code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a ... | 183 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def UpperCAmelCase__ ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : float = 0.0 , UpperCAmelCase_ : float = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 ... | 185 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
... | 40 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
__a = 42
__a = 42
class lowerCAmelCase__ :
... | 40 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normaliz... | 320 |
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
# Check if the input is valid
if not len(UpperCamelCase__ ) == len(UpperCamelCase__ ) == 3:
raise ValueError("""Please enter a valid equation... | 294 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
),
'''... | 278 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
_snake_case = tau * frequency / samplerate
_snake_case ... | 278 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def A... | 328 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, r... | 328 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
A : List[Any] = TypeVar("T")
class lowerCamelCase (Generic[T] ):
"""simple docstring"""
def __init__( self : Optional[int] ... | 356 | import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main... | 305 | 0 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_a = logging.get_logger(... | 209 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Dict = logging.get_logger(__name__)
def a__ ( snake_case__ ) ... | 291 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE : Any = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
'''confi... | 218 |
import doctest
from collections import deque
import numpy as np
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : List[Any] ) -> None:
SCREAMING_SNAKE_CASE__ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE__ = [1, 2, 3, 4]
def lowercas... | 218 | 1 |
import random
from typing import Any
def _a ( SCREAMING_SNAKE_CASE : str ) -> list[Any]:
"""simple docstring"""
for _ in range(len(SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase: List[Any] = random.randint(0 , len(SCREAMING_SNAKE_CASE ) - 1 )
... | 322 |
'''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 vocab first, and then a tiny model - so the outcome... | 41 | 0 |
from __future__ import annotations
from typing import Any
class lowerCamelCase :
'''simple docstring'''
def __init__( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 0 ) -> None:
UpperCAmelCase_ , UpperCAmelCa... | 145 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
i... | 145 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,... | 125 |
'''simple docstring'''
snake_case_ : List[str] = {
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3",
... | 125 | 1 |
"""simple docstring"""
import os
def _snake_case ( UpperCAmelCase_ : str = "matrix.txt" ):
with open(os.path.join(os.path.dirname(UpperCAmelCase_ ) , UpperCAmelCase_ ) ) as in_file:
A__ = in_file.read()
A__ =... | 69 |
"""simple docstring"""
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_available():
from PIL i... | 69 | 1 |
"""simple docstring"""
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowerCamelCase_ : Any = 0b101_100_... | 81 |
import argparse
import json
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... | 38 | 0 |
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
lowercase_... | 269 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transfor... | 269 | 1 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterToke... | 185 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classe... | 185 | 1 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 371 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def SCREAMING_SNAKE_CASE__ ( ) -> None:
assert or_gate(0 ,0 ) == 0
assert or_gate(0 ,1 ) == 1
assert or_gate(1 ,0 ) ==... | 176 | 0 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def UpperCAmelCase__ ( _A : list[list[float]] ):
'''simple docstring'''
a__ =Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for ... | 188 |
def UpperCAmelCase__ ( _A : dict ):
'''simple docstring'''
a__ =set()
# To detect a back edge, keep track of vertices currently in the recursion stack
a__ =set()
return any(
node not in visited and depth_first_search(_A , _A , _A , _A )
for n... | 188 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _a ( ... | 326 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def ... | 326 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
... | 183 |
"""simple docstring"""
import os
def lowerCamelCase__ ( ) -> List[Any]:
with open(os.path.dirname(_lowerCamelCase ) + '/grid.txt' ) as f:
lowerCamelCase_ = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCamelCas... | 183 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case : str = {"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailab... | 134 |
from __future__ import annotations
_snake_case : Union[str, Any] = []
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
for i in range(len(__lowerCamelCase ) ):
if board[row][i] == 1:
... | 134 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ = 1_000_000 ) -> int:
lowercase : Optional[Any] = 1
lowercase : List[Any] = 1
lowercase : Any = {1: 1}
for inputa in range(2 , SCREAMING_SNAKE_CASE__ ):
