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 |
|---|---|---|---|---|
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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_co... | 352 |
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 con... | 47 | 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 transformers.utils ... | 353 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase , lowercase ) -> List[Any]:
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should ... | 47 | 0 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('''fixtures/spiece.model''')
@require_sentencepi... | 354 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
__A ={
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''... | 47 | 0 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memor... | 355 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A =pd.read_csv('''sample_data.csv''', header=None)
__A =df.shape[:1][0]
# If you're using som... | 47 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# ... | 356 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('''fixtures/spiece.model''')
@require_sentencepi... | 47 | 0 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__A =version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def lowerCamelCase_ ( lowerCamelCase__ , lowerC... | 357 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__A ={
'''iou_prediction_head.layers.0''': '''io... | 47 | 0 |
"""simple docstring"""
from math import pi
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return 2 * pi * radius * (angle / 3_6_0)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 358 |
__A ='''
# 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 git+https://github.com/huggingface/transformers.git
'''
__A =[{'''typ... | 47 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
"""junnyu/roformer_chinese_small""": """https://huggingface.co... | 359 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase = 16 , lowercase = 88 , lowercase = None , lowercase = 1 , lowerca... | 47 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A_ )
class _SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ = field(default='image-classificatio... | 360 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return math.pow(lowerCamelCase__ , 2 ) - a
def lowerCamelCase_ ( lowerCamelCase__ ):
return 2 * x
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = ... | 47 | 0 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__A : Dict =logging.getLogger(__name__)
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
def __init__( s... | 361 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = get_failure_array(lowerCamelCase__ )
# 2) Step through text searching for pattern
lowerCamelCase_ , lowerCamelCase_ = 0, 0 # index into tex... | 47 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, loa... | 362 |
import os
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(grid[0] )
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
# Check vertically, horizontally, di... | 47 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = 0
lowerCamelCase_ = len(_lowerCamelCase )
for i in range(n - 1 ):
for j in range(i + 1 , _lowerCamelCase ):
if arr[i] > arr[j]:
num_inversions += 1
return num_i... | 363 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTo... | 47 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A ={
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
'''BlipTex... | 364 |
from collections import defaultdict
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = first_str.lower().strip()
lowerCamelCase_ = second_str.lower().strip()
# Remove whitespace
lowerCamelCase_ = first_str.replace(" " ... | 47 | 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 applicab... | 365 |
from sklearn.metrics import recall_score
import datasets
__A ='''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
'''
__A ='''
Arg... | 47 | 0 |
from math import loga
def lowerCamelCase_ ( lowerCamelCase__ ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise TypeError("Input value must be a 'int' type" )
return 0 if (a... | 366 |
from __future__ import annotations
import math
def lowerCamelCase_ ( lowerCamelCase__ ):
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
... | 47 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNeta... | 367 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A ={
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeTowerConfig''',
'''BridgeTowerTextConfig''',
... | 47 | 0 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def lowerCamelCase_ ( lowerCamelCase... | 368 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__A =True
except ImportError:
__A =... | 47 | 0 |
import argparse
import struct
import unittest
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase ) -> None:
lowerCamelCase_ = data
# Initialize hash values
lowerCamelCase_ = [
0x6a_09e_667,
0xbb_67a_e85,
0x3c_6ef_372,... | 369 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTest... | 47 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def lowerCamelCase_ ( lowerCamelCase__ ):
if "cls_token" in name:
lowerCamelCase_ = name.replace("cls_token" , "vit.embedd... | 370 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__=False ):
lowerCamelCase_ = OmegaConf.load(lowerCamelCase__ )
if display:
print(yaml.dump(Omega... | 47 | 0 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__A ="<<<<<<< This should probably be modified because it mentions: "
__A ="=======\n>>>>>>>\n"
__A =[
"TextEncoderCon... | 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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A =logging.... | 47 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = []
if len(__a ) == 1:
return [nums.copy()]
for _ in range(len(__a ) ):
lowerCamelCase_ = nums.pop(0 )
lowerCamelCase_ = permute(__a )
for perm in permutations:
... | 350 |
import copy
import re
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 'hp'
lowerCAmelCase__ = {}
lowerCAmelCase__ = None
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase , lowercase ) -> Tuple:
lowerCamelCase_ = prefix
... | 47 | 0 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__A ='''\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Ba... | 351 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__A ={
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
'''processing_t... | 47 | 0 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase_ ( ):
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from o... | 352 |
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 con... | 47 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
if not (isinstance(_A , _A ) and isinstance(_A , _A )):
raise ValueError("longest_common_substring() takes two strings for inputs" )
lowerCamelCase_ = len(_A )
lowerCamelCase_ ... | 353 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase , lowercase ) -> List[Any]:
