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 |
|---|---|---|---|---|
import qiskit
def A__( __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Optional[Any] = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
_snake_case : List[str] = qiskit.QuantumCircuit(__lowerCAmelCase , __l... | 652 |
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 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowercase_ : List[str] = logging.getLogger()
@unittest.skip("Temporarily d... | 652 |
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 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : List[str] = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
_UpperCamelCase : Optional[int] = "timm_backbone"
... | 652 |
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 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCOND... | 652 |
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 | 1 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def A__( __lowerCAmelCase ):
return input_array.reshape((input_array.size, 1) )
def A__( __lowerCAmelCase , __lowerCAmelCase , ... | 652 |
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 | 1 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase ):
if nth_term == "":
return [""]
_snake_case : int = int(__lowerCAmelCase )
_snake_case : Union[str, Any] = int(__lowerCAmelCase )
_snake_case : list[str] ... | 652 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ : Optional[Any] = {
'''configuration_vision_text_dual_encoder''': ['''VisionTextDualEnc... | 652 |
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 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql impo... | 652 |
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 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : int = logging.get_logger(__name__)
lowercase_ : Optional[int] = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class ... | 652 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : Any = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
... | 652 |
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 | 1 |
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 |
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 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data i... | 652 |
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 | 1 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A__( __lowerCAmelCase = 2_00_00_00 ):
_snake_case : list[int] = [0]
_snake_case : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_number... | 652 |
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 | 1 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
... | 652 |
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 | 1 |
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... | 652 |
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 | 1 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import depr... | 652 |
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 | 1 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transforme... | 652 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase_ : List[Any] = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
lowercase_ : Union[str, Any] ... | 652 |
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 | 1 |
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Check if the input is valid
if not len(__lowerCAmelCase ) == len(__lowerCAmelCase ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
ra... | 652 |
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 | 1 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowercase_ : List[Any] = loggin... | 652 |
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 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 652 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : Optional[int] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise Opti... | 652 |
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 | 1 |
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if index == r:
for j in range(__lowerCAmelCase ):
print(data[j] , end=' ' )
print(' ' )
return
# When no more elem... | 652 |
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 | 1 |
import random
def A__( __lowerCAmelCase ):
_snake_case : List[str] = num - 1
_snake_case : Optional[int] = 0
while s % 2 == 0:
_snake_case : List[str] = s // 2
t += 1
for _ in range(5 ):
_snake_case : Dict ... | 652 |
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 | 1 |
import math
def A__( __lowerCAmelCase ):
_snake_case : int = [True] * n
_snake_case : str = False
_snake_case : int = False
_snake_case : Optional[int] = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
_snake_case ... | 652 |
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 | 1 |
class lowercase : # Public class to implement a graph
"""simple docstring"""
def __init__( self : Any , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : list[list[bool]] ):
'''simple docstring'''
... | 652 |
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 | 1 |
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 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : Dict = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if not is_torch_availabl... | 652 |
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 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( a_ ):
"""simple docstring"""
_UpperCamelCase : Union[str, Any] = (UnCLIPScheduler,)
def __UpperCAmelCase ... | 652 |
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 | 1 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None ):
if version.parse(hfh.__version__ ).release < version.parse('0.11.0' ).release:
# o... | 652 |
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 | 1 |
import datasets
from .evaluate import evaluate
lowercase_ : Union[str, Any] = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv... | 652 |
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 | 1 |
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 |
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 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable... | 652 |
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 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transforme... | 652 |
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 | 1 |
def A__( __lowerCAmelCase ):
return "".join([hex(__lowerCAmelCase )[2:].zfill(2 ).upper() for byte in list(__lowerCAmelCase )] )
def A__( __lowerCAmelCase ):
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(__lowerCAmelCase ) % 2) !... | 652 |
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 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attenti... | 652 |
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 | 1 |
import math
import unittest
def A__( __lowerCAmelCase ):
assert isinstance(__lowerCAmelCase , __lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or ... | 652 |
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 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import loa... | 652 |
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 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : List[Any] = logging.get_logger(__name__)
lowercase_ : Union[str, Any] = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json'... | 652 |
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 | 1 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOC... | 652 |
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 | 1 |
def A__( __lowerCAmelCase ):
_snake_case : Dict = int(__lowerCAmelCase )
if n_element < 1:
_snake_case : Tuple = ValueError('a should be a positive number' )
raise my_error
_snake_case : Dict = [1]
_snake_case , _snake_ca... | 652 |
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 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
_UpperCamelCase : float
_UpperCamelCase : TreeNode | None = None
_UpperCamelCase : TreeNode | None = None
def ... | 652 |
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 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 652 |
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 | 1 |
import numpy as np
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1E-12 , __lowerCAmelCase = 1_00 , ):
assert np.shape(__lowerCAmelCase )[0] == np.shape(__lowerCAmelCase )[1]
