code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Any = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt... | 121 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeli... | 121 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case__ ( __snake_case ):
'''simple docstring'''
__A = '''ClapFeatureExtractor'''
__A = ('''RobertaTokenizer''', ... | 121 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acceler... | 121 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _lowerCAmelCase (... | 121 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _lowerCAmelCase ( ):
print('''Making key files...''' )
make_key_files('''rsa''' , 1_0_2_4 )... | 121 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 121 | 1 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 121 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 121 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _lowerCAmelCase ( __magic_name__ :Any ):
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class snake_cas... | 121 |
def _lowerCAmelCase ( __magic_name__ :list ):
if any(not isinstance(__magic_name__ , __magic_name__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(__magic_name__ ... | 121 | 1 |
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 diffuse... | 121 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.jso... | 121 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _lowerCAmelCase ( __magic_name__ :Tuple , __magic_name__ :Any=7 ):
UpperCAmelCase_ = None
if token is not None:
UpperCA... | 121 |
def _lowerCAmelCase ( __magic_name__ :list[list[int]] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :set ):
UpperCAmelCase_, UpperCAmelCase_ = len(__magic_name__ ), len(grid[0] )
if (
min(__magic_name__ , __ma... | 121 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case__ ( __snake_case ):
'''simple docstring'''
@staticmethod
@abstractmethod
def UpperCamelCase ( lowerCAmelCase_ : ArgumentPa... | 121 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class snake_case__ ( __snake_case ):
... | 121 | 1 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate mode... | 121 |
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _lowerCAmelCase ( __magic_name_... | 121 | 1 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :List[str] ,... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 | 1 |
from PIL import Image
def _lowerCAmelCase ( __magic_name__ :Image , __magic_name__ :int ):
UpperCAmelCase_ = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
def contrast(__magic_name__ :int ) -> int:
return int(1_2_8 + factor * (c ... | 121 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case__ :
'''simple docstring'''
__A = 42
__A = None
__A = None
_lowerCamelCas... | 121 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = [True] * limit
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = True
for i in range(3 , int(limit**0.5 + 1 ) ... | 121 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class snake_case__ :
'''simple docstring'''
__A = 42
__A = None
__A = None
def _lowerCAmelCase ( __magi... | 121 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase : str = logging.getLogger(__name__)
if __name__ ==... | 121 | 1 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _lowerCAmelCase ( __magic_name__ :Optional[Any] , __magic_name__ :List[Any] , __magic_name__ :str , __magic_name__ :Dict=5 ):
# Adapted from https://github.com/pyt... | 121 |
import os
from math import logaa
def _lowerCAmelCase ( __magic_name__ :str = "base_exp.txt" ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) )... | 121 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational im... | 121 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>'
_lowerCamelCase : Dict = [0] * 100 + [1] * 10
random.shuffle(choice... | 121 | 1 |
import requests
_lowerCamelCase : Optional[Any] = 'YOUR API KEY'
def _lowerCAmelCase ( __magic_name__ :str , __magic_name__ :str = giphy_api_key ):
UpperCAmelCase_ = '''+'''.join(query.split() )
UpperCAmelCase_ = F'''https://api.gip... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int | str] ):
create_state_space_tree(__magic_name__ , [] , 0 , [0 for i in range(len(__magic_name__ ) )] )
def _lowerCAmelCase ( ... | 121 | 1 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_lowerCamelCase : List[str] = logging.getLogger(__name__)
@dataclass
class snake_case__ ( __sn... | 121 |
# 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 switchi... | 121 | 1 |
from __future__ import annotations
import math
def _lowerCAmelCase ( __magic_name__ :int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even... | 121 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase : Union[str, Any] = {
'tiny.en': 'https://openaipublic.azu... | 121 | 1 |
# 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 switchi... | 121 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_na... | 121 | 1 |
import inspect
import unittest
from transformers import ViTMSNConfig
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 ConfigTester... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
... | 121 | 1 |
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 OptionalDependencyNotAvailab... | 121 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possib... | 121 | 1 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_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 ...te... | 121 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeli... | 121 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _lowerCAmelCase ( __magic_name__ :str , __magic_name__ :str = "cpu" , __magic_name__ :Union[str, None] = None ):
UpperCAmelCase_ = torch.load(__magic_name__ , map_loc... | 121 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acceler... | 121 | 1 |
import unittest
from transformers import AutoTokenizer, FalconConfig, 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_co... | 121 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers im... | 121 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 121 | 1 |
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=__snake_case ):
'''simple docstring'''
