code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transforme... | 528 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 623 | 0 |
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
lowercase : Optional[Any] =... | 302 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""... | 623 | 0 |
"""simple docstring"""
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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
... | 420 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mo... | 623 | 0 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _lowercase ( UpperCAmelCase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = ['''image_processor''', '''tokenizer''']
SCREAMING_SNAKE_C... | 696 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, ... | 623 | 0 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class A_ :
'''simple docstring'''
def __init__(self , lowercase__ ) -> Any:
if isinstance(_... | 303 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if n == 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return 0
elif n == 2:
return 1
else:
lowerCamelCase = [0, 1]
for i i... | 623 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
... | 576 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowercase( UpperCAmelCase__ = "laptop" ):
"""simple docstring"""
lowerCamelCase = F"""https://www.amazon.in/laptop/s?k={product}"""
l... | 623 | 0 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 309 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_):
"""simple docstring"""
_A = ['transformers', 'torch', 'note_seq']
def __init__(self , *__a , **__a ):
... | 623 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A : int = {
'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MA... | 349 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a_ : Any = logging.get_logger(__n... | 623 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoF... | 225 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from acceler... | 623 | 0 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
lowerCAmelCase__ = tuple[int, int]
class lowercase_ :
"""simple docstring"""
def __init__( self : List[str] ,lowercase__ : int ,lowercase__ : str ):
__lowe... | 41 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.u... | 623 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_... | 202 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 623 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_b... | 528 |
import socket
def __lowercase( ):
"""simple docstring"""
lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase = socket.gethostname()
lowerCamelCase = 12312
sock.connect((host, port... | 623 | 0 |
from __future__ import annotations
from math import pi
def A_ ( A__ , A__ , A__ ) -> int:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if inductance < 0:
... | 302 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
_A = (PNDMScheduler,)
_A = (('num_inference_steps', 50),)
... | 623 | 0 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenize... | 420 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
a_ : Tuple = datasets.logging.get_logger(__name__)
a_ : List[str] = '\\n@InProceedings{moosavi2019minimum,\n... | 623 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkou... | 696 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
... | 623 | 0 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A_ : Union[str, Any] = TypeVar('_T')
class A_ ( Generic[_T] ):
'''simple docstring'''
def __init__(self , lowercase__ = None ) -> Optional[int]:
__UpperCAmelCase ... | 303 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 623 | 0 |
def UpperCAmelCase__( __UpperCAmelCase : Union[str, Any] ):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(UpperCAmelCase__ ) == 0:
raise ValueError('Input list ... | 576 |
from __future__ import annotations
from math import pow, sqrt
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argume... | 623 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConf... | 309 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Au... | 623 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : Optional[int] = logging.ge... | 349 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 623 | 0 |
lowerCamelCase__ = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_... | 225 |
a_ : Dict = 8.314462 # Unit - J mol-1 K-1
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
... | 623 | 0 |
'''simple docstring'''
def _A ( A__ , A__ ):
"""simple docstring"""
while b:
__lowercase , __lowercase = b, a % b
return a
def _A ( A__ , A__ ):
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase... | 41 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCA... | 623 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
lowerCA... | 202 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
re... | 623 | 0 |
"""simple docstring"""
def _lowerCamelCase( a , a = False ):
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 1_0 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3_1_7_0_4_4_0_6_4_6... | 528 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 623 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Any = logging.get_logger(__name__)
lowercase : int = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
'google/fnet-large': 'https://huggingface.co/... | 302 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""... | 623 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def lowercase ( a__ : Dict="ro" , a__ : List[Any]="en" , a__ : int="wmt16" , a__ : List[Any]=None ) -> Optional[int]:
try:
import datasets
... | 420 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mo... | 623 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase : Union[str, Any] ={
'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}
try:
if not is_torch_a... | 696 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, ... | 623 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
A_ : Optional[Any] = '__DUMMY_TRANSFORMERS_USER__'
A_ : List[Any] = 'Dummy User'
A_ : Optional[Any] = 'h... | 303 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if n == 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return 0
elif n == 2:
return 1
else:
lowerCamelCase = [0, 1]
for i i... | 623 | 0 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__magic_name__ = 'src/transformers'
