | 25 |
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 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from tran... | 26 |
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 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : Opti... | 27 |
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'''
import inspect
import unittest
class _a ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
'''simple docstring'''
tr... | 28 |
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 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ..... | 29 |
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 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def l... | 30 |
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 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def UpperCAmelCase_ ( __UpperCAmelCase : Tuple , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : int ) -> Dict:
SCREAMING_SNAKE_CASE_ = 0
if start < end... | 31 |
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 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Dict ... | 32 |
# 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : List[str] = logging.get_logger(__name__)
lowerCamelCase__ : Dict = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/... | 33 |
__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 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is... | 34 |
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 |
def a ( A__ = 1_0_0_0 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
... | 35 |
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 collections import defaultdict
from math import gcd
def UpperCamelCase_ ( __a = 1_500_000 ) -> int:
a__ : defaultdict = defaultdict(__a )
a__ : Optional[int] = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + ... | 37 |
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 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_co... | 38 |
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 |
import unittest
from transformers import DonutProcessor
lowerCAmelCase_ = '''naver-clova-ix/donut-base'''
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : Union[str, Any] ) ->Any:
... | 39 |
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 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def UpperCamelCase ( snake_case__ : str ) -> None:
UpperCamelCase , UpperCamelCase : Tuple = analyze_text(snake_case__ )
UpperCamelCas... | 40 |
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 |
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