... | 20 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requir... | 20 | 1 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( a , unittest.TestCase ):
__A = ... | 262 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1 / sqrt(2 ) ) -> IIRFilter:
lowerCamelCase =tau * frequency / samplerate
lowerCamelCase =sin(_UpperCAmelCase )
... | 262 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if i... | 52 | '''simple docstring'''
def snake_case__ ( _A: int , _A: int ) -> int:
'''simple docstring'''
while a != 0:
lowerCAmelCase , lowerCAmelCase = b % a, a
return b
def snake_case__ ( _A: int , _A: int ) -> int:
'''simple do... | 272 | 0 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgument... | 357 |
'''simple docstring'''
a_ : List[Any] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def a_ ( __snake_case : int ) -> int:
"""simple docstring"""
lowerCamelCase_ =0
while number:
# I... | 6 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Tuple = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
... | 274 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ... | 127 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt models at https://huggingf... | 364 | """simple docstring"""
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A = get_tests_dir("fixtu... | 2 | 0 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
def ... | 248 |
from __future__ import annotations
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : List[str] = str(a__)
return len(a__) == 9 and set(a__) == set("""123456789""")
def _UpperCAmelCase ( ):
'''simple docstring'''
for base_num in range(9_9_9_9 , 4_9... | 248 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import Ba... | 354 |
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_inputs
if is_tor... | 257 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
... | 64 |
def UpperCamelCase__( UpperCamelCase__ : Dict )->Dict:
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
A__ = len(UpperCamelCase__ )
A__ = ... | 193 | 0 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.process... | 350 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
UpperCAmelCase_ : List[Any] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculat... | 318 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_trajectory_transformer''': [
'''TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Traj... | 108 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase__ ( self ):
"""simple... | 108 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ :Union[str, Any] = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': ['''C... | 245 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __A ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ):
"""simple docstring"""
__Upper... | 245 | 1 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
_lowerCAmelCase = TypeVar('''T''')
class lowerCAmelCase_( Generic... | 37 |
'''simple docstring'''
from __future__ import annotations
class A__ :
def __init__( self :Union[str, Any] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :str ) -> Optional[int]:
'''simple docstring''... | 276 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : List[Any] = {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/res... | 313 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
'''simple docstring'''
lowercase_ , lowercase_ = np.shape(__lowerCAmelCase )
if rows !... | 313 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCAmelCase ( snake_c... | 79 |
'''simple docstring'''
from math import factorial
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : int, SCREAMING_SNAKE_CASE__ : int ) -> int:
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number,... | 125 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMSchedule... | 23 |
"""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 A_ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( sel... | 23 | 1 |
def a_ ( SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
if num < 0:
return False
_lowerCamelCase : int =num
_lowerCamelCase : int =0
while num > 0:
_lowerCamelCase : Optional[Any] =rev_num * 10 ... | 199 |
import os
import pytest
from attr import dataclass
lowerCamelCase = 'us-east-1' # defaults region
@dataclass
class A :
UpperCamelCase__ : str
UpperCamelCase__ : Dict ='arn:aws:iam::558105141721:role/sagemaker_execution_role'
UpperCamelCase__ : int ... | 199 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
_lowerCamelCase : Any = ''''''
_lowerCamelCase : Any = ''''''
_lowerCamelCase : Any = ''''''
_lowerCamelCase : List[Any] = 1 # (0 is vertical, 1 is horizontal)... | 371 |
import random
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
@staticmethod
def A ( lowercase : str ):
'''simple docstring'''
_snake_case = [ord(lowercase ) for i in text]
_snake_case = []
_snake_case = ... | 130 | 0 |
def _snake_case ( lowerCAmelCase : list ):
"""simple docstring"""
if len(lowerCAmelCase ) <= 1:
return [tuple(lowerCAmelCase )]
SCREAMING_SNAKE_CASE_ : Tuple = []
def generate(lowerCAmelCase : int , lowerCAmelCase : list ):
if k == 1:
res.ap... | 18 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_( snake_case : list[int] ):
'''simple docstring'''