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should ... | 47 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.ut... | 354 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
__A ={
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''... | 47 | 0 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
def... | 355 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A =pd.read_csv('''sample_data.csv''', header=None)
__A =df.shape[:1][0]
# If you're using som... | 47 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ = 1_0**9 ):
lowerCamelCase_ = 1
lowerCamelCase_ = 2
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
p... | 356 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('''fixtures/spiece.model''')
@require_sentencepi... | 47 | 0 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto im... | 357 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__A ={
'''iou_prediction_head.layers.0''': '''io... | 47 | 0 |
"""simple docstring"""
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if height >= 1:
move_tower(height - 1 , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
move_disk(lowerCamelCase_... | 358 |
__A ='''
# 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 git+https://github.com/huggingface/transformers.git
'''
__A =[{'''typ... | 47 | 0 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import... | 359 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase = 16 , lowercase = 88 , lowercase = None , lowercase = 1 , lowerca... | 47 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__A =lo... | 360 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return math.pow(lowerCamelCase__ , 2 ) - a
def lowerCamelCase_ ( lowerCamelCase__ ):
return 2 * x
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = ... | 47 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase_ ( lowerCamelCase__ ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def lowerCamelCase_ ( lowe... | 361 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = get_failure_array(lowerCamelCase__ )
# 2) Step through text searching for pattern
lowerCamelCase_ , lowerCamelCase_ = 0, 0 # index into tex... | 47 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''google/mobilenet_v1_1.0_224''': '''https://huggingfac... | 362 |
import os
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(grid[0] )
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
# Check vertically, horizontally, di... | 47 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__A =logging.get_logger(__name__)
__A ={'''vocab_file''': ... | 363 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTo... | 47 | 0 |
import argparse
from collections import defaultdict
import yaml
__A ='''docs/source/en/_toctree.yml'''
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = defaultdict(_a )
lowerCamelCase_ = []
lowerCamelCase_ = []
for doc in doc_list:
... | 364 |
from collections import defaultdict
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = first_str.lower().strip()
lowerCamelCase_ = second_str.lower().strip()
# Remove whitespace
lowerCamelCase_ = first_str.replace(" " ... | 47 | 0 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils im... | 365 |
from sklearn.metrics import recall_score
import datasets
__A ='''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
'''
__A ='''
Arg... | 47 | 0 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enab... | 366 |
from __future__ import annotations
import math
def lowerCamelCase_ ( lowerCamelCase__ ):
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
... | 47 | 0 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
... | 367 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A ={
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeTowerConfig''',
'''BridgeTowerTextConfig''',
... | 47 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ ):
if isinstance(__lowerCamelCase , __lowerCamelCase ):
raise TypeError("\'float\' object cannot be interpreted as an integer" )
if isinstance(__lowerCamelCase , __lowerCamelCase ):
raise TypeError("\'str\' object cannot be ... | 368 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__A =True
except ImportError:
__A =... | 47 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A ={
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftFormerOnnxConfig",
]
}
try:
if ... | 369 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTest... | 47 | 0 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokeniz... | 370 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__=False ):
lowerCamelCase_ = OmegaConf.load(lowerCamelCase__ )
if display:
print(yaml.dump(Omega... | 47 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational ... | 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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A =logging.... | 47 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
lowerCamelCase_ = 1
lowerCamelCase_ = 1
while repunit:
lowerCamelCase_ = (1_0 * repunit + 1) % divisor
repunit_index += 1
return repunit_in... | 350 |
import copy
import re
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 'hp'
lowerCAmelCase__ = {}
lowerCAmelCase__ = None
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase , lowercase ) -> Tuple:
lowerCamelCase_ = prefix
... | 47 | 0 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, 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, ids_tensor, random_attention_... | 351 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__A ={
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
'''processing_t... | 47 | 0 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__A =version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def lowerCamelCase_ ( lowerCamelCase__ , lower... | 352 |
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 con... | 47 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''... | 353 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase , lowercase ) -> List[Any]:
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should ... | 47 | 0 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.... | 354 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
__A ={
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''... | 47 | 0 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class _SCREAMING_SNAKE_CASE ( nn.Module ):
lowerCAmelCase__ = 42
lowerCAmelCase__ = jnp.floataa
def SCREAMING_SNAKE_CASE_( self ) -> str:
lowerCamelCase_ = nn.Conv(
self.out_cha... | 355 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A =pd.read_csv('''sample_data.csv''', header=None)
__A =df.shape[:1][0]
# If you're using som... | 47 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = generate_pascal_triangle(lowerCamelCase__ )
for row_idx in range(lowerCamelCase__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " )
# Print row values
... | 356 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('''fixtures/spiece.model''')
@require_sentencepi... | 47 | 0 |
