code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from collections import defaultdict
from math import ceil, sqrt
def A__( __lowerCAmelCase = 1_00_00_00 , __lowerCAmelCase = 10 ):
_snake_case : defaultdict = defaultdict(__lowerCAmelCase )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_l... | 712 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Dict = list(range(len(__lowerCAmelCase ) ) )
_snake_case : Optional[int] = [v / w for v, w in zip(__lowerCAmelCase , __lowerCAmelCase )]
in... | 652 | 0 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL ... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_cani... | 652 | 0 |
import requests
from bsa import BeautifulSoup
def A__( __lowerCAmelCase = "https://www.worldometers.info/coronavirus" ):
_snake_case : Optional[int] = BeautifulSoup(requests.get(lowerCAmelCase__ ).text , 'html.parser' )
_snake_case : str = soup.findAll('h1' )
_snake_cas... | 714 |
import math
def A__( __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
return False
# All prime... | 652 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common impor... | 715 |
import torch
from transformers import AutoModel
class lowercase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Dict="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
... | 652 | 0 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def A__( __lowerCAmelCase ):
_snake_case : Optional[int] ... | 716 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( ... | 652 | 0 |
'''simple docstring'''
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if exponent == 1:
return base
if exponent % 2 == 0:
_snake_case : Any = _modexpt(_lowerCamelCase , exponent // 2 , _lowerCamelCase ) % modulo_value
... | 717 |
def A__( __lowerCAmelCase ):
assert column_title.isupper()
_snake_case : List[Any] = 0
_snake_case : List[str] = len(__lowerCAmelCase ) - 1
_snake_case : Dict = 0
while index >= 0:
_snake_case : List[str] = (ord(column_ti... | 652 | 0 |
import numpy as np
def A__( __lowerCAmelCase ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase_ : List[str] = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
D... | 652 | 0 |
import math
def A__( __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
return False
# All prime... | 719 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase_ : Optional[int] = object()
# For specifying empty leaf dict `{}`
lowercase_ : List[Any] = ... | 652 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : int = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig"... | 720 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ : Any = lo... | 652 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.t... | 721 |
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
_snake_case : Any = str(abs(__lowerCAmelCase ) )
_snake_case : List[str] = [list(__lowerCAmelC... | 652 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ : Tuple = {
'''configuration_mobilebert''': [
'''MOBILEBERT_PRETRAINED_C... | 700 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase_ : Tuple = logging.getLogger(__name__)
class lowercase ( a_ ):
"""simple docstri... | 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case , _snake_case : Optional[Any] = len(__SCREAMING_SNAKE_CASE ), len(grid[0] )
if (
min(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) < 0
or row == ... | 701 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.sta... | 652 | 0 |
class lowercase :
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCamelCase_ : str ):
'''simple docstring'''
_snake_case : Tuple = n
_snake_case : Tuple = [None] * self.n
... | 702 |
import functools
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
raise ValueError('The parameter days should be a list of i... | 652 | 0 |
import math
def A__( __lowerCAmelCase = 1_00 ):
_snake_case : Tuple = sum(i * i for i in range(1 , n + 1 ) )
_snake_case : Optional[int] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
... | 703 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase_ : str = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : int , *low... | 652 | 0 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase ( nn.Module ):
"""simple docstring"""
def __init__( self : int , lowerCamelCase_ : int = 16 , lowerCamelCa... | 704 |
from math import factorial
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-neg... | 652 | 0 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# prepare kernel
# the kernel size have to be o... | 705 |
lowercase_ : Tuple = '''
# 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/tr... | 652 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL impor... | 706 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[Any] = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''to... | 652 | 0 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = No... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : Optional[int] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
... | 652 | 0 |
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
lowercase_ : Dic... | 708 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase_ : Optional[Any] = pytest.mark.integration
@pytest.mark.parametr... | 652 | 0 |
'''simple docstring'''
lowercase_ : Tuple = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'... | 709 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# Initialise PyTorch model
... | 652 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRo... | 710 |
import itertools
import math
def A__( __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
return False
... | 652 | 0 |
import functools
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Validation
if not isinstance(_snake_case , _snake_case ) or not all(isinstance(_snake_case , _snake_case ) for day in days ):
