code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_A : Optional[int] = get_tests... | 100 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Tuple ,lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : Union[str, Any] ,lowerCAmelCase_ : Optional[Any]=1024 ... | 220 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
def UpperCamelCase__ ( a__ ):
'''simple docstring'... | 713 | '''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__name__)
lowerca... | 58 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import M... | 333 |
import os
def __UpperCamelCase ( ):
"""simple docstring"""
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCAmelCase = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
... | 333 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
__A ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
__A ={
'voc... | 703 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__A =logging.get_logger(__name__)
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAme... | 113 | 0 |
from collections import deque
from math import floor
from random import random
from time import time
class _lowerCamelCase :
"""simple docstring"""
def __init__( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ : Optional[int] ... | 285 |
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_gpu, skip_mps
from ..pipeline_params import UNCONDITIONA... | 285 | 1 |
import random
def UpperCamelCase ( __lowercase : int ):
'''simple docstring'''
A_ : Tuple = num - 1
A_ : Optional[Any] = 0
while s % 2 == 0:
A_ : Optional[int] = s // 2
t += 1
for _ in range(5 ):
A_ ... | 70 | import numpy as np
_UpperCAmelCase = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
... | 70 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-... | 18 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSc... | 688 | 0 |
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CO... | 380 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowercase_ = '<<<<<<< This should probably be modified because it mentions: '
lowercase_ = ... | 380 | 1 |
"""simple docstring"""
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... | 530 | """simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCamelCase_ ( __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
def is_in_circle(__lowerCAmelCase , __lowerCAm... | 530 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase : Dict = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechCon... | 516 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
... | 516 | 1 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone im... | 357 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def A_ ( __lowercase , __lowercase , __lowercase = 1 , __lowercase = 1 , __lowercase = 1.0e4 , __lowercase = False , __lowercase = 1.0 , ):
assert timesteps.ndim == 1, "Timesteps ... | 357 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
... | 183 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''voc... | 183 | 1 |
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.... | 24 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : int = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
'JukeboxV... | 587 | 0 |
from __future__ import annotations
import math
def A__ ( lowerCamelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not pr... | 670 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 670 | 1 |
import math
from collections.abc import Iterator
from itertools import takewhile
def __UpperCAmelCase ( a_):
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 n... | 198 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case_ )
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
lowerCAmelCase = field(default... | 198 | 1 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( lowercase__ ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> ... | 700 |
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_transformer import PriorTransformer
fr... | 626 | 0 |
from __future__ import annotations
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase = None , UpperCamelCase = None , UpperCamelCase = False , ) -> tuple[int, float, str]:
lowerCAmelCase__ : Union[str, Any] = cipher_alphabet or [chr(A__ ) for ... | 678 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : Optional[Any] = {'configuration_reformer': ['REFOR... | 649 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encode... | 664 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 664 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case_ ( UpperCAmelCase_ ):
'''simple docstring'''
__UpperCamelCase = ['''image_processor''', '''tokenizer''']
__UpperCamelCase = '''CLIPImageProcessor'''... | 375 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.jso... | 375 | 1 |
'''simple docstring'''
import argparse
import os
# New Code #
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
... | 466 | '''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_commo... | 466 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputW... | 193 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
UpperCamelCase_ : List[str] = logging.get_logger(__name__)
class __lowercase ( __snake_case ):
def __init__(self : int , *snake_case : Optional[Any] ... | 461 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must b... | 703 |
"""simple docstring"""
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.war... | 573 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require... | 92 |
def _UpperCAmelCase ( A ):
'''simple docstring'''
for i in range(len(A ) - 1 , 0 , -1 ):
UpperCAmelCase__ =False
for j in range(A , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
UpperCAme... | 625 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
"s... | 700 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_UpperCAmelCase : Tup... | 288 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_p... | 459 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowercase = 4
lowercase = 3
class UpperCamelCase_ ( snake_case_ ):
'''sim... | 198 | 0 |
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 TFModelTesterMix... | 291 |
# 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
#
# U... | 291 | 1 |
def a__ ( A_ ):
'''simple docstring'''
return 10 - x * x
def a__ ( A_, A_ ):
'''simple docstring'''
if equation(SCREAMING_SNAKE_CASE__ ) * equation(SCREAMING_SNAKE_CASE__ ) >= 0:
raise ValueError("""Wrong space!""" )
__magi... | 529 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 603 | 0 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowercase__ :
UpperCamelCase_ = 42 # [batch_size x 3]