import qiskit
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
lowerCamelCase_ = qiskit.QuantumCircuit(A__ , A__ )
# Apply X ... | 357 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__A ={
'''iou_prediction_head.layers.0''': '''io... | 47 | 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 _SCREAMING_SNAKE_CASE ( SCR... | 358 |
__A ='''
# 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 git+https://github.com/huggingface/transformers.git
'''
__A =[{'''typ... | 47 | 0 |
"""simple docstring"""
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_param... | 359 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase = 16 , lowercase = 88 , lowercase = None , lowercase = 1 , lowerca... | 47 | 0 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _SCREAMING_SNAKE_CASE ( u... | 360 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return math.pow(lowerCamelCase__ , 2 ) - a
def lowerCamelCase_ ( lowerCamelCase__ ):
return 2 * x
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = ... | 47 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__A : List[str] =logging.get_logger(__name__)
def lowerCamelCase_ ... | 361 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = get_failure_array(lowerCamelCase__ )
# 2) Step through text searching for pattern
lowerCamelCase_ , lowerCamelCase_ = 0, 0 # index into tex... | 47 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 42
lowerCAmelCase__ = None
lowerCAmelCase__ = N... | 362 |
import os
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(grid[0] )
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
# Check vertically, horizontally, di... | 47 | 0 |
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_torch_available():
impo... | 363 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTo... | 47 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A ={
'''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''Perceiver... | 364 |
from collections import defaultdict
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = first_str.lower().strip()
lowerCamelCase_ = second_str.lower().strip()
# Remove whitespace
lowerCamelCase_ = first_str.replace(" " ... | 47 | 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_tor... | 365 |
from sklearn.metrics import recall_score
import datasets
__A ='''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
'''
__A ='''
Arg... | 47 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
lowerCAmelCase__ = ["""image_processor""", """tokenizer"""]
lowerCAmelCase__ = """CLIPImageProcessor"""
low... | 366 |
from __future__ import annotations
import math
def lowerCamelCase_ ( lowerCamelCase__ ):
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
... | 47 | 0 |
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('''fixtures/spiece.model''')
@require_sent... | 367 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A ={
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeTowerConfig''',
'''BridgeTowerTextConfig''',
... | 47 | 0 |
from math import factorial
__A ={str(d): factorial(d) for d in range(1_0)}
def lowerCamelCase_ ( lowerCamelCase__ ):
return sum(DIGIT_FACTORIAL[d] for d in str(a__ ) )
def lowerCamelCase_ ( ):
lowerCamelCase_ = 7 * factorial(9 ) + 1
return sum(i for i i... | 368 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__A =True
except ImportError:
__A =... | 47 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaV... | 369 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTest... | 47 | 0 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None ... | 370 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__=False ):
lowerCamelCase_ = OmegaConf.load(lowerCamelCase__ )
if display:
print(yaml.dump(Omega... | 47 | 0 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A =logging.... | 47 | 0 |
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
pass
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
pass
class _SCREAMING_SNAKE_CASE :
def __init__( self ) -> Tuple:
lowerCamelCase_ = [
[],
[],
[],
... | 350 |
import copy
import re
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 'hp'
lowerCAmelCase__ = {}
lowerCAmelCase__ = None
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase , lowercase ) -> Tuple:
lowerCamelCase_ = prefix
... | 47 | 0 |
__A =[
[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 lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
# Return Tru... | 351 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__A ={
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
'''processing_t... | 47 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A ={'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_av... | 352 |
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 con... | 47 | 0 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import Sp... | 353 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase , lowercase ) -> List[Any]:
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should ... | 47 | 0 |
from __future__ import annotations
import queue
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase ) -> Union[str, Any]:
lowerCamelCase_ = data
lowerCamelCase_ = None
lowerCamelCase_ = None
def lowerCamelCase_ ( ):
... | 354 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
__A ={
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''... | 47 | 0 |
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
__A ='sshleifer/bart-tiny-random'
__A ='patrickvonplaten/t5-tiny-r... | 355 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A =pd.read_csv('''sample_data.csv''', header=None)
__A =df.shape[:1][0]
# If you're using som... | 47 | 0 |
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 _SCREAMING_SNAKE_CASE ( A__ ):
lowerCAmelCase__ ... | 356 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('''fixtures/spiece.model''')
@require_sentencepi... | 47 | 0 |
from manim import *
class _SCREAMING_SNAKE_CASE ( a_ ):
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ = Rectangle(height=0.5 , width=0.5 )
lowerCamelCase_ = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
... | 357 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__A ={
'''iou_prediction_head.layers.0''': '''io... | 47 | 0 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__A ={
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'''
... | 358 |
__A ='''
# 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 git+https://github.com/huggingface/transformers.git
'''
__A =[{'''typ... | 47 | 0 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBer... | 359 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase = 16 , lowercase = 88 , lowercase = None , lowercase = 1 , lowerca... | 47 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''facebook/levit-128S''': '''https://huggingface.co/faceb... | 360 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return math.pow(lowerCamelCase__ , 2 ) - a
def lowerCamelCase_ ( lowerCamelCase__ ):
