code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
# Copyright 2023 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 applic... | 20 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = int(_lowerCAmelCase )
assert noofclusters < len(_lowerCAmelCase )
# Find out the dimensionality
Upper... | 629 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_ind... | 21 |
import os
def A_ ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : ... | 629 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class A__ :
_UpperCAmelCase :Union[str, Any] = None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int ... | 629 | 0 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGen... | 23 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 629 | 0 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase (_lowerCamelCase : list[float] , _lowerCamelCase : Dict )-> List[Any]:
'''simple docstring'''
print(f'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumera... | 24 |
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
UpperCamelCase : Tuple = u
for i in range(1 , _lowerCAmelCase ):
UpperCamelCase : Any = temp * (u - i)
return temp
def A_ ( ) -> ... | 629 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_v... | 25 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__lowerCamelCase : str = TypeVar("""T""")
class A__ ( Generic[T] ):
def __init__( ... | 629 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import Shap... | 26 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__low... | 629 | 0 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"{price_plus_tax(100, 0.2_5) = }")
print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) ... | 27 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 629 | 0 |
'''simple docstring'''
import sys
from collections import defaultdict
class _a :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : ... | 28 |
__lowerCamelCase : Any = 9.8_0_6_6_5
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = g ) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
raise... | 629 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ ):
lowerCamelCase_ = str(lowerCAmelCase__ )
return n == n[::-1]
def lowercase ( lowerCAmelCase__ = 1_000_000 ):
lowerCamelCase_ = 0
for i in range(1 ,lowerCAmelCase... | 29 |
import numpy as np
import qiskit
def A_ ( _lowerCAmelCase = 8 , _lowerCAmelCase = None ) -> str:
UpperCamelCase : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
UpperCamelCase... | 629 | 0 |
import os
import sys
import unittest
__a = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_in... | 30 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, glo... | 629 | 0 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
) | 31 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
UpperCamelCase : Union[str, Any] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - эт... | 629 | 0 |
def A__ ( SCREAMING_SNAKE_CASE_ : int = 10_00 ) -> int:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = 1, 1
_UpperCAmelCase = []
for i in range(1 , n + 1 ):
_UpperCAmelCase = prev_numerato... | 32 |
def A_ ( _lowerCAmelCase = 1000 ) -> int:
UpperCamelCase : Optional[int] = -1
UpperCamelCase : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase : Optional[Any] = (n *... | 629 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemak... | 33 |
def A_ ( _lowerCAmelCase ) -> bool:
UpperCamelCase : List[Any] = 0
for ch in input_str:
UpperCamelCase : Optional[Any] = ord(_lowerCAmelCase )
UpperCamelCase : Optional[Any] = pow(2 , _lowerCAmelCase )
# If we already turned on bit for ... | 629 | 0 |
"""simple docstring"""
from __future__ import annotations
SCREAMING_SNAKE_CASE_ = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( _lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,):
"""simple docstrin... | 34 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 629 | 0 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def a ( A__ ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = {}
SCREAMING_SNAKE_CASE__ : int = job['''started... | 35 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : List[Any] = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig"""... | 629 | 0 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils impor... | 36 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
UpperCamelCase : List[Any] = [1]
for i in range(2 , _lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase : Tuple =... | 629 | 0 |
from __future__ import annotations
def UpperCamelCase_ ( __a , __a ) -> list[list[int]]:
a__ : list[list[int]] = []
a__ : list[int] = []
a__ : List[Any] = 0
a__ : Dict = sum(__a )
create_state_space_tree(__a , __a , __a ,... | 37 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_confi... | 629 | 0 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = CustomTokenizer
pass
| 38 |
def A_ ( _lowerCAmelCase ) -> bool:
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def A_ ( _lowerCAmelCase ) -> int:
return int(_lowerCAmelCase ) + int(str(_lowerCAmelCase )[::-1] )
def A_ ( _lowerCAmelCase = 1_0000 ) -> int:
UpperCamelCase... | 629 | 0 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
cla... | 39 |
__lowerCamelCase : str = 6_5521
def A_ ( _lowerCAmelCase ) -> int:
UpperCamelCase : Any = 1
UpperCamelCase : str = 0
for plain_chr in plain_text:
UpperCamelCase : List[Any] = (a + ord(_lowerCAmelCase )) % MOD_ADLER
UpperCamelCase... | 629 | 0 |
import math
import random
def UpperCamelCase ( snake_case__ : float , snake_case__ : bool = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__UpperCAmelCase = 0.02