__magic_name__ = 'docs/... | 576 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowercase( UpperCAmelCase__ = "laptop" ):
"""simple docstring"""
lowerCamelCase = F"""https://www.amazon.in/laptop/s?k={product}"""
l... | 623 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = '▁'
snake_case = ... | 309 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_):
"""simple docstring"""
_A = ['transformers', 'torch', 'note_seq']
def __init__(self , *__a , **__a ):
... | 623 | 0 |
'''simple docstring'''
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, ... | 349 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a_ : Any = logging.get_logger(__n... | 623 | 0 |
from __future__ import annotations
from random import random
class SCREAMING_SNAKE_CASE :
def __init__( self : int , __lowercase : str = None ):
'''simple docstring'''
__a = value
__a = random()
__a = None
__a ... | 225 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from acceler... | 623 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class lowercase_ (UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Tuple ):
self.test()
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = ... | 41 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.u... | 623 | 0 |
from collections import Counter
from timeit import timeit
def lowerCAmelCase ( UpperCamelCase__ : Optional[int] = "" , ) -> str:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2... | 202 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 623 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase( a , a , a , a ): # noqa: E741
while r - l > 1:
__a = (l + r) // 2
if v[m] >= key:
__a = m
else:
__a ... | 528 |
import socket
def __lowercase( ):
"""simple docstring"""
lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase = socket.gethostname()
lowerCamelCase = 12312
sock.connect((host, port... | 623 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase : Dict = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependency... | 302 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
_A = (PNDMScheduler,)
_A = (('num_inference_steps', 50),)
... | 623 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
UpperCAmelCase = 500_000
UpperCAmelCase = os.path.split(__file__)
UpperCAmelCase = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FI... | 420 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
a_ : Tuple = datasets.logging.get_logger(__name__)
a_ : List[str] = '\\n@InProceedings{moosavi2019minimum,\n... | 623 | 0 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
fr... | 696 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
... | 623 | 0 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ... | 303 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 623 | 0 |
import requests
from bsa import BeautifulSoup
def UpperCAmelCase__( __UpperCAmelCase : Dict , __UpperCAmelCase : Dict ):
__snake_case : str = BeautifulSoup(requests.get(UpperCAmelCase__ , params=UpperCAmelCase__ ).content , 'html.parser'... | 576 |
from __future__ import annotations
from math import pow, sqrt
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argume... | 623 | 0 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distribut... | 309 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Au... | 623 | 0 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
A : Tuple = datasets.logging.get_logger(__name__)
A : List[str] = '\\n@InP... | 349 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 623 | 0 |
import mpmath # for roots of unity
import numpy as np
class SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : List[str]=None , __lowercase : Union[str, Any]=None ):
'''simple docstring'''
__a = list(poly_a or [0] )[:]... | 225 |
a_ : Dict = 8.314462 # Unit - J mol-1 K-1
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
... | 623 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_token... | 41 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCA... | 623 | 0 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCAmelCase : Dict = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
lowerCAmelCase : Optional[int] = None
def lowerCAmelCase ( ) -> Dict:
"... | 202 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
re... | 623 | 0 |
"""simple docstring"""
import os
import string
import sys
SCREAMING_SNAKE_CASE__:List[str] = 1 << 8
SCREAMING_SNAKE_CASE__:Union[str, Any] = {
'tab': ord("""\t"""),
'newline': ord("""\r"""),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 ... | 528 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 623 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 302 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""... | 623 | 0 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: ... | 420 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mo... | 623 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _UpperCamelCase ( lowercase__ , lowercase__=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split('''.''' )[n_shave_prefix_segments:] )
else:
... | 696 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, ... | 623 | 0 |
import operator as op
def __a ( SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__UpperCAmelCase = []
__UpperCAmelCase = lambda SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int(x / y ) # noqa: E731 integer division opera... | 303 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
if n == 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return 0
elif n == 2:
return 1
else:
lowerCamelCase = [0, 1]
for i i... | 623 | 0 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCAmelCase__( __UpperCAmelCase : Any , __UpperCAmelCase : Any ... | 576 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowercase( UpperCAmelCase__ = "laptop" ):
"""simple docstring"""
lowerCamelCase = F"""https://www.amazon.in/laptop/s?k={product}"""
l... | 623 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def A_ ( _lowerCamelCase : Union[str, Any] ):
_lowerCAmelCase = SwinConfig(image_size=192 )
if ... | 309 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=UpperCAmelCase_):
"""simple docstring"""
_A = ['transformers', 'torch', 'note_seq']
def __init__(self , *__a , **__a ):
