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 __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_a... | 271 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
A__ : List[str] = logging.get_logger(__name__)
class UpperCAmelCase_ (_lowercase ):
"""simple docstring"""
def __init__( self , *SCREAMIN... | 13 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 0 |
import re
import subprocess
import sys
a_ :Any = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
a_ :List[Any] = subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode("utf-8").split()
a_ :Optional[int] = '''|'''.join(sys.argv[1:])
a_ :Tup... | 478 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 115 | 0 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__ ( a__ , a__ , a__ ) ->int:
'''simple docstring'''
_UpperCamelCase = TaCo... | 547 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 115 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...featur... | 135 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 115 | 0 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any]=1 ) -> Union[str, Any]:
"""simple ... | 71 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case : Dict = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MA... | 441 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETR... | 115 | 0 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
snake_case_ = logging.getLogger()
def lowerCamelCas... | 592 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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 Con... | 115 | 0 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def UpperCamelCase_ ( __a , __a , **__a ) -> str:
a__ : str = AutoConfig.from_pretrained(__a , **__a )
a__ : Optional[int] = AutoModelForSeqaSeqLM.from_config(__a ... | 37 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfo... | 115 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
... | 240 |
"""simple docstring"""
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 A_ (__a , __a , __a , __a , __a ):
'''simple d... | 115 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_lowercase )
class UpperCAmelCase ( _lowercase ):
_lowercase: List[Any] ... | 207 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unispeech-large-1... | 271 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def UpperCAmelCase__ ( ) -> Optional[int]:
__lowerCamelCase : Dict = {}
__lowerCamelCase : List[Any] = 2
while True:
__lower... | 13 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 115 | 0 |
from __future__ import annotations
import os
from collections.abc import Mapping
a_ :int = tuple[int, int]
class snake_case__ :
"""simple docstring"""
def __init__( self : int, _snake_case : set[int], _snake_case : Mapping[EdgeT, int] ) ->None:
... | 478 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 0 |
import random
def lowerCAmelCase__ ( a__ ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase = num - 1
_UpperCamelCase = 0
while s % 2 == 0:
_UpperCamelCase = s // 2
t += 1
for _ in range(5 ):
_UpperCamelCase = ran... | 547 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 0 |
'''simple docstring'''
import math
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : str ):
'''simple docstring'''
A: List[str] = 0
A: int = 0
while num > 0:
A: str = num % 8
A: Dict = octal + (remainder * ma... | 135 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 115 | 0 |
'''simple docstring'''
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_rob... | 71 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not ... | 115 | 0 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
_snake_case : Tuple = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "... | 441 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_util... | 115 | 0 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_commo... | 592 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : ... | 115 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDi... | 37 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a ):
'''simple docstring'''
A_ = 0.00
A_ = 0
for resistor in resistors:
if resistor <= 0:
A_ = f'Resistor at index {index} has a negative or zero value!'
