code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
def _a ( a :int ) -> Tuple:
a = []
a = set({'''(''', '''[''', '''{'''} )
a = set({''')''', ''']''', '''}'''} )
a = {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
for i in range(len(a ) ):
if s[i] in open_brackets:
stack.appen... | 0 |
def A_ ( _lowerCAmelCase = 50 ) -> int:
UpperCamelCase : List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_co... | 52 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
SCREAMING_SNAKE_CASE_: int =logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__(self : Optional[int] , *__a : Optional[Any... | 1 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : List[Any] = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A_ ( _lowerCAmelCase ) -> dict[str, str]:
UpperCamelCase : Optional[An... | 52 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_commo... | 2 |
from sklearn.metrics import fa_score
import datasets
__lowerCamelCase : List[Any] = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
__lowerCamelCase : List[Any] = ... | 52 | 0 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : typing.Counter[int] = Counter()
for base in range(1 , max_p... | 3 |
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.text_encoder impo... | 52 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/r... | 4 |
class A__ :
def __init__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = set_counts
UpperCamelCase : int = max(A_ )
UpperCamelCase : Optional[Any] = len(A_ )
UpperCamelCase : ... | 52 | 0 |
from __future__ import annotations
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> None:
"""simple docstring"""
_lowercase =len(__snake_case )
# If row is equal to the ... | 5 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONF... | 52 | 0 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - usefu... | 6 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""facebook/c... | 52 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( _Uppe... | 7 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def A_ ( ) -> List[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.rais... | 52 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# Checks if the entire collection has been sorted
if len(SCREAMING_SNAKE_CASE__ ) <= 1 or n <= 1:
return
insert_next(SCREAMING_SNAKE_CASE__ ... | 8 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Optional[int] = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except O... | 52 | 0 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ('''dense.weight''', '''att... | 9 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__lowerCamelCase : List[Any] = """
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
... | 52 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test... | 10 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""roberta-b... | 52 | 0 |
from __future__ import annotations
def _UpperCAmelCase (UpperCamelCase__ : list ):
if not nums:
raise ValueError("List is empty" )
return sum(UpperCamelCase__ ) / len(UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod... | 11 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__lowerCamelCase : str = logging.get_logger(__name__) # pylint: disable=invalid-name
class A__ ( __snake_... | 52 | 0 |
from __future__ import annotations
import bisect
def lowerCamelCase__ ( A__ : list[int] , A__ : int , A__ : int = 0 , A__ : int = -1 ):
'''simple docstring'''
if hi < 0:
__lowerCamelCase = len(A__ )
... | 12 |
import functools
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
UpperCamelCase : Optional[int] = len(_lowerCAmelCase )
UpperCamelCase : List[str] = len(_lowerCAmelCase )
@functools.cache
def min_distance(_lowerCAmelCase , _lowerCAme... | 52 | 0 |
class __lowercase :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any]):
SCREAMING_SNAKE_CASE_: List[str] = name
SCREAMING_SNAKE_CASE_: Union[str, Any] = val
... | 13 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceF... | 52 | 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 ...tokenization_uti... | 14 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class A__ :
def __init__( self , A_ , A_ , A_ , A_ , A_ , A_=0.2 , A_=0.2 ):
'''simple docstring'''
UpperCamelCase : ... | 52 | 0 |
def UpperCAmelCase ( a_ ) -> float:
"""simple docstring"""
__A = 0
while len(a_ ) > 1:
__A = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
__A = files.index(min(a_ ) )
temp... | 15 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dime... | 52 | 0 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__... | 16 |
from math import sqrt
def A_ ( _lowerCAmelCase ) -> bool:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