return len(set(snake_case ) ) == len(snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 85 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmel... | 28 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
__UpperCAmelCase = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
__UpperCAmelCase... | 28 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = [0] * len(lowercase_ )
for i in range(1 , len(lowercase_ ) ):
# use last results for better performance - dynamic programming
UpperCAmelCase ... | 78 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
A_ = logging.get_logger(__name__)
class _snake_case ( _a ):
def __init__( self : Optional[int] ,*SCREAMING_SNAKE_CASE__ : ... | 139 | 0 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class _snake_case ( _lowercase... | 371 | 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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
f... | 342 | 0 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap-... | 72 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
lowerCamelCase__ = """us-east-1""" # defaults region
@dataclass
class A__ :
A_ : str
A_ : Union[str, Any] = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
A_ : ... | 86 | 0 |
import os
UpperCamelCase__ : Optional[int] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> int:
"""simple docstring"""
a = 0
a = 0
wh... | 330 |
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> Union[str, Any]:
"""simple docstring"""
stooge(snake_case_, 0, len(snake_case_ ) - 1 )
return arr
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_, snake_case_ ) -> ... | 330 | 1 |
from math import factorial
def __UpperCamelCase ( _A , _A ):
# 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 positive integers... | 278 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = args.log_outputs
lowerCAmelCase_ = ... | 278 | 1 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
"""simple docstring"""
A__ , ... | 154 | """simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase__( __A ... | 154 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Any = {
'huggingface/informer-tourism-monthly': (
'https://hugg... | 336 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils im... | 121 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( __A : list[int] , __A : int ) -> list[int]:
"""simple docstring"""
a_ : int = 0
a_ : Any = len(__A ) - 1
while i < j:
if nums[i]... | 362 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Dict = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependenc... | 120 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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_... | 31 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 0 |
'''simple docstring'''
import random
def UpperCAmelCase_ ( __lowerCamelCase : int ,__lowerCamelCase : str ,__lowerCamelCase : Any ):
lowercase_ :List[Any] = a[left_index]
lowercase_ :Union[str, Any] = left_index + 1
for j in range(left_i... | 147 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : dict ):
lowercase_ :set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowercase_ :set[int] = set()
return any(
node not in... | 147 | 1 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = f'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
... | 289 | """simple docstring"""
UpperCAmelCase__ = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ):
... | 289 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin... | 69 |
"""simple docstring"""
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_available():
from PIL i... | 69 | 1 |
import itertools
import math
def _a ( a :int ) -> 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 primes
return False
# All... | 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_inputs
if is_t... | 329 | 0 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa... | 362 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.... | 135 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowerCAmelCase = logging.get_logger(__name__)
class A_ ( A__ ):
"""simple docstring"""
def __init__( self :List[str] ... | 126 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerat... | 126 | 1 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_a : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"""... | 364 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 100 | 0 |
def lowerCamelCase__ ( A__ : int , A__ : int ):
'''simple docstring'''
return 1 if input_a == input_a else 0
def lowerCamelCase__ ( ):
'''simple docstring'''
assert xnor_gate(0 , 0 ) == 1
assert xnor_ga... | 12 | '''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 145 | 0 |
import os
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
UpperCamelCase__ : List[str] = len(grid[0] )
UpperCamelCase__ : Union[str, Any] = len(__lowerCAmelCase )
UpperCamelCase__ : Optional[An... | 196 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[int] =logging.get_logger(__name__)
lowerCamelCase : Optional[Any] ={
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface... | 196 | 1 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transf... | 242 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 242 | 1 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingf... | 367 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( A_ ):
lowercase__ = ['''image_processor''', '''tokenizer''']
lowercase__ = '''AutoImageProcessor'''
