code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_lowercase: Dict = logging.get_logger(__name__)
class _lowercase ( _a ):
def __init__(self , *lowerCamelCase_ , **lowerCamelCase_ ):
"""simple docstring"""
w... | 370 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_ber... | 71 | 0 |
import re
def a( A : Tuple ) -> list:
"""simple docstring"""
return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]" , str_ )]
def a( A : Optional[int] ) -> str:
"""simple docstring"""
a = split_input(str_ )
return "".join(
... | 371 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a( A : List[s... | 71 | 0 |
import re
from filelock import FileLock
try:
import nltk
_lowercase: Optional[Any] = True
except (ImportError, ModuleNotFoundError):
_lowercase: List[Any] = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def a( A : O... | 350 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 71 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase: Union[str, Any] = logging.get_logger(__name__)
_lowercase: str = {
... | 351 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 71 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
... | 352 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "ViTImageProcessor"
__A ... | 71 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 353 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_... | 71 | 0 |
from __future__ import annotations
def a( A : List[Any] ) -> Tuple:
"""simple docstring"""
a = len(_lowerCAmelCase )
# We need to create solution object to save path.
a = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lowerCAme... | 354 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: Optional[Any] = logging.get_logger(__name__)
_lowercase: Any = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/c... | 71 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
... | 355 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from... | 71 | 0 |
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
_lowercase: Tuple = False
try:
_lowercase: Optional[int] ... | 356 |
import os
def a( ) -> List[str]:
"""simple docstring"""
with open(os.path.dirname(A ) + "/grid.txt" ) as f:
a = [] # noqa: E741
for _ in range(20 ):
l.append([int(A ) for x in f.readline().split()] )
a = 0
# ... | 71 | 0 |
def a( A : int ) -> List[Any]:
"""simple docstring"""
a = generate_pascal_triangle(A )
for row_idx in range(A ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " )
# Print row values
for col_idx ... | 357 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert impor... | 71 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_to... | 358 |
def a( A : int = 200 ) -> int:
"""simple docstring"""
a = [1, 2, 5, 10, 20, 50, 100, 200]
a = [0] * (pence + 1)
a = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(A , pence + 1 , 1 ):
... | 71 | 0 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
... | 359 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a( A : Optional[Any] ) -> Tuple:
"""simple docstring"""
... | 71 | 0 |
"""simple docstring"""
_lowercase: List[str] = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 1000000,
"gigajoule": 1000000000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 3600000,
"newtonmeter": 1.0,
"calorie_nutr": 4186.8,
"kilocalorie_nutr": 41868... | 360 |
def a( ) -> str:
"""simple docstring"""
a = 0
for i in range(1 , 1001 ):
total += i**i
return str(A )[-10:]
if __name__ == "__main__":
print(solution())
| 71 | 0 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformer... | 361 |
# using dfs for finding eulerian path traversal
def a( A : int , A : Optional[Any] , A : Any , A : Optional[int]=None ) -> List[str]:
"""simple docstring"""
a = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
... | 71 | 0 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class _lowercase :
"""simple docstring"""
__A = field(
... | 362 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowercase: Optional[Any] = "\\n@misc{chen2021evaluating,\n title={Evaluating Large ... | 71 | 0 |
import numpy as np
def a( A : Optional[Any] , A : List[str] , A : Any , A : Any , A : List[Any] ) -> int:
"""simple docstring"""
a = int(np.ceil((x_end - xa) / h ) )
a = np.zeros((n + 1,) )
a = ya
a = ... | 363 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchm... | 71 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowercase: Optional[Any] = logging.get_logger(__name__)
class _lowercase ( __SCREAMING_SNAKE_CASE, __... | 364 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowercase: List[Any] = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd... | 71 | 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_p... | 365 |
def a( A : int , A : float , A : float ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def a( A : float , A : float , A : float ) -> float:
"""simple docstring"""
return round(float((moles * 0.0_... | 71 | 0 |
def a( A : List[Any] , A : Dict , A : Union[str, Any] , A : Dict ) -> List[str]:
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
a = mf_knapsack(i - 1 , SC... | 366 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 71 | 0 |
def a( A : int , A : int ) -> Dict:
"""simple docstring"""
return int((input_a, input_a).count(0 ) == 0 )
def a( ) -> Any:
"""simple docstring"""
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ... | 367 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaa... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase: str = {
