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 |
|---|---|---|---|---|
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowercase ( snake_case ):
"""simple docstring"""
__magic_name__ :Optional[Any] = [
'''encoder.version''',
'''decoder.version''',
... | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docs... | 617 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureEx... | 1 |
"""simple docstring"""
import qiskit
def _A ( _a : int , _a : int ):
"""simple docstring"""
A = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
A = qiskit.QuantumCircu... | 617 | 0 |
import os
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> List[Any]:
_A = len(grid[0] )
_A = len(_snake_case )
_A = 0
_A = 0
_A = 0
# Check vertically, horizontally, diagonally at the same time (only wor... | 2 |
"""simple docstring"""
from statistics import mean, stdev
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = min(_a )
A = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) ... | 617 | 0 |
'''simple docstring'''
from string import ascii_uppercase
lowerCAmelCase : Union[str, Any] = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase : Dict = dict(enumerate(ascii_uppercase))
def A_( A : str , A : str):
Upp... | 3 |
"""simple docstring"""
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> tuple[list[int], list[int]]:
A = [ord(lowerCamelCase_ ) for i in text]
A = []... | 617 | 0 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 4 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
... | 617 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
self.test()
def _lower... | 5 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> Any:
A = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
A... | 617 | 0 |
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: list ):
if any(not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(UpperCamelCase__ ) ):
... | 6 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 617 | 0 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from f... | 7 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 0 |
'''simple docstring'''
import math
def _lowerCAmelCase ( __snake_case : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all e... | 8 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = list(_a )
A = list(_a )
A ... | 617 | 0 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobe... | 9 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase =get_tests_... | 617 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW model... | 10 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mo... | 617 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A , __A = False):
"""simple docstring"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3... | 11 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 617 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
lowerCamelCase__ : List[str] = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/... | 12 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : list[float] , _a : list[float] ):
"""simple docstring"""
A = sorted(numsa + numsa )
A , A = divmod(len(_a ) , 2 )
if mod == 1:
... | 617 | 0 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
A__ : Union[str, Any] = logging.get_logger(__name__)
A__ : Union[str, Any] = {name: getattr(tran... | 13 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *_a : int ):
"""simple docstring"""
if not isinstance(_a , _a ):
A = list(_a ... | 617 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at htt... | 14 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 617 | 0 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def UpperCamelCase ( __magic_name__ : List[str] ) -> Dict: #... | 15 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 617 | 0 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from dataset... | 16 |
"""simple docstring"""
from math import pow, sqrt
def _A ( *_a : float ):
"""simple docstring"""
A = len(_a ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _a : float , _a : float ):
... | 617 | 0 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.... | 17 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 0 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
_lowerCAmelCase = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_lowerCAmelCase = ""
_lowerCAmelCase = ""
# append eac... | 18 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
A = {}
def UpperCamelCase__ ( self ) -> None:
print(self.vertex )
for i in self.vertex:
... | 617 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a = {
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mask2For... | 19 |
"""simple docstring"""
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
UpperCAmelCase =logging.get_logge... | 617 | 0 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():... | 20 |
"""simple docstring"""
def _A ( _a : Optional[int] , _a : str ):
"""simple docstring"""
A = 0
A = len(_a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[le... | 617 | 0 |
from scipy.stats import spearmanr
import datasets
UpperCAmelCase_ : Optional[Any] = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no corre... | 21 |
"""simple docstring"""
UpperCAmelCase =[
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _A ( _a : Dict , _a : int , _a : Dict , _a : ... | 617 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require... | 22 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docs... | 617 | 0 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
snake_case__ : Optional[int] = logging.g... | 23 |
"""simple docstring"""
import qiskit
def _A ( _a : int , _a : int ):
"""simple docstring"""
A = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
A = qiskit.QuantumCircu... | 617 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
class lowerCAmelCase ( __lowerCAmelCase):
def __init__( self , *__SCREAMING_SNAKE_CAS... | 24 |
"""simple docstring"""
from statistics import mean, stdev
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = min(_a )
A = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) ... | 617 | 0 |
