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 |
|---|---|---|---|---|
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__A ='''\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Juli... | 47 |
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 | 1 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__A =collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''tes... | 47 |
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 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A =logging.get_logger(__name__)
__A ={
'''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2... | 47 |
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 | 1 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__A =False
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
pass
@slow
@require_tor... | 47 |
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 | 1 |
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ = 0 ):
lowerCamelCase_ = length or len(lowerCamelCase__ )
lowerCamelCase_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
lowerCamelCase_ , lowerCamelCase_... | 47 |
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 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__A =logging.get_logger(__name__)
__A =R'''
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
... | 47 |
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 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 47 |
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 | 1 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = False ):
if radian_mode:
return [magnitude * cos(lowerCamelCase__ ),... | 47 |
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 | 1 |
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=snake_case_ ):
lowerCAmelCase__ = ['torch', 'scipy']
def __init__( self , *lowercase , **lowercase ) -> Optional[Any]:
requires_backends(self , ["torch", "scipy"]... | 47 |
__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 | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 47 |
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 | 1 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test... | 47 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return math.pow(lowerCamelCase__ , 2 ) - a
def lowerCamelCase_ ( lowerCamelCase__ ):
return 2 * x
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = ... | 47 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
... | 47 |
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 | 1 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCamelCase_ ( lowerCamelCase__="ro" , lowerCamelCase__="en" , lowerCamelCase__="wmt16" , lowerCamelCase__=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportE... | 47 |
import os
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(grid[0] )
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
# Check vertically, horizontally, di... | 47 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
... | 47 |
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 | 1 |
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''': '''... | 47 |
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 | 1 |
import numpy as np
from PIL import Image
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = np.array(lowerCamelCase__ )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )... | 47 |
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 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''studio-ousia/luke-large''': '''https://huggingface.co/studio-ousia/lu... | 47 |
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 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def SCREAMING_SNAKE_CASE_( self ) -> Any:
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
... | 47 |
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 | 1 |
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... | 47 |
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 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''huggingface/time-series-transformer-tourism-monthly''': (
'''https://huggingface.co/huggingface/time-series-transformer-tourism-mon... | 47 |
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 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase_ ( ):
lowerCamelCase_ = ArgumentParser(
description=(
"PyTorch TPU distributed training launc... | 47 |
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 | 1 |
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... | 47 |
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 | 1 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
i... | 47 |
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 | 1 |
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ , lowerCamelCase_ = [], []
while len(lowerCamelCase__ ) > 1:
lowerCamelCase_ , lowerCamelCase_ = min(lowerCamelCase__ ), max(lowerCamelCase__ )
start.append(lowerCamelCase__ )
... | 47 |
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 | 1 |
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... | 47 |
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 | 1 |
import unittest
from transformers import LiltConfig, 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 ModelTesterMixin, ids_t... | 47 |
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 | 1 |
import os
import sys
import unittest
__A =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapp... | 47 |
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 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ... | 47 |
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 | 1 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils im... | 47 |
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 | 1 |
def lowerCamelCase_ ( lowerCamelCase__ ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
... | 47 |
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 | 1 |
from __future__ import annotations
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase = 6 ) -> None:
lowerCamelCase_ = None
lowerCamelCase_ = None
self.create_linked_list(lowercase )
def SCREAMING_SNAKE_CASE_( s... | 47 |
__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 | 1 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = int(np.ceil((x_end - xa) / step_size ) )
lowerCamelCase_ ... | 47 |
