code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
A__: Any = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAIN... | 276 |
'''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_sq... | 276 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
... | 276 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__: List[str] = logging.get_logger(__name__)
A__: Union[str, Any] ... | 276 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers... | 276 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils ... | 276 | 1 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" ,[
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""... | 276 |
'''simple docstring'''
from __future__ import annotations
import requests
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> dict:
_a : Any =F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
return requests.get(_UpperCAmelCa... | 276 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.ut... | 276 |
'''simple docstring'''
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, apply_forward_hook
from .modeling_utils import ModelMixin... | 276 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
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_... | 276 |
'''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, id... | 276 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
A__: int = ... | 276 |
'''simple docstring'''
from math import isqrt
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 ,isqrt(_UpperCAmelCase ) + 1 ) )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 10**6 ) -... | 276 | 1 |
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
... | 276 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F40... | 276 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
... | 276 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" ,[
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""... | 276 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class A__ :
__UpperCamelCase : Optional[str] = field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of mo... | 276 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Dict = logging.get_logger(__name__)
A__: Tuple = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/reso... | 276 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : List[str] ) -> List[str]:
if "model" in orig_key:
_a : str =orig_key.replace("""model.""" ... | 276 |
'''simple docstring'''
class A__ :
def __init__( self :List[str] ) -> List[Any]:
'''simple docstring'''
_a : Tuple =0
_a : Any =0
_a : int ={}
... | 276 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__: List[str] = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAI... | 276 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A__: Union[str, Any] = input('''Enter image url: ''').strip()
print(F"Downloading image from {url} ...")
A__: Tuple = Be... | 276 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Sequence[float] ,_UpperCAmelCase : bool = False ) -> float:
if not arr:
return 0
_a : Dict =0 if allow_empty_subarrays else float(... | 276 |
'''simple docstring'''
A__: Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+http... | 276 | 1 |
'''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, id... | 276 |
'''simple docstring'''
A__: Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: Any = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: int = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''T... | 276 | 1 |
'''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
A__: Tuple = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.... | 276 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : str
__UpperCamelCase : int
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> li... | 276 | 1 |
'''simple docstring'''
A__: Optional[Any] = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ... | 276 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__: List[str] = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_M... | 276 | 1 |
'''simple docstring'''
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_... | 276 |
'''simple docstring'''
class A__ :
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
_a : dict[str, TrieNode] ={} # Mapping from char to TrieNode
_a : List[str] =False
... | 276 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__: Tuple = logging.get_logger(__name__)
A__: int = ... | 276 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A__: str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Optional... | 276 | 1 |
'''simple docstring'''
import sys
from collections import defaultdict
class A__ :
def __init__( self :Optional[Any] ) -> List[str]:
'''simple docstring'''
_a : List[Any] =[]
def _... | 276 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as ... | 276 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__: str = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIV... | 276 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
A__: int = ... | 276 | 1 |
'''simple docstring'''
A__: List[Any] = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
... | 276 |
'''simple docstring'''
from __future__ import annotations
class A__ :
def __init__( self :Union[str, Any] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :str ) -> Optional[int]:
'''simple docstring''... | 276 | 1 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
A__: Dict = logging.getLogger(__name__)
class A__ :
... | 276 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e... | 276 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class A__ ( UpperCAmelCase__ ):
# `... | 276 |
'''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_sq... | 276 | 1 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
A__: str = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\... | 276 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__: List[str] = logging.get_logger(__name__)
A__: Union[str, Any] ... | 276 | 1 |
'''simple docstring'''
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, req... | 276 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils ... | 276 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
_a : Dict =str(bin(_UpperCAmelCase ) )[2:] # ... | 276 |
'''simple docstring'''
from __future__ import annotations
import requests
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> dict:
_a : Any =F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
return requests.get(_UpperCAmelCa... | 276 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__: List[str] ... | 276 |
'''simple docstring'''
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, apply_forward_hook
from .modeling_utils import ModelMixin... | 276 | 1 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class A__ ( UpperCAmelCase__ ):
def __UpperCAmelCase ( self :Optional[Any] ) -> int:
... | 276 |
'''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, id... | 276 | 1 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def... | 276 |
'''simple docstring'''
from math import isqrt
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 ,isqrt(_UpperCAmelCase ) + 1 ) )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 10**6 ) -... | 276 | 1 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : List[Any]=None ) -> List[str]:
... | 276 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F40... | 276 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: int = logging.get_logger(__name__)
A__: Optional[int] = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/r... | 276 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" ,[
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""... | 276 | 1 |
'''simple docstring'''
A__: Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+http... | 276 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Dict = logging.get_logger(__name__)
A__: Tuple = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/reso... | 276 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoC... | 276 |
'''simple docstring'''
class A__ :
def __init__( self :List[str] ) -> List[Any]:
'''simple docstring'''
_a : Tuple =0
_a : Any =0
_a : int ={}
... | 276 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipe... | 276 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A__: Union[str, Any] = input('''Enter image url: ''').strip()
print(F"Downloading image from {url} ...")
