code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def a__ ( lowercase : str ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = img.shape[0], img.shape[1]
# converting each pixel's color to its nega... | 360 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
lowercase__ : Optional[Any] = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __ini... | 287 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : Optional[int] = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
]
}
try:
... | 361 |
'''simple docstring'''
from math import isclose, sqrt
def a__ ( lowercase : float, lowercase : float, lowercase : float ) -> tuple[float, float, float]:
"""simple docstring"""
_UpperCamelCase = point_y / 4 / point_x
_UpperCamelCase = 2 * normal_grad... | 287 | 0 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def a__ ( lowercase : List[str], lowercase : Union[str, Any], lowercase : List[Any] ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase ... | 362 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[Any] = logging.get_logger(__name__)
lowercase__ : Tuple = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwr... | 287 | 0 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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_tensor, random_attention_mask
... | 363 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTes... | 287 | 0 |
'''simple docstring'''
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 t... | 364 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowercase : list[int], lowercase : list[int], lowercase : int ) -> tuple[float, list[float]]:
"""simple docstring"""
_UpperCamelCase = list(range(len(lowercase ) ) )
_UpperCamelC... | 287 | 0 |
'''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def a__ ( lowercase : Optional[int], lowercase : Optional[Any]=(), lowercase : Union[str... | 365 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Optional[Any] = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
... | 287 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def a__ ( lowercase : str = "AAPL" ) -> str:
"""simple docstring"""
_UpperCamelCase = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
_UpperCamelCase = BeautifulSoup(requests.get(... | 366 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowercase__ : List[Any] = parse(importlib.metadata.version('torch'))
def a__ ( lowercase : Union[str, Version], lo... | 287 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Conditi... | 367 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def a__ ( lowercase : Tuple ) -> Dict:
"""simple docstring"""
_U... | 287 | 0 |
'''simple docstring'''
lowercase__ : List[str] = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import Strea... | 368 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def a__ ( lowercase : str ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = img.shape[0], img.shape[1]
# converting each pixel's color to its nega... | 287 | 0 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
fro... | 369 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 287 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ : List[str] = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Pix2StructCo... | 370 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self : Tuple ) -> int:
'''simple d... | 287 | 0 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_... | 371 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 287 | 0 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__lowerCAmelCase = '''http://www.mocksite.com/f... | 288 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def snake_case_ ( snake_case , snake_case , snake_case = False ) -> list[float]:
if radian_mode:
return [magn... | 288 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__lowerCAmelCase = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io ... | 288 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ... | 288 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = '... | 288 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def snake_case_ ( snake_case ... | 288 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCAmelCase = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}
try:
if not is_torch_... | 288 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models ... | 288 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__lowerCAmelCase = {
'''configuration_swiftformer''': [
'''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SwiftFormerConfig''',
... | 288 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[int]:
lowercase__: Tuple = 0
lowercase__: str = len(snake_case ) - 1
while i < j:
if nums[i] + nums[j] =... | 288 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers imp... | 288 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inpu... | 288 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def snake_case_ ( snake... | 288 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
Ope... | 288 | 1 |
from ..utils import DummyObject, requires_backends
class __a ( metaclass=__UpperCamelCase ):
__lowercase : List[str] = ['onnx']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
... | 288 |
def snake_case_ ( snake_case ) -> list[int]:
lowercase__: Dict = [0 for i in range(len(snake_case ) )]
# initialize interval's left pointer and right pointer
lowercase__ , lowercase__: Union[str, Any] = 0, 0
... | 288 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avail... | 288 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configur... | 288 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
Ope... | 288 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__lowerCAmelCase = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "Proceeding... | 288 | 1 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
''' Distilla... | 288 |
from math import pow
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case , ) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then... | 288 | 1 |
from math import sqrt
def snake_case_ ( snake_case = 1_00_00_00 ) -> int:
lowercase__: int = 0
lowercase__: int = 0
lowercase__: int
while num_cuboids <= limit:
max_cuboid_size += 1