lowercase__ ... | 230 | 0 |
lowercase : List[str] = tuple[float, float, float]
lowercase : int = tuple[float, float, float]
def A_ ( A__ , A__ ) -> Vectorad:
a__ : Optional[int] = end_pointa[0] - end_pointa[0]
a__ : str = end_pointa[1] - end_pointa[1]
... | 99 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@requi... | 99 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase_ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
lowercase_ = {
... | 20 | from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
... | 20 | 1 |
def A ( _lowerCamelCase = 50_000_000 ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = set()
_lowerCAmelCase : Any = int((limit - 24) ** (1 / 2) )
_lowerCAmelCase : Tuple = set(range(3 , prime_square_limi... | 36 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/gi... | 323 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 1000 ):
lowerCAmelCase = -1
lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowerCAmelCase = (n * n - 2 * a * n... | 358 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from... | 309 | 0 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
... | 69 |
# Copyright 2022 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 applica... | 336 | 0 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common impor... | 240 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class a ( _a ):
"""simple docstring"""
def __init__( self : Optional[Any] ) -> int:
# test for the above condition
self.tes... | 240 | 1 |
"""simple docstring"""
import argparse
import json
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_... | 45 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : str , lowerCAmelCase__ : list[str] ) -> str:
__a = ''''''
for word_or_phrase in separated:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise Exception('''join(... | 45 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 ) -> None:
"""simple docstring"""
... | 183 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
SCREAMING_SNAKE_CASE__ = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
SCREAMING_SNA... | 183 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisionConfig',
... | 24 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaToke... | 24 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
if num < 0:
return False
lowerCamelCase__ : int = num
lowerCamelCase__ : int = 0
while num > 0:
lowerCamelCase__ : Optional[int] = rev_num * 10 + (num % 10)
num /... | 316 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = []
lowerCamelCase__ : List[str] = []
lowerCamelCase__ : Tuple = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
... | 316 | 1 |
"""simple docstring"""
def _A ( UpperCamelCase_ : int) -> Dict:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
__lowercase = 1
__lowercase = 1
while repunit:
__lowercase = (10 * repunit + 1) % divisor
... | 17 |
def a_ ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
assert x is not None
assert y is not None
_lowerCamelCase : Dict =len(SCREAMING_SNAKE_CASE__ )
_lowerCamelCase : Optional[Any] ... | 199 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import t... | 362 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :List[str] = logging.get_logger(__name__)
a :Union[str, Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve... | 56 | 0 |
'''simple docstring'''
def a ( __a ) -> list:
'''simple docstring'''
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
UpperCamelCase__ :Optional[int] = gray_code_sequence_string(__a )
... | 97 |
'''simple docstring'''
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... | 97 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 60_08_51_47_51_43 ) -> int:
'''simple docstring'''
try:
_A = int(_snake_case )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or casta... | 271 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 271 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : Tuple = str(bin(__snake_case ) )[2:] # remove the leading "0b"
_Upper... | 145 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params im... | 29 | 0 |
class lowercase :
def __init__( self , snake_case = "" , snake_case = False ):
# Mapping from the first character of the prefix of the node
snake_case_ = {}
# A node will be a leaf if the tree contains its word
snake_c... | 352 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
sna... | 200 | 0 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__... | 30 |
'''simple docstring'''
def __lowerCamelCase ( ) -> Tuple:
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def __lowerCamelCase ( lowerCAmelCase_ ) -> List[Any]:
_a : Any = 1
_a : Tuple = 2
while i * i... | 89 | 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 lowercase_ :
@property
def lowerCamelCase_ ( self ):
"""simple doc... | 358 |
def lowerCamelCase__ ( a__ : Optional[int] , a__ : Any ) -> Optional[Any]:
UpperCamelCase_ = 0
UpperCamelCase_ = len(a__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_co... | 261 | 0 |
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 _snake_case( SCREAMING_SNAKE_CASE__ ) ... | 20 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[str]:
if index == r:
for j in range(SCREAMING_SNAKE_CASE__ ):
prin... | 20 | 1 |
"""simple docstring"""
class lowercase__ :