'''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LxmertConfig''']... | 368 |
import math
from numpy import inf
from scipy.integrate import quad
def a( A : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
return quad(A , 0 , A , args=(A) )[0]
def a( A : float , A :... | 71 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowercase: Dict = logging.get_logger(__name__)
_lowercase: Dict ... | 369 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ... | 71 | 0 |
from __future__ import annotations
def a( A : list[float] ) -> float:
"""simple docstring"""
a = 0.00
a = 0
for resistor in resistors:
if resistor <= 0:
a = f'''Resistor at index {index} has a negative or zero value!''... | 370 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_ber... | 71 | 0 |
import functools
from typing import Any
def a( A : str , A : List[Any] ) -> Any:
"""simple docstring"""
if not isinstance(A , A ) or len(A ) == 0:
raise ValueError("the string should be not empty string" )
if not isinstance(A , A ) or not all(
... | 371 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a( A : List[s... | 71 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def a( A : Namespace ) -> str:
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , ... | 350 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 71 | 0 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
l... | 351 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 71 | 0 |
import torch
from torch import nn
class _lowercase ( nn.Module ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=1 , lowerCamelCase_=False ):
"""simple docstring"""
super().__... | 352 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "ViTImageProcessor"
__A ... | 71 | 0 |
def a( A : Dict ) -> Optional[Any]:
"""simple docstring"""
a = len(UpperCAmelCase_ )
a = sum(UpperCAmelCase_ )
a = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
a = True
f... | 353 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_... | 71 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def a( A : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = {}
a ... | 354 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: Optional[Any] = logging.get_logger(__name__)
_lowercase: Any = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/c... | 71 | 0 |
class _lowercase :
"""simple docstring"""
def __init__(self ):
"""simple docstring"""
a = {} # Mapping from char to TrieNode
a = False
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
for word in words:
... | 355 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from... | 71 | 0 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def a( A : float , A : float , A : bool = False ) -> list[float]:
"""simple docstring"""
if radian_mode:
return [magnitude * cos(low... | 356 |
import os
def a( ) -> List[str]:
"""simple docstring"""
with open(os.path.dirname(A ) + "/grid.txt" ) as f:
a = [] # noqa: E741
for _ in range(20 ):
l.append([int(A ) for x in f.readline().split()] )
a = 0
# ... | 71 | 0 |
import os
from pathlib import Path
def a( A : int , A : int , A : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = {
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение - это здорово, не так ли?'... | 357 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert impor... | 71 | 0 |
from __future__ import annotations
def a( A : Dict , A : Optional[Any] = None , A : Tuple = None , A : Dict = False , ) -> Dict:
"""simple docstring"""
a = cipher_alphabet or [chr(_lowercase ) for i in range(97 , 123 )]
# If the argu... | 358 |
def a( A : int = 200 ) -> int:
"""simple docstring"""
a = [1, 2, 5, 10, 20, 50, 100, 200]
a = [0] * (pence + 1)
a = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(A , pence + 1 , 1 ):
... | 71 | 0 |
import operator as op
_lowercase: str = "scaler.pt"
_lowercase: List[str] = "pytorch_model"
_lowercase: List[str] = "random_states"
_lowercase: Optional[int] = "optimizer"
_lowercase: Any = "scheduler"
_lowercase: Optional[int] = "pytorch_mod... | 359 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a( A : Optional[Any] ) -> Tuple:
"""simple docstring"""
... | 71 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import gcd
def a( A : int = 150_0000 ) -> Union[str, Any]:
"""simple docstring"""
a = defaultdict(_a )
a = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in ... | 360 |
def a( ) -> str:
"""simple docstring"""
a = 0
for i in range(1 , 1001 ):
total += i**i
return str(A )[-10:]
if __name__ == "__main__":
print(solution())
| 71 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase: Any = logging.get_logger(__name__)
_lowercase: str = {
"facebook/data2vec-text-bas... | 361 |
# using dfs for finding eulerian path traversal
def a( A : int , A : Optional[Any] , A : Any , A : Optional[int]=None ) -> List[str]:
"""simple docstring"""
a = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
... | 71 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowercase: List[Any] = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
a... | 362 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowercase: Optional[Any] = "\\n@misc{chen2021evaluating,\n title={Evaluating Large ... | 71 | 0 |
from __future__ import annotations
_lowercase: List[Any] = list[tuple[int, int]]
_lowercase: Tuple = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0,... | 363 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchm... | 71 | 0 |
import argparse