A__: Tuple = Be... | 276 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
ex... | 276 |
'''simple docstring'''
A__: Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+http... | 276 | 1 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils ... | 276 |
'''simple docstring'''
A__: Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: Any = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: int = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''T... | 276 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
_a : Optional[int] =_modexpt(_UpperCAmelCa... | 276 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : str
__UpperCamelCase : int
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> li... | 276 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassif... | 276 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__: List[str] = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_M... | 276 | 1 |
'''simple docstring'''
from __future__ import annotations
import requests
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> dict:
_a : Any =F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
return requests.get(_UpperCAmelCa... | 276 |
'''simple docstring'''
class A__ :
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
_a : dict[str, TrieNode] ={} # Mapping from char to TrieNode
_a : List[str] =False
... | 276 | 1 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : float ,_UpperCAmelCase : float ,_UpperCAmelCase : float ,) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError(... | 276 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A__: str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Optional... | 276 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
A__: str = 1.054571817E-34 # unit of ℏ : J * s
A__: int = 3E8 # unit of c : m... | 276 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as ... | 276 | 1 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as ... | 276 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
A__: int = ... | 276 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
_a : int =F"Input value of [number={number}] must be an integer"
raise TypeError(_UpperCAmelCase... | 276 |
'''simple docstring'''
from __future__ import annotations
class A__ :
def __init__( self :Union[str, Any] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :str ) -> Optional[int]:
'''simple docstring''... | 276 | 1 |
'''simple docstring'''
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def SCREAMIN... | 276 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e... | 276 | 1 |
'''simple docstring'''
from string import ascii_uppercase
A__: Tuple = {str(ord(c) - 55): c for c in ascii_uppercase}
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ) -> str:
if isinstance(_UpperCAmelCase ,_UpperCAmelCase )... | 276 |
'''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_sq... | 276 | 1 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import ... | 276 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__: List[str] = logging.get_logger(__name__)
A__: Union[str, Any] ... | 276 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A__: int = logging.get_logger(__name__) # pylint: disable=invalid-name
... | 276 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils ... | 276 | 1 |
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> int:
_a : str =prime_factors(_UpperCAmelCase )
if is_square_free(_UpperCAmelCase ... | 276 |
'''simple docstring'''
from __future__ import annotations
import requests
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> dict:
_a : Any =F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
return requests.get(_UpperCAmelCa... | 276 | 1 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class A__ ( UpperCAmelCase__ ):
def __UpperCAmelCase ( self :Dict , SCREAMING_SNAKE_CASE :List[str]=None , SCREAMING_SNAKE_CASE ... | 276 |
'''simple docstring'''
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, apply_forward_hook
from .modeling_utils import ModelMixin... | 276 | 1 |
'''simple docstring'''
from __future__ import annotations
class A__ :
def __init__( self :Union[str, Any] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :str ) -> Optional[int]:
'''simple docstring''... | 276 |
'''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, id... | 276 | 1 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class A__ ( unittest.TestCase ):
def __UpperCAmel... | 276 |
'''simple docstring'''
from math import isqrt
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 ,isqrt(_UpperCAmelCase ) + 1 ) )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 10**6 ) -... | 276 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 276 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F40... | 276 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : float ,_UpperCAmelCase : list[float] ) -> float:
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError("""Ca... | 276 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" ,[
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""... | 276 | 1 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
A__: str = '''src/transformers'''
# Matches is_xxx_available()
A__: List[str] = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = ... | 276 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Dict = logging.get_logger(__name__)
A__: Tuple = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/reso... | 276 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Union[str, Any] = logging.get_logger(__name__)
A__: Dict = {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface... | 276 |
'''simple docstring'''
class A__ :
def __init__( self :List[str] ) -> List[Any]:
'''simple docstring'''
_a : Tuple =0
_a : Any =0
_a : int ={}
... | 276 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : list ) -> list:
if any(not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in ... | 276 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A__: Union[str, Any] = input('''Enter image url: ''').strip()
print(F"Downloading image from {url} ...")