from PIL import Image
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = image.size
SCREAMING_SNAKE_CASE : Tuple = 0
SCREAMING_SNAKE_CASE : Tuple = image.load()
for i in range(_a):
for j in range(_a):
SCREAMING_SNAKE_CASE : ... | 25 |
"""simple docstring"""
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> tuple[list[int], list[int]]:
A = [ord(lowerCamelCase_ ) for i in text]
A = []... | 617 | 0 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> int:
"""simple docstring"""
__snake_case : List[str] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _a ( ... | 26 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
... | 617 | 0 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx im... | 27 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> Any:
A = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
A... | 617 | 0 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int = 10_00 ):
"""simple docstring"""
return sum(e for e in range(3 ,__UpperCamelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 28 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 617 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ = (3, 9, -11, 0, 7, 5, 1, -1)
A_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __lowerCamelCase :
a__: int
a__: Node | No... | 29 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 0 |
def lowerCamelCase__ ( _lowercase = 10 , _lowercase = 22 ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = range(1 , _lowercase )
UpperCAmelCase_ : Optional[int] = range(1 , _lowercase )
return sum(
1 for power in powers for base in base... | 30 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = list(_a )
A = list(_a )
A ... | 617 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ : str = logging.get_logger(__name__)
lowerCamelCase__ : List[str] = {
'vocab_file': 'vocab... | 31 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase =get_tests_... | 617 | 0 |
from numpy import exp, pi, sqrt
def A__ ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : float = 0.0 , SCREAMING_SNAKE_CASE_ : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2... | 32 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mo... | 617 | 0 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
snake_case__ = F"""Input value of [number={number}] must be an integer"""
raise TypeError(__lowerCAmelCase )
if number < 1:
snake_case__ = F"""... | 33 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 617 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'nielsr/canine-s': 2048,
}
# Unicode de... | 34 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : list[float] , _a : list[float] ):
"""simple docstring"""
A = sorted(numsa + numsa )
A , A = divmod(len(_a ) , 2 )
if mod == 1:
... | 617 | 0 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
a_ :Tuple = object()
# For specifying empty leaf dict `{}`
a_ :str = object()
def a ( A__ , A__ ... | 35 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *_a : int ):
"""simple docstring"""
if not isinstance(_a , _a ):
A = list(_a ... | 617 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__low... | 36 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 617 | 0 |
def UpperCamelCase_ ( __a ) -> float:
return 10 - x * x
def UpperCamelCase_ ( __a , __a ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(__a ) * equation(__a ) >= 0:
raise ValueError("Wrong space!" ... | 37 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( __magic_name__ : Tuple , __magic_name__ : List[str] , __magic_name__ : int , __magic_name__ : Union[str, Any] ) -> Optional[Any]: # noqa: E741
'''simple docstring'''
... | 38 |
"""simple docstring"""
from math import pow, sqrt
def _A ( *_a : float ):
"""simple docstring"""
A = len(_a ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _a : float , _a : float ):
... | 617 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
lo... | 39 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available... | 40 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
A = {}
def UpperCamelCase__ ( self ) -> None:
print(self.vertex )
for i in self.vertex:
... | 617 | 0 |
'''simple docstring'''
def _A ( A__ , A__ , A__ ):
"""simple docstring"""
if len(A__ ) != len(A__ ):
raise ValueError('''The length of profit and weight must be same.''' )
if max_weight <= 0:
raise ValueError('''max_weight must greater than zero.''' )
if any(p < 0 for p ... | 41 |
"""simple docstring"""
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
UpperCAmelCase =logging.get_logge... | 617 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig... | 42 |
"""simple docstring"""
def _A ( _a : Optional[int] , _a : str ):
"""simple docstring"""
A = 0
A = len(_a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[le... | 617 | 0 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Optional[Any] = Down... | 43 |
"""simple docstring"""
UpperCAmelCase =[
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _A ( _a : Dict , _a : int , _a : Dict , _a : ... | 617 | 0 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any]=None ):
"""simple docstring"""... | 44 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docs... | 617 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A ( lowercase__ : dict ) -> tuple:
return (data["data"], d... | 45 |
"""simple docstring"""
import qiskit
def _A ( _a : int , _a : int ):
"""simple docstring"""
A = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
A = qiskit.QuantumCircu... | 617 | 0 |
"""simple docstring"""
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowerCAmelCase : List[str] = 300 # TEMPERATURE (unit = K)
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ) -> float:
'''simple ... | 46 |
"""simple docstring"""
from statistics import mean, stdev
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = min(_a )
A = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) ... | 617 | 0 |
def UpperCAmelCase__ ( lowerCamelCase_ : str ):
if n_term == "":
return []
__a : list = []
for temp in range(int(lowerCamelCase_ ) ):