__A = ['''torch''', '''scipy''']
def __init__( self : str , *lowerCAmelCase_ : Union... | 121 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :list , __magic_name__ :list , __magic_name__ :int ):
if len(__magic_name__ ) != len(__magic_name__ ):
raise ValueError('''The length of profit and weight must be same.''' )
if max_weight <= 0:
... | 121 |
def _lowerCAmelCase ( __magic_name__ :list ):
if any(not isinstance(__magic_name__ , __magic_name__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(__magic_name__ ... | 121 | 1 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCamelCase : str = 10
def _lowerCAmelCase ( __magic_name__ :int , __magic... | 121 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.jso... | 121 | 1 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class ... | 121 |
def _lowerCAmelCase ( __magic_name__ :list[list[int]] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :set ):
UpperCAmelCase_, UpperCAmelCase_ = len(__magic_name__ ), len(grid[0] )
if (
min(__magic_name__ , __ma... | 121 | 1 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase : Union[str, Any] = {
'tiny.en': 'https://openaipublic.azu... | 121 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class snake_case__ ( __snake_case ):
... | 121 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 |
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _lowerCAmelCase ( __magic_name_... | 121 | 1 |
class snake_case__ :
'''simple docstring'''
def __init__( self : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : int ) -> str:
UpperCAmelCase_ = None
... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 121 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case__ :
'''simple docstring'''
__A = 42
__A = None
__A = None
_lowerCamelCas... | 121 | 1 |
import numpy
class snake_case__ :
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase_ : numpy.ndarray , lowerCAmelCase_ : numpy.ndarray ) -> None:
UpperCAmelCase_ = input_array
... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = [True] * limit
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = True
for i in range(3 , int(limit**0.5 + 1 ) ... | 121 | 1 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def _lowerCAmelCase ( __magic_name__ :List[Any] ):
UpperCAmelCase_ = test_file.split(os... | 121 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase : str = logging.getLogger(__name__)
if __name__ ==... | 121 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 |
import os
from math import logaa
def _lowerCAmelCase ( __magic_name__ :str = "base_exp.txt" ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) )... | 121 | 1 |
from __future__ import annotations
from collections.abc import Callable
_lowerCamelCase : str = list[list[float | int]]
def _lowerCAmelCase ( __magic_name__ :Matrix , __magic_name__ :Matrix ):
UpperCAmelCase_ = len(__magic_name__ )
... | 121 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>'
_lowerCamelCase : Dict = [0] * 100 + [1] * 10
random.shuffle(choice... | 121 | 1 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avai... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int | str] ):
create_state_space_tree(__magic_name__ , [] , 0 , [0 for i in range(len(__magic_name__ ) )] )
def _lowerCAmelCase ( ... | 121 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : str = {
'micr... | 121 |
# 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 switchi... | 121 | 1 |
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,
)
_lowerCamelCase : List[Any] = {
'c... | 121 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase : Union[str, Any] = {
'tiny.en': 'https://openaipublic.azu... | 121 | 1 |
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... | 121 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_na... | 121 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
Vilt... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
... | 121 | 1 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _lowerCAmelCase ( __magic_name__ :List[str] ):
if "model" in orig_key:
UpperCAmelCase_ = orig_key.replace('''model.''' , '''''' )
if "norm1" in ori... | 121 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possib... | 121 | 1 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.n... | 121 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeli... | 121 | 1 |
from PIL import Image
def _lowerCAmelCase ( __magic_name__ :Image , __magic_name__ :float ):
def brightness(__magic_name__ :int ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueErr... | 121 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acceler... | 121 | 1 |
_lowerCamelCase : Any = 8.314462 # Unit - J mol-1 K-1
def _lowerCAmelCase ( __magic_name__ :float , __magic_name__ :float , __magic_name__ :float ):
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Ente... | 121 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 | 1 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_na... | 121 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 121 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepIn... | 121 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 121 | 1 |
# Copyright 2023 The HuggingFace Inc. 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
#
# Unl... | 121 |
def _lowerCAmelCase ( __magic_name__ :list ):
if any(not isinstance(__magic_name__ , __magic_name__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(__magic_name__ ... | 121 | 1 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
... | 121 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.jso... | 121 | 1 |
import argparse
import os
import re
import packaging.version
_lowerCamelCase : List[str] = 'examples/'
_lowerCamelCase : str = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(... | 121 |
def _lowerCAmelCase ( __magic_name__ :list[list[int]] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :set ):
UpperCAmelCase_, UpperCAmelCase_ = len(__magic_name__ ), len(grid[0] )
if (
min(__magic_name__ , __ma... | 121 | 1 |
import sys
from collections import defaultdict
class snake_case__ :
'''simple docstring'''
def __init__( self : Optional[int] ) -> str:
UpperCAmelCase_ = []