# Ensure proper dimensionality.
assert np.shape(__lowerCAmelCase )[0] == np.shape... | 652 |
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 | 1 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokeni... | 652 |
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 | 1 |
from math import factorial
def A__( __lowerCAmelCase = 20 ):
_snake_case : List[Any] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_snake_case : int = n // 2
return int(factorial(__lowerCAmelCase ) / (factori... | 652 |
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 | 1 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def A__( ):
_snake_case... | 652 |
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 | 1 |
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 |
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 | 1 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accelerat... | 652 |
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 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
lowercase_ : Union[str, Any] = logging.get_... | 652 |
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 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
lowercase_ : str = {
'''t5-small''': '''https://hugg... | 652 |
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 | 1 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,... | 652 |
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 | 1 |
from scipy.stats import pearsonr
import datasets
lowercase_ : List[Any] = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the a... | 652 |
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 | 1 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if len(__lowerCAmelCase ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(__lowerCAmelCase )
or left < -len(__lowerCAmelCas... | 652 |
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 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from... | 652 |
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 | 1 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase_ : List[str] = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstr... | 652 |
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 | 1 |
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 |
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 | 1 |
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 |
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 | 1 |
from collections.abc import Sequence
def A__( __lowerCAmelCase , __lowerCAmelCase = False ):
if not arr:
return 0
_snake_case : Optional[int] = 0 if allow_empty_subarrays else float('-inf' )
_snake_case : List[Any] = 0.0
for num in arr:
... | 652 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ : int = {
'''configuration_roberta_prelayernorm''': [
'''ROBERTA_PRELAYERNORM_... | 652 |
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 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : str = logging.get_logger(__name__)
lowercase_ : Optional[int] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowercase ( ... | 652 |
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 | 1 |
from __future__ import annotations
import queue
class lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase_ : Optional[int] ):
'''simple docstring'''
_snake_case : Tuple =... | 652 |
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 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigT... | 652 |
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 | 1 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
lowercase_ : Tuple = '''\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, A... | 652 |
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 | 1 |
lowercase_ : int = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
lowercase_ : Union[str, Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : List[Any] = True
... | 652 |
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 | 1 |
import math
def A__( __lowerCAmelCase = 1_00 ):
_snake_case : str = sum(i * i for i in range(1 , n + 1 ) )
_snake_case : Optional[Any] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
... | 652 |
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 | 1 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.uti... | 652 |
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 | 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
lowercase_ : Tuple = get_tests_dir(''... | 652 |
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 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
lowercase_ : Tuple = get_logger(__name__)
lowercase_ : Union[str, Any] = r'''
Args:
input_ids (`jnp.n... | 652 |
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 | 1 |
from math import factorial
def A__( __lowerCAmelCase = 1_00 ):
return sum(map(__lowerCAmelCase , str(factorial(__lowerCAmelCase ) ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 652 |
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 | 1 |
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... | 652 |
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 | 1 |
# 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... | 652 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Union[str, Any] = {
'''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', '''M2M100Onn... | 652 |
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 | 1 |
from __future__ import annotations
def A__( __lowerCAmelCase ):
return [ord(__lowerCAmelCase ) - 96 for elem in plain]
def A__( __lowerCAmelCase ):
return "".join(chr(elem + 96 ) for elem in encoded )
def A__( ):
_snake_case : int = encode(input('-> ' ... | 652 |
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 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowercase_ : str = False
class lowercase ( unittest.TestCase ):
... | 652 |
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 | 1 |
lowercase_ : int = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase_ : int = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase_ : List[str] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Frid... | 652 |
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 | 1 |
from string import ascii_uppercase
lowercase_ : Optional[int] = {str(ord(c) - 55): c for c in ascii_uppercase}
def A__( __lowerCAmelCase , __lowerCAmelCase ):
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('int() can\'t convert non-string with... | 652 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ : Optional[Any] = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CO... | 652 |
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 | 1 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
... | 652 |
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 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : List[Any] = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Optio... | 652 |
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 | 1 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassificat... | 652 |
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 | 1 |
from __future__ import annotations
from math import pow, sqrt
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance == 0:
ret... | 652 |
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 | 1 |
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 |
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 | 1 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from tra... | 652 |
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 | 1 |
from jiwer import compute_measures
import datasets
lowercase_ : Union[str, Any] = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: impro... | 652 |
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 | 1 |
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 |
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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ : Optional[int] = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MA... | 652 |
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 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As ... | 652 |
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 | 1 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
lowercase_ : List[str] = logging.getLogger(__na... | 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 numpy as np
from PIL import Image
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Tuple = np.array(__A )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is not a square matrix' )
_snake_case : List... | 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 functools import lru_cache
@lru_cache
def A__( __lowerCAmelCase ):
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 1_00 * 2**20, 9_00 * 2**20] )
def A__( __lowerCA... | 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 os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowercase_ : Any = logging.get_... | 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 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowercase ( lowercase__ , lowercase__ ):
"""simple docstring"""
... | 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 warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 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 requests
lowercase_ : Tuple = "YOUR API KEY"
def A__( __lowerCAmelCase , __lowerCAmelCase = giphy_api_key ):
_snake_case : Dict = '+'.join(query.split() )
_snake_case : str = F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_ke... | 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 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 tran... | 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'''
from __future__ import annotations
from typing import Any
def A__( __lowerCAmelCase ):
if not postfix_notation:
return 0
_snake_case : List[Any] = {'+', '-', '*', '/'}
_snake_case : int = []
for token in po... | 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 collections.abc import Generator
from math import sin
def A__( __lowerCAmelCase ):
if len(lowerCamelCase__ ) != 32:
raise ValueError('Input must be of length 32' )
_snake_case : Dict = b""
for i in [3, 2, 1, 0]:
... | 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
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A__( __lowerCAmelCase ):
if "img_encoder.pos_embed" in name:
_snake_case : List[str] = name.replace('img_encoder.pos... | 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 |
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