series.append(f'''1/{temp + 1}''' if series else '1' )
return series
if __name__ =... | 47 |
"""simple docstring"""
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> tuple[list[int], list[int]]:
A = [ord(lowerCamelCase_ ) for i in text]
A = []... | 617 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class A ( SCREAMING_SN... | 48 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
... | 617 | 0 |
"""simple docstring"""
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pi... | 49 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> Any:
A = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
A... | 617 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 50 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 617 | 0 |
'''simple docstring'''
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,
)
if is_sentencepiece_available():
from ..ta... | 51 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 0 |
"""simple docstring"""
import torch
from transformers import AutoModel
class __lowercase ( torch.nn.Module ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase="sayef/fsner-bert-base-uncased" ):
su... | 52 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = list(_a )
A = list(_a )
A ... | 617 | 0 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import Dat... | 53 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase =get_tests_... | 617 | 0 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, Imag... | 54 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mo... | 617 | 0 |
def UpperCAmelCase ( a_ = 1_0_0_0 ) -> int:
"""simple docstring"""
__A = 3
__A = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
a += 1
return result
if __nam... | 55 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 617 | 0 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a ... | 56 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : list[float] , _a : list[float] ):
"""simple docstring"""
A = sorted(numsa + numsa )
A , A = divmod(len(_a ) , 2 )
if mod == 1:
... | 617 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertToken... | 57 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *_a : int ):
"""simple docstring"""
if not isinstance(_a , _a ):
A = list(_a ... | 617 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowerCAmelCase ( __UpperCamelCase :... | 58 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 617 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
... | 59 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 617 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
# See all Cvt models at https://h... | 60 |
"""simple docstring"""
from math import pow, sqrt
def _A ( *_a : float ):
"""simple docstring"""
A = len(_a ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _a : float , _a : float ):
... | 617 | 0 |
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
UpperCamelCase = get_tests_dir('fixtures/spiece.mo... | 61 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 0 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCamelCase__ ( lowercase , lowercase=None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = None
if token ... | 62 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
A = {}
def UpperCamelCase__ ( self ) -> None:
print(self.vertex )
for i in self.vertex:
... | 617 | 0 |
def lowerCamelCase__ ( __lowerCamelCase : List[Any] , __lowerCamelCase : Union[str, Any] ):
__UpperCAmelCase : Tuple = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( ... | 63 |
"""simple docstring"""
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
UpperCAmelCase =logging.get_logge... | 617 | 0 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowercase_ : List[Any] = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def A__ ( snake_case_ : str = "mumbai" ):
SCREAMING_SNAKE_CASE__: List[str]... | 64 |
"""simple docstring"""
def _A ( _a : Optional[int] , _a : str ):
"""simple docstring"""
A = 0
A = len(_a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[le... | 617 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_ava... | 65 |
"""simple docstring"""
UpperCAmelCase =[
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _A ( _a : Dict , _a : int , _a : Dict , _a : ... | 617 | 0 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
UpperCamelCase = object()
# For specifying empty leaf dict `{}`
UpperCamelCase = object()
def __magic_name__ (... | 66 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docs... | 617 | 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
... | 67 |
"""simple docstring"""
import qiskit
def _A ( _a : int , _a : int ):
"""simple docstring"""
A = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
A = qiskit.QuantumCircu... | 617 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/c... | 68 |
"""simple docstring"""
from statistics import mean, stdev
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = min(_a )
A = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) ... | 617 | 0 |
'''simple docstring'''
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 Paddin... | 69 |
"""simple docstring"""
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> tuple[list[int], list[int]]:
A = [ord(lowerCamelCase_ ) for i in text]
A = []... | 617 | 0 |
import cmath
import math
def _SCREAMING_SNAKE_CASE ( lowercase : float , lowercase : float , lowercase : float , lowercase : float ):
'''simple docstring'''
lowerCamelCase_ = math.radians(lowercase )
... | 70 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : tuple[int, int] , _SCREAMING_SNAKE_CASE : int ) -> list[tuple[int, int]]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] ... | 71 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> Any:
A = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
A... | 617 | 0 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_... | 72 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 617 | 0 |
def lowerCamelCase__ (_UpperCAmelCase):
if not numbers:
return 0
if not isinstance(_UpperCAmelCase , (list, tuple)) or not all(