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 | 1 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
__A =version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_tokenize
__A ='''\
@inproceedings{banarjee20... | 47 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return math.pow(lowerCamelCase__ , 2 ) - a
def lowerCamelCase_ ( lowerCamelCase__ ):
return 2 * x
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = ... | 47 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
... | 47 |
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 | 1 |
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 |
import os
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(grid[0] )
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
# Check vertically, horizontally, di... | 47 | 1 |
__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 |
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 | 1 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vi... | 47 |
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 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class _SCREAMING_SNAKE_CASE :
... | 47 |
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 | 1 |
from typing import List
import numpy as np
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = {key: len(lowerCamelCase__ ) for key, value in gen_kwargs.items() if isinstance(lowerCamelCase__ , lowerCamelCase__ )}
if len(set(lists_lengths.values() ) ) > 1:
... | 47 |
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 | 1 |
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 |
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 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
__A =logging.ge... | 47 |
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 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def __init__( self , lowercase , ... | 47 |
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 | 1 |
import os
def lowerCamelCase_ ( ):
with open(os.path.dirname(lowerCamelCase__ ) + "/grid.txt" ) as f:
lowerCamelCase_ = [] # noqa: E741
for _ in range(2_0 ):
l.append([int(lowerCamelCase__ ) for x in f.readline().split()] )
lowerCamelCase_ ... | 47 |
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 | 1 |
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return [sentence[i : i + ngram_size] for i in range(len(lowerCamelCase__ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 47 |
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 | 1 |
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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQ... | 47 |
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 | 1 |
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 ={
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/... | 47 |
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 | 1 |
def lowerCamelCase_ ( lowerCamelCase__ = 1_0_0_0_0_0_0 ):
lowerCamelCase_ = set(range(3 , lowerCamelCase__ , 2 ) )
primes.add(2 )
for p in range(3 , lowerCamelCase__ , 2 ):
if p not in primes:
continue
primes.difference_upda... | 47 |
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 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = list(range(len(lowerCamelCase__ ) ) )
lowerCamelCase_ = [v / w for v, w in zip(lowerCamelCase__ , lowerCamelCase__ ... | 47 |
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 | 1 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__A =logging.getLogger(__name__)
__A =tf.data.AUTOTUNE
... | 47 |
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 | 1 |
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowerCamelCase_ = set()
return any(
node not in visited and depth_first_search(lowerCamelCase__ , lowerCamelC... | 47 |
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 | 1 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _SCREAMING_... | 47 |
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 | 1 |
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... | 47 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A ={
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
'''configuration_maskformer_swin''': ['''MaskFormerSw... | 47 |
__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 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import ... | 47 |
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 | 1 |
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 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return math.pow(lowerCamelCase__ , 2 ) - a
def lowerCamelCase_ ( lowerCamelCase__ ):
return 2 * x
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = ... | 47 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriev... | 47 |
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 | 1 |
import sys
import turtle
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ):
my_pen.up()
... | 47 |
import os
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(grid[0] )
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
# Check vertically, horizontally, di... | 47 | 1 |
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return int((input_a, input_a).count(1 ) != 0 )
def lowerCamelCase_ ( ):
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 ... | 47 |
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 | 1 |
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__ = Non... | 47 |
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 | 1 |
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 |
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 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sql... | 47 |
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 | 1 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import... | 47 |
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 | 1 |
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... | 47 |
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 | 1 |
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 |
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 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__A =Mapping[str, np.ndarray]