UpperCamelCase : List[Any] = True
# 0 and 1 are none primes.
if number <= 1:
Upp... | 52 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMi... | 17 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCamelCase : str = """src/transformers"""
# This is to m... | 52 | 0 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__lowerCamelCase : Optional[int] = logging.getLogger(__name__)
if is_torch_tpu_available(che... | 18 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
__lowerCamelCase : str = 100
__lowerCamelCase : Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__lowerCamelCase : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if pri... | 52 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase_ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_gate(1 , 1 ) == 1
if __name_... | 19 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : Optional[int] = int(_lowerCAmelCase )
if decimal in (0, 1): # Exit cases for the recursion
return str(_lowerCAmelCase )
UpperCamelCase , UpperCamelCase : Dict = divmod(_lowerCAmelCase , 2 )... | 52 | 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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity... | 20 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 52 | 0 |
from manim import *
class _lowerCamelCase( _a ):
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : Union[str, Any] = Rectangle(height=0.5, width=0.5)
_lowercase : List[Any] ... | 21 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 52 | 0 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range... | 22 |
def A_ ( _lowerCAmelCase = 50 ) -> int:
UpperCamelCase : List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_co... | 52 | 0 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import log... | 23 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : List[Any] = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A_ ( _lowerCAmelCase ) -> dict[str, str]:
UpperCamelCase : Optional[An... | 52 | 0 |
snake_case_ = [
(1000, 'M'),
(900, 'CM'),
(500, 'D'),
(400, 'CD'),
(100, 'C'),
(90, 'XC'),
(50, 'L'),
(40, 'XL'),
(10, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def lowerCamelCase__ ( snake_case_ : str ... | 24 |
from sklearn.metrics import fa_score
import datasets
__lowerCamelCase : List[Any] = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
__lowerCamelCase : List[Any] = ... | 52 | 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 FlaxXLMRobertaM... | 25 |
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.text_encoder impo... | 52 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@r... | 26 |
class A__ :
def __init__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = set_counts
UpperCamelCase : int = max(A_ )
UpperCamelCase : Optional[Any] = len(A_ )
UpperCamelCase : ... | 52 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : Dict = {
'distilb... | 27 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONF... | 52 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __lowerCamelCase ( A__ ) ... | 28 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""facebook/c... | 52 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__UpperCAmelCase = {
'sample_size': 32,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 2,
'num_... | 29 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def A_ ( ) -> List[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.rais... | 52 | 0 |
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Optional[int] = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except O... | 52 | 0 |
'''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(... | 31 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__lowerCamelCase : List[Any] = """
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
... | 52 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def SCREAMING_SNAKE_CASE_ ( __A : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a_ : Di... | 32 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""roberta-b... | 52 | 0 |
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...u... | 33 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__lowerCamelCase : str = logging.get_logger(__name__) # pylint: disable=invalid-name
class A__ ( __snake_... | 52 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor... | 34 |
import functools
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
UpperCamelCase : Optional[int] = len(_lowerCAmelCase )
UpperCamelCase : List[str] = len(_lowerCAmelCase )
@functools.cache
def min_distance(_lowerCAmelCase , _lowerCAme... | 52 | 0 |
'''simple docstring'''
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 Option... | 35 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceF... | 52 | 0 |
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 TokenizerTesterMixin
@require_tokenizers
class ... | 36 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class A__ :
def __init__( self , A_ , A_ , A_ , A_ , A_ , A_=0.2 , A_=0.2 ):
'''simple docstring'''
UpperCamelCase : ... | 52 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _SCR... | 37 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dime... | 52 | 0 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDi... | 38 |
from math import sqrt
def A_ ( _lowerCAmelCase ) -> bool:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
UpperCamelCase : List[Any] = True
# 0 and 1 are none primes.
if number <= 1:
Upp... | 52 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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 import (
... | 39 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCamelCase : str = """src/transformers"""
# This is to m... | 52 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
d... | 40 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
__lowerCamelCase : str = 100
__lowerCamelCase : Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__lowerCamelCase : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if pri... | 52 | 0 |
'''simple docstring'''
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto imp... | 41 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : Optional[int] = int(_lowerCAmelCase )
if decimal in (0, 1): # Exit cases for the recursion
return str(_lowerCAmelCase )
UpperCamelCase , UpperCamelCase : Dict = divmod(_lowerCAmelCase , 2 )... | 52 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@datacla... | 42 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 52 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 43 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 52 | 0 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from .... | 44 |
def A_ ( _lowerCAmelCase = 50 ) -> int:
UpperCamelCase : List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_co... | 52 | 0 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self ... | 45 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : List[Any] = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A_ ( _lowerCAmelCase ) -> dict[str, str]:
UpperCamelCase : Optional[An... | 52 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_availa... | 46 |
from sklearn.metrics import fa_score
import datasets