... | 623 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : Union[str, Any] = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classica... | 349 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a_ : Any = logging.get_logger(__n... | 623 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowerCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE ( UpperCAme... | 225 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from acceler... | 623 | 0 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accel... | 41 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.u... | 623 | 0 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class a :
SCREAMING_SNAKE_CASE__ : List[Any] = 42
SCREAMING_SNAKE_... | 202 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 623 | 0 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_availabl... | 528 |
import socket
def __lowercase( ):
"""simple docstring"""
lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase = socket.gethostname()
lowerCamelCase = 12312
sock.connect((host, port... | 623 | 0 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() an... | 302 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase_):
"""simple docstring"""
_A = (PNDMScheduler,)
_A = (('num_inference_steps', 50),)
... | 623 | 0 |
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase = 1.6_021E-19 # units = C
def lowercase ( a__ : Tuple , a__ : Dict , a__ : Dict , ) -> Optional[int]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
... | 420 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
a_ : Tuple = datasets.logging.get_logger(__name__)
a_ : List[str] = '\\n@InProceedings{moosavi2019minimum,\n... | 623 | 0 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, l... | 696 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
... | 623 | 0 |
import warnings
warnings.warn(
'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
'`from accelerate import find_executable_batch_size` to avoid this warning.',
FutureWarning,
)
| 303 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner im... | 623 | 0 |
"""simple docstring"""
def lowercase__ ( lowercase_ = 50 ) -> int:
"""simple docstring"""
_UpperCamelCase : Optional[int] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 ,5 ):
... | 624 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.util... | 624 | 1 |
"""simple docstring"""
def lowercase__ ( lowercase_ = 1_000 ) -> int:
"""simple docstring"""
_UpperCamelCase : Optional[int] = 2**power
_UpperCamelCase : List[str] = str(lowercase_ )
_UpperCamelCase : str = ... | 624 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :Optional[Any] = ["image_processor", "tokenizer"]
... | 624 | 1 |
"""simple docstring"""
lowerCamelCase__ = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCamel... | 624 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfi... | 624 | 1 |
"""simple docstring"""
def lowercase__ ( lowercase_ ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase : List[Any] = len(lowercase_ )
for i in range(length - 1 ):
_UpperCamelCase : str = i
f... | 624 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
lowerCamelCase__ = False
lowerCamelCase__ = True
lowerCamelCase__ = False
if __name__ == "__main__":
lowerCamelCas... | 624 | 1 |
"""simple docstring"""
lowerCamelCase__ = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
lowerCamelCase__ = frozenset(["prom... | 624 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowercase__ ( lowercase_ ) -> Tuple:
"""simple docstring"""
_UpperCamelCase : Optional[int] = [
... | 624 | 1 |
"""simple docstring"""
def lowercase__ ( lowercase_ ,lowercase_ ) -> float:
"""simple docstring"""
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" ... | 624 |
"""simple docstring"""
def lowercase__ ( lowercase_ ) -> list:
"""simple docstring"""
if len(lowercase_ ) <= 1:
return [tuple(lowercase_ )]
_UpperCamelCase : Optional[Any] = []
def generate(lowercase_ ,lowercase_ ... | 624 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 624 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGenerat... | 624 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 624 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_sched... | 624 | 1 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :str = "EncodecFeatu... | 624 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 624 | 1 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCamelCase__ = {
"n_samples": 64,
"horizon": 32,
"num_inference_steps": 20,
"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network
... | 624 |
"""simple docstring"""
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 624 | 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 transforms
from torchvis... | 624 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ ,lowercase_=5 ) -> List[Any]:
"""simple docstring"""
assert masked_input.count("<mask>" ) == 1
... | 624 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.util... | 624 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
Stab... | 624 | 1 |
"""simple docstring"""
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : List[Any] ) -> Tuple:
_UpperCamelCase : List[str] = 0
_UpperCamelCase : Union[str, Any] = 0
_UpperCamelCase : int ... | 624 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequ... | 624 | 1 |
"""simple docstring"""
def lowercase__ ( lowercase_ ,lowercase_ ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def lowercase__ ( ) -> None:
"""simple docstring"""
print("Truth Table of NOR Gat... | 624 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 624 | 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.utils import floats_tensor, lo... | 624 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ) -> np.ndarray:
"""simple docstring"""
_UpperCamelCase : List[str] = int(np.ceil((x_... | 624 | 1 |
"""simple docstring"""
from collections import defaultdict
def lowercase__ ( lowercase_ ) -> int:
"""simple docstring"""
_UpperCamelCase : List[Any] = 1
_UpperCamelCase : Optional[Any] = True
for v in tree[start]:
... | 624 |
"""simple docstring"""
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
lowerCamelCase__ = False
try:
lowerCamelC... | 624 | 1 |
"""simple docstring"""
from math import factorial
def lowercase__ ( lowercase_ ,lowercase_ ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k" )
return factorial(l... | 624 |
"""simple docstring"""
import numpy as np