raise ValueError('The parameter days should be a list of integers' )
if... | 711 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbar... | 652 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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_co... | 712 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Dict = list(range(len(__lowerCAmelCase ) ) )
_snake_case : Optional[int] = [v / w for v, w in zip(__lowerCAmelCase , __lowerCAmelCase )]
in... | 652 | 0 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
lowercase_ : str = '''T5Config'... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_cani... | 652 | 0 |
from ... import PretrainedConfig
lowercase_ : Any = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class lowercase ( UpperCAmelCase__ ):
"""simple docstring"""
_UpperCamelCase : str = NEZHA_P... | 714 |
import math
def A__( __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
return False
# All prime... | 652 | 0 |
from __future__ import annotations
from typing import Generic, TypeVar
lowercase_ : List[Any] = TypeVar('''T''')
class lowercase ( Generic[T] ):
"""simple docstring"""
def __init__( self : List[Any] , lowerCamelCase_ : T ):
'''simple d... | 715 |
import torch
from transformers import AutoModel
class lowercase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Dict="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
... | 652 | 0 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
UpperCamelCase_ : Any = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def A__( __lowerCAmelCase = "mumbai" ):
_snake_case : Tuple ... | 716 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( ... | 652 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase ( ... | 717 |
def A__( __lowerCAmelCase ):
assert column_title.isupper()
_snake_case : List[Any] = 0
_snake_case : List[str] = len(__lowerCAmelCase ) - 1
_snake_case : Dict = 0
while index >= 0:
_snake_case : List[str] = (ord(column_ti... | 652 | 0 |
def A__( __lowerCAmelCase ):
_snake_case : Dict = [0] * len(__lowerCAmelCase )
for i in range(1 , len(__lowerCAmelCase ) ):
# use last results for better performance - dynamic programming
_snake_case : Optional[Any] = prefix_result[i - 1]
while j > 0... | 718 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase_ : List[str] = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
D... | 652 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sag... | 719 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase_ : Optional[int] = object()
# For specifying empty leaf dict `{}`
lowercase_ : List[Any] = ... | 652 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : Any = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolv... | 720 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ : Any = lo... | 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase ):
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
_snake_case : Optional[int] = str(bin(_UpperCamelCase ) )[2:] # remove the leading "0b"
_snake_case : Optional[int] = ... | 721 |
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
_snake_case : Any = str(abs(__lowerCAmelCase ) )
_snake_case : List[str] = [list(__lowerCAmelC... | 652 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils ... | 700 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase_ : Tuple = logging.getLogger(__name__)
class lowercase ( a_ ):
"""simple docstri... | 652 | 0 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
lowercase_ : int = {
'''nielsr/canine-s''': 2048,
}
# Unicode defi... | 701 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.sta... | 652 | 0 |
from collections import defaultdict
def A__( __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Tuple = first_str.lower().strip()
_snake_case : List[Any] = second_str.lower().strip()
# Remove whitespace
_snake_case : Optional[int] = first_str.... | 702 |
import functools
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
raise ValueError('The parameter days should be a list of i... | 652 | 0 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
... | 703 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase_ : str = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : int , *low... | 652 | 0 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 704 |
from math import factorial
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-neg... | 652 | 0 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def A__( ):
_snake_case : ... | 705 |
lowercase_ : Tuple = '''
# 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/tr... | 652 | 0 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base i... | 706 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[Any] = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''to... | 652 | 0 |
def A__( ):
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
lowercase_ : List[str] = generate_large_matrix()
lowercase_ : Optional[Any] = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : Optional[int] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
... | 652 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : List[str] = logging.get_logger(__name__)
lowercase_ : int = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/reso... | 708 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase_ : Optional[Any] = pytest.mark.integration
@pytest.mark.parametr... | 652 | 0 |
'''simple docstring'''
lowercase_ : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_9344,
"knot": 1.852,
}
lowercase_ : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_7777_7778,
"mph": 0.6_2137_1192,
"knot": 0.5_3995_6803... | 709 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# Initialise PyTorch model
