code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> list[list[int]]:
UpperCamelCase : list[list[int]] = []
create_all_state(1 , _lowerCAmelCase , _lowerCAmelCase , [] , _lowerCAmelCase )
return resul... | 38 |
from __future__ import annotations
__lowerCamelCase : Optional[int] = """Muhammad Umer Farooq"""
__lowerCamelCase : Tuple = """MIT"""
__lowerCamelCase : Optional[int] = """1.0.0"""
__lowerCamelCase : int = """Muhammad Umer Farooq"""
__lowerCamelCase : Optional[int] ... | 38 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__lowerCamelCase : Dict = logging.getLogger()
@unittest.skip('Temporarily disable the doc test... | 38 |
from __future__ import annotations
def A_ ( _lowerCAmelCase ) -> list[int]:
UpperCamelCase : Optional[Any] = [True] * limit
UpperCamelCase : Optional[Any] = False
UpperCamelCase : List[str] = False
UpperCamelCase : Tuple = True
for i in... | 38 | 1 |
import sys
import turtle
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ) -> None... | 38 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class A__ ( __snake_case ):
def __init__( self , A_ , A_ = None , A_ = None , A_ = False , ... | 38 | 1 |
from PIL import Image
def A_ ( _lowerCAmelCase ) -> Image:
UpperCamelCase , UpperCamelCase : List[Any] = image.size
UpperCamelCase : Union[str, Any] = 0
UpperCamelCase : List[str] = image.load()
for i in range(_lowerCAmelCase ):
for j in range... | 38 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 38 | 1 |
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 : Tuple = logging.get_logger(__name__)
__lowerCamelCa... | 38 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__snake_case ):
_UpperCAmelCase :Tuple = ['note_seq']
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
requires_backends(self , ["note_seq"] ... | 38 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test... | 38 |
import math
import tensorflow as tf
from packaging import version
def A_ ( _lowerCAmelCase ) -> Any:
UpperCamelCase : List[Any] = tf.convert_to_tensor(_lowerCAmelCase )
UpperCamelCase : Any = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype )... | 38 | 1 |
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
UpperCamelCase : Dict = u
for i in range(1 , _lowerCAmelCase ):
UpperCamelCase : Optional[Any] = temp * (u - i)
return temp
def A_ ... | 38 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_f... | 38 | 1 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common... | 38 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A_ ( ) -> Dict:
UpperCamelCase : Tuple = ArgumentParser(
description=(
"PyTorch TPU distributed training laun... | 38 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
... | 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Union[str, Any] = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""",... | 38 | 1 |
import math
import tensorflow as tf
from packaging import version
def A_ ( _lowerCAmelCase ) -> Any:
UpperCamelCase : List[Any] = tf.convert_to_tensor(_lowerCAmelCase )
UpperCamelCase : Any = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype )... | 38 |
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_video_inputs
if is_torch_available():
import t... | 38 | 1 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__lowerCamelCase : str = None
try:
import msvcrt
except ImportError:
__lowerCamelCase : str = None
try:
import fcntl
except ImportError:
__lowerCamelCase : List[Any] ... | 38 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lo... | 38 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_test... | 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : int = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Conv... | 38 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 38 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__lowerCamelCase : str = None
try:
import msvcrt
except ImportError:
__lowerCamelCase : str = None
try:
import fcntl
except ImportError:
__lowerCamelCase : List[Any] ... | 38 | 1 |
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
__low... | 38 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None ... | 38 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.u... | 38 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tens... | 38 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils im... | 38 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : Tuple = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""camembert-base""": """h... | 38 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : str = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class A__ ( __snake_case ):
... | 38 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
return int(input_a == input_a == 0 )
def A_ ( ) -> None:
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(F"""| 0 | 0 | {nor_gate(0 , 0 )} |"""... | 38 | 1 |
from math import pow
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution... | 38 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( __snake_ca... | 38 | 1 |
def A_ ( _lowerCAmelCase ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
UpperCamelCase : Tuple = gray_code_sequence_string(_lowerCAmelCase )
#
# conver... | 38 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
__lowerCamelCase : Dict ... | 38 | 1 |
def A_ ( _lowerCAmelCase ) -> bool:
UpperCamelCase : Optional[int] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 38 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar("""KT""")
__lowerCamelCase : Dict = TypeVar("""VT""")
class A__ ( Generic[KT, VT] ):
def __init__( self , A_ = "root" , ... | 38 | 1 |
__lowerCamelCase : 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,
"kilocalorie_nutr": 418_6800.00,
... | 38 |