UpperCamelCase_ = 42 # [batch_size x 3]
UpperCamelCase_ = 42 # [batch_size x 3]
UpperCamelCase_ = 4... | 34 | from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def A ( _lowercase ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = analyze_text(_lowercase )
SCREAMING_SNAKE_CASE ... | 34 | 1 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__a: Union[str, Any] = (
"This metric will be removed from the library... | 152 |
import os
from datetime import datetime as dt
from github import Github
__lowerCamelCase : Optional[int] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def lowerCamelCase_() -> L... | 323 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__lowerCAmelCase = logging.get_logger(_... | 129 |
import re
from filelock import FileLock
try:
import nltk
__lowerCAmelCase = True
except (ImportError, ModuleNotFoundError):
__lowerCAmelCase = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def __lowerCam... | 129 | 1 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase__ = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def snake_case_ ( A_ : list[float] )... | 83 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
... | 156 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
if not all(char in '''01''' for char in bin_string ):
raise ValueError('''Non-binary value was passed to the function''' )
if not bin_string:
raise ValueError('''Empty string was passed to the function... | 523 |
"""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 UpperCAmelCase_ ( unittest.TestCase ):
def _lowerCamelCase ( self ) -> List[str]:
... | 523 | 1 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import Aut... | 614 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : List[str] = logging.get_logger(__name__)
UpperCamelCase__ : str = {
... | 614 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int , lowercase : int ) -> int:
while a != 0:
_a , _a = b % a, a
return b
def _lowerCamelCase ( lowercase : int , lowercase : int ) -> int:
... | 700 |
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _lowerCamelCase ( lowercase : Union[str, Any] , ... | 521 | 0 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_t... | 56 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
UpperCAmelCase_ ... | 255 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def __A ( a_ : int , a_ : int = 2 , a_ : int = 1 , a_ : int = 3 , )-> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('''The input value cannot be le... | 18 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def __A ( a_ : float , a_ : float , a_ : bool = False )-> list[float]:
'''simple docstring'''
if radian_... | 18 | 1 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __SCREAMING_SNAKE_... | 620 | import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import Te... | 613 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_token... | 720 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class a__ ( nn.Module ):
"""simple docstring"""
UpperCAmelCase__ :... | 446 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( A__ ):
... | 174 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowerCAmelCa... | 174 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _lowercase ( UpperCamelCase_ ) -> str:
'''s... | 400 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ) -> list[float]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , ... | 400 | 1 |
def __snake_case ( lowerCAmelCase_ ) -> float:
SCREAMING_SNAKE_CASE__ = 0
while len(lowerCAmelCase_ ) > 1:
SCREAMING_SNAKE_CASE__ = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
SCREAMING_SNAKE_... | 100 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avai... | 195 | 0 |
import random
def lowercase_ (A : List[str] , A : Tuple , A : int ):
snake_case__ : List[str] = a[left_index]
snake_case__ : Optional[int] = left_index + 1
for j in range(left_index + 1 , A ):
if a[j] < ... | 716 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since ... | 243 | 0 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""facebook/encodec_24khz""": """https://huggingface.co/fa... | 19 | 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( __lowerCamelCase ):
__a = test_file.split(os.path.sep )
if components[0:2] != ["te... | 559 | 0 |
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_image_in... | 700 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
... | 484 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Dict = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
... | 51 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging ... | 39 | 0 |
'''simple docstring'''
from math import factorial
def __a ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k ... | 340 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImag... | 340 | 1 |
"""simple docstring"""
def _A (__a ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 512 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrateg... | 512 | 1 |
import argparse
import json
from tqdm import tqdm
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=A__ , default='''biencode... | 391 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( A__: Optional[int] , A__: List[Any] , A__: str ):
'''simp... | 391 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class lowerCamelCase_( unittest.TestCase, A__ ):
'''simple docstring'''
def snake_case__ ( self ):
_lowerCamelCase = load_tool('''tex... | 661 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
... | 661 | 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 ...test_modeling_common... | 295 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Any
class UpperCamelCase :
def __init__( self , snake_case__ = None ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[str] = value
_SCREAMING_SNAKE_CASE ... | 295 | 1 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __magic_name__ ( unittest.TestCase ):
def lowerCAmelCase... | 446 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
lowercase =[8, 5, 9, 7]
lowercase =[
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowercase =[
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, ... | 446 | 1 |
from abc import ABC, abstractmethod
from typing import List, Optional
class snake_case_ (lowercase__ ):
"""simple docstring"""
def __init__( self):
"""simple docstring"""
self.test()
def A_ ( self):
"""simple docstring""... | 455 |
import logging
from transformers import PretrainedConfig
__lowerCamelCase = logging.getLogger(__name__)
__lowerCamelCase = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class ... | 455 | 1 |
"""simple docstring"""