def UpperCamelCase ( self : ... | 121 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class snake_case__ ( __snake_case ):
... | 121 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class snake_case__ ( ctypes.Structure ):
'''simple docstring'''
__A = [('''siz... | 121 |
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _lowerCAmelCase ( __magic_name_... | 121 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase : str = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 | 1 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = [True] * limit
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = True
for i in range(3 , int(limit**0.5 + 1 ) ... | 121 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case__ :
'''simple docstring'''
__A = 42
__A = None
__A = None
_lowerCamelCas... | 121 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
def wrapper(*__magic_name__ :str , **__magic_name__ :Any ... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = [True] * limit
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = True
for i in range(3 , int(limit**0.5 + 1 ) ... | 121 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Tuple = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 121 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase : str = logging.getLogger(__name__)
if __name__ ==... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :int ):
if number > 0:
raise ValueError('''input must be a negative integer''' )
UpperCAmelCase_ = len(bin(__magic_name__ )[3:] )
UpperCAmelCase_ = bin(abs(__magic_name__ ) - (1 << binary_number_le... | 121 |
import os
from math import logaa
def _lowerCAmelCase ( __magic_name__ :str = "base_exp.txt" ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) )... | 121 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.jso... | 121 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>'
_lowerCamelCase : Dict = [0] * 100 + [1] * 10
random.shuffle(choice... | 121 | 1 |
import math
import qiskit
def _lowerCAmelCase ( __magic_name__ :int = 1 , __magic_name__ :int = 1 , __magic_name__ :int = 1 ):
if (
isinstance(__magic_name__ , __magic_name__ )
or isinstance(__magic_name__ , __magi... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int | str] ):
create_state_space_tree(__magic_name__ , [] , 0 , [0 for i in range(len(__magic_name__ ) )] )
def _lowerCAmelCase ( ... | 121 | 1 |
from __future__ import annotations
import math
import random
from typing import Any
class snake_case__ :
'''simple docstring'''
def __init__( self : str ) -> None:
UpperCAmelCase_ = []
UpperCAmelCase_ = ... | 121 |
# 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 switchi... | 121 | 1 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_lowerCamelCase : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_p... | 121 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase : Union[str, Any] = {
'tiny.en': 'https://openaipublic.azu... | 121 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : ... | 121 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_na... | 121 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def _lowerCAmelCase ( __magic_name__ :str , __magic_name__ :str , __magic_name__ :Optional[int] , __magic_name__ :Optional[int] ):
Up... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
... | 121 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
class snake_case__ ( __snake_case ):
'''simple docstring'''
def __i... | 121 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possib... | 121 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from... | 121 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeli... | 121 | 1 |
import torch
def _lowerCAmelCase ( ):
if torch.cuda.is_available():
UpperCAmelCase_ = torch.cuda.device_count()
else:
UpperCAmelCase_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main(... | 121 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acceler... | 121 | 1 |
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
from ...test_tokenization_co... | 121 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 | 1 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : List[str] = logging.get_logger(__name__)
def _lowerCAmelCase ( __magi... | 121 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 121 | 1 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _lowerCAmelCase ( __magic_name__ :Any ):
UpperCAmelCase_ = args.pruning_method
UpperCAmelCase_ = args.threshold
... | 121 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 121 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class snake_case__ :
'''simple docstring'''
def __init__( self : int , lowerCAmelCase_ : Any ) -> Optional[Any]:
... | 121 |
def _lowerCAmelCase ( __magic_name__ :list ):
if any(not isinstance(__magic_name__ , __magic_name__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(__magic_name__ ... | 121 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_availab... | 121 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.jso... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :str , __magic_name__ :str ):
if len(__magic_name__ ) != len(__magic_name__ ):
raise ValueError('''String lengths must match!''' )
UpperCAmelCase_ = 0
for chara, chara in zip(__magic_name__ , ... | 121 |
def _lowerCAmelCase ( __magic_name__ :list[list[int]] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :set ):
UpperCAmelCase_, UpperCAmelCase_ = len(__magic_name__ ), len(grid[0] )
if (
min(__magic_name__ , __ma... | 121 | 1 |
from __future__ import annotations
class snake_case__ :
'''simple docstring'''
def __init__( self : str , lowerCAmelCase_ : int ) -> None:
UpperCAmelCase_ = order
# a_{0} ... a_{k}
U... | 121 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class snake_case__ ( __snake_case ):
... | 121 | 1 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int] ):
if len(__magic_name__ ) == 0:
return array
UpperCAmelCase_, UpperCAmelCase_ = min(__magic_name__ ), max(__magic_name__ )
# Compute the vari... | 121 |
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _lowerCAmelCase ( __magic_name_... | 121 | 1 |
from __future__ import annotations
_lowerCamelCase : Union[str, Any] = 'Muhammad Umer Farooq'
_lowerCamelCase : Dict = 'MIT'