def UpperCamelCase ... | 40 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__lo... | 629 | 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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
... | 41 |
from typing import Any
def A_ ( _lowerCAmelCase ) -> list[Any]:
if not input_list:
return []
UpperCamelCase : List[str] = [input_list.count(_lowerCAmelCase ) for value in input_list]
UpperCamelCase : Dict = max(_lowerCAmelCase ) # Gets the maximum count in... | 629 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> tuple[float, float]:
if not all(isi... | 42 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = int(_lowerCAmelCase )
assert noofclusters < len(_lowerCAmelCase )
# Find out the dimensionality
Upper... | 629 | 0 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
lowerCAmelCase = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem impo... | 43 |
import os
def A_ ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : ... | 629 | 0 |
'''simple docstring'''
UpperCAmelCase_ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 100_0000,
"gigajoule": 10_0000_0000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 360_0000,
"newtonmeter": 1.0,
"calorie_nutr": 4186.8,
"kilocalo... | 44 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class A__ :
_UpperCAmelCase :Union[str, Any] = None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int ... | 629 | 0 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate impor... | 45 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 629 | 0 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
return abs(_lowerCamelCase ) if a == 0 else greatest_common_divisor(b % a , _lowerCamelCase )
def lowerCamelCase_( _lowerCamelCase , ... | 46 |
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
UpperCamelCase : Tuple = u
for i in range(1 , _lowerCAmelCase ):
UpperCamelCase : Any = temp * (u - i)
return temp
def A_ ( ) -> ... | 629 | 0 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__... | 47 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__lowerCamelCase : str = TypeVar("""T""")
class A__ ( Generic[T] ):
def __init__( ... | 629 | 0 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def A ( UpperCamelCase_ : List[Any] , UpperCamelCase_ : int , UpperCamelCase_ : List[Any] ) -> Dict:
'''simple docstring'''
lowerCAmelC... | 48 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__low... | 629 | 0 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _Uppe... | 49 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 629 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase : Dict = {
'configuration_perceiver': ['PERCEIVER_PRETRAINE... | 50 |
__lowerCamelCase : Any = 9.8_0_6_6_5
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = g ) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
raise... | 629 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 51 |
import numpy as np
import qiskit
def A_ ( _lowerCAmelCase = 8 , _lowerCAmelCase = None ) -> str:
UpperCamelCase : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
UpperCamelCase... | 629 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :float , a_ :float , a_ :float , ) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0) != 1:
raise ValueError('''You cannot supply more or less than 2 va... | 52 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, glo... | 629 | 0 |
import re
def a_ ( lowerCAmelCase_ : str ):
__lowerCAmelCase = re.compile(R'^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$' )
if match := re.search(lowerCAmelCase_, lowerCAmelCase_ ):
return match.string == phone
return False
if __name__ == "__main__":
... | 53 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
UpperCamelCase : Union[str, Any] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - эт... | 629 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase )
class A ( __lowercase ):
# `task` is not a ClassVar since we want it to be part of the `asdict` ... | 54 |
def A_ ( _lowerCAmelCase = 1000 ) -> int:
UpperCamelCase : Optional[int] = -1
UpperCamelCase : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase : Optional[Any] = (n *... | 629 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 55 |
def A_ ( _lowerCAmelCase ) -> bool:
UpperCamelCase : List[Any] = 0
for ch in input_str:
UpperCamelCase : Optional[Any] = ord(_lowerCAmelCase )
UpperCamelCase : Optional[Any] = pow(2 , _lowerCAmelCase )
# If we already turned on bit for ... | 629 | 0 |
'''simple docstring'''
from typing import Any
def _a (lowercase__ : list , lowercase__ : list , lowercase__ : dict , lowercase__ : dict , lowercase__ : dict , ) -> list:
"""simple docstring"""
_validation(
lowercase_... | 56 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 629 | 0 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
A_ : int = [
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a c... | 57 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : List[Any] = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig"""... | 629 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : List[str] = {
'''configuration_m... | 58 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
UpperCamelCase : List[Any] = [1]
for i in range(2 , _lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase : Tuple =... | 629 | 0 |
from __future__ import annotations
from math import pi
def lowerCAmelCase_ ( __a , __a , __a ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be... | 59 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_confi... | 629 | 0 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCa... | 60 |