import os
import re
import packaging.version
_lowercase: List[Any] = "examples/"
_lowercase: Union[str, Any] = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(r"^__version__\s+=\... | 364 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowercase: List[Any] = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd... | 71 | 0 |
def a( A : int = 6008_5147_5143 ) -> Dict:
"""simple docstring"""
try:
a = int(lowerCAmelCase__ )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must b... | 365 |
def a( A : int , A : float , A : float ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def a( A : float , A : float , A : float ) -> float:
"""simple docstring"""
return round(float((moles * 0.0_... | 71 | 0 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _lowercase ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def UpperCamelCase_ (self ):
"""simple docstring"""
return... | 366 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 71 | 0 |
def a( A : Tuple ) -> str:
"""simple docstring"""
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
a = ''... | 367 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaa... | 71 | 0 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
_lowercase: Any ... | 368 |
import math
from numpy import inf
from scipy.integrate import quad
def a( A : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
return quad(A , 0 , A , args=(A) )[0]
def a( A : float , A :... | 71 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreT... | 369 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ... | 71 | 0 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def a( A : Dict[str, torch.Tensor] ) -> List[str]:
"""simple docstring"""
a = []
a = []
a = []
... | 370 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_ber... | 71 | 0 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from .... | 371 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a( A : List[s... | 71 | 0 |
from typing import Any
class _lowercase :
"""simple docstring"""
def __init__(self , lowerCamelCase_ ):
"""simple docstring"""
a = data
a = None
def __repr__(self ):
"""simple docstring"""
return F'''Node({self.data})'''
clas... | 350 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 71 | 0 |
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 ModelTesterMixin, ids_tensor
from... | 351 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 71 | 0 |
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = "philschmid/bart-large-cnn-samsum"
__A = (
"This is a tool that summarizes an English... | 352 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "ViTImageProcessor"
__A ... | 71 | 0 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATUR... | 353 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase: Union[str, Any] = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],... | 354 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: Optional[Any] = logging.get_logger(__name__)
_lowercase: Any = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/c... | 71 | 0 |
def a( ) -> List[str]:
for n in range(1 , 100_0000 ):
yield n * (n + 1) // 2
def a( A : int ) -> int:
a = 1
a = 2
while i * i <= n:
a = 0
while n % i == 0:
n //= i
multiplici... | 355 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from... | 71 | 0 |
def a( A : str ) -> bool:
"""simple docstring"""
a = [int(UpperCAmelCase_ ) for i in ip_va_address.split("." ) if i.isdigit()]
return len(UpperCAmelCase_ ) == 4 and all(0 <= int(UpperCAmelCase_ ) <= 254 for octet in octets )
if __name__ == "__main__":
_lowercase: str ... | 356 |
import os
def a( ) -> List[str]:
"""simple docstring"""
with open(os.path.dirname(A ) + "/grid.txt" ) as f:
a = [] # noqa: E741
for _ in range(20 ):
l.append([int(A ) for x in f.readline().split()] )
a = 0
# ... | 71 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 357 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert impor... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowercase: str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOn... | 358 |
def a( A : int = 200 ) -> int:
"""simple docstring"""
a = [1, 2, 5, 10, 20, 50, 100, 200]
a = [0] * (pence + 1)
a = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(A , pence + 1 , 1 ):
... | 71 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.... | 359 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a( A : Optional[Any] ) -> Tuple:
"""simple docstring"""
... | 71 | 0 |
"""simple docstring"""
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase: Optional[int] = get_... | 360 |
def a( ) -> str:
"""simple docstring"""
a = 0
for i in range(1 , 1001 ):
total += i**i
return str(A )[-10:]
if __name__ == "__main__":
print(solution())
| 71 | 0 |
from __future__ import annotations
from fractions import Fraction
def a( A : Optional[int] , A : Dict ) -> bool:
"""simple docstring"""
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
de... | 361 |
# using dfs for finding eulerian path traversal
def a( A : int , A : Optional[Any] , A : Any , A : Optional[int]=None ) -> List[str]:
"""simple docstring"""
a = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
... | 71 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase: Optional[Any] = "▁"
_lowercase: Tuple = {"vocab_file": "sp... | 362 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowercase: Optional[Any] = "\\n@misc{chen2021evaluating,\n title={Evaluating Large ... | 71 | 0 |