... | 288 |
import torch
from diffusers import StableDiffusionPipeline
__lowerCAmelCase = '''path-to-your-trained-model'''
__lowerCAmelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
__lowerCAmelCase = '''A photo of sks dog in a bucket'''
... | 288 | 1 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import Tok... | 288 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 288 | 1 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertT... | 288 |
import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
from transform... | 288 | 1 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.... | 288 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeatur... | 288 | 1 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __a ( __UpperCamelCase ):
def __lt__( self , lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstri... | 288 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_dif... | 288 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class __a ( __U... | 288 |
from collections import deque
from math import floor
from random import random
from time import time
class __a :
def __init__( self ) -> Dict:
'''simple docstring'''
lowercase__: Dict = {}
def SCREAMING_SNAKE_CASE__ ( s... | 288 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
class __a ( __UpperCamelCase ):
def __init__( self , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> Optional[Any]:
... | 288 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __a ( tf.keras.layers.Layer ):
def __init__( self , ... | 288 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 288 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = '... | 288 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
__lowerCAmelCase = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true... | 288 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case_ ( snake_case = 3 ) -> qiskit.result.counts.Counts:
if isinstance(snake_case , snake_case ):
raise... | 288 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 288 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[str]:
if nth_term == "":
return [""]
lowercase__: Tuple = int(snake_case )
lowercase__: int = int(snake_ca... | 288 | 1 |
def snake_case_ ( snake_case = 1_00_00_00 ) -> int:
lowercase__: Dict = limit + 1
lowercase__: Optional[Any] = [0] * limit
for first_term in range(1 , snake_case ):
for n in range(snake_case , ... | 288 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
... | 288 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 288 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def snake_case_ ( snake_case , snake_case , snake_case = False ) -> list[float]:
if radian_mode:
return [magn... | 288 | 1 |
import copy
import re
class __a :
__lowercase : Dict = 'hp'
__lowercase : List[Any] = {}
__lowercase : Tuple = None
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , lowerCAmelCase__ , lowerCAmelCase_... | 288 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ... | 288 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configur... | 288 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def snake_case_ ( snake_case ... | 288 | 1 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case ) -> List[Any]:
# load base mo... | 288 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models ... | 288 | 1 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 288 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[int]:
lowercase__: Tuple = 0
lowercase__: str = len(snake_case ) - 1
while i < j:
if nums[i] + nums[j] =... | 288 | 1 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
c... | 288 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inpu... | 288 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase... | 288 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
Ope... | 288 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_config... | 288 |
def snake_case_ ( snake_case ) -> list[int]:
lowercase__: Dict = [0 for i in range(len(snake_case ) )]
# initialize interval's left pointer and right pointer
lowercase__ , lowercase__: Union[str, Any] = 0, 0
... | 288 | 1 |
import math
import qiskit
def snake_case_ ( snake_case = 1 , snake_case = 1 , snake_case = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(snake_case , snake_case )
or isinstance(snake_case , snake_... | 288 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configur... | 288 | 1 |
from __future__ import annotations
def snake_case_ ( snake_case ) -> bool:
lowercase__: Optional[Any] = str(snake_case )
return n == n[::-1]
def snake_case_ ( snake_case = 1_00_00_00 ) -> int:
lowercase__: ... | 288 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__lowerCAmelCase = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "Proceeding... | 288 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers... | 288 |
from math import pow
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case , ) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then... | 288 | 1 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_... | 288 |
import torch
from diffusers import StableDiffusionPipeline
__lowerCAmelCase = '''path-to-your-trained-model'''
__lowerCAmelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
__lowerCAmelCase = '''A photo of sks dog in a bucket'''
... | 288 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __a ( __UpperCamelCase , __UpperCamelCase ):
__lowerca... | 288 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 288 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def snake_case_ ( snake_case ) -> List[str]:
def wrapper(*snake_case , **snake_case ):
lowercase... | 288 |
import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
from transform... | 288 | 1 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
__lowerCAmelCase = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Au... | 288 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeatur... | 288 | 1 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> int:
# Checks if the entire collection has been sorted
if len(snake_case ) <= 1 or n <= 1:
return
insert_next(snake_case , n - 1 )