... | 115 | 0 |
import qiskit
def lowerCamelCase_ ( UpperCamelCase__ : Tuple = 2 ):
'''simple docstring'''
UpperCamelCase__ = qubits
# Using Aer's simulator
UpperCamelCase__ = qiskit.Aer.get_backend('''aer_simulator''' )
#... | 240 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 0 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class UpperCAmelCase ( _lowercase ):
def __init__( self : List[A... | 207 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import... | 115 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distr... | 271 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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
f... | 13 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 0 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class snake_case__ ( _lowercase ):
"""simple docstring"""
def __lt__( self : List[Any], _snake_case : Optional[Any] ) ->Any... | 478 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 115 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_t... | 547 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 115 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[int] ={
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''... | 135 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 115 | 0 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
_lowerCamelCase = lo... | 71 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case : int = {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAva... | 441 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETR... | 115 | 0 |
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_available... | 592 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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 Con... | 115 | 0 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=_lowercase ):
"""simple docstring"""
_lowercase = ['transformers', 'torch', 'note_seq']
def __init__( self : str , *lowerCamelCase__ : Union[str, Any] , **lowerCamelCase__ : str ):
... | 37 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfo... | 115 | 0 |
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
lowercase = [
'''EAGER''',
'''AOT_EAGER''',
'''INDUCTOR''',
'''NVFUSER''',
... | 240 |
"""simple docstring"""
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 A_ (__a , __a , __a , __a , __a ):
'''simple d... | 115 | 0 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple d... | 207 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 0 |
def lowerCAmelCase_ ( lowercase: Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase: str = int(__a )
if n_element < 1:
_UpperCamelCase: List[Any] = ValueError('''a should be a positive number''' )
raise my_error
_UpperCamelCase: List[... | 271 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 0 |
'''simple docstring'''
from collections import namedtuple
A__ : Tuple = namedtuple("""from_to""", """from_ to""")
A__ : Union[str, Any] = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.0_0_1, 1000),
'''kilolitre''': from_to(1, 1),
'''gallon''': from_to(0.0_0_... | 13 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 115 | 0 |
import argparse
import os
import re
a_ :Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a_ :Optional[int] = re.compile(R"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pa... | 478 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 0 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase__ ( a__ , a__ , a__ = 10**-10 ) ->Optional[int]:
'''simple docstring'''
_UpperCamelCase = a
while True:
_UpperCamelCase... | 547 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : List[str] ):
'''simple docstring'''
A: Any = [[0 for _ in range(__a )] for _ in range(m + 1 )]
for i in range(m + 1 ):
A: Union[str, Any] = 1
for n in ra... | 135 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 115 | 0 |
'''simple docstring'''
from maths.prime_check import is_prime
def a__ ( _SCREAMING_SNAKE_CASE : Tuple ) -> List[str]:
"""simple docstring"""
if not isinstance(__a , __a ):
UpperCAmelCase_ : str = F'''Input value of [number={number}] must b... | 71 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not ... | 115 | 0 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_snake_case : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( _lowercase ):
'''simple docstring'''
def __init__( self ... | 441 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_util... | 115 | 0 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowercase : Union[str, Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
#... | 116 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
f... | 116 | 1 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import ... | 116 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowerCamelCase__ ( __lowercase ):
snake_case : List[Any] = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case ... | 116 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : int = {
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConf... | 116 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"""microsoft/un... | 116 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase : Tuple = logging.get_logger(__name__)
lowercase ... | 116 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase : Tuple = {"""vocab_file""": ""... | 116 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a (datasets.BeamBasedBuilder ):
... | 116 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Any = {
"""huggingface/autoformer-t... | 116 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase : Optional[int] = logging.get_logger(__name__)
lowerc... | 116 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutpu... | 116 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
lowercase : Lis... | 116 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gp... | 116 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( __lowercase ):
snake_case : Dict = 0
snake_case : Optional[Any] = len(__lowercase )
for i in range(n - 1 ):
for j in range(i + 1 , __lowercase ):
... | 116 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _a :
'''simple docstring'''
def __init__( self ,__a ,__a ,__a ) -> Tuple:
if dst_width < 0 or... | 116 | 1 |
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
... | 116 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Optional[Any] = {
"""google/pegasus-large""": """https://huggingface.co/... | 116 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( __lowercase , __lowercase ):
if b == 0:
return (1, 0)
((snake_case) , (snake_case)) : List[Any] = extended_euclid(__lowercase , a % b ... | 116 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from .... | 116 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor im... | 116 |
'''simple docstring'''
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
)
lowercase : Any = lo... | 116 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase : Dict = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 116 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXCon... | 116 | 1 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import C... | 116 |
'''simple docstring'''
def lowerCamelCase__ ( __lowercase ):
if not isinstance(__lowercase , __lowercase ):
snake_case : int = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__lowercase )
... | 116 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : Any = {"""... | 116 |
'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_sch... | 116 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
low... | 116 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Any = ["""flax"""]
def __init__( self ,*__a ,**__a ... | 116 | 1 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
lowercase : Any = Fals... | 116 |
'''simple docstring'''
import numpy as np
lowercase : str = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
... | 116 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Dict = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
"""https:... | 116 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Union[str, Any] = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 116 | 1 |
'''simple docstring'''
import argparse
import copy
def lowerCamelCase__ ( __lowercase ):
snake_case : Optional[int] = {}
with open(__lowercase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbour... | 116 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_pr... | 116 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transf... | 116 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowercase : List[str] = """."""
