code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/encodec_24khz": "https://huggingface.co/facebook/encod... | 217 | '''simple docstring'''
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_co... | 272 | 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,
Sta... | 329 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__lowercase = logging.get_logger(__name__)
class a__( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , *__... | 272 | 0 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
snake_case_ : List[Any] = logging.... | 83 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''Ins... | 272 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class __snake_case ( lowerCAmelCase__ )... | 348 | '''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
f... | 272 | 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_availab... | 267 | '''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_prop... | 272 | 0 |
from __future__ import annotations
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
... | 313 | '''simple docstring'''
def snake_case__ ( _A: int , _A: int ) -> int:
'''simple docstring'''
while a != 0:
lowerCAmelCase , lowerCAmelCase = b % a, a
return b
def snake_case__ ( _A: int , _A: int ) -> int:
'''simple do... | 272 | 0 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def _lowerCamelCase ( lowercase : int , lowercase : List[str] ) -> int:
_a = Mock()
... | 63 | '''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def snake_case__ ( _A: jnp.ndarray , _A: int , _A: float = 1 , _A: float = 1 , _A: float = 1.0e4 , _A: bool = False , _A: float = 1.0 , )... | 272 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureE... | 310 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if not is_to... | 272 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_... | 258 | '''simple docstring'''
from math import sqrt
def snake_case__ ( _A: int = 1000000 ) -> int:
'''simple docstring'''
lowerCAmelCase = 0
lowerCAmelCase = 0
lowerCAmelCase = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides ... | 272 | 0 |
'''simple docstring'''
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def lowerCAmelCase (__A , __A , __A=0):
"""simple docstring"""
if name is None:
_a = None
else:
_a = '''.''' * ... | 211 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowercase = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNe... | 272 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionPr... | 173 | '''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 272 | 0 |
"""simple docstring"""
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, TrainingJob... | 217 | '''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 272 | 0 |
from functools import lru_cache
@lru_cache
def lowerCAmelCase__ ( a__: int ) -> int:
'''simple docstring'''
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ ... | 329 | '''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import ... | 272 | 0 |
'''simple docstring'''
snake_case_ : Union[str, Any] = 'Alexander Joslin'
import operator as op
from .stack import Stack
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : Union[str, Any] = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
_UpperCamelCase ... | 83 | '''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class a__( enum.En... | 272 | 0 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 10**-10 )-> float:
'''simple docstring'''
UpperCAmelCase :... | 348 | '''simple docstring'''
def snake_case__ ( _A: str ) -> list[int]:
'''simple docstring'''
lowerCAmelCase = [0 for i in range(len(_A ) )]
# initialize interval's left pointer and right pointer
lowerCAmelCase , lowerCAmelCase = 0, 0
for i in range(1 , le... | 272 | 0 |
'''simple docstring'''
from collections import namedtuple
UpperCAmelCase : str = namedtuple('from_to', 'from_ to')
UpperCAmelCase : Dict = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.0_0_1, 1_0_0_0),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0_4_5_4, 2_... | 267 | '''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class a__( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase_ : str = '''EncodecFeatureExtractor'''
Upper... | 272 | 0 |
import os
import string
import sys
a__ : Dict = 1 << 8
a__ : Any = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'''right''': 67 + ARROW_KEY_FLAG,
'''left''': 68 + ARR... | 313 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a__( unittest.TestCase ):
... | 272 | 0 |
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate... | 63 | '''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def snake_case__ ( _A: str ) -> str:
'''simple docstring'''
if not sentence:
return ""
lowerCAmelCase = dict(zip(_A , _A ) )
return lower_to_upper.get(sentence[0] , sentence[0] ) ... | 272 | 0 |
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_met... | 310 | '''simple docstring'''
import os
import string
import sys
__lowercase = 1 << 8
__lowercase = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 2_7,
'''up''': 6_5 + ARROW_KEY_FLAG,
'''down''': 6_6 + ARROW_KEY_FLAG,
'''right''': 6_7 + ARROW_KEY_FLAG,
... | 272 | 0 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from dataset... | 273 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 273 | 1 |
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, BlipaProcessor, BlipImageProc... | 273 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 1 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fro... | 273 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCho... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> float:
'''simple docstring'''
UpperCAmelCase = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in... | 273 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 273 | 1 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__A : Optional[Any] = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and ... | 273 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 273 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logg... | 273 |
import cva
import numpy as np
class A_ :
def __init__( self , _A , _A ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase = k
UpperCAmelCase = window_size
else:
raise ValueError('''invalid k value''' )
def __... | 273 | 1 |
__A : Tuple = [
[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 __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) ... | 273 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 1 |
from ...processing_utils import ProcessorMixin
class A_ (a_ ):
UpperCAmelCase__ = ['''image_processor''', '''feature_extractor''']
UpperCAmelCase__ = '''TvltImageProcessor'''
UpperCAmelCase__ = '''TvltFeatureExtractor'''