def __magic_name__ ( UpperCamelCase : list[int] ) -> list[int]:
a__ = len(UpperCamelCase )
for i in range(UpperCamelCase ):
for j in range(i + 1 , UpperCamelCase ):
if numbers[j] < numbers[i]:
a__ , ... | 273 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import F... | 273 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowerCAmelCase : Dict = (720, 1_280) # Height, Width
__lowerCAmelCase : int = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowe... | 702 |
from math import factorial
__lowerCAmelCase : Dict = {str(d): factorial(d) for d in range(10)}
def UpperCAmelCase_ ( __lowerCAmelCase ) -> int:
return sum(DIGIT_FACTORIAL[d] for d in str(__lowerCAmelCase ) )
def UpperCAmelCase_ ( ) -> int:
__lowercas... | 284 | 0 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class A_ :
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( sel... | 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_a... | 84 | 1 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def _lowercase ( UpperCamelCase__ : np.ndarray, UpperCamelCase__ : tuple[int, int], UpperCamelCase__ : tuple[int, int], UpperCamelCase__ : bool, ):
__A : Optional[Any] ... | 718 |
'''simple docstring'''
from collections.abc import Sequence
def _lowercase ( UpperCamelCase__ : Sequence[float], UpperCamelCase__ : float ):
return sum(c * (x**i) for i, c in enumerate(UpperCamelCase__ ) )
def _lowercase ( UpperCamelCase__ : Sequenc... | 540 | 0 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Any =logging.get_logger(__name__)
lowerCAmelCase__ : Union[str, Any] ={... | 101 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
a : Optional[int] = logging.get_logger(__name__)
def lowercase ( __magic_name__ ):
'''simple docstring'... | 679 | 0 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 721 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availabl... | 315 | 0 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = (KDPMaDiscret... | 42 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resol... | 512 | 0 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): # noqa: E741
while r - l > 1:
lowerCAmelCase_ : int =(l + ... | 305 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowercase = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config... | 305 | 1 |
"""simple docstring"""
import argparse
import struct
import unittest
class __snake_case :
"""simple docstring"""
def __init__( self :Optional[Any] , UpperCamelCase__ :bytes ):
_a = data
# Initialize hash values
_a = [
... | 388 |
"""simple docstring"""
from __future__ import annotations
import math
def __a ( a, a ):
"""simple docstring"""
_a = u
for i in range(1, a ):
_a = temp * (u - i)
return temp
def __a ( ):
"""sim... | 388 | 1 |
def _snake_case ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
__UpperCAmelCase = generate_large_matrix()
__UpperCAmelCase = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1,... | 716 |
"""simple docstring"""
from __future__ import annotations
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A=None ) -> Tuple:
lowerCAmelCase_ :Optional[int] = data
lowerCAmelCase_ :List[Any] = None
... | 256 | 0 |
from functools import reduce
snake_case_ : Any = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'6689664895044524452316... | 488 | """simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def UpperCamelCase ( ) ->Optional[int]:
_lowerCamelCase : int = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
_lowerCamelCas... | 434 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
a : Union[str, Any] = logging.get_logger(__name__)
a : Dict = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dp... | 701 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available... | 31 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetu... | 26 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
__UpperCamelCase = "examples/"
__UpperCamelCase = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init... | 26 | 1 |
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : float , ):
"""simple docstring"""
lowerCamelCase_... | 625 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def snake_case__ ( __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : int ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] =x
lowerC... | 625 | 1 |
"""simple docstring"""
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def __snake_case ( __A : Union[str, Any] , __A : Any , __A : Tuple , __A : Union[str, Any] , __A ... | 265 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin... | 265 | 1 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 703 |
'''simple docstring'''
import os
import jsonlines
import numpy as np
from tqdm import tqdm
snake_case = 20_48
snake_case = 40_96
snake_case = 42
snake_case = os.environ.pop("""PROCESS_TRAIN""", """false""")
snake_case = {"""null""": 0, """s... | 568 | 0 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
def __init__( self : Tuple , SCREAMING_SNAKE_CASE : Optional[Any]="" , SCREAMING_SNAKE_CASE : Union[str, Any]="trai... | 496 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def __lowerCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowercase__ : Optional[int] = [
"encoder.version",
"decoder.version",
"mode... | 496 | 1 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base impor... | 700 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
low... | 24 | 0 |
"""simple docstring"""
class snake_case_ :
"""simple docstring"""
def __init__( self , __a ):
"""simple docstring"""
A__ = n
A__ = [None] * self.n
A__ = 0 # index of the first element
A__ ... | 260 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
SCREAMING_SNAKE_CASE : Tuple ... | 260 | 1 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision... | 512 | '''simple docstring'''
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_... | 512 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transfo... | 49 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Any = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'... | 49 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/co... | 702 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
... | 22 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowercase__ ( A__ ):
a_ ="Wav2Vec2FeatureExtractor"
a_ ... | 339 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Dict ={"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "P... | 136 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
UpperCAmelCase = (720, 1280) # Height, Width
UpperCAmelCase = (0.4, 0.6) # if height or width lower than this scale, drop it.