if __name__ == "__main__":
lowercase : List... | 116 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( __lowercase , __lowercase ):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"""{price_plus_tax(100, 0.2_5) = }""")
print(F"""{price_plus_tax(1_2_5.5_0, 0.0_5) = }""")
| 116 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
lowercase : int = logging.getLogger(__name__)
class _a (a__ ):
'''simple docstring'''
lowerCAmelCase_ : Union[str, A... | 116 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowercase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Neg... | 116 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase : Tuple = logging.get_logger(__name__)
lowercase : Dict = {
"""ut/deta""": """htt... | 116 | 1 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowercase : D... | 116 |
'''simple docstring'''
from __future__ import annotations
class _a :
'''simple docstring'''
def __init__( self ,__a = 0 ) -> str:
snake_case : List[Any] = key
def snake_case_... | 116 | 1 |
'''simple docstring'''
import random
def lowerCamelCase__ ( __lowercase ):
snake_case : List[str] = num - 1
snake_case : Dict = 0
while s % 2 == 0:
snake_case : List[str] = s // 2
... | 116 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
f... | 116 | 1 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
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 im... | 116 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowerCamelCase__ ( __lowercase ):
snake_case : List[Any] = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case ... | 116 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase : Optional[Any] = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHI... | 116 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"""microsoft/un... | 116 | 1 |
'''simple docstring'''
from collections import deque
def lowerCamelCase__ ( __lowercase ):
snake_case : Dict = len(__lowercase )
snake_case : Any = deque()
snake_case : str = [False for _ in range(__lo... | 116 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase : Tuple = {"""vocab_file""": ""... | 116 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase : Optional[int] = {"""con... | 116 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Any = {
"""huggingface/autoformer-t... | 116 | 1 |
'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_sch... | 116 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutpu... | 116 | 1 |
'''simple docstring'''
import cva
import numpy as np
class _a :
'''simple docstring'''
def __init__( self ,__a ,__a ) -> str:
if k in (0.04, 0.06):
snake_case : Optional[int] ... | 116 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gp... | 116 | 1 |
'''simple docstring'''
import os
def lowerCamelCase__ ( ):
snake_case : Optional[int] = os.path.dirname(os.path.realpath(__lowercase ) )
snake_case : Dict = os.path.join(__lowercase , """triangle.txt""" )
w... | 116 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _a :
'''simple docstring'''
def __init__( self ,__a ,__a ,__a ) -> Tuple:
if dst_width < 0 or... | 116 | 1 |
'''simple docstring'''
import functools
def lowerCamelCase__ ( __lowercase , __lowercase ):
# Validation
if not isinstance(__lowercase , __lowercase ) or not all(isinstance(__lowercase , __lowercase ) for day in days ):
... | 116 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Optional[Any] = {
"""google/pegasus-large""": """https://huggingface.co/... | 116 | 1 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class _a (unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ) -> Union[str, Any]:
... | 116 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from .... | 116 | 1 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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 .... | 116 |
'''simple docstring'''
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
)
lowercase : Any = lo... | 116 | 1 |
'''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
lowercase : Dict = """... | 116 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXCon... | 116 | 1 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def lowerCamelCase__ ( ... | 116 |
'''simple docstring'''
def lowerCamelCase__ ( __lowercase ):
if not isinstance(__lowercase , __lowercase ):
snake_case : int = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__lowercase )
... | 116 | 1 |
'''simple docstring'''
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils impor... | 116 |
'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_sch... | 116 | 1 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
lowercase : List[str] = 6_3_7_8_1_3_7.0
lowercase : Union[str, Any] = 6_3_5_6_7_5_2.3_1_4_2_4_5
lowercase : str = 637_8137
def lowerCamelCase... | 116 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Any = ["""flax"""]
def __init__( self ,*__a ,**__a ... | 116 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
... | 116 |
'''simple docstring'''
import numpy as np
lowercase : str = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
... | 116 | 1 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_... | 116 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Union[str, Any] = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 116 | 1 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from .... | 116 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_pr... | 116 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
... | 116 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowercase : List[str] = """."""