from PIL import Image
def A_ ( _lowerCAmelCase ) -> Image:
UpperCamelCase , UpperCamelCase : List[Any] = image.size
UpperCamelCase : Union[str, Any] = 0
UpperCamelCase : List[str] = image.load()
for i in range(_lowerCAmelCase ):
for j in range... | 38 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__lowerCamelCase : Dict = logging.getLogger(__name__)
class A__ ( __snake_case ):
def __init__( self , ... | 38 |
from math import loga
def A_ ( _lowerCAmelCase ) -> int:
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("Input value must be a 'int' type" )
return 0 if (a == 0) else int(loga(a & -a ) ... | 38 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, ... | 38 |
from __future__ import annotations
__lowerCamelCase : Optional[int] = """Muhammad Umer Farooq"""
__lowerCamelCase : Tuple = """MIT"""
__lowerCamelCase : Optional[int] = """1.0.0"""
__lowerCamelCase : int = """Muhammad Umer Farooq"""
__lowerCamelCase : Optional[int] ... | 38 | 1 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as comp... | 38 |
from __future__ import annotations
def A_ ( _lowerCAmelCase ) -> list[int]:
UpperCamelCase : Optional[Any] = [True] * limit
UpperCamelCase : Optional[Any] = False
UpperCamelCase : List[str] = False
UpperCamelCase : Tuple = True
for i in... | 38 | 1 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__snake_case ):
_UpperCAmelCase :Tuple = ['note_seq']
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
requires_backends(self , ["note_seq"] ... | 38 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class A__ ( __snake_case ):
def __init__( self , A_ , A_ = None , A_ = None , A_ = False , ... | 38 | 1 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> List[str]:
assert ... | 38 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 38 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( __snake_case ):
_UpperCAmelCase :Any = (PNDMScheduler,)
_UpperCAmelCase :List[str] = (('num_inference_steps', 5_0),)
def ... | 38 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__snake_case ):
_UpperCAmelCase :Tuple = ['note_seq']
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
requires_backends(self , ["note_seq"] ... | 38 | 1 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__lowerCamelCase : Union[str, Any] = """\
"""
__lowerCamelCase : Dict = """
Perplexity (PPL) is one of the most... | 38 |
import math
import tensorflow as tf
from packaging import version
def A_ ( _lowerCAmelCase ) -> Any:
UpperCamelCase : List[Any] = tf.convert_to_tensor(_lowerCAmelCase )
UpperCamelCase : Any = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype )... | 38 | 1 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
__lowerCamelCas... | 38 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_f... | 38 | 1 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__lowerCamelCase : Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
def A_ ... | 38 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A_ ( ) -> Dict:
UpperCamelCase : Tuple = ArgumentParser(
description=(
"PyTorch TPU distributed training laun... | 38 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Optional[Any] = {
"""configuration_... | 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Union[str, Any] = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""",... | 38 | 1 |
def A_ ( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
UpperCamelCase : Any = str(abs(_lowerCAmelCase ) )
UpperCamelCase : Dict = [list(_lowerCAmelCase ) fo... | 38 |
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_video_inputs
if is_torch_available():
import t... | 38 | 1 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> str:
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCamelCase : Any = str(bin(_lowerCAmelCase ) )[2:] # remove the leading "0b"
UpperCamelCase : List[str] = str(bin(... | 38 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lo... | 38 | 1 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEAT... | 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : int = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Conv... | 38 | 1 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def A_ ( _lowerCAmelCase , _lowerCAmelCase = True , _lowerCAmelCase = math.inf , _lowerCAmelCase = -math.inf , _lowerCAmelCase = math.inf , _lowerCAmelCase = -mat... | 38 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__lowerCamelCase : str = None
try:
import msvcrt
except ImportError:
__lowerCamelCase : str = None
try:
import fcntl
except ImportError:
__lowerCamelCase : List[Any] ... | 38 | 1 |
import requests
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> None:
UpperCamelCase : List[Any] = {"Content-Type": "application/json"}
UpperCamelCase : Dict = requests.post(_lowerCAmelCase , json={"text": message_body} , headers=_lowerCA... | 38 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None ... | 38 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHyb... | 38 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tens... | 38 | 1 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
__lowerCamelCase : int = yaml.safe_load(
"""\
name: \"\"
allow_empty: false
allow_empty_text: true
subsections:
- name: \"Da... | 38 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : Tuple = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""camembert-base""": """h... | 38 | 1 |
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_video_inputs
if is_torch_available():
import t... | 38 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
return int(input_a == input_a == 0 )
def A_ ( ) -> None:
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(F"""| 0 | 0 | {nor_gate(0 , 0 )} |"""... | 38 | 1 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(_lowerCAmelCase ):