... | 288 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_dif... | 288 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__lowerCAmelCase = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 288 |
from collections import deque
from math import floor
from random import random
from time import time
class __a :
def __init__( self ) -> Dict:
'''simple docstring'''
lowercase__: Dict = {}
def SCREAMING_SNAKE_CASE__ ( s... | 288 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 288 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __a ( tf.keras.layers.Layer ):
def __init__( self , ... | 288 | 1 |
def snake_case_ ( snake_case ) -> Dict:
lowercase__: str = []
lowercase__: str = []
lowercase__: List[str] = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
... | 288 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = '... | 288 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__lowerCAmelCase = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "Proceeding... | 288 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case_ ( snake_case = 3 ) -> qiskit.result.counts.Counts:
if isinstance(snake_case , snake_case ):
raise... | 288 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compar... | 288 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[str]:
if nth_term == "":
return [""]
lowercase__: Tuple = int(snake_case )
lowercase__: int = int(snake_ca... | 288 | 1 |
from manim import *
class __a ( __UpperCamelCase ):
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
lowercase__: Dict = Rectangle(height=0.5 , width=0.5 )
lowercase__: str ... | 288 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
... | 288 | 1 |
import argparse
import os
import re
import packaging.version
__lowerCAmelCase = '''examples/'''
__lowerCAmelCase = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__versi... | 288 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def snake_case_ ( snake_case , snake_case , snake_case = False ) -> list[float]:
if radian_mode:
return [magn... | 288 | 1 |
from math import isqrt
def snake_case_ ( snake_case ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(snake_case ) + 1 ) )
def snake_case_ ( snake_case = 10**6 ) -> int:
lowercase__: Opt... | 288 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ... | 288 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models ... | 288 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def snake_case_ ( snake_case ... | 288 | 1 |
import math
def snake_case_ ( snake_case , snake_case ) -> float:
if (
not isinstance(snake_case , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('power_factor mus... | 288 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models ... | 288 | 1 |
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def snake_case_ ( snake_case ) -> Any:
return 1 / (1 + np.exp(-z ))
def... | 288 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[int]:
lowercase__: Tuple = 0
lowercase__: str = len(snake_case ) - 1
while i < j:
if nums[i] + nums[j] =... | 288 | 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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
lo... | 288 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inpu... | 288 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def snake_case_ ( snake_case ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
lowerca... | 288 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
Ope... | 288 | 1 |
def snake_case_ ( snake_case ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
lowercase__: Any = sorted(string.lower() )
return len(snake_case ) ... | 288 |
def snake_case_ ( snake_case ) -> list[int]:
lowercase__: Dict = [0 for i in range(len(snake_case ) )]
# initialize interval's left pointer and right pointer
lowercase__ , lowercase__: Union[str, Any] = 0, 0
... | 288 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer... | 288 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configur... | 288 | 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 Con... | 288 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__lowerCAmelCase = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "Proceeding... | 288 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 288 |
from math import pow
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case , ) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then... | 288 | 1 |
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_available, is_torc... | 288 |
import torch
from diffusers import StableDiffusionPipeline
__lowerCAmelCase = '''path-to-your-trained-model'''
__lowerCAmelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
__lowerCAmelCase = '''A photo of sks dog in a bucket'''
... | 288 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __a ( __UpperCamelCase ):
__lowercase :... | 288 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 288 | 1 |
def snake_case_ ( snake_case , snake_case ) -> list:
lowercase__: Union[str, Any] = len(snake_case )
lowercase__: Optional[Any] = []
for i in range(len(snake_case ) - pat_len + 1 ):
lowercase__:... | 288 |
import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
from transform... | 288 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCAmelCase = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/mas... | 288 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeatur... | 288 | 1 |
# Copyright 2023 The HuggingFace Inc. 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 re... | 288 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_dif... | 288 | 1 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[int]:
lowercase__: Tuple = 0
lowercase__: str = len(snake_case ) - 1
while i < j:
if nums[i] + nums[j] =... | 288 |
from collections import deque
from math import floor
from random import random
from time import time
class __a :
def __init__( self ) -> Dict:
'''simple docstring'''
lowercase__: Dict = {}
def SCREAMING_SNAKE_CASE__ ( s... | 288 | 1 |
from math import pow
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case , ) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then... | 288 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __a ( tf.keras.layers.Layer ):