... | 652 | 0 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=13_37 , num_examples=... | 710 |
import itertools
import math
def A__( __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
return False
... | 652 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if versi... | 711 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbar... | 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
def update_area_of_max_square(__lowerCAmelCase , __lowerCAmelCase ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
_snake_case : List[str] = update_area_of_max_square(l... | 712 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Dict = list(range(len(__lowerCAmelCase ) ) )
_snake_case : Optional[int] = [v / w for v, w in zip(__lowerCAmelCase , __lowerCAmelCase )]
in... | 652 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : List[str] = {
"configuration_xlm_roberta_xl": [
"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaXLConfig",
"XLMRobertaXLOnnxCo... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_cani... | 652 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def A__( __lowerCAmelCase=None , __lowerC... | 714 |
import math
def A__( __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
return False
# All prime... | 652 | 0 |
lowercase_ : Optional[Any] = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def A__( __lowerCAmel... | 715 |
import torch
from transformers import AutoModel
class lowercase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Dict="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
... | 652 | 0 |
def A__( __lowerCAmelCase = 10_00 ):
_snake_case : List[Any] = -1
_snake_case : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_snake_case : Optional[Any] = (n * n - 2 * a * ... | 716 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( ... | 652 | 0 |
'''simple docstring'''
def A__( __lowerCAmelCase , __lowerCAmelCase ):
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueError('Cash flows list cannot be empty' )
_snake_case : List[str] = ... | 717 |
def A__( __lowerCAmelCase ):
assert column_title.isupper()
_snake_case : List[Any] = 0
_snake_case : List[str] = len(__lowerCAmelCase ) - 1
_snake_case : Dict = 0
while index >= 0:
_snake_case : List[str] = (ord(column_ti... | 652 | 0 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ... | 718 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase_ : List[str] = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
D... | 652 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
lowercase_ : Union[str, Any] = l... | 719 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase_ : Optional[int] = object()
# For specifying empty leaf dict `{}`
lowercase_ : List[Any] = ... | 652 | 0 |
from __future__ import annotations
from math import pi
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if inductance < 0:
raise Val... | 720 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ : Any = lo... | 652 | 0 |
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
... | 721 |
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
_snake_case : Any = str(abs(__lowerCAmelCase ) )
_snake_case : List[str] = [list(__lowerCAmelC... | 652 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params i... | 700 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase_ : Tuple = logging.getLogger(__name__)
class lowercase ( a_ ):
"""simple docstri... | 652 | 0 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowercase :
"""simple docstring"""
_UpperCamelCase : torch.Tensor # [batch_size x 3]
_UpperCamelCase : torch.Tensor # [batch_size x 3]
_UpperCa... | 701 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.sta... | 652 | 0 |
import argparse
import copy
def A__( __lowerCAmelCase ):
_snake_case : Any = {}
with open(SCREAMING_SNAKE_CASE_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_snake_case : Tuple = []
_list... | 702 |
import functools
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
raise ValueError('The parameter days should be a list of i... | 652 | 0 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowercase_ : Tuple = TypeVar('''KEY''')
lowercase_ : Union[str, Any] = TypeVar('''VAL''')
@dataclass(frozen=a_ , slots=a_ )
class lowercase... | 703 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase_ : str = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : int , *low... | 652 | 0 |
import requests
def A__( __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : str = {'Content-Type': 'application/json'}
_snake_case : int = requests.post(__lowercase , json={'text': message_body} , headers=__lowercase )
if response.status_code != 2_00:
... | 704 |
from math import factorial
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-neg... | 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if index == number_of_items:
return 0
_snake_case : Dict = 0
_snake_case : List[str] = 0
_snake_case : List[str] = knapsack(__... | 705 |
lowercase_ : Tuple = '''
# 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/tr... | 652 | 0 |
'''simple docstring'''
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def A__( __lowerCAmelCase ):
_snake_case : List[Any] = [
'decoder.version',
'decoder.out... | 706 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[Any] = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''to... | 652 | 0 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
lowercase_ : Any = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.par... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : Optional[int] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
... | 652 | 0 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class lowercase :
"""simple docstring"""
def __UpperCAmelCase ( self : Optional[Any] , lowerCamelCase_ : Optional[int] ... | 708 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase_ : Optional[Any] = pytest.mark.integration
