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 TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ : str = {
"configuration_blenderbot_small": [
"... | 185 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAU... | 132 | 0 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCamelCase ( UpperCAmelCase__ : ndarray ) -> float:
return np.dot(UpperCAmelCase__ , UpperCAmelCase__ )
class __magic_name__ ... | 21 | '''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_stagin... | 21 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A__ ( unittest.TestCase ):
@p... | 52 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONF... | 52 | 1 |
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'''{price_plus_tax(1_00, 0.25) = }''')
print(f'''{price_plus_tax(125.50, 0.05) = }''')
| 213 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class A__ ( snake_case__ ):
"""simple docstring"""
def __init__( self , *__snake_... | 213 | 1 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a_ = str(bin(UpperCAmelCase ) )[2:] # remove the leading "0b"
a_ ... | 243 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( SCREAMING_SNAKE_CASE_ ):
a_ : int = (KDPMaDiscreteScheduler,)
a_ : List[str] =... | 243 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common imp... | 350 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 46 | 0 |
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class _snake_case ( __a ):
'''simple docstring'''
... | 345 |
"""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
__magic_name__ = logging.get_logger(__name_... | 100 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from tran... | 163 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"t5-small": "https://huggingface.co/t5-smal... | 163 | 1 |
def UpperCamelCase_( lowerCamelCase_ = 1000 ) -> int:
_lowercase : Tuple = -1
_lowercase : List[str] = 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
_lowercase : ... | 21 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
SCREAMING_SNAKE_CASE : Any = logging.get_logg... | 21 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, ... | 117 |
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
_A = logging.get_logger(__name__)
_A = {'vocab_file': 'vocab.txt', 'token... | 117 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase__( __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE ... | 213 | """simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.... | 213 | 1 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
__SCREAMING_SNAKE_CASE :Tuple = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weig... | 362 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
_UpperCAmelCase = set()
# Replace all the whitespace in our sentence
_UpperCAmelCase ... | 156 | 0 |
# Function to print upper half of diamond (pyramid)
def UpperCamelCase_( lowerCamelCase_ ) -> List[str]:
for i in range(0 , lowerCamelCase_ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(' ' , end='' )
for _ in range(0 , i + 1 )... | 21 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
... | 46 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
... | 361 | from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowercase :
lowercase__ : torch.Tensor # [batch_size x 3]
lowercase__ : torch.Tensor # [batch_size x 3]
lowercase__ : torch.Tensor # [batch_size x 3]
... | 206 | 0 |
'''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 ... | 163 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
... | 163 | 1 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : Any = {name: getattr(t... | 367 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( SCREAMING_SNAKE_CASE : Optional[Any]... | 51 | 0 |
import numpy as np
from PIL import Image
def _a ( lowerCamelCase: np.ndarray , lowerCamelCase: int , lowerCamelCase: int ) -> np.ndarray:
'''simple docstring'''
__A = np.array(lowerCamelCase )
if arr.shape[0] != a... | 117 |
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
snake_case__ : List[Any] = logging.get_logg... | 117 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_lowerCamelCase : Union[str, Any] = datasets.load_iris()
_lowerCamelCase : Union[str, Any] = np.array(data["""data"""])
_lowerCamelCase : ... | 231 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"""configuration_speech_to_text""": ["""SPEECH_TO... | 231 | 1 |
from typing import Any
import numpy as np
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[str] ) -> bool:
"""simple docstring"""
return np.array_equal(__lowerCAmelCase , matrix.conjugate().T )
def __SCREAMING_SNAKE_CASE ( __UpperCame... | 219 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__lowerCAmelCase : str = False
__lowerCAmelCase : List[str] = True
__lowerCAmelCase : Union[str, Any] = False
... | 156 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
'''configuration_megatron_bert''': ['''MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegatronBertConfig'''],
}
try:
if not is_torch_available():
rais... | 365 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [int(_A ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(_A ) == 4 and all(0 <= int(_A ) <= 254 for octet in octets )
if __name__ == "__main__":
_A = input().strip()
_A = '''va... | 167 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Interpo... | 6 |
'''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
lowerCamelCase :Tuple = logging.get_logger(__name... | 206 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class ... | 362 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
... | 307 | 0 |