__A =Mapping[str, Any] # Is a nested dict.
__A =0.01
@dataclasses.dataclass(frozen=snake_case_ ... | 47 |
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 | 1 |
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... | 47 |
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 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__A ='''src/diffusers'''
__A ='... | 47 |
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 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
__A ='''▁'''
__A ={'''vocab_file''': '''vocab.txt''', '''sentencepie... | 47 |
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 | 1 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}
cla... | 47 |
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 | 1 |
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 diffusers.utils import float... | 47 |
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 | 1 |
def lowerCamelCase_ ( lowerCamelCase__ ):
return "".join(chr(ord(lowerCamelCase__ ) - 3_2 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 47 |
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 | 1 |
import os
import time
import numpy as np
import onnxruntime as ort
__A ='''1'''
__A ='''0'''
__A ='''1'''
__A =ort.SessionOptions()
__A =ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
__A =['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
__A =... | 47 |
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 | 1 |
__A ='''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def lowerCamelCase_ ( ):
lowerCamelCase_ = input("Enter message: " )
lowerCamelCase_ = input("Enter key [alphanumeric]: " )
lowerCamelCase_ = input("Encrypt/Decrypt [e/d]: " )
if mode.lower().startswith("e" ... | 47 |
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 | 1 |
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 |
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 | 1 |
from math import factorial
def lowerCamelCase_ ( lowerCamelCase__ = 1_0_0 ):
return sum(int(lowerCamelCase__ ) for x in str(factorial(lowerCamelCase__ ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 47 |
__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 | 1 |
from __future__ import annotations
from statistics import mean
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = [0] * no_of_processes
lowerCamelCase_ = [0] * no_of_processes
# Initialize remaining_time to w... | 47 |
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 | 1 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def __init__( self , lowercase="" , lowercase="train" ) -> Union[str, Any]:
assert os.path.isdir(lowercase )
lowerCamelCase_ ... | 47 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return math.pow(lowerCamelCase__ , 2 ) - a
def lowerCamelCase_ ( lowerCamelCase__ ):
return 2 * x
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = ... | 47 | 1 |
from __future__ import annotations
__A =[
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ):
lowerCamelCase_ ... | 47 |
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 | 1 |
import os
from pathlib import Path
def lowerCamelCase_ ( ):
from torch.utils.cpp_extension import load
lowerCamelCase_ = Path(lowerCamelCase__ ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
lowerCamelCase_ = [
root / filename
for file... | 47 |
import os
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(grid[0] )
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
# Check vertically, horizontally, di... | 47 | 1 |
__A ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__A =[{'''type''': '''code''', '''content''': INSTALL_... | 47 |
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 | 1 |
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 |
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 | 1 |
__A =2_5_6
# Modulus to hash a string
__A =1_0_0_0_0_0_3
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = len(lowerCamelCase__ )
if p_len > t_len:
return False
lowerCame... | 47 |
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 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__A =TypeVar('''T''')
class _SCREAMING_SNAKE_CASE ( Generic[T] ):
def __init__( self , lo... | 47 |
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 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = 0
if start < end:
lowerCamelCase_ = randint(lowerCamelCase__ , lowe... | 47 |
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 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif stress < 0:
raise... | 47 |
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 | 1 |
def lowerCamelCase_ ( ):
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
__A =generate_large_matrix()
__A =(
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3... | 47 |
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 | 1 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin i... | 47 |
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 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = []
if isin... | 47 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A ={'''configuration_mbart''': ['''MBART_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 47 |
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 | 1 |
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 |
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 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _SCREAMING_SNAKE_CASE ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowerCAmelCase__ = [('size', ctypes.c_int... | 47 |
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 | 1 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mode... | 47 |
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 | 1 |
import os
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 logging
__A =logging.get_logger(__name__)
__A ={'''vocab_file''': '''sentencepiece.bpe.model'''}
__A ... | 47 |
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 | 1 |
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 |
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 | 1 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_mod... | 47 |
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 | 1 |
from __future__ import annotations
from collections import namedtuple
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
ra... | 47 |
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 | 1 |
import math
from collections.abc import Iterator
from itertools import takewhile
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, a... | 47 |
__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 | 1 |
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 |
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 | 1 |
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 import ... | 47 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return math.pow(lowerCamelCase__ , 2 ) - a
def lowerCamelCase_ ( lowerCamelCase__ ):
return 2 * x
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = ... | 47 | 1 |
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase ) -> None:
lowerCamelCase_ = size
lowerCamelCase_ = [0] * size
lowerCamelCase_ = [0] * size
@staticmethod
def SCREAMING_SNAKE_CASE_( lowercase ) -> int:
return index | (... | 47 |
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 | 1 |
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