for j in range(_lowerCAmelCase ):
if dist[i][j] != float("inf" ):
print(int(dist[i][j] ) , end="\t" )
e... | 38 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( __snake_ca... | 38 | 1 |
from math import factorial, pi
def A_ ( _lowerCAmelCase , _lowerCAmelCase = 30 ) -> float:
if not isinstance(_lowerCAmelCase , (int, float) ):
raise ValueError("maclaurin_sin() requires either an int or float for theta" )
if not isinstance(_lowerCAmelCase , _l... | 38 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
__lowerCamelCase : Dict ... | 38 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( __snake_ca... | 38 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar("""KT""")
__lowerCamelCase : Dict = TypeVar("""VT""")
class A__ ( Generic[KT, VT] ):
def __init__( self , A_ = "root" , ... | 38 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 38 |
from PIL import Image
def A_ ( _lowerCAmelCase ) -> Image:
UpperCamelCase , UpperCamelCase : List[Any] = image.size
UpperCamelCase : Union[str, Any] = 0
UpperCamelCase : List[str] = image.load()
for i in range(_lowerCAmelCase ):
for j in range... | 38 | 1 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lower... | 38 |
from math import loga
def A_ ( _lowerCAmelCase ) -> int:
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("Input value must be a 'int' type" )
return 0 if (a == 0) else int(loga(a & -a ) ... | 38 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParse... | 38 |
from __future__ import annotations
__lowerCamelCase : Optional[int] = """Muhammad Umer Farooq"""
__lowerCamelCase : Tuple = """MIT"""
__lowerCamelCase : Optional[int] = """1.0.0"""
__lowerCamelCase : int = """Muhammad Umer Farooq"""
__lowerCamelCase : Optional[int] ... | 38 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepie... | 38 |
from __future__ import annotations
def A_ ( _lowerCAmelCase ) -> list[int]:
UpperCamelCase : Optional[Any] = [True] * limit
UpperCamelCase : Optional[Any] = False
UpperCamelCase : List[str] = False
UpperCamelCase : Tuple = True
for i in... | 38 | 1 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceC... | 38 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class A__ ( __snake_case ):
def __init__( self , A_ , A_ = None , A_ = None , A_ = False , ... | 38 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A__ ( __snake_case ):
@staticmethod
@abstractmethod
def __UpperCamelCase( A_ ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def __... | 38 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 38 | 1 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__lowerCamelCase : Optional[int] = _LazyModule(... | 38 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__snake_case ):
_UpperCAmelCase :Tuple = ['note_seq']
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
requires_backends(self , ["note_seq"] ... | 38 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__lowerCamelCase : Dict = logging.getLogger(__name__)
if is_torch_tpu_available(check_... | 38 |
import math
import tensorflow as tf
from packaging import version
def A_ ( _lowerCAmelCase ) -> Any:
UpperCamelCase : List[Any] = tf.convert_to_tensor(_lowerCAmelCase )
UpperCamelCase : Any = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype )... | 38 | 1 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 38 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_f... | 38 | 1 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCamelCase : Any = logging.get_logge... | 38 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A_ ( ) -> Dict:
UpperCamelCase : Tuple = ArgumentParser(
description=(
"PyTorch TPU distributed training laun... | 38 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available... | 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Union[str, Any] = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""",... | 38 | 1 |
def A_ ( _lowerCAmelCase ) -> int:
if not head:
return True
# split the list to two parts
UpperCamelCase , UpperCamelCase : Dict = head.next, head
while fast and fast.next:
UpperCamelCase : Union[str, Any] = fast.next.next
UpperCamelCase : Dict = ... | 38 |
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_video_inputs
if is_torch_available():
import t... | 38 | 1 |
from __future__ import annotations
def A_ ( _lowerCAmelCase ) -> float:
if not nums:
raise ValueError("List is empty" )
return sum(_lowerCAmelCase ) / len(_lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 38 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lo... | 38 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A__ ( __snake_case ):
_UpperCAmelCase :str = ... | 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : int = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Conv... | 38 | 1 |
import re
from filelock import FileLock
try:
import nltk
__lowerCamelCase : int = True
except (ImportError, ModuleNotFoundError):
__lowerCamelCase : Any = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def ... | 38 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__lowerCamelCase : str = None
try:
import msvcrt
except ImportError:
__lowerCamelCase : str = None
try:
import fcntl
except ImportError:
__lowerCamelCase : List[Any] ... | 38 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__lowerCamelCase : Tuple = {
... | 38 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None ... | 38 | 1 |
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,
XLMRo... | 38 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tens... | 38 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase : Optional[int] = logging.get_logger(__name__)