def __init__( self : List[Any] , snake_case__ : int , snake_case__ : Optional[int]=None , snake_case__ : Union[str, Any]=None ):
lowerCamelCase_ : Union[str, Any] =data
lowerC... | 209 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
A__ : int = {
'n_samples': 64,
'horizon': 32,
'num_inference_steps': 20,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value network
's... | 209 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCamelCase_ : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailab... | 81 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Optional[Any] =logging.get_logger(__name__)
lowerCAmelCase : Tuple ={
'''facebook/vit-mae-base''': '''https://huggingface.co/faceb... | 223 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Any = logging.get_logger(__name__)
__snake_case : Union[str, Any] = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config... | 58 |
"""simple docstring"""
__snake_case : Dict = 65_521
def _lowercase ( __snake_case ) -> int:
__lowerCAmelCase : str = 1
__lowerCAmelCase : Union[str, Any] = 0
for plain_chr in plain_text:
__lowerCAmelCase... | 58 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class... | 252 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : List[str] = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
"XCLIPVisionConfig... | 252 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase : Optional[int] = {
"configuration_efficientformer": [
"EFFICIENTFORMER... | 208 |
def _SCREAMING_SNAKE_CASE ( lowercase : str ):
'''simple docstring'''
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... | 208 | 1 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.sched... | 283 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILIm... | 283 | 1 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a__ ( __lowercase = True , *__lowercase , **__lowercase ) -> str:
if not is_tqdm_availa... | 163 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_si... | 163 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('You cannot supply more or les... | 96 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'h... | 339 | 0 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
lowercase : Any = logging.get_logger(__name__)
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase) ... | 225 |
def A_ ( A__ ) -> int:
if not isinstance(A__ , A__ ):
raise TypeError('only integers accepted as input' )
else:
a__ : List[Any] = str(abs(A__ ) )
a__ : Optional[int] = [list(A__ ) for char in range(le... | 225 | 1 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_c... | 255 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase: List[str] = {}
try:
if not is_s... | 255 | 1 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : str = [0 for i in range(r + 1 )]
# nc0 = 1
__snake_case : Any = 1
for i in range(1 , n + 1 ):
# to compute current ro... | 369 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
if index == r:
for j in range(__lowerCamelCase ):
print(data[j] , ... | 134 | 0 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
a : Any = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
a : Optional[int] ... | 56 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , low... | 171 | 0 |
"""simple docstring"""
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class lowerCAmelCase__ ( ... | 350 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
UpperCAmelCase__ = logging.get_logger(__n... | 40 | 0 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_... | 10 |
'''simple docstring'''
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
_lowerCAmelCase = '''src/tr... | 298 | 0 |
# 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 by appl... | 105 | # 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 by appl... | 105 | 1 |
'''simple docstring'''
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
@... | 47 |
"""simple docstring"""
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _lowerCamelCase ( a_ ):
def _lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
return [
... | 242 | 0 |
from scipy.stats import spearmanr
import datasets
__UpperCAmelCase = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations ... | 42 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QFormerConfig''',
'''Blip... | 42 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
_lowercase : Optional[int] =logging.getLogger(__name__)
if is_torch_tpu_... | 170 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase_ ( _lowercase : Dict , _lowercase : str , _lowercase : str , _lowercase : Optional[Any]=1024) -> List[Any]:... | 170 | 1 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_avai... | 363 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Union[str, Any] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
... | 150 | 0 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_UpperCAmelCase = """\
@misc{chen2021evaluating,
title={Evaluating Large Language... | 140 | import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_UpperCAmelCase = """sshleifer/bart-tiny-random"""
_Uppe... | 140 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_... | 116 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _A ( _lowerCamelCase ):
_UpperCamelCase : Tuple = (PNDMScheduler,)
_UpperCamelCase : Optional[int] = (('''num_inference_steps'''... | 116 | 1 |
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