UpperCAmelCase = 1 / 100
UpperCAmelCase ... | 565 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowercase = set()
return any(
node not in visited and depth_first_search(__SCREAMING_SNAKE_CASE , __SCREAMING_SNA... | 565 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : Dict ):
"""simple docstring"""
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(__UpperCAmelCase ,... | 561 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _int... | 194 | 0 |
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,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
S... | 684 |
import os
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
with open(os.path.dirname(lowercase ) + """/grid.txt""" ) as f:
snake_case : Tuple = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowercase ) for x in f.readline().split()] )
snake_cas... | 684 | 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 import AttentionProcessor, Attn... | 110 |
"""simple docstring"""
from itertools import permutations
def lowerCamelCase ( _snake_case ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
UpperCAmelCase__ : List[str] = ... | 110 | 1 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 701 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCamelCase: Optional[int] =TypeVar('T')
class __lowercase( Generic[T] ):
"""simple docstring"""
def __init__( self : str , _lowerCAmelCase : T ... | 585 | 0 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
a__ : int = (
'This metric will be removed fr... | 51 |
def lowerCamelCase ( a_ ) -> list:
lowerCAmelCase_ = len(a_ )
for _ in range(a_ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
lowerCAmelCase_ , low... | 318 | 0 |
def lowerCamelCase_ ( lowerCAmelCase: str )-> int:
_snake_case : Any = hex_num.strip()
if not hex_num:
raise ValueError('No value was passed to the function' )
_snake_case : str = hex_num[0] == '-'
if is_negative:
_snake_case : List[str] ... | 720 |
from functools import reduce
lowerCAmelCase_ = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66... | 669 | 0 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def A ( __snake_case: Optional[int] = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() )... | 545 |
def lowercase ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 ,999 )
for b in range(_a ,999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"""{solution() = }""") | 137 | 0 |
import re
def UpperCamelCase_ ( lowerCAmelCase__ ):
"""simple docstring"""
if len(re.findall("[ATCG]" , lowerCAmelCase__ ) ) != len(lowerCAmelCase__ ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , "TAGC" ) )
... | 587 | from __future__ import annotations
import math
def UpperCamelCase_ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
"""simple docstring"""
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if not sc... | 587 | 1 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _SCREAMING_SNAKE_CASE ... | 70 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowercase__ :List[Any] = Lock()
def lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase... | 522 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 701 | '''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atten... | 179 | 0 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_ve... | 221 | import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
a_ = 4
a_ = 3
class UpperCAmelCase__ ( snake_case ):
"""simple docstring"""
... | 221 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chan... | 459 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tes... | 459 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeat... | 325 |
def _A ( _lowercase = 1_00 ) -> int:
"""simple docstring"""
__UpperCamelCase = 0
__UpperCamelCase = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main_... | 1 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowerCamelCase... | 711 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-smal... | 686 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase_ : List[str] = {"""configuration_dpt""": ["""DPT_PRETRAINED... | 435 | import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A... | 486 | 0 |
import operator as op
def A__ ( lowerCamelCase ) -> Optional[int]:
UpperCamelCase_: Optional[int] = []
UpperCamelCase_: Optional[int] = lambda lowerCamelCase , lowerCamelCase : int(x / y ) # noqa: E731 integer division operation
UpperCame... | 704 |
import random
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False ) -> dict:
UpperCamelCase_: dict = {i: [] for i in range(lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability ... | 670 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__magic_name__ : str = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import... | 281 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common impor... | 281 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _snake_case (_snake_case : int = 3) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case):
raise TypeError('number ... | 557 |
# 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
#
# Unless required ... | 557 | 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 ...test_modeling_commo... | 691 | """simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention,... | 656 | 0 |
import os
from pathlib import Path
def A__ ( ):
from torch.utils.cpp_extension import load
SCREAMING_SNAKE_CASE__: int= Path(snake_case_ ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
SCREAMING_SNAKE_CASE__: Any= [
root / filename
for filename in... | 107 | lowercase_ : Optional[int] = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_availa... | 107 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a : Union[str, Any] = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHI... | 69 |
from math import factorial
def UpperCamelCase ( snake_case__ , snake_case__):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter pos... | 659 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase_ : Any = False
while is_sorted is False: # Until all the indices are traversed keep looping
lowercase_ : Any = True
for i in range(0 , ... | 438 | '''simple docstring'''
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase__ ( _snake_case , unittest.TestCase ):
... | 438 | 1 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
__UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class __lowercase... | 65 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> None:
'''simple docstring'''
_UpperCAmelCase = generate_pascal_triangle(__lowercase )
for row_idx in range(__lowercase ):
# Print left spaces
for _ in range(num_rows - ro... | 236 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
snake_case_ : List[str] = [True] * (num + 1)
snake_case_ : str = 2
whil... | 267 |
'''simple docstring'''
# Copyright (c) 2021-, NVIDIA CORPORATION. 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... | 267 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.