def __init__( self , _A , _A ):
... | 273 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 1 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
impo... | 273 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __SCREAMING_SNAKE_CASE ( ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = ArgumentParser(
descripti... | 273 |
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_in_pytorch_model
from transformer... | 273 | 1 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
print(F"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(UpperCamelCase__ ):
print(F"""{i}\t\t{d}""" )
... | 273 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 1 |
import argparse
import os
import re
import packaging.version
__A : Optional[Any] = "examples/"
__A : List[Any] = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\"... | 273 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : int = {
"BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgetower-base/blob... | 273 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversat... | 273 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
UpperCAmelCase = 6
UpperCAmelCase = 1
UpperCAmelCase = 1901
UpperCAmelCase = 0
while year < 200... | 273 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 273 | 1 |
from scipy.stats import spearmanr
import datasets
__A : Dict = "\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 correlation.\nPositive correlations ... | 273 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 | 1 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
UpperCAmelCase = ('''dense.wei... | 273 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 1 |
import gc
import threading
import time
import psutil
import torch
class A_ :
def __init__( self ):
'''simple docstring'''
UpperCAmelCase = psutil.Process()
UpperCAmelCase = False
def _lowercase ( self ):
'''simple docstring'''
... | 273 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : Union[str, Any] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 273 | 1 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Dict:
'''simple docstring'''
UpperCAmelCase = args.pruning_method
UpperCAmelCase ... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 1 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A : ... | 273 |
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... | 273 | 1 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__A : Dict = 50_000
__A : str = 5_000
__A , __A : List[Any] = os.path.split(__file__)
__A : str = os.path.join(RESULTS_BASEPATH, "results", ... | 273 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (a_ ):
UpperCAmelCase__ = (UnCLIPScheduler,)
def _lowercase ( self , **_A ):
'''simple docstring'''
UpperCAmelCase = {
'''num_t... | 273 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 273 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 1 |
from __future__ import annotations
import math
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if num <= 0:
UpperCAmelCase = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(UpperCamelCase__ )
... | 273 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCho... | 273 | 1 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 273 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 273 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__A : List[Any] = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch... | 273 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
UpperCAmelCase = len(UpperCamelCase__ )
UpperCAmelCase = max(UpperCamelCase__ )
UpperCAmelCase ... | 273 |
import cva
import numpy as np
class A_ :
def __init__( self , _A , _A ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase = k
UpperCAmelCase = window_size
else:
raise ValueError('''invalid k value''' )
def __... | 273 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : str = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/c... | 273 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 1 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils ... | 273 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 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_ (a_ ):
def __init__( self , ... | 273 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Tuple = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
"conve... | 273 |
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_in_pytorch_model
from transformer... | 273 | 1 |
import logging
from transformers.configuration_utils import PretrainedConfig
__A : List[str] = logging.getLogger(__name__)
class A_ (a_ ):
UpperCAmelCase__ = '''masked_bert'''
def __init__( self , _A=3_0_5_2_2 , _A=7_6_8 , _A=1_2 , _A=1... | 273 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
__A : Optional[Any] = logging.get_logger(__name__)
class A_ (a_ ):
def __init__( self , *_A , **_A ):
'''simple docstring'''
warnings.warn(
... | 273 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acce... | 273 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 1 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__A : Union[str, Any] = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def __SCREAMING_SNAKE_CASE ( Uppe... | 273 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__A : Union[str, Any] = logging.get_logger(__name__)
class A_ (a_ ):
UpperCAmelCase__ = '''upernet'''
def __init__( self , ... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list:
'''simple docstring'''
if len(UpperCamelCase__ ) < 2:
return collection
def circle_sort_util(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> bool:
UpperCAmelCase = False
if low ==... | 273 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 273 | 1 |
from __future__ import annotations
from math import pow, sqrt
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> dict[str, float]:
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('''... | 273 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
__A : Dict = 3
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> int:
'''simple docstring'''
print('''Generating primitive root of p''' )
while True:
... | 273 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class A_ (unittest.TestCase ):
UpperCAmelCase__ = JukeboxTokenizer
UpperCAmelCase__ = {
'''artist''': '''Zac Brown Band''',
'''genres''': '''Country''',
'''lyrics'... | 273 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : Union[str, Any] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> str:
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCamelCase__ , int(b / 2 ) ) * actual_power(UpperCamelCase__ , int(b / 2 ) )
else:
return a... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> str:
'''simple d... | 273 |