if __name__ == "__main__":
lowercase : List... | 116 | 1 |
'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, ... | 116 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
lowercase : int = logging.getLogger(__name__)
class _a (a__ ):
'''simple docstring'''
lowerCAmelCase_ : Union[str, A... | 116 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[Any] = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_... | 116 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase : Tuple = logging.get_logger(__name__)
lowercase : Dict = {
"""ut/deta""": """htt... | 116 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase : List[Any] = {
"""configuration_resnet""": ["... | 116 |
'''simple docstring'''
from __future__ import annotations
class _a :
'''simple docstring'''
def __init__( self ,__a = 0 ) -> str:
snake_case : List[Any] = key
def snake_case_... | 116 | 1 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python u... | 116 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
f... | 116 | 1 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .pa... | 116 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowerCamelCase__ ( __lowercase ):
snake_case : List[Any] = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case ... | 116 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : Dict = logging.get_logger(__name__)
lowercase : ... | 116 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"""microsoft/un... | 116 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowercase ... | 116 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase : Tuple = {"""vocab_file""": ""... | 116 | 1 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, ... | 116 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Any = {
"""huggingface/autoformer-t... | 116 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( __lowercase , __lowercase ):
if len(__lowercase ) < k or k < 0:
raise ValueError("""Invalid Input""" )
snake_case : Dict = sum(array[:k] ... | 116 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutpu... | 116 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( __lowercase ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("""The given input must be positive""" )
# get the generated string sequence
snake_case... | 116 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gp... | 116 | 1 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
lowercase : Optional[Any] = datasets.logging.get_logger(__name__)
lowercase : Optional[int] = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricar... | 116 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _a :
'''simple docstring'''
def __init__( self ,__a ,__a ,__a ) -> Tuple:
if dst_width < 0 or... | 116 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _a :
'''simple docstring'''
def __init__( self ) -> None:
snake_case : list[Any] = []... | 116 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Optional[Any] = {
"""google/pegasus-large""": """https://huggingface.co/... | 116 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowercase : Union[str, Any] = 1.0_5457_1817E-34 # unit of ℏ : J * s
lowercase : Any... | 116 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from .... | 116 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase : Tuple = {"""vocab_file""": ""... | 116 |
'''simple docstring'''
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
)
lowercase : Any = lo... | 116 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutpu... | 116 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXCon... | 116 | 1 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_uti... | 116 |
'''simple docstring'''
def lowerCamelCase__ ( __lowercase ):
if not isinstance(__lowercase , __lowercase ):
snake_case : int = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__lowercase )
... | 116 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def lowerCamelCase__ ( __lowercase , __lowercase , __lowercase , __lowercase ):
snake_case : Optiona... | 116 |
'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_sch... | 116 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_in... | 116 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a (metaclass=a__ ):
'''simple docstring'''
lowerCAmelCase_ : Any = ["""flax"""]
def __init__( self ,*__a ,**__a ... | 116 | 1 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
lowercase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
lowercase : Optional[Any] = ty... | 116 |
'''simple docstring'''
import numpy as np
lowercase : str = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
... | 116 | 1 |
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