def A_ ( snake_case : int = 1000 ) -> int:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase = 1, 1
__UpperCamelCase = []
for i in range(1 , n + 1 ):
__UpperCamelCase = prev_numerator + ... | 328 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : int = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "Deber... | 51 | 0 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
snake_case : Tuple = '''http://www.mocksite.com... | 362 |
from __future__ import annotations
def __lowercase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str , __lowerCAmelCase : List[str] ): # noqa: E741
while r - l > 1:
a__ = (l + r... | 109 | 0 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class _lowerCAmelCase :
def __a ( self , _UpperCamelCase ) -> Optional[Any]:
raise NotImplementedError()
def __a (... | 231 |
import functools
def lowerCamelCase__ ( __lowerCAmelCase : str , __lowerCAmelCase : str ):
"""simple docstring"""
lowerCAmelCase_ = len(__lowerCAmelCase )
lowerCAmelCase_ = len(__lowerCAmelCase )
@functools.cache
def min_distance(__lowerCAmelCase : ... | 231 | 1 |
import math
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = [True] * n
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_... | 364 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
a__ : Optional[Any] = logging.get_logger(__name__)
a__ ... | 19 | 0 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class U... | 226 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : int = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/m... | 167 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''tokenization_r... | 354 | """simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
__UpperCamelCase =FileLock(str(tmpdir / '''foo.lock''' ) )
__UpperCamelCase =F... | 85 | 0 |
'''simple docstring'''
def snake_case_ ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase_ : Optional[int] = F'''Input... | 93 |
def a_ ( _A = 1000 ) -> int:
"""simple docstring"""
return sum(e for e in range(3 , _A ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 307 | 0 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ... | 36 |
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,
_is_whitespace,
)
from transformers.testing_utils ... | 36 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : List[Any]... | 14 |
"""simple docstring"""
from __future__ import annotations
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
'''simple docstring'''
UpperCAmelCase : Any = data
UpperCAmelCase : Node | None = None
UpperCAmelCase : ... | 109 | 0 |
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 A(__a: str ):
... | 22 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A(__a: Any , __a: Union[str, Any] , __a: List[str] ):
lowerCAmelCase_ = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelle... | 22 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_G... | 168 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A ={
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
... | 19 | 0 |
# 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 requi... | 367 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ = "cpu" , lowerCamelCase__ = None ):
"""simple docstring"""
lowercase__ : Any = torch.load(lowerCamelCase__ , map_locat... | 121 | 0 |
"""simple docstring"""
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable... | 290 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCamelCase_( snake_case : Tuple ):
'''simple docstring'''
snake_case_ = FileLock(str(tmpdir / "foo.lock" ) )
snake_cas... | 85 | 0 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
... | 367 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
... | 124 | 0 |
from __future__ import annotations
def A ( _lowerCamelCase ):
'''simple docstring'''
if len(_lowerCamelCase ) < 2:
raise ValueError("Monogons and Digons are not polygons in the Euclidean space" )
if any(i <= 0 for i in nums ):
raise Valu... | 36 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transfo... | 36 | 1 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = len(SCREAMING_SNAKE_CASE_ )
for i in range(length - 1 ):
lowercase__ = i
for k in range(i + 1 , SCREAMING_SNAKE_CASE_ ):
if collection[k] < collection[least]:
lowercase__... | 224 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Inte... | 224 | 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_c... | 22 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import T... | 22 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
... | 351 |
def lowerCamelCase__ ( a__ : List[Any] ) -> Optional[int]:
UpperCamelCase_ = len(a__ )
while cur > 1:
# Find the maximum number in arr
UpperCamelCase_ = arr.index(max(arr[0:cur] ) )
# Reverse f... | 261 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _a ( lowerCamelCase ):
for param in module.parameters():
lowerCamelCase : Union[str, Any] = False
def _a ( ):
lowerCamelCase : Union[str, Any] = '''cuda''' if torch.cuda... | 287 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],... | 121 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 370 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCAmelCase: Any = argparse.ArgumentParser()
parser.add_argument('--dump_... | 96 | 0 |
import unittest
import numpy as np
def __UpperCamelCase ( _A , _A , _A , _A = None , ):
lowerCAmelCase_ = np.shape(_A )
lowerCAmelCase_ = np.shape(_A )
lowerCAmelCase_ = np.shape(_A )
if shape_a[0] != shape_b[0]:
lowerCAmelCase_ ... | 278 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase : Optional[int] = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],... | 124 | 0 |
UpperCAmelCase : Union[str, Any] = 0 # The first color of the flag.