def A_ ( _lowerCAmelCase ) -> bool:
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def A_ ( _lowerCAmelCase ) -> int:
return int(_lowerCAmelCase ) + int(str(_lowerCAmelCase )[::-1] )
def A_ ( _lowerCAmelCase = 1_0000 ) -> int:
UpperCamelCase... | 629 | 0 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...ut... | 61 |
__lowerCamelCase : str = 6_5521
def A_ ( _lowerCAmelCase ) -> int:
UpperCamelCase : Any = 1
UpperCamelCase : str = 0
for plain_chr in plain_text:
UpperCamelCase : List[Any] = (a + ord(_lowerCAmelCase )) % MOD_ADLER
UpperCamelCase... | 629 | 0 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophe... | 62 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__lo... | 629 | 0 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:READM... | 63 |
from typing import Any
def A_ ( _lowerCAmelCase ) -> list[Any]:
if not input_list:
return []
UpperCamelCase : List[str] = [input_list.count(_lowerCAmelCase ) for value in input_list]
UpperCamelCase : Dict = max(_lowerCAmelCase ) # Gets the maximum count in... | 629 | 0 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _lowerCamelCase :
__a = None
def UpperCamelCase_ ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE__: Union[str, Any]= self.feature_extraction_class(**self.... | 64 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = int(_lowerCAmelCase )
assert noofclusters < len(_lowerCAmelCase )
# Find out the dimensionality
Upper... | 629 | 0 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED... | 65 |
import os
def A_ ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : ... | 629 | 0 |
import requests
from bsa import BeautifulSoup
def __magic_name__ ( SCREAMING_SNAKE_CASE = "AAPL" ) -> str:
_lowercase : str = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
_lowercase : int = BeautifulSoup(requests.get(SCREAMING... | 66 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class A__ :
_UpperCAmelCase :Union[str, Any] = None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int ... | 629 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case = logging.get_logger(__name__)
... | 67 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 629 | 0 |
from __future__ import annotations
def lowercase__ ( A_: list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in rang... | 68 |
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
UpperCamelCase : Tuple = u
for i in range(1 , _lowerCAmelCase ):
UpperCamelCase : Any = temp * (u - i)
return temp
def A_ ( ) -> ... | 629 | 0 |
'''simple docstring'''
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as ... | 69 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__lowerCamelCase : str = TypeVar("""T""")
class A__ ( Generic[T] ):
def __init__( ... | 629 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, D... | 70 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__low... | 629 | 0 |
'''simple docstring'''
from typing import Any
class _snake_case :
def __init__( self ,_snake_case ):
UpperCAmelCase_ : Union[str, Any] = data
UpperCAmelCase_ : List[str] = None
class _snake_case :
def __init__( se... | 71 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 629 | 0 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCamelCase ( lowercase_ : Any , lo... | 72 |
__lowerCamelCase : Any = 9.8_0_6_6_5
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = g ) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
raise... | 629 | 0 |
import os
from distutils.util import strtobool
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
for e in env_keys:
SCREAMING_SNAKE_CASE = int(os.environ.get(_UpperCAmelCase , -1))
if val >= 0:
return val
return default
def lo... | 73 |
import numpy as np
import qiskit
def A_ ( _lowerCAmelCase = 8 , _lowerCAmelCase = None ) -> str:
UpperCamelCase : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
UpperCamelCase... | 629 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
}
lo... | 74 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, glo... | 629 | 0 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import Tokeni... | 75 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
UpperCamelCase : Union[str, Any] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - эт... | 629 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def __UpperCAmelCase ( ):
from torch.utils.cpp_extension import load
__lowercase : List[Any] = Path(__UpperCamelCase ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
... | 76 |
def A_ ( _lowerCAmelCase = 1000 ) -> int:
UpperCamelCase : Optional[int] = -1
UpperCamelCase : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase : Optional[Any] = (n *... | 629 | 0 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionP... | 77 |
def A_ ( _lowerCAmelCase ) -> bool:
UpperCamelCase : List[Any] = 0
for ch in input_str:
UpperCamelCase : Optional[Any] = ord(_lowerCAmelCase )
UpperCamelCase : Optional[Any] = pow(2 , _lowerCAmelCase )
# If we already turned on bit for ... | 629 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
SCREAMING_SNAKE_CASE_: Dict =generate_large_matrix()
SCREAMING_SNAKE_CASE_: List[... | 78 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 629 | 0 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
... | 79 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : List[Any] = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig"""... | 629 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : List[Any] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_AR... | 80 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