A__: Tuple = Be... | 276 | 1 |
'''simple docstring'''
class A__ :
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
_a : dict[str, TrieNode] ={} # Mapping from char to TrieNode
_a : List[str] =False
... | 276 |
'''simple docstring'''
A__: Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+http... | 276 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 3 ,_UpperCAmelCase : int = 7 ,_UpperCAmelCase : int = 1000000 ) -> int:
_a : Dict =0
_a : Optional[int] =1
for current_denominator in range(1 ,limit + 1 )... | 276 |
'''simple docstring'''
A__: Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: Any = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: int = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''T... | 276 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__: List[Any] = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
... | 276 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : str
__UpperCamelCase : int
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> li... | 276 | 1 |
'''simple docstring'''
import logging
import os
from .state import PartialState
class A__ ( logging.LoggerAdapter ):
@staticmethod
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE :Union[str, Any] ) -> Tuple:
... | 276 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__: List[str] = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_M... | 276 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A__: List[str] = {'''configuration_plbart''': [''... | 276 |
'''simple docstring'''
class A__ :
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
_a : dict[str, TrieNode] ={} # Mapping from char to TrieNode
_a : List[str] =False
... | 276 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
_a : Tuple =1
_a : List[str] =2
while i * i <= n:
_a : Tuple =0
while n % i == 0:
... | 276 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A__: str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Optional... | 276 | 1 |
'''simple docstring'''
import math
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> bool:
_a : Dict =math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( _Up... | 276 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as ... | 276 | 1 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE_ ( _Up... | 276 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
A__: int = ... | 276 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> Any:
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
... | 276 |
'''simple docstring'''
from __future__ import annotations
class A__ :
def __init__( self :Union[str, Any] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :str ) -> Optional[int]:
'''simple docstring''... | 276 | 1 |
'''simple docstring'''
A__: Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: Any = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: int = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''T... | 276 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e... | 276 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__: Optional[int] = {'''configuration_unispeech''': ['''UNISPEECH... | 276 |
'''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_sq... | 276 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from... | 276 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__: List[str] = logging.get_logger(__name__)
A__: Union[str, Any] ... | 276 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
A__: str = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM f... | 276 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils ... | 276 | 1 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
Bert... | 276 |
'''simple docstring'''
from __future__ import annotations
import requests
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> dict:
_a : Any =F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
return requests.get(_UpperCAmelCa... | 276 | 1 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class A__ ( UpperCAmelCase__ , UpperCAmelCase__ ):
@register_to_config
def _... | 276 |
'''simple docstring'''
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, apply_forward_hook
from .modeling_utils import ModelMixin... | 276 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Dict = logging.get_logger(__name__)
A__: Optional[int] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/... | 276 |
'''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, id... | 276 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class A__ ( UpperCAmelCase__ ... | 276 |
'''simple docstring'''
from math import isqrt
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 ,isqrt(_UpperCAmelCase ) + 1 ) )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 10**6 ) -... | 276 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipe... | 276 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F40... | 276 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 100 ) -> int:
_a : Dict =set()
_a : str =0
_a : List[str] =n + 1 # maximum limit
for a in range(2 ,_UpperCAmelCase ):
fo... | 276 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" ,[
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""... | 276 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Optional[int] = logging.get_logger(__name__)
A__: Tuple = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-ba... | 276 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Dict = logging.get_logger(__name__)
A__: Tuple = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/reso... | 276 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> Dict:
_a : Dict =SwinConfig(... | 276 |
'''simple docstring'''
class A__ :
def __init__( self :List[str] ) -> List[Any]:
'''simple docstring'''
_a : Tuple =0
_a : Any =0
_a : int ={}
... | 276 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__: Optional[int] ... | 276 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A__: Union[str, Any] = input('''Enter image url: ''').strip()
print(F"Downloading image from {url} ...")