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ = 1e-12 ,lowercase_ = 100 ,) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(lowercase_ )[0] == np.shape(lowercase_ )[1]
# Ensure pr... | 624 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 624 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
lowerCamelCase__ = "examples/"
lowerCamelCase__ = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=... | 624 | 1 |
"""simple docstring"""
from math import factorial
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Union[str, Any] , __a : Optional[Any] , __a : List[str] ) -> Optional[Any]:
_UpperCamelCase : List[str] = ... | 624 |
"""simple docstring"""
import random
from typing import Any
def lowercase__ ( lowercase_ ) -> list[Any]:
"""simple docstring"""
for _ in range(len(lowercase_ ) ):
_UpperCamelCase : Dict = random.randint(0 ,len(lowercase_ ... | 624 | 1 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCamelCase__ = {"UserAgent": UserAgent().random}
def lowercase__ ( lowercase_ ) -> dict:
"""simple docst... | 624 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
... | 624 | 1 |
"""simple docstring"""
from __future__ import annotations
lowerCamelCase__ = []
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ ) -> bool:
"""simple docstring"""
for i in range(len(lowercase_ ) ):
if board[row][i] == 1:
... | 624 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
lowerCamelCase__ = "src/transformers"
# Matches is_xxx_available()
lowerCamelCase__ = re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
lowerCamelCase__ = re.compile(R"^_i... | 624 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 624 |
"""simple docstring"""
import re
def lowercase__ ( lowercase_ ) -> bool:
"""simple docstring"""
_UpperCamelCase : List[Any] = re.compile(
r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$" )
return bool(re.se... | 624 | 1 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def lowercase__ ( lowercase_ ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number <... | 624 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.util... | 624 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __SCREAMING_SNAKE_CASE ( _UpperC... | 624 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :Optional[Any] = ["image_processor", "tokenizer"]
... | 624 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowercase__ ( lowercase_ ) -> Any:
"""simple docstring"""
return DownloadCommand(args.model ,args.cache_dir ,args.force ,args.trust_remote_code )
... | 624 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfi... | 624 | 1 |
"""simple docstring"""
def lowercase__ ( lowercase_ ,lowercase_ ) -> str:
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
_UpperCamelCase : List[Any] = str(... | 624 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
lowerCamelCase__ = False
lowerCamelCase__ = True
lowerCamelCase__ = False
if __name__ == "__main__":
lowerCamelCas... | 624 | 1 |
"""simple docstring"""
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
def __init__( self : Any , __a : List[str]="" , __a : List[Any]="trai... | 624 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowercase__ ( lowercase_ ) -> Tuple:
"""simple docstring"""
_UpperCamelCase : Optional[int] = [
... | 624 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_sched... | 624 |
"""simple docstring"""
def lowercase__ ( lowercase_ ) -> list:
"""simple docstring"""
if len(lowercase_ ) <= 1:
return [tuple(lowercase_ )]
_UpperCamelCase : Optional[Any] = []
def generate(lowercase_ ,lowercase_ ... | 624 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ = 100 ,) -> float:
"""simple docstring"""
_UpperCamelCase : Any = x_start
... | 624 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGenerat... | 624 | 1 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tr... | 624 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_sched... | 624 | 1 |
"""simple docstring"""
def lowercase__ ( lowercase_ ,lowercase_ ) -> str:
"""simple docstring"""
if not (isinstance(lowercase_ ,lowercase_ ) and isinstance(lowercase_ ,lowercase_ )):
raise ValueError("longest_common_substring() takes two str... | 624 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 624 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.... | 624 |
"""simple docstring"""
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 624 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https:/... | 624 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ ,lowercase_=5 ) -> List[Any]:
"""simple docstring"""
assert masked_input.count("<mask>" ) == 1
... | 624 | 1 |
"""simple docstring"""
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 624 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
Stab... | 624 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 624 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequ... | 624 | 1 |
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def lowercase__ ( lowercase_ ) -> Dict:
"""simple docstring"""
_UpperCamelCase : Tuple = min(lowercase_ ) # min() finds the minimum value
_UpperCamelCase : Dict ... | 624 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 624 | 1 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
... | 624 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ) -> np.ndarray:
"""simple docstring"""
_UpperCamelCase : List[str] = int(np.ceil((x_... | 624 | 1 |
"""simple docstring"""
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
lowerCamelCase__ = yaml.safe_load(
"\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubsections:... | 624 |
"""simple docstring"""
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
lowerCamelCase__ = False
try:
lowerCamelC... | 624 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rando... | 624 |
"""simple docstring"""
import numpy as np
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ = 1e-12 ,lowercase_ = 100 ,) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(lowercase_ )[0] == np.shape(lowercase_ )[1]
# Ensure pr... | 624 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
def lowercase__ ( lowercase_ ) ->... | 624 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
lowerCamelCase__ = "examples/"
lowerCamelCase__ = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=... | 624 | 1 |
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