UpperCamelCase : List[Any] = [1]
for i in range(2 , _lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase : Tuple =... | 629 | 0 |
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 transformers.utils import WEIG... | 81 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_confi... | 629 | 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(__na... | 82 |
def A_ ( _lowerCAmelCase ) -> bool:
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def A_ ( _lowerCAmelCase ) -> int:
return int(_lowerCAmelCase ) + int(str(_lowerCAmelCase )[::-1] )
def A_ ( _lowerCAmelCase = 1_0000 ) -> int:
UpperCamelCase... | 629 | 0 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_se... | 83 |
__lowerCamelCase : str = 6_5521
def A_ ( _lowerCAmelCase ) -> int:
UpperCamelCase : Any = 1
UpperCamelCase : str = 0
for plain_chr in plain_text:
UpperCamelCase : List[Any] = (a + ord(_lowerCAmelCase )) % MOD_ADLER
UpperCamelCase... | 629 | 0 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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... | 84 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__lo... | 629 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case ( UpperCamel... | 85 |
from typing import Any
def A_ ( _lowerCAmelCase ) -> list[Any]:
if not input_list:
return []
UpperCamelCase : List[str] = [input_list.count(_lowerCAmelCase ) for value in input_list]
UpperCamelCase : Dict = max(_lowerCAmelCase ) # Gets the maximum count in... | 629 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
imp... | 86 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = int(_lowerCAmelCase )
assert noofclusters < len(_lowerCAmelCase )
# Find out the dimensionality
Upper... | 629 | 0 |
import re
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
"""simple docstring"""
if len(re.findall('''[ATCG]''' , lowercase_ ) ) != len(lowercase_ ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.maketrans('''ATCG''' , ... | 87 |
import os
def A_ ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : ... | 629 | 0 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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-... | 88 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class A__ :
_UpperCAmelCase :Union[str, Any] = None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int ... | 629 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalD... | 89 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 629 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPE... | 90 |
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
UpperCamelCase : Tuple = u
for i in range(1 , _lowerCAmelCase ):
UpperCamelCase : Any = temp * (u - i)
return temp
def A_ ( ) -> ... | 629 | 0 |
"""simple docstring"""
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] ,A_ : Union[str, Any] ,A_ : List[Any] ) -> Union[str, Any]:
A = name
A = val
def __str__( self : Dict ) -> Tuple:
return F'{s... | 91 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__lowerCamelCase : str = TypeVar("""T""")
class A__ ( Generic[T] ):
def __init__( ... | 629 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , UpperCAmelCase__ : Union[str, Any]=2 , UpperCAmelCa... | 92 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__low... | 629 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
"""configuration_layoutlmv3""": [
"""L... | 93 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 629 | 0 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class U... | 94 |
__lowerCamelCase : Any = 9.8_0_6_6_5
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = g ) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
raise... | 629 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 95 |
import numpy as np
import qiskit
def A_ ( _lowerCAmelCase = 8 , _lowerCAmelCase = None ) -> str:
UpperCamelCase : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
UpperCamelCase... | 629 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase = {
'configuration_roberta_prelayernorm': [
... | 96 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, glo... | 629 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path ... | 97 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
UpperCamelCase : Union[str, Any] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - эт... | 629 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase__ : str = {
'configuration_... | 98 |
def A_ ( _lowerCAmelCase = 1000 ) -> int:
UpperCamelCase : Optional[int] = -1
UpperCamelCase : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase : Optional[Any] = (n *... | 629 | 0 |
import random
from typing import Any
def a (lowerCAmelCase__ ):
for _ in range(len(lowerCAmelCase__ ) ):
__a = random.randint(0 , len(lowerCAmelCase__ ) - 1 )
__a = random.randint(0 , len(lowerCAmelCase__ ) - 1 )
__a , __a = data[b], da... | 99 |
def A_ ( _lowerCAmelCase ) -> bool:
UpperCamelCase : List[Any] = 0
for ch in input_str:
UpperCamelCase : Optional[Any] = ord(_lowerCAmelCase )
UpperCamelCase : Optional[Any] = pow(2 , _lowerCAmelCase )
# If we already turned on bit for ... | 629 | 0 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_A : Optional[int] = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
pri... | 100 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 629 | 0 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassi... | 101 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : List[Any] = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig"""... | 629 | 0 |