UpperCAmelCase : Optional[int] = 1 # The second color of the flag.
UpperCAmelCase : int = 2 # The third color of the flag.
UpperCAmelCase : Any = (red, white, blue)
def __lowerCamelCase ( ... | 353 |
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
UpperCAmelCase : Optional[Any] = logging.get_logger(... | 66 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
... | 224 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"""The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionImg2ImgPipeline` instead."""
)
| 224 | 1 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
fr... | 352 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingS... | 253 | 0 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __UpperCAmelCase ( __lowerCamelCase = "" ) -> dict[str, float]:
lowercase__ : Optional[int] = url or '''https://www.imdb.com/ch... | 16 | """simple docstring"""
def _lowerCamelCase( a = 1_0_0_0 ):
__a = 3
__a = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
a += 1
return re... | 261 | 0 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invali... | 356 |
"""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 | 0 |
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoder... | 40 |
"""simple docstring"""
def _snake_case ( lowercase__ , lowercase__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : List[Any] = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Tuple = b... | 96 | 0 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
Requ... | 211 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''facebook/xlm-roberta-xl''': '''https://h... | 211 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__: Union[str, Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docs... | 23 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig... | 66 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase__(A , A , A ) ->Optional[int]:
... | 150 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
a : Optional[Any] = [
"""Prosecutor: \"No videos were used in the crash investigat... | 150 | 1 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__UpperCamelCase : int ... | 106 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase : Dict = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutL... | 253 | 0 |
from collections import defaultdict
from math import gcd
def __lowercase ( a__ = 1_50_00_00 ) -> int:
__SCREAMING_SNAKE_CASE = defaultdict(a__ )
__SCREAMING_SNAKE_CASE = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_... | 118 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
c... | 118 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl""": ["""CTRLTok... | 256 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCAmelCase__( lowercase : Option... | 326 | 0 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable... | 141 |
from typing import Any
def UpperCamelCase (lowercase_: list ) -> list[Any]:
if not input_list:
return []
A__ : Any = [input_list.count(lowercase_ ) for value in input_list]
A__ : List[Any] = max(lowercase_ ) # Gets the maximum count in the input list.