class A__ ( __snake_case , __snake_case... | 38 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : Tuple = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""camembert-base""": """h... | 38 | 1 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..... | 38 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
return int(input_a == input_a == 0 )
def A_ ( ) -> None:
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(F"""| 0 | 0 | {nor_gate(0 , 0 )} |"""... | 38 | 1 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None ... | 38 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( __snake_ca... | 38 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__lowerCamelCase : Union[str, Any] = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MA... | 38 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
__lowerCamelCase : Dict ... | 38 | 1 |
def A_ ( ) -> Optional[Any]:
UpperCamelCase : List[str] = []
UpperCamelCase : Any = 1
while len(_lowerCAmelCase ) < 1e6:
constant.append(str(_lowerCAmelCase ) )
i += 1
UpperCamelCase : Any = "".join(_lowerCAmelCase )
return (
int(constant[... | 38 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar("""KT""")
__lowerCamelCase : Dict = TypeVar("""VT""")
class A__ ( Generic[KT, VT] ):
def __init__( self , A_ = "root" , ... | 38 | 1 |
from manim import *
class A__ ( __snake_case ):
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase : str = Rectangle(height=0.25 , ... | 38 |
from PIL import Image
def A_ ( _lowerCAmelCase ) -> Image:
UpperCamelCase , UpperCamelCase : List[Any] = image.size
UpperCamelCase : Union[str, Any] = 0
UpperCamelCase : List[str] = image.load()
for i in range(_lowerCAmelCase ):
for j in range... | 38 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 38 |
from math import loga
def A_ ( _lowerCAmelCase ) -> int:
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("Input value must be a 'int' type" )
return 0 if (a == 0) else int(loga(a & -a ) ... | 38 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_f... | 38 |
from __future__ import annotations
__lowerCamelCase : Optional[int] = """Muhammad Umer Farooq"""
__lowerCamelCase : Tuple = """MIT"""
__lowerCamelCase : Optional[int] = """1.0.0"""
__lowerCamelCase : int = """Muhammad Umer Farooq"""
__lowerCamelCase : Optional[int] ... | 38 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_ut... | 38 |
from __future__ import annotations
def A_ ( _lowerCAmelCase ) -> list[int]:
UpperCamelCase : Optional[Any] = [True] * limit
UpperCamelCase : Optional[Any] = False
UpperCamelCase : List[str] = False
UpperCamelCase : Tuple = True
for i in... | 38 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 38 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class A__ ( __snake_case ):
def __init__( self , A_ , A_ = None , A_ = None , A_ = False , ... | 38 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : int = {
"""vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
# See all... | 38 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 38 | 1 |
def A_ ( _lowerCAmelCase ) -> bool:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("check_bouncy() accepts only integer arguments" )
UpperCamelCase : Dict = str(_lowerCAmelCase )
UpperCamelCase : Tuple = "".join(sorted(_lowerCAmel... | 38 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__snake_case ):
_UpperCAmelCase :Tuple = ['note_seq']
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
requires_backends(self , ["note_seq"] ... | 38 | 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 app... | 38 |
import math
import tensorflow as tf
from packaging import version
def A_ ( _lowerCAmelCase ) -> Any:
UpperCamelCase : List[Any] = tf.convert_to_tensor(_lowerCAmelCase )
UpperCamelCase : Any = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype )... | 38 | 1 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
depr... | 38 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_f... | 38 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class A__ :
def __init__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = str(id_ )
UpperCamelCase : Dict = None
UpperCame... | 38 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A_ ( ) -> Dict:
UpperCamelCase : Tuple = ArgumentParser(
description=(
"PyTorch TPU distributed training laun... | 38 | 1 |
from math import factorial
def A_ ( _lowerCAmelCase = 100 ) -> int:
return sum(int(_lowerCAmelCase ) for x in str(factorial(_lowerCAmelCase ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Union[str, Any] = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""",... | 38 | 1 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 38 |
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_video_inputs
if is_torch_available():
import t... | 38 | 1 |
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... | 38 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lo... | 38 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slo... | 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : int = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Conv... | 38 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_AR... | 38 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__lowerCamelCase : str = None
try:
import msvcrt
except ImportError:
__lowerCamelCase : str = None
try:
import fcntl
except ImportError:
__lowerCamelCase : List[Any] ... | 38 | 1 |
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 A__ ( __snake_case ):
def ... | 38 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None ... | 38 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCamelCase : Optional[int] = {
"""configuration_rag""": ["""RagConfig"""],
"""retrieval_rag""": ["""RagRetriever"""],