@pytest.mark.parametr... | 652 | 0 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowercase_ : Optional[int] = logging.get_logger(__name__)
lowercase_ : Any ... | 709 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# Initialise PyTorch model
... | 652 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import To... | 710 |
import itertools
import math
def A__( __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
return False
... | 652 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 711 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbar... | 652 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowercase_ : List[str] = logging.... | 712 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Dict = list(range(len(__lowerCAmelCase ) ) )
_snake_case : Optional[int] = [v / w for v, w in zip(__lowerCAmelCase , __lowerCAmelCase )]
in... | 652 | 0 |
def A__( __lowerCAmelCase ):
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
_snake_case : Optional[int] = 4
_snake_case : Tuple = (1 << p) - 1
for _ in range(p - 2 ):
_snake_case : Optional[A... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_cani... | 652 | 0 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceC... | 714 |
import math
def A__( __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
return False
# All prime... | 652 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@re... | 715 |
import torch
from transformers import AutoModel
class lowercase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Dict="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
... | 652 | 0 |
from numpy import exp, pi, sqrt
def A__( __lowerCAmelCase , __lowerCAmelCase = 0.0 , __lowerCAmelCase = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 716 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( ... | 652 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : List[str] = logging.get_logger(__name__)
lowercase_ : List[Any] = {
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingfac... | 717 |
def A__( __lowerCAmelCase ):
assert column_title.isupper()
_snake_case : List[Any] = 0
_snake_case : List[str] = len(__lowerCAmelCase ) - 1
_snake_case : Dict = 0
while index >= 0:
_snake_case : List[str] = (ord(column_ti... | 652 | 0 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils im... | 718 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase_ : List[str] = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
D... | 652 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowercase_ : int = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_M... | 719 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase_ : Optional[int] = object()
# For specifying empty leaf dict `{}`
lowercase_ : List[Any] = ... | 652 | 0 |
from collections.abc import Callable
import numpy as np
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : List[Any] = int(np.ceil((x_end - xa) / step_size ) )
_snake_case : Tuple = np.ze... | 720 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ : Any = lo... | 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if height >= 1:
move_tower(height - 1 , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
move_disk(__lowerCAmelCase , __lowerCAmelCase )
move_tower... | 721 |
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
_snake_case : Any = str(abs(__lowerCAmelCase ) )
_snake_case : List[str] = [list(__lowerCAmelC... | 652 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def A__( __lowerCAmelCase ) -> Dict:
for param in module.parameters():
_snake_case : List[str] = False
def A__( ) -> List[str]:
_snake_case : Dict = '''cuda... | 700 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase_ : Tuple = logging.getLogger(__name__)
class lowercase ( a_ ):
"""simple docstri... | 652 | 0 |
from datetime import datetime as dt
import os
from github import Github
lowercase_ : List[Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def A__( ):
... | 701 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.sta... | 652 | 0 |
def A__( __lowerCAmelCase ):
return "".join([hex(lowerCamelCase_ )[2:].zfill(2 ).upper() for byte in list(lowerCamelCase_ )] )
def A__( __lowerCAmelCase ):
if (len(lowerCamelCase_ ) % 2) != 0:
raise ValueError(
'Base16 encoded data is invalid:\nData does not ha... | 702 |
import functools
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
raise ValueError('The parameter days should be a list of i... | 652 | 0 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_com... | 703 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase_ : str = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : int , *low... | 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase ):
return int(input_a == input_a == 0 )
def A__( ):
print('Truth Table of NOR Gate:' )
print('| Input 1 | Input 2 | Output |' )
print(F'''| 0 | 0 | {nor_gate(0 , 0 )} |''' )
print(F'''| ... | 704 |
from math import factorial
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-neg... | 652 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
... | 705 |
lowercase_ : Tuple = '''
# 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/tr... | 652 | 0 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
... | 706 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[Any] = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''to... | 652 | 0 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowercase ( a_ ):
"""simple docstring"""
de... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : Optional[int] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
... | 652 | 0 |
import requests
lowercase_ : Optional[int] = '''''' # <-- Put your OpenWeatherMap appid here!