def __init__( self , ... | 288 | 1 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_dif... | 288 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = '... | 288 | 1 |
from math import factorial
def snake_case_ ( snake_case , snake_case ) -> int:
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
... | 288 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case_ ( snake_case = 3 ) -> qiskit.result.counts.Counts:
if isinstance(snake_case , snake_case ):
raise... | 288 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__lowerCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__low... | 288 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[str]:
if nth_term == "":
return [""]
lowercase__: Tuple = int(snake_case )
lowercase__: int = int(snake_ca... | 288 | 1 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__lowerCAmelCase = logging.get_logger(__name__)
def... | 288 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
... | 288 | 1 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils impor... | 288 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def snake_case_ ( snake_case , snake_case , snake_case = False ) -> list[float]:
if radian_mode:
return [magn... | 288 | 1 |
__lowerCAmelCase = 2_56
# Modulus to hash a string
__lowerCAmelCase = 1_00_00_03
def snake_case_ ( snake_case , snake_case ) -> bool:
lowercase__: List[Any] = len(snake_case )
lowercase__: int = len... | 288 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ... | 288 | 1 |
def snake_case_ ( snake_case=2_81_23 ) -> Optional[int]:
lowercase__: Any = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ... | 288 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def snake_case_ ( snake_case ... | 288 | 1 |
__lowerCAmelCase = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1''',
'''hf-doc-b... | 288 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models ... | 288 | 1 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
__lowerCAmelCase = logging.getLogger()
def snake_case_ (... | 288 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[int]:
lowercase__: Tuple = 0
lowercase__: str = len(snake_case ) - 1
while i < j:
if nums[i] + nums[j] =... | 288 | 1 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
... | 288 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inpu... | 288 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 288 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
Ope... | 288 | 1 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from .... | 288 |
def snake_case_ ( snake_case ) -> list[int]:
lowercase__: Dict = [0 for i in range(len(snake_case ) )]
# initialize interval's left pointer and right pointer
lowercase__ , lowercase__: Union[str, Any] = 0, 0
... | 288 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeatur... | 288 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configur... | 288 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transforme... | 288 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__lowerCAmelCase = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "Proceeding... | 288 | 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 require... | 288 |
from math import pow
def snake_case_ ( snake_case , snake_case , snake_case , snake_case , snake_case , ) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then... | 288 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, ... | 288 |
import torch
from diffusers import StableDiffusionPipeline
__lowerCAmelCase = '''path-to-your-trained-model'''
__lowerCAmelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
__lowerCAmelCase = '''A photo of sks dog in a bucket'''
... | 288 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __a ( __UpperCame... | 288 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 288 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCAmelCase = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, glo... | 288 |
import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
from transform... | 288 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case_ ( snake_case = 3 ) -> qiskit.result.counts.Counts:
if isinstance(snake_case , snake_case ):
raise... | 288 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeatur... | 288 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase = {
'''configuration_distilbert''': [
'''DISTILBERT_... | 288 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_dif... | 288 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto imp... | 288 |
from collections import deque
from math import floor
from random import random
from time import time
class __a :
def __init__( self ) -> Dict:
'''simple docstring'''
lowercase__: Dict = {}
def SCREAMING_SNAKE_CASE__ ( s... | 288 | 1 |
from __future__ import annotations
from math import gcd
def snake_case_ ( snake_case , snake_case = 2 , snake_case = 1 , snake_case = 3 , ) -> int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
if n... | 288 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __a ( tf.keras.layers.Layer ):
def __init__( self , ... | 288 | 1 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
def snake_case_ ( snake_case ... | 288 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = '... | 288 | 1 |
import os
import numpy
import onnx
def snake_case_ ( snake_case , snake_case ) -> int:
lowercase__: Optional[Any] = a.name
lowercase__: int = b.name
lowercase__: Union[str, Any] = ''
... | 288 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case_ ( snake_case = 3 ) -> qiskit.result.counts.Counts:
if isinstance(snake_case , snake_case ):
raise... | 288 | 1 |
import fire
from utils import calculate_rouge, save_json
def snake_case_ ( snake_case , snake_case , snake_case=None , **snake_case ) -> Optional[int]:
lowercase__: str = [x.strip() for x in open(snake_case ).readlines... | 288 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[str]:
if nth_term == "":
return [""]
lowercase__: Tuple = int(snake_case )
lowercase__: int = int(snake_ca... | 288 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifie... | 288 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
... | 288 | 1 |
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