# G... | 141 | 1 |
'''simple docstring'''
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 lowerCAmelCase (__A):
""... | 211 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at ... | 211 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
fr... | 369 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = ... | 340 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLI... | 150 | """simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {"vocab_file": "vocab.json"}
SCREAMING_S... | 150 | 1 |
import math
UpperCamelCase__ = 10
UpperCamelCase__ = 7
UpperCamelCase__ = BALLS_PER_COLOUR * NUM_COLOURS
def _a ( SCREAMING_SNAKE_CASE_ : int = 20 ):
__lowerCAmelCase = math.comb(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CA... | 102 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, ... | 102 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A : List[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try:
i... | 118 | from functools import lru_cache
@lru_cache
def a__ ( __UpperCamelCase ):
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 118 | 1 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] ):
"""simple docstring"""
__lowerCamelCase = Counter()
for base in range(1 , max_perimeter + 1 ):
... | 359 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).jso... | 348 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, sl... | 141 |
'''simple docstring'''
import math
class lowerCAmelCase :
def snake_case ( self : Optional[int] , __lowercase : list[list[float]] , __lowercase : list[int] ):
"""simple docstring"""
__lowercase =0.0
... | 141 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowerCamelCase_ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase_ : Dict = {name: getattr(transformers, name + """... | 359 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigT... | 223 | 0 |
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... | 24 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _a ( UpperCamelCase_ : str , UpperCamelCase_ : int , UpperCamelCase_ : List[str]=1_024 , UpperCamelCase_ ... | 340 | 0 |
"""simple docstring"""
import math
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> int:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multi... | 359 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowercase__ : Optional[int] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
lowercase... | 180 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, r... | 102 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER... | 102 | 1 |
"""simple docstring"""
import numpy
# List of input, output pairs
__lowercase = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__lowercase = (((515, 22, 13), 555), ((61, 35, 49), 150))
__lowercase = [2, 4, 1,... | 363 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if not is_torch_... | 85 | 0 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neur... | 97 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers... | 348 | 0 |
"""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 tests
... | 73 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : List[str] ) -> Optional[Any]:
_lowerCamelCase = len(lowercase_ )
while cur > 1:
# Find the maximum number in arr
_lowerCamelCase = arr.index(max(arr[0:cur] ) )
# Reve... | 73 | 1 |
from __future__ import annotations
_UpperCAmelCase : Optional[Any] =8.988E9 # units = N * m^s * C^-2
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
lowerCAmelCase_ : int = a... | 262 |
'''simple docstring'''
lowerCAmelCase : str ='''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install g... | 223 | 0 |
from __future__ import annotations
from math import pow, sqrt
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('''... | 367 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Tuple:
"""simple docstring"""
A__ = AutoConfig.from_pretrained(lowercase_ )... | 231 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.t... | 71 | import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
f... | 180 | 0 |
def A_ ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(_lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 140 |
from pathlib import Path
import numpy as np
from PIL import Image
def A_ ( _lowerCAmelCase ) -> np.ndarray:
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_989 * r + 0.5_870 * g + 0.1_140 * ... | 140 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransfor... | 100 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
class _snake_case ( lowercase_ ):
lower... | 85 | 0 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _snake_case ( A ) -> Dict:
if not is_accelerate_available():
return method
lowerC... | 228 |
'''simple docstring'''
def _snake_case ( A = 10 , A = 22 ) -> int:
lowerCAmelCase__ = range(1 , A )
lowerCAmelCase__ = range(1 , A )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) =... | 228 | 1 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class A_ :
def __init__( self : Optional[Any]):
__lowerCamelCase : Dict = ''
__lowerCamelCase : str = ''
__lowerCamelCase : ... | 73 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a ={
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask2former-swin-small-coco-... | 73 | 1 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Rand... | 281 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case : Union[str, Any] = logging.get_logger(__name__)
snake_case : List[str] ... | 281 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _a ( unittest.TestCase ):
de... | 147 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .... | 231 | 0 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transfo... | 371 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HAS... | 116 | 0 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCamelCase ( __lowercase : int ,__lowercase : int ,__lowercase : float = 1 / sqrt(2 ) ):