__lowerCamelCase : List[Any] = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
__lowerCamelCase : List[Any] = ... | 52 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_comm... | 47 |
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.text_encoder impo... | 52 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
requ... | 48 |
class A__ :
def __init__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = set_counts
UpperCamelCase : int = max(A_ )
UpperCamelCase : Optional[Any] = len(A_ )
UpperCamelCase : ... | 52 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__snake_case :Optional[int] ... | 49 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONF... | 52 | 0 |
from string import ascii_uppercase
_UpperCAmelCase : List[str] = {str(ord(c) - 55): c for c in ascii_uppercase}
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> str:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError(... | 50 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""facebook/c... | 52 | 0 |
def A (__A : list[int] , __A : int ) -> bool:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
UpperCAmelCase_ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each ar... | 51 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def A_ ( ) -> List[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.rais... | 52 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class snake_case :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : s... | 53 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Optional[int] = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except O... | 52 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def UpperCAmelCase__ (lowerCAmelCase_ = 100_0000 , lowerCAmelCase_ = 10 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = defaultdict(lowerCAmelCase_ )
for outer_widt... | 54 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__lowerCamelCase : List[Any] = """
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
... | 52 | 0 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : int = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 55 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""roberta-b... | 52 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Dict = {
'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip... | 56 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__lowerCamelCase : str = logging.get_logger(__name__) # pylint: disable=invalid-name
class A__ ( __snake_... | 52 | 0 |
"""simple docstring"""
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = []
__lowerCAmelCase = 1
while len(_UpperCamelCase ) < 1e6:
constant.append(str(_UpperCamelCase ) )
i += 1
__lowerCAmelCase = "".join(_UpperCamelCase... | 57 |
import functools
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
UpperCamelCase : Optional[int] = len(_lowerCAmelCase )
UpperCamelCase : List[str] = len(_lowerCAmelCase )
@functools.cache
def min_distance(_lowerCAmelCase , _lowerCAme... | 52 | 0 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager impor... | 58 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceF... | 52 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class UpperCAmelCase :
def __init__(self : List[str] ) -> Any:
'''simple docstring'''
snake_case : Tuple = ""
snake_case : ... | 59 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class A__ :
def __init__( self , A_ , A_ , A_ , A_ , A_ , A_=0.2 , A_=0.2 ):
'''simple docstring'''
UpperCamelCase : ... | 52 | 0 |
"""simple docstring"""
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
snake_case__ : Dict = False
try:
snak... | 60 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dime... | 52 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = '▁'
_... | 61 |
from math import sqrt
def A_ ( _lowerCAmelCase ) -> bool:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
UpperCamelCase : List[Any] = True
# 0 and 1 are none primes.
if number <= 1:
Upp... | 52 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 62 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCamelCase : str = """src/transformers"""
# This is to m... | 52 | 0 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_dev... | 63 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
__lowerCamelCase : str = 100
__lowerCamelCase : Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__lowerCamelCase : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if pri... | 52 | 0 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 64 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : Optional[int] = int(_lowerCAmelCase )
if decimal in (0, 1): # Exit cases for the recursion
return str(_lowerCAmelCase )
UpperCamelCase , UpperCamelCase : Dict = divmod(_lowerCAmelCase , 2 )... | 52 | 0 |
import math
def lowerCAmelCase_ ( __A ) -> bool:
'''simple docstring'''
return math.sqrt(__A ) * math.sqrt(__A ) == num
def lowerCAmelCase_ ( __A ) -> bool:
'''simple docstring'''
UpperC... | 65 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 52 | 0 |
"""simple docstring"""
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
return 1 if input_a == input_a else 0
def A_ ( ):
'''simple docstring'''
assert xnor_gate(0, 0 ) == 1
assert xnor_gate(0, 1 ) == 0
assert xnor_gate(1, 0 ... | 66 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 52 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transf... | 67 |
def A_ ( _lowerCAmelCase = 50 ) -> int:
UpperCamelCase : List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_co... | 52 | 0 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-touri... | 68 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : List[Any] = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A_ ( _lowerCAmelCase ) -> dict[str, str]:
UpperCamelCase : Optional[An... | 52 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = ... | 69 |
from sklearn.metrics import fa_score
import datasets
__lowerCamelCase : List[Any] = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
__lowerCamelCase : List[Any] = ... | 52 | 0 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class UpperCAmelCase ( nn.Module ):
def __init__( self : int , __snake_case : int = 16 , __snake_... | 70 |
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.text_encoder impo... | 52 | 0 |
def A ( a_ ,a_ ) -> str:
if not (isinstance(a_ ,a_ ) and isinstance(a_ ,a_ )):
raise ValueError('longest_common_substring() takes two strings for inputs' )
__UpperCamelCase : str =len(a_ )
__U... | 71 |
class A__ :
def __init__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = set_counts
UpperCamelCase : int = max(A_ )
UpperCamelCase : Optional[Any] = len(A_ )
UpperCamelCase : ... | 52 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase__ = '''docs/source/en/_toctree.yml'''
def snake_case_ ( A_ : str ):
'''simple docstring'''
_lowerCamelCase : List[Any] = defaultdic... | 72 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONF... | 52 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a ="""src/diffusers""... | 73 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""facebook/c... | 52 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def _snake_case ( snake_case__ : Dict ):
A = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model... | 74 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def A_ ( ) -> List[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.rais... | 52 | 0 |
'''simple docstring'''
def a_ ( __snake_case : int = 400_0000 ) -> int:
"""simple docstring"""
lowerCamelCase_ =[]
lowerCamelCase_, lowerCamelCase_ =0, 1
while b <= n:
if b % 2 == 0:
even_fib... | 75 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Optional[int] = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except O... | 52 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = token... | 76 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__lowerCamelCase : List[Any] = """
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
... | 52 | 0 |
"""simple docstring"""
import inspect
import unittest
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> List[str]:
try:
import diffusers # noqa: F401
except ImportError:
assert F... | 77 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""roberta-b... | 52 | 0 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
__UpperCamelCase = (DDPMScheduler,)
def ... | 78 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__lowerCamelCase : str = logging.get_logger(__name__) # pylint: disable=invalid-name
class A__ ( __snake_... | 52 | 0 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCamelCase_ = parse(importlib.metadata.version('''torch'''))
def __lowercase ( __lowercase , __lowercase ... | 79 |
import functools
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
UpperCamelCase : Optional[int] = len(_lowerCAmelCase )
UpperCamelCase : List[str] = len(_lowerCAmelCase )
@functools.cache
def min_distance(_lowerCAmelCase , _lowerCAme... | 52 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a__ : List[Any] = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIV... | 80 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceF... | 52 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
@staticmethod
@abstractmethod
def SCREAMING_SNAKE_CASE ( __A ) ... | 81 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class A__ :
def __init__( self , A_ , A_ , A_ , A_ , A_ , A_=0.2 , A_=0.2 ):
'''simple docstring'''
UpperCamelCase : ... | 52 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
A__ = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""": """https://huggingface.c... | 82 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dime... | 52 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import Fl... | 83 |
from math import sqrt
def A_ ( _lowerCAmelCase ) -> bool:
assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
UpperCamelCase : List[Any] = True
# 0 and 1 are none primes.
if number <= 1:
Upp... | 52 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class _SCREAMING_SNAKE_CASE ( ... | 84 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCamelCase : str = """src/transformers"""
# This is to m... | 52 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mode... | 85 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
__lowerCamelCase : str = 100
__lowerCamelCase : Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__lowerCamelCase : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if pri... | 52 | 0 |
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization... | 86 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : Optional[int] = int(_lowerCAmelCase )
if decimal in (0, 1): # Exit cases for the recursion
return str(_lowerCAmelCase )
UpperCamelCase , UpperCamelCase : Dict = divmod(_lowerCAmelCase , 2 )... | 52 | 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 ... | 87 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 52 | 0 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_A ) , """Tatoeba ... | 88 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 52 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
tr... | 89 |
def A_ ( _lowerCAmelCase = 50 ) -> int:
UpperCamelCase : List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_co... | 52 | 0 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self... | 90 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : List[Any] = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A_ ( _lowerCAmelCase ) -> dict[str, str]:
UpperCamelCase : Optional[An... | 52 | 0 |
"""simple docstring"""
UpperCAmelCase_ : Dict = range(2, 20 + 1)
UpperCAmelCase_ : Union[str, Any] = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def _A (__a , __a , __a , __a ) -> Any:
"""si... | 91 |
from sklearn.metrics import fa_score
import datasets
__lowerCamelCase : List[Any] = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
__lowerCamelCase : List[Any] = ... | 52 | 0 |
from __future__ import annotations
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : list[float] ):
return np.maximum(0 , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 92 |
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.text_encoder impo... | 52 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
f... | 93 |
class A__ :
def __init__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = set_counts
UpperCamelCase : int = max(A_ )
UpperCamelCase : Optional[Any] = len(A_ )
UpperCamelCase : ... | 52 | 0 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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_pip... | 94 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONF... | 52 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : str = logging.get_logger(__name__)
UpperCAmelCase : str = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __lowerC... | 95 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""facebook/c... | 52 | 0 |
"""simple docstring"""
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... | 96 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def A_ ( ) -> List[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.rais... | 52 | 0 |
'''simple docstring'''
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_t... | 97 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Optional[int] = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except O... | 52 | 0 |
"""simple docstring"""
def a_ ( lowerCamelCase = 1_0**1_2 ):
UpperCAmelCase__ = 1
UpperCAmelCase__ = 0
UpperCAmelCase__ = 1
UpperCAmelCase__ = 1
while numerator <= 2 * min_total - 1:
prev_numerat... | 98 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__lowerCamelCase : List[Any] = """
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
... | 52 | 0 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase : Union[str, Any] = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
lowercase : Union[str, Any] = None
def A_ ( ) -> Dict:
a__ : Dict... | 99 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""roberta-b... | 52 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import gcd
def _lowerCAmelCase ( UpperCamelCase_ = 150_0000 ):
__SCREAMING_SNAKE_CASE = defaultdict(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
f... | 100 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__lowerCamelCase : str = logging.get_logger(__name__) # pylint: disable=invalid-name
class A__ ( __snake_... | 52 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.