_lowerCamelCase : List[Any] = '1.0.0'
_lowerCamelCase : int = 'Muhammad Umer Farooq'
_lowerCamelCase : List[str] ... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 | 1 |
import requests
from bsa import BeautifulSoup
def _lowerCAmelCase ( __magic_name__ :str = "https://www.worldometers.info/coronavirus" ):
UpperCAmelCase_ = BeautifulSoup(requests.get(__magic_name__ ).text , '''html.parser''' )
UpperCAmelCase_ =... | 121 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case__ :
'''simple docstring'''
__A = 42
__A = None
__A = None
_lowerCamelCas... | 121 | 1 |
from heapq import heappop, heappush
import numpy as np
def _lowerCAmelCase ( __magic_name__ :np.ndarray , __magic_name__ :tuple[int, int] , __magic_name__ :tuple[int, int] , __magic_name__ :bool , ):
UpperCAmelCase_, UpperCAmelCase_ = grid.s... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :int ):
UpperCAmelCase_ = [True] * limit
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = True
for i in range(3 , int(limit**0.5 + 1 ) ... | 121 | 1 |
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_pipeline_test,
nested_simplify,... | 121 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase : str = logging.getLogger(__name__)
if __name__ ==... | 121 | 1 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : str = {'vocab_file': 'vocab.json'}
_lowerCamelCase ... | 121 |
import os
from math import logaa
def _lowerCAmelCase ( __magic_name__ :str = "base_exp.txt" ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) )... | 121 | 1 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import AN... | 121 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>'
_lowerCamelCase : Dict = [0] * 100 + [1] * 10
random.shuffle(choice... | 121 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : str = {
'SenseTime/deformable-detr': 'https://huggingf... | 121 |
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ :list[int | str] ):
create_state_space_tree(__magic_name__ , [] , 0 , [0 for i in range(len(__magic_name__ ) )] )
def _lowerCAmelCase ( ... | 121 | 1 |
_lowerCamelCase : Optional[Any] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
_lowerCame... | 121 |
# 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 switchi... | 121 | 1 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transfo... | 121 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase : Union[str, Any] = {
'tiny.en': 'https://openaipublic.azu... | 121 | 1 |
from __future__ import annotations
import math
_lowerCamelCase : int = '2020.9.26'
_lowerCamelCase : List[str] = 'xcodz-dot, cclaus, dhruvmanila'
def _lowerCAmelCase ( __magic_name__ :float , __magic_name__ :float , __magic_name__ :fl... | 121 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ :Union[str, Any] , __magic_na... | 121 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def _lowerCAmelCase ( __magic_name__ :List[Any] , __magic_name__ :int ):
UpperCAmelCase_ = int(__magic_name__ )
assert noofclusters < len(__magic_name__ )
# F... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
... | 121 | 1 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _lowerCAmelCase ( ... | 121 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int , __magic_name__ :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possib... | 121 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_imag... | 121 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeli... | 121 | 1 |
class snake_case__ :
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase_ : str ) -> Optional[Any]:
UpperCAmelCase_ = val
UpperCAmelCase_ = None
UpperCAmelCase_ = N... | 121 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acceler... | 121 | 1 |
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,
AutoModelForSequence... | 121 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 | 1 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>'
_lowerCamelCase : Dict = [0] * 100 + [1] * 10
random.shuffle(choice... | 121 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import ena... | 121 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : str = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 121 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 121 | 1 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _lowerCAmelCase ( __magic_name__ :List[str] ): # p... | 121 |
def _lowerCAmelCase ( __magic_name__ :list ):
if any(not isinstance(__magic_name__ , __magic_name__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(__magic_name__ ... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :int , __magic_name__ :int ):
return base * power(__magic_name__ , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
_lowerCamelCas... | 121 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.jso... | 121 | 1 |
def _lowerCAmelCase ( __magic_name__ :list[list[int]] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :set ):
UpperCAmelCase_, UpperCAmelCase_ = len(__magic_name__ ), len(grid[0] )
if (
min(__magic_name__ , __ma... | 121 |
def _lowerCAmelCase ( __magic_name__ :list[list[int]] , __magic_name__ :int , __magic_name__ :int , __magic_name__ :set ):
UpperCAmelCase_, UpperCAmelCase_ = len(__magic_name__ ), len(grid[0] )
if (
min(__magic_name__ , __ma... | 121 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_lowerCamelCase : Tuple = False
class snake_case__ ... | 121 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class snake_case__ ( __snake_case ):
... | 121 | 1 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_... | 121 |
def _lowerCAmelCase ( __magic_name__ :str ):
UpperCAmelCase_ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _lowerCAmelCase ( __magic_name_... | 121 | 1 |
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=__snake_case ):
'''simple docstring'''
__A = ['''flax''']
def __init__( self : Union[str, Any] , *lowerCAmelCase_ : List[An... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : List[Any] = {
'Salesforce/blip-vqa-base': 'https://huggingface.c... | 121 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case__ :
'''simple docstring'''
__A = 42
__A = None
__A = None
_lowerCamelCas... | 121 | 1 |
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