A__: Tuple = Be... | 276 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
A__: int = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise Opt... | 276 |
'''simple docstring'''
A__: Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+http... | 276 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A__: int = {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
im... | 276 |
'''simple docstring'''
A__: Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: Any = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__: int = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''T... | 276 | 1 |
'''simple docstring'''
import string
from math import logaa
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ,_UpperCAmelCase : str ) -> int:
_a : Dict =document.translate(
str.maketrans("""""" ,"""""" ,string.punctuation ) ).replace("""\n... | 276 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : str
__UpperCamelCase : int
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> li... | 276 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> str:
if number > 0:
raise ValueError("""input must be a negative integer""" )
_a : int =len(bin(_UpperCAmelCase )[3:] )
_a : Tuple =bin(abs(_Uppe... | 276 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__: List[str] = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_M... | 276 | 1 |
'''simple docstring'''
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
... | 276 |
'''simple docstring'''
class A__ :
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
_a : dict[str, TrieNode] ={} # Mapping from char to TrieNode
_a : List[str] =False
... | 276 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer... | 276 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A__: str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Optional... | 276 | 1 |
'''simple docstring'''
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, apply_forward_hook
from .modeling_utils import ModelMixin... | 276 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as ... | 276 | 1 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_f... | 276 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
A__: int = ... | 276 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A__: Union[str, Any] = input('''Enter image url: ''').strip()
print(F"Downloading image from {url} ...")
A__: Tuple = Be... | 276 |
'''simple docstring'''
from __future__ import annotations
class A__ :
def __init__( self :Union[str, Any] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :str ) -> Optional[int]:
'''simple docstring''... | 276 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
A__: Union[str, Any] = logging.get_logger(__name__)
class A__ ( UpperCAmelCase__ ):
def __init__( sel... | 276 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e... | 276 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
A__: Optional[int] = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWit... | 276 |
'''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_sq... | 276 | 1 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : list[int | float] ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> int | float:
if len(_UpperCAmelCase ) == 0:
raise ValueError("""find_max() arg is... | 276 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__: List[str] = logging.get_logger(__name__)
A__: Union[str, Any] ... | 276 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Optional[Any] = logging.get_logger(__name__)
A__: Any = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co... | 276 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils ... | 276 | 1 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Op... | 276 |
'''simple docstring'''
from __future__ import annotations
import requests
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> dict:
_a : Any =F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
return requests.get(_UpperCAmelCa... | 276 | 1 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def SC... | 276 |
'''simple docstring'''
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, apply_forward_hook
from .modeling_utils import ModelMixin... | 276 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedTo... | 276 |
'''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, id... | 276 | 1 |
'''simple docstring'''
import gc
import threading
import time
import psutil
import torch
class A__ :
def __init__( self :int ) -> Tuple:
'''simple docstring'''
_a : int =psutil.Process()
... | 276 |
'''simple docstring'''
from math import isqrt
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 ,isqrt(_UpperCAmelCase ) + 1 ) )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 10**6 ) -... | 276 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : np.ndarray ,_UpperCAmelCase : np.ndarray ,_UpperCAmelCase : np.ndarray ,_UpperCAmelCase : np.ndarray | None = None ,) -> np.ndarray:
_a : int =np.... | 276 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F40... | 276 | 1 |
'''simple docstring'''
from math import isqrt
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 ,isqrt(_UpperCAmelCase ) + 1 ) )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 10**6 ) -... | 276 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" ,[
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""... | 276 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__: Optional[Any] = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
... | 276 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Dict = logging.get_logger(__name__)
A__: Tuple = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/reso... | 276 | 1 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Namespace ) -> Union[str, Any]:
return ConvertCommand(
args.model_type ,... | 276 |
'''simple docstring'''
class A__ :
def __init__( self :List[str] ) -> List[Any]:
'''simple docstring'''
_a : Tuple =0
_a : Any =0
_a : int ={}
... | 276 | 1 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> list[int]:
_a : Optional[Any] =2
_a : List[str] =[]
while i * i <= n:
if n % i:
i += 1... | 276 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A__: Union[str, Any] = input('''Enter image url: ''').strip()
print(F"Downloading image from {url} ...")
A__: Tuple = Be... | 276 | 1 |
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