def a( A : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = 0
a = len(SCREAMING_SNAKE_CASE_ )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE_ ):
if arr[i] > arr[j]:
num_inversions... | 363 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchm... | 71 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowercase: str = pd.read_csv("sample_data.csv", header=None)
_lowercase: List[Any] ... | 364 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowercase: List[Any] = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd... | 71 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowercase: Dict = logging.get_logger(__name__)
_lowercase: List[str] = {
"google/bit-50": "https://hugg... | 365 |
def a( A : int , A : float , A : float ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def a( A : float , A : float , A : float ) -> float:
"""simple docstring"""
return round(float((moles * 0.0_... | 71 | 0 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
_lowercase: Any = datasets.utils.logging.get_logger(__name__)
@dataclass
class _low... | 366 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 71 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: str = logging.get_logger(__name__)
_lowercase: Optional[Any] = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class ... | 367 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaa... | 71 | 0 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase_ (self ):
"""simple docstring"""
a = get_activation("swish" ... | 368 |
import math
from numpy import inf
from scipy.integrate import quad
def a( A : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
return quad(A , 0 , A , args=(A) )[0]
def a( A : float , A :... | 71 | 0 |
from __future__ import annotations
def a( A : Optional[Any] , A : str , A : Dict , A : Optional[int] ) -> Tuple: # noqa: E741
"""simple docstring"""
while r - l > 1:
a = (l + r) // 2
if v[m] >= key:
a = m... | 369 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ... | 71 | 0 |
from __future__ import annotations
def a( A : list[int] ) -> str:
"""simple docstring"""
if len(__lowerCAmelCase ) == 0:
return array
a , a = min(__lowerCAmelCase ), max(__lowerCAmelCase )
# Compute the variables
a = _max - _min +... | 370 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_ber... | 71 | 0 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
... | 371 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a( A : List[s... | 71 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece... | 350 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 71 | 0 |
_lowercase: Union[str, Any] = 8.3_144_598
def a( A , A ) -> float:
"""simple docstring"""
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to... | 351 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 71 | 0 |
from __future__ import annotations
from typing import Any
class _lowercase :
"""simple docstring"""
def __init__(self , lowerCamelCase_ ):
"""simple docstring"""
a = num_of_nodes
a = []
a = {}
def UpperCamelCase_ (self... | 352 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "ViTImageProcessor"
__A ... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase: int = {
"configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "W... | 353 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_... | 71 | 0 |
import pprint
import requests
_lowercase: List[Any] = "https://zenquotes.io/api"
def a( ) -> list:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def a( ) -> list:
"""simple docstring"""
... | 354 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: Optional[Any] = logging.get_logger(__name__)
_lowercase: Any = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/c... | 71 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ... | 355 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from... | 71 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determi... | 356 |
import os
def a( ) -> List[str]:
"""simple docstring"""
with open(os.path.dirname(A ) + "/grid.txt" ) as f:
a = [] # noqa: E741
for _ in range(20 ):
l.append([int(A ) for x in f.readline().split()] )
a = 0
# ... | 71 | 0 |
import re
def a( A : str ) -> bool:
"""simple docstring"""
a = re.compile(
r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$" )
return bool(re.search(A , A ) )
if __name__ == "__main__":
_lowercase: Tuple = "009470234322... | 357 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert impor... | 71 | 0 |
def a( A : int ) -> bool:
"""simple docstring"""
if num < 0:
return False
a = num
a = 0
while num > 0:
a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
im... | 358 |
def a( A : int = 200 ) -> int:
"""simple docstring"""
a = [1, 2, 5, 10, 20, 50, 100, 200]
a = [0] * (pence + 1)
a = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(A , pence + 1 , 1 ):
... | 71 | 0 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _lowercase ( lowerCAmelCase ):
... | 359 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a( A : Optional[Any] ) -> Tuple:
"""simple docstring"""
... | 71 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultis... | 360 |
def a( ) -> str:
"""simple docstring"""
a = 0
for i in range(1 , 1001 ):
total += i**i
return str(A )[-10:]
if __name__ == "__main__":
print(solution())
| 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase: int = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfi... | 361 |
# using dfs for finding eulerian path traversal
def a( A : int , A : Optional[Any] , A : Any , A : Optional[int]=None ) -> List[str]:
"""simple docstring"""
a = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
... | 71 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "ViTImagePr... | 362 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowercase: Optional[Any] = "\\n@misc{chen2021evaluating,\n title={Evaluating Large ... | 71 | 0 |
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: Union[str, Any] = {
"andreasmadsen/efficient... | 363 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchm... | 71 | 0 |
from __future__ import annotations
from random import random
class _lowercase :
"""simple docstring"""
def __init__(self , lowerCamelCase_ = None ):
"""simple docstring"""
a = value
a = random()
a = None
a = None
... | 364 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowercase: List[Any] = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd... | 71 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils impo... | 365 |
def a( A : int , A : float , A : float ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def a( A : float , A : float , A : float ) -> float:
"""simple docstring"""
return round(float((moles * 0.0_... | 71 | 0 |
from bisect import bisect
from itertools import accumulate
def a( A : Optional[int] , A : List[Any] , A : List[str] , A : str ) -> Union[str, Any]:
"""simple docstring"""
a = sorted(zip(A , A ) , key=lambda A : x[0] / x[... | 366 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 71 | 0 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
_lowercase: List[str] = logging.get_logger(__name__)
_lowercase: Union[str, Any] = {name: getattr(transformers, name + "Fas... | 367 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaa... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase: Tuple = {"configuration_xglm": ["XGLM_PRETRAINED_... | 368 |
import math
from numpy import inf
from scipy.integrate import quad
def a( A : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
return quad(A , 0 , A , args=(A) )[0]
def a( A : float , A :... | 71 | 0 |
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 transformers.utils ... | 369 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ... | 71 | 0 |
import random
from .binary_exp_mod import bin_exp_mod
def a( A : Tuple , A : Dict=1000 ) -> Tuple:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
a = n - 1
a = 0
... | 370 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_ber... | 71 | 0 |
def a( A : int , A : float , A : float ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def a( A : float , A : float , A : float ) -> float:
"""simple docstring"""
return round(float((moles * 0.0_... | 371 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a( A : List[s... | 71 | 0 |
from collections import deque
def a( A : str ) -> List[Any]:
"""simple docstring"""
a = len(A )
a = deque()
a = [False for _ in range(A )]
a = [-1 for _ in range(A )]
a = index_of[:]
def strong_connect(A :... | 350 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 71 | 0 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase_ (self ):
"""sim... | 351 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 71 | 0 |
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 AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, g... | 352 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ["image_processor", "tokenizer"]
__A = "ViTImageProcessor"
__A ... | 71 | 0 |
_lowercase: Optional[int] = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/t... | 353 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_... | 71 | 0 |
import logging
from transformers import PretrainedConfig
_lowercase: Tuple = logging.getLogger(__name__)
_lowercase: Union[str, Any] = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/con... | 354 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: Optional[Any] = logging.get_logger(__name__)
_lowercase: Any = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/c... | 71 | 0 |
class _lowercase :
"""simple docstring"""
def __init__(self , lowerCamelCase_ ):
"""simple docstring"""
a = set_counts
a = max(lowerCamelCase_ )
a = len(lowerCamelCase_ )
a = [1] * num_sets
a = list... | 355 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from... | 71 | 0 |
def a( A : int , A : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def a( ) -> None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == ... | 356 |
import os
def a( ) -> List[str]:
"""simple docstring"""
with open(os.path.dirname(A ) + "/grid.txt" ) as f:
a = [] # noqa: E741
for _ in range(20 ):
l.append([int(A ) for x in f.readline().split()] )
a = 0
# ... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase: Dict = {
"configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
"tokenization_mvp": ["MvpTokenizer"],
}... | 357 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert impor... | 71 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowercase ( lowerCAmelCase )... | 358 |
def a( A : int = 200 ) -> int:
"""simple docstring"""
a = [1, 2, 5, 10, 20, 50, 100, 200]
a = [0] * (pence + 1)
a = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(A , pence + 1 , 1 ):
... | 71 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def a( A : List[Any] ) -> List[str]:... | 359 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a( A : Optional[Any] ) -> Tuple:
"""simple docstring"""
... | 71 | 0 |
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