return 2 * x
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = ... | 47 | 0 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging... | 361 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = get_failure_array(lowerCamelCase__ )
# 2) Step through text searching for pattern
lowerCamelCase_ , lowerCamelCase_ = 0, 0 # index into tex... | 47 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}
class _SCREAMING_SNAKE_CASE ( a__ ):
lowerCAmelCase__ ... | 362 |
import os
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(grid[0] )
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
# Check vertically, horizontally, di... | 47 | 0 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(lowerCamelCase__ , max_perimeter + 1 ... | 363 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTo... | 47 | 0 |
__A =[
(1_0_0_0, '''M'''),
(9_0_0, '''CM'''),
(5_0_0, '''D'''),
(4_0_0, '''CD'''),
(1_0_0, '''C'''),
(9_0, '''XC'''),
(5_0, '''L'''),
(4_0, '''XL'''),
(1_0, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''IV'''),
(1, '''I'''),
]
def lowerCamelCase_ ( ... | 364 |
from collections import defaultdict
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = first_str.lower().strip()
lowerCamelCase_ = second_str.lower().strip()
# Remove whitespace
lowerCamelCase_ = first_str.replace(" " ... | 47 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A ={
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMTokenizer'],
}
try:
if not is_torch_availabl... | 365 |
from sklearn.metrics import recall_score
import datasets
__A ='''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
'''
__A ='''
Arg... | 47 | 0 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/mai... | 366 |
from __future__ import annotations
import math
def lowerCamelCase_ ( lowerCamelCase__ ):
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
... | 47 | 0 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from... | 367 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A ={
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeTowerConfig''',
'''BridgeTowerTextConfig''',
... | 47 | 0 |
from heapq import heappop, heappush
import numpy as np
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ):
lowerCamelCase_ , lowerCamelCase_ = grid.shape
lowerCamelCase_ = [-1, 1, 0, 0]
lower... | 368 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__A =True
except ImportError:
__A =... | 47 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_... | 369 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTest... | 47 | 0 |
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=_a ):
lowerCAmelCase__ = ["""speech"""]
def __init__( self , *lowercase , **lowercase ) -> List[str]:
requires_backends(self , ["speech"] )
class _SCREAM... | 370 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__=False ):
lowerCamelCase_ = OmegaConf.load(lowerCamelCase__ )
if display:
print(yaml.dump(Omega... | 47 | 0 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 42
lowerCAmelCase__ = Non... | 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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A =logging.... | 47 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict ={
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 350 |
import copy
import re
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 'hp'
lowerCAmelCase__ = {}
lowerCAmelCase__ = None
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase , lowercase ) -> Tuple:
lowerCamelCase_ = prefix
... | 47 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A ={
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig... | 351 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__A ={
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
'''processing_t... | 47 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A ={
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
"""convert_funnel_o... | 352 |
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 con... | 47 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ ):
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise TypeError("'str' object cannot be... | 353 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase , lowercase ) -> List[Any]:
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should ... | 47 | 0 |
from statistics import mean
import numpy as np
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = 0
# Number of processes finished
lowerCamelCase_ = 0
# Displays the finished proce... | 354 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
__A ={
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''... | 47 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class _SCREA... | 355 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A =pd.read_csv('''sample_data.csv''', header=None)
__A =df.shape[:1][0]
# If you're using som... | 47 | 0 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp... | 356 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('''fixtures/spiece.model''')
@require_sentencepi... | 47 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase_ ( lowerCamelCase__ )... | 357 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__A ={
'''iou_prediction_head.layers.0''': '''io... | 47 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from d... | 358 |
__A ='''
# 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 git+https://github.com/huggingface/transformers.git
'''
__A =[{'''typ... | 47 | 0 |
"""simple docstring"""
from __future__ import annotations
__A =[-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
__A =[-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = []
lowerCamelCa... | 359 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase = 16 , lowercase = 88 , lowercase = None , lowercase = 1 , lowerca... | 47 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig... | 360 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return math.pow(lowerCamelCase__ , 2 ) - a
def lowerCamelCase_ ( lowerCamelCase__ ):
return 2 * x
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = ... | 47 | 0 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__A : Optional[int] =(
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6S ... | 361 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = get_failure_array(lowerCamelCase__ )
# 2) Step through text searching for pattern
lowerCamelCase_ , lowerCamelCase_ = 0, 0 # index into tex... | 47 | 0 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impor... | 362 |
import os
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(grid[0] )
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
# Check vertically, horizontally, di... | 47 | 0 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__A =logging.get_logger(__name__) # pylint: disable=invalid-name
class _SCREAMING_SNAKE_CASE ( snake_case__ ):
... | 363 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTo... | 47 | 0 |
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