lowercase_ : Optional[int] = '''https://api.openweathermap.org/data/2.5/'''
def A__( __lowerCAmelCase = "Chicago" , __lowerCAmelCase = APPID ):
return requests.get(URL_... | 708 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase_ : Optional[Any] = pytest.mark.integration
@pytest.mark.parametr... | 652 | 0 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from t... | 709 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# Initialise PyTorch model
... | 652 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def A__( __lowerCAmelCase ):
_snake_case : List[str] = int(number**0.5 )
return number == sq * sq
def A__( __lowerCAmelCase , __lowerC... | 710 |
import itertools
import math
def A__( __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
return False
... | 652 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : str = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Informe... | 711 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbar... | 652 | 0 |
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 TaTokenizer
else:
lo... | 712 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Dict = list(range(len(__lowerCAmelCase ) ) )
_snake_case : Optional[int] = [v / w for v, w in zip(__lowerCAmelCase , __lowerCAmelCase )]
in... | 652 | 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 lowercase ( _UpperCamelCase ):
"""s... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_cani... | 652 | 0 |
from bisect import bisect
from itertools import accumulate
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Optional[int] = sorted(zip(UpperCAmelCase__ , UpperCAmelCase__ ) , key=lambda __lowerCAmelCase : x[0] / x[1] , reverse=U... | 714 |
import math
def A__( __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
return False
# All prime... | 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if height >= 1:
move_tower(height - 1 , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
move_disk(UpperCAmelCase__ , UpperCAmelCase__ )
move_tower(height - 1 , UpperCAm... | 715 |
import torch
from transformers import AutoModel
class lowercase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Dict="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
... | 652 | 0 |
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
UpperCamelCase_ : List[str] = logging.get_logger(__name__)
UpperCa... | 716 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( ... | 652 | 0 |
'''simple docstring'''
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_sentencepiec... | 717 |
def A__( __lowerCAmelCase ):
assert column_title.isupper()
_snake_case : List[Any] = 0
_snake_case : List[str] = len(__lowerCAmelCase ) - 1
_snake_case : Dict = 0
while index >= 0:
_snake_case : List[str] = (ord(column_ti... | 652 | 0 |
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=__lowerCAmelCase ):
"""simple docstring"""
_UpperCamelCase : Any = ["flax"]
def __init__( self : Optional[Any] , *lowerCamelCase_ : List[str] , **lowerCamelCase_ :... | 718 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase_ : List[str] = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
D... | 652 | 0 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 719 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase_ : Optional[int] = object()
# For specifying empty leaf dict `{}`
lowercase_ : List[Any] = ... | 652 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : str = logging.get_logger(__name__)
class lowercase ( lowercase_ ):
"""simple docstring"""
_UpperCamelCase : int = '''encoder-decod... | 720 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ : Any = lo... | 652 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...to... | 721 |
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
_snake_case : Any = str(abs(__lowerCAmelCase ) )
_snake_case : List[str] = [list(__lowerCAmelC... | 652 | 0 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import AN... | 700 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase_ : Tuple = logging.getLogger(__name__)
class lowercase ( a_ ):
"""simple docstri... | 652 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A__( __lowerCAmelCase ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/C... | 701 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.sta... | 652 | 0 |
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