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... | 273 | 1 |
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_pipelines_common import ANY
if is_vision_av... | 273 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> str:
'''simple docstring'''
UpperCAmelCase = [0 for i in range(r + 1 )]
# nc0 = 1
UpperCAmelCase = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
... | 273 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 273 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : Union[str, Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class A_ (a_ ):
UpperCAmelCase__ ... | 273 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
cl... | 273 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCho... | 273 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : Tuple = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory-transformer-halfch... | 273 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 273 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , Upper... | 273 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 273 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class A_ (unittest.TestCase ):
UpperCAmelCase__ = inspect.getfile(accelerate.test_uti... | 273 |
import cva
import numpy as np
class A_ :
def __init__( self , _A , _A ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase = k
UpperCAmelCase = window_size
else:
raise ValueError('''invalid k value''' )
def __... | 273 | 1 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_... | 273 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 1 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __SCREAMING_SNAKE_CASE ( UpperCa... | 273 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 1 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeM... | 273 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 1 |
import math
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> float:
'''simple docstring'''
UpperCAmelCase = xa
UpperCAmelCase = xa
while True:
if x_n == x_na or function(... | 273 |
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_in_pytorch_model
from transformer... | 273 | 1 |
from __future__ import annotations
__A : List[Any] = list[tuple[int, int]]
__A : Union[str, Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
... | 273 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A : List[Any] = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"GroupViTOnnxConfig",
... | 273 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 1 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=Fa... | 273 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 1 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils imp... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 1 |
from math import factorial
__A : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> int:
'''simple docstring'''
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise TypeError(... | 273 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 273 | 1 |
import operator as op
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = []
UpperCAmelCase = lambda UpperCamelCase__ , UpperCamelCase__ : int(x / y ) # noqa: E731 integer division operation
UpperCAmelCase ... | 273 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 | 1 |
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_in_pytorch_model
from transformer... | 273 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 1 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_b... | 273 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : Union[str, Any] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 273 | 1 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__A : Optional[int] = logging.getLogger(__name__)
if __name__ == "__main__":
_... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say tha... | 273 |
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... | 273 | 1 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils imp... | 273 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 1 |
from math import pow
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> tuple[int, int]:
'''simple docstring'''
if current_sum == needed_sum:
# If the sum of the powers is equal... | 273 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 273 | 1 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import ... | 273 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[str] = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConfig",
],
}... | 273 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCho... | 273 | 1 |
from collections.abc import Sequence
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(UpperCamelCase__ ) )
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCam... | 273 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 273 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_avail... | 273 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ = 100 ) -> int:
'''simple docstring'''
UpperCAmelCase = set()
UpperCAmelCase = 0
UpperCAmelCase = n + 1 # maximum limit
for a in range(2 , UpperCamelCase__ ):
for b in range(2 , UpperCamelCase... | 273 |
import cva
import numpy as np
class A_ :
def __init__( self , _A , _A ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase = k
UpperCAmelCase = window_size
else:
raise ValueError('''invalid k value''' )
def __... | 273 | 1 |
__A : str = "Tobias Carryer"
from time import time
class A_ :
def __init__( self , _A , _A , _A , _A=int(time() ) ): # noqa: B008
'''simple docstring'''
UpperCAmelCase = multiplier
UpperCAmelCase = increme... | 273 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get... | 273 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ (a_ ):
UpperCAmelCase__ = ['''i... | 273 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 |
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_in_pytorch_model
from transformer... | 273 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Op... | 273 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 1 |
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 BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A : Any = logging.get_logger(__name__)
_... | 273 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_imag... | 273 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ = 100 ) -> int:
'''simple docstring'''
UpperCAmelCase = n * (n + 1) * (2 * n + 1) / 6
UpperCAmelCase = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'{solution... | 273 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A : Tuple = {
"configuration_clip": [
"CLIP_PRETRA... | 273 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 1 |
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