'''simple docstring'''
A_ : int = tau * frequency / samplera... | 140 | from typing import Any
import numpy as np
def UpperCamelCase ( __lowercase : np.ndarray ):
'''simple docstring'''
return np.array_equal(__lowercase ,matrix.conjugate().T )
def UpperCamelCase ( __lowercase : np.ndarray ,__lowercase ... | 140 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase__ (A ) ->Optional[Any]:
... | 365 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a : Dict = """
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 impl... | 150 | 0 |
from __future__ import annotations
def __A ( __lowerCamelCase ) -> int:
if not nums:
return 0
a = nums[0]
a = 0
for num in nums[1:]:
a , a = (
max_excluding + num,
max(__lowerCamelCase , __lowerCame... | 228 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate impor... | 228 | 1 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...util... | 363 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_M... | 42 | 0 |
def lowerCAmelCase_ ( _snake_case : int ) -> list:
'''simple docstring'''
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__magic_name__ : str = gray_code_sequence_string(_snake_case )
#
# convert them to ... | 281 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils impo... | 281 | 1 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
snake_case__ : Union[str, Any] = logging.get_logger(__name__)
def _a ( lowerCamelCase: Optional[Any] ) -> Dict:
... | 355 |
from __future__ import annotations
snake_case__ : Dict = [True] * 1000001
snake_case__ : int = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
snake_case__ : str = Fa... | 250 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase__ ( metaclass=SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
__UpperCAmelCase : List[str] = ["torch", "transformers", "onnx"]
def __init__( self : str ,*_a : ... | 271 |
def __UpperCamelCase ( _lowerCAmelCase = 100_0000 ) -> int:
"""simple docstring"""
A : str = limit + 1
A : Tuple = [0] * limit
for first_term in range(1 , _lowerCAmelCase ):
for n in range(_lowerCAmelCase , _lowerCAmelCa... | 116 | 0 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> np.ndarray:
A__ = np.array(lowercase_ )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a squa... | 230 |
"""simple docstring"""
SCREAMING_SNAKE_CASE = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
SCREAMIN... | 230 | 1 |
def lowerCamelCase__ ( A__ : Optional[Any]=28123 ):
'''simple docstring'''
__lowerCamelCase = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
... | 12 | """simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
SCREAMING_SNAKE_CASE__ = 2_048
SCREAMING_SNAKE_CASE__ = 4_096
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = os.environ.pop("PROCESS_TRAIN", "false")
SCREAMING_SNAKE_CA... | 150 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import Patchi... | 227 |
'''simple docstring'''
import string
import numpy
def lowerCAmelCase__ ( lowerCamelCase : int ,lowerCamelCase : int ):
return b if a == 0 else greatest_common_divisor(b % a ,lowerCamelCase )
class __lowerCamelCase :
"""simple docstring"""
a ... | 227 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case_ : List[str] = logging.get_logger(__name__)
snake_case_ : str = {
"SenseTime/deformable-detr": "https://huggingface.co/sensetime/... | 83 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get... | 42 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__:Optional[Any] = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_c... | 353 | """simple docstring"""
from __future__ import annotations
def _lowerCamelCase( a , a , a , a , a , ):
__a = len(a )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
... | 268 | 0 |
# 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 a... | 87 |
'''simple docstring'''
from __future__ import annotations
import requests
def _A ( snake_case ) -> dict:
_lowercase : Dict = F'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(snake_case ).json()
def _A ( snake_... | 250 | 0 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_... | 115 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE_:Any = {
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config""",
... | 115 | 1 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class a ( unittest.TestCase ):
@require_torch
de... | 230 |
import random
from .binary_exp_mod import bin_exp_mod
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase=1000 ) -> str:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
... | 230 | 1 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class low... | 267 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeniz... | 267 | 1 |
import math
import unittest
def a( A : int ) -> bool:
"""simple docstring"""
assert isinstance(A , A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return Tr... | 227 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _a ( _lowercase : List[str] ):
'''simple docstring'''
def wrapper(*_lowercase : int , **... | 353 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class a :
"""simple docstring"""
SCREAMING_SNAKE_CASE : torch.Tensor # [batch_size x 3]
SCREAMING_SNAKE_CASE : torch.Tenso... | 240 | 0 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _snake_case ( lowerCAmelCas... | 18 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Uppe... | 268 | 0 |
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 .transformer_engine import ... | 103 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_p... | 103 | 1 |
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