"""tokenization_rag""": ["""R... | 38 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tens... | 38 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class A__ ( nn.Module ):
... | 38 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : Tuple = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""camembert-base""": """h... | 38 | 1 |
def A_ ( _lowerCAmelCase ) -> int:
if n == 1 or not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return 0
elif n == 2:
return 1
else:
UpperCamelCase : Tuple = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
... | 38 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
return int(input_a == input_a == 0 )
def A_ ( ) -> None:
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(F"""| 0 | 0 | {nor_gate(0 , 0 )} |"""... | 38 | 1 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
__lowerCamelCase : Dict ... | 38 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( __snake_ca... | 38 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar("""KT""")
__lowerCamelCase : Dict = TypeVar("""VT""")
class A__ ( Generic[KT, VT] ):
def __init__( self , A_ = "root" , ... | 38 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
__lowerCamelCase : Dict ... | 38 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils im... | 38 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar("""KT""")
__lowerCamelCase : Dict = TypeVar("""VT""")
class A__ ( Generic[KT, VT] ):
def __init__( self , A_ = "root" , ... | 38 | 1 |
from math import pi
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 38 |
from PIL import Image
def A_ ( _lowerCAmelCase ) -> Image:
UpperCamelCase , UpperCamelCase : List[Any] = image.size
UpperCamelCase : Union[str, Any] = 0
UpperCamelCase : List[str] = image.load()
for i in range(_lowerCAmelCase ):
for j in range... | 38 | 1 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowerCamelCase : int = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 38 |
from math import loga
def A_ ( _lowerCAmelCase ) -> int:
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("Input value must be a 'int' type" )
return 0 if (a == 0) else int(loga(a & -a ) ... | 38 | 1 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
f... | 38 |
from __future__ import annotations
__lowerCamelCase : Optional[int] = """Muhammad Umer Farooq"""
__lowerCamelCase : Tuple = """MIT"""
__lowerCamelCase : Optional[int] = """1.0.0"""
__lowerCamelCase : int = """Muhammad Umer Farooq"""
__lowerCamelCase : Optional[int] ... | 38 | 1 |
from __future__ import annotations
from typing import Any
class A__ :
def __init__( self , A_ ):
'''simple docstring'''
UpperCamelCase : str = num_of_nodes
UpperCamelCase : list[list[int]] = []
UpperCamelCase : ... | 38 |
from __future__ import annotations
def A_ ( _lowerCAmelCase ) -> list[int]:
UpperCamelCase : Optional[Any] = [True] * limit
UpperCamelCase : Optional[Any] = False
UpperCamelCase : List[str] = False
UpperCamelCase : Tuple = True
for i in... | 38 | 1 |
__lowerCamelCase : Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowerCamelCase : Dict = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowerCamelCase : str = {
0: """Sunday""",
1: """Monday""",
2: """Tuesday""",
3: """Wednesday""",
4: """Thursday""",
5: "... | 38 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class A__ ( __snake_case ):
def __init__( self , A_ , A_ = None , A_ = None , A_ = False , ... | 38 | 1 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCamelCase : Optional[Any] = """."""
# Internal Tensor... | 38 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 38 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Huggi... | 38 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__snake_case ):
_UpperCAmelCase :Tuple = ['note_seq']
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
requires_backends(self , ["note_seq"] ... | 38 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavL... | 38 |
import math
import tensorflow as tf
from packaging import version
def A_ ( _lowerCAmelCase ) -> Any:
UpperCamelCase : List[Any] = tf.convert_to_tensor(_lowerCAmelCase )
UpperCamelCase : Any = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype )... | 38 | 1 |
import numpy as np
__lowerCamelCase : Union[str, Any] = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""",... | 38 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_f... | 38 | 1 |
from math import factorial
__lowerCamelCase : List[Any] = {str(d): factorial(d) for d in range(10)}
def A_ ( _lowerCAmelCase ) -> int:
return sum(DIGIT_FACTORIAL[d] for d in str(_lowerCAmelCase ) )
def A_ ( ) -> int:
UpperCamelCase : Optional[Any] = 7 *... | 38 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A_ ( ) -> Dict:
UpperCamelCase : Tuple = ArgumentParser(
description=(
"PyTorch TPU distributed training laun... | 38 | 1 |
import os
def A_ ( ) -> List[str]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : int = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : str = 0
... | 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Union[str, Any] = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""",... | 38 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tens... | 38 |
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_video_inputs
if is_torch_available():
import t... | 38 | 1 |
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 : Any = logging.get_logger(__name__)
__lowerCamelCase : int = {
... | 38 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lo... | 38 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
imp... | 38 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : int = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Conv... | 38 | 1 |
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