"""simple docstring"""
import torch
from diffusers import DiffusionPipeline
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self , _A , _A ):
'''simple docstring'''
... | 102 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
UpperCamelCase : List[Any] = [1]
for i in range(2 , _lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase : Tuple =... | 629 | 0 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
snake_case = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
... | 103 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_confi... | 629 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
A__ : Tuple = "SpeechT5FeatureExtractor"
A__ : List[Any] = "SpeechT5Tokenizer"
... | 104 |
def A_ ( _lowerCAmelCase ) -> bool:
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def A_ ( _lowerCAmelCase ) -> int:
return int(_lowerCAmelCase ) + int(str(_lowerCAmelCase )[::-1] )
def A_ ( _lowerCAmelCase = 1_0000 ) -> int:
UpperCamelCase... | 629 | 0 |
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> bool:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number ... | 105 |
__lowerCamelCase : str = 6_5521
def A_ ( _lowerCAmelCase ) -> int:
UpperCamelCase : Any = 1
UpperCamelCase : str = 0
for plain_chr in plain_text:
UpperCamelCase : List[Any] = (a + ord(_lowerCAmelCase )) % MOD_ADLER
UpperCamelCase... | 629 | 0 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def lowerCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : Dict ) -> str:
'''simple docstring'''
... | 106 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__lo... | 629 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_UpperCAmelCase : Dict = '''
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)
'''
_UpperCAmelCase : ... | 107 |
from typing import Any
def A_ ( _lowerCAmelCase ) -> list[Any]:
if not input_list:
return []
UpperCamelCase : List[str] = [input_list.count(_lowerCAmelCase ) for value in input_list]
UpperCamelCase : Dict = max(_lowerCAmelCase ) # Gets the maximum count in... | 629 | 0 |
import re
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> bool:
_UpperCAmelCase = 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(__snake_case , __snake_case ) )
if __name__ ==... | 108 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = int(_lowerCAmelCase )
assert noofclusters < len(_lowerCAmelCase )
# Find out the dimensionality
Upper... | 629 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMi... | 109 |
import os
def A_ ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : ... | 629 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, ... | 119 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class A__ :
_UpperCAmelCase :Union[str, Any] = None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int ... | 629 | 0 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_lowerCAmelCase : List[Any] = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that... | 46 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 629 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase : Optional[... | 305 |
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
UpperCamelCase : Tuple = u
for i in range(1 , _lowerCAmelCase ):
UpperCamelCase : Any = temp * (u - i)
return temp
def A_ ( ) -> ... | 629 | 0 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class A_ ( nn.Module ):
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = jnp.floataa
def _UpperCAmelCase ( self : str ):
__a = nn.Conv(
self.out_channels , kernel_size=(3,... | 197 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__lowerCamelCase : str = TypeVar("""T""")
class A__ ( Generic[T] ):
def __init__( ... | 629 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowercase ( __snake_case ):
_lowercase : Dict = ''
_lowercase : str = (
None # protocol passed i... | 203 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__low... | 629 | 0 |
'''simple docstring'''
import os
def _lowerCamelCase ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
_a = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in ... | 692 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 629 | 0 |
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
a ={
"""tiny.en""": """https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32acc... | 652 |
__lowerCamelCase : Any = 9.8_0_6_6_5
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = g ) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
raise... | 629 | 0 |
'''simple docstring'''
_lowerCAmelCase = range(2, 2_0 + 1)
_lowerCAmelCase = [1_0**k for k in range(ks[-1] + 1)]
_lowerCAmelCase = {}
def _lowerCAmelCase ( lowercase : str , lowercase : Tuple , lowercase : Optional[int] , l... | 161 |
import numpy as np
import qiskit
def A_ ( _lowerCAmelCase = 8 , _lowerCAmelCase = None ) -> str:
UpperCamelCase : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
UpperCamelCase... | 629 | 0 |
from __future__ import annotations
from random import random
class UpperCamelCase__ :
def __init__( self : int, __lowerCamelCase : List[str] = None ) -> Any:
UpperCamelCase__ : Any = value
UpperCamelCase__ : List[Any] = random()
... | 344 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, glo... | 629 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowercase = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowercase = _LazyModule(__name__, glob... | 370 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
UpperCamelCase : Union[str, Any] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - эт... | 629 | 0 |
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