isinstance(_UpperCAmelCase , _UpperCAmelCase) for number in numbers):
raise ValueError('numbers must be an iterable of integers')
... | 73 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 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_indices
lowercase_... | 74 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = list(_a )
A = list(_a )
A ... | 617 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
... | 75 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase =get_tests_... | 617 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
if n == 1 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
return 0
elif n == 2:
return 1
else:
__lowercase : int = [0, 1]
for i in range(2 , n + 1 ):
... | 76 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mo... | 617 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 77 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 617 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
SCREAMING_SNAKE_CASE_: Tuple =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Tuple ={
'Visual-Attention-Network/van-base': (
'https://huggingface.co/Visual-Attention-Network/van-base/... | 78 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : list[float] , _a : list[float] ):
"""simple docstring"""
A = sorted(numsa + numsa )
A , A = divmod(len(_a ) , 2 )
if mod == 1:
... | 617 | 0 |
from numpy import exp, pi, sqrt
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase = 0.0 , __lowerCamelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
i... | 79 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *_a : int ):
"""simple docstring"""
if not isinstance(_a , _a ):
A = list(_a ... | 617 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
... | 80 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 617 | 0 |
import os
from collections.abc import Iterator
def lowerCAmelCase_ ( __lowerCamelCase = "." ):
for dir_path, dir_names, filenames in os.walk(__lowerCamelCase ):
__snake_case : Tuple = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
... | 81 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 617 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
... | 82 |
"""simple docstring"""
from math import pow, sqrt
def _A ( *_a : float ):
"""simple docstring"""
A = len(_a ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _a : float , _a : float ):
... | 617 | 0 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_av... | 83 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository c... | 84 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
A = {}
def UpperCamelCase__ ( self ) -> None:
print(self.vertex )
for i in self.vertex:
... | 617 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
... | 85 |
"""simple docstring"""
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
UpperCAmelCase =logging.get_logge... | 617 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a :Optional[Any] = {'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARC... | 86 |
"""simple docstring"""
def _A ( _a : Optional[int] , _a : str ):
"""simple docstring"""
A = 0
A = len(_a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[le... | 617 | 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
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : ... | 87 |
"""simple docstring"""
UpperCAmelCase =[
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _A ( _a : Dict , _a : int , _a : Dict , _a : ... | 617 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase__ ( A_ ):
def __init__( ... | 88 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docs... | 617 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 89 |
"""simple docstring"""
import qiskit
def _A ( _a : int , _a : int ):
"""simple docstring"""
A = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
A = qiskit.QuantumCircu... | 617 | 0 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_comm... | 90 |
"""simple docstring"""
from statistics import mean, stdev
def _A ( _a : list , _a : int = 3 ):
"""simple docstring"""
A = min(_a )
A = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) ... | 617 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow... | 91 |
"""simple docstring"""
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> tuple[list[int], list[int]]:
A = [ord(lowerCamelCase_ ) for i in text]
A = []... | 617 | 0 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
UpperCamelCase_ = 3
def _lowerCAmelCase ( __magic_name__ : int ) -> int:
print('''Generating primitive root of p''' )
... | 92 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
... | 617 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 93 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> Any:
A = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
A... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[int] , __A : int ) -> list[int]:
"""simple docstring"""
lowercase : List[Any] =0
lowercase : Tuple =len(__A ) - 1
while i < j:
if nu... | 94 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 617 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''huggingface/informer-tourism-monthly''': (
'''https://hugg... | 95 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 0 |
"""simple docstring"""
from timeit import timeit
__lowerCamelCase = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# En... | 96 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = list(_a )
A = list(_a )
A ... | 617 | 0 |
import numpy as np
def a ( snake_case__: np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 97 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase =get_tests_... | 617 | 0 |
'''simple docstring'''
lowercase__ : List[str] = 'Alexander Joslin'
import operator as op
from .stack import Stack
def a__ ( lowercase : str ) -> int:
"""simple docstring"""
_UpperCamelCase = {'''*''': op.mul, '''/''': op.truediv, ''... | 98 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mo... | 617 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
... | 99 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 617 | 0 |
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