code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def _a ( lowercase__ : int = 10 , lowercase__ : int = 22 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = range(1 , lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = range(1 , lowercase__ )
return sum(
1 for p... | 85 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[... | 85 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
def _a ( lowercase__ : List[str] ,... | 85 | def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | 1 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join #... | 85 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 1 |
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch', 'torchsde']
def __init__( self : Tuple , *a_ : Any , **a_ : List[Any] )-> Any:
"""simple docstring"""
requires_ba... | 85 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _a ( lowercase__ : List[str] , lowercase_... | 85 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 1 |
from collections.abc import Sequence
from queue import Queue
class snake_case :
def __init__( self : Optional[int] , a_ : Tuple , a_ : str , a_ : Union[str, Any] , a_ : Optional[Any]=None , a_ : List[str]=None )-> str:
"""simple d... | 85 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = ... | 85 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 1 |
from __future__ import annotations
import pandas as pd
def _a ( lowercase__ : list[int] , lowercase__ : list[int] , lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0] * no_of_processes
SCREAMING_SNAKE_CASE__ : ... | 85 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese... | 85 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 1 |
from statistics import mean
import numpy as np
def _a ( lowercase__ : list , lowercase__ : list , lowercase__ : list , lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
# Number of processes finished... | 85 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : int = {
"configuration_... | 85 | 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,
Stable... | 85 | 1 |
from __future__ import annotations
def _a ( lowercase__ : list[list[int]] ):
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(lowercase__ ) ):
... | 85 | import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | 1 |
def _a ( lowercase__ : int = 1_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = set()
SCREAMING_SNAKE_CASE__ : List[str] = 0
SCREAMING_SNAKE_CASE__ : Any = n + 1 # maximum limit
for a in range(2 , low... | 85 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'AutoImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self ... | 85 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 85 | import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( lowercase__ : int = 3 ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('number of qubits... | 85 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class snake_case ( UpperCamelCase_ ):
lowercase_ = (DPMSolverSDESched... | 85 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | 1 |
from string import ascii_lowercase, ascii_uppercase
def _a ( lowercase__ : str ):
'''simple docstring'''
if not sentence:
return ""
SCREAMING_SNAKE_CASE__ : Tuple = dict(zip(lowercase__ , lowercase__ ) )
return lower_to_upper.get(sentenc... | 85 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Opti... | 85 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
... | 85 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 85 | 1 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 1 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfi... | 85 | def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_... | 85 | 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 Tensor... | 85 | 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 TokenizerTesterMixin
SCREAMING_SNAKE_CAS... | 85 | 1 |
SCREAMING_SNAKE_CASE__ : Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE__ : List[Any] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE__ : List[str] = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
... | 85 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | 1 |
def _a ( lowercase__ : int , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def _a ( lowercase__ : float , lowercase__ : float , lowercase__ : float ):
... | 85 | from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 85 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Dict = {
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if not is_torch_available():
... | 85 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[... | 85 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | 1 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
class snake_case :
lowercase_ = None
@experimental
def _a ( lowerc... | 85 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 1 |
def _a ( lowercase__ : str ):
'''simple docstring'''
assert column_title.isupper()
SCREAMING_SNAKE_CASE__ : str = 0
SCREAMING_SNAKE_CASE__ : List[Any] = len(lowercase__ ) - 1
SCREAMING_SNAKE_CASE__ : str = 0
while inde... | 85 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 1 |
from __future__ import annotations
def _a ( lowercase__ : list ):
'''simple docstring'''
if not nums:
raise ValueError('List is empty' )
return sum(lowercase__ ) / len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod(... | 85 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 1 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-tes... | 85 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_avai... | 85 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[int] = ... | 85 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridCon... | 85 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 1 |
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 TokenizerTesterMixin
SCREAMING_SNAKE_CAS... | 85 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 1 |
from typing import Any
import numpy as np
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return np.array_equal(lowercase__ , matrix.conjugate().T )
def _a ( lowercase__ : np.ndarray , lowercase__ : np.ndarray ):
'''s... | 85 | 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,
Stable... | 85 | 1 |
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self : Any , a_ : int , a_ : int , a_ : float = 0 )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : ... | 85 | import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | 1 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Imag... | 85 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'AutoImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self ... | 85 | 1 |
def _a ( lowercase__ : str , lowercase__ : bool = False ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Any = f'''Expected string as input, found {type(lowercase__ )}'''
raise ValueError(l... | 85 | import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( lowercase__ : int = 3 ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('number of qubits... | 85 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE__ : str = {
"configuration_squeezebert": [
"SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SqueezeBertConfig",... | 85 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.t... | 85 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Opti... | 85 | 1 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
fr... | 85 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 85 | 1 |
import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class snake_case ( UpperCamelCase_ ):
def __init__( self : Optional[Any] , a_ : List[str] , a_ : List[str] )-> List[Any]:
"""simple docstring"""
SCREA... | 85 | def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_... | 85 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 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 TokenizerTesterMixin
SCREAMING_SNAKE_CAS... | 85 | 1 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 85 | 1 |
import requests
SCREAMING_SNAKE_CASE__ : Union[str, Any] = "YOUR API KEY"
def _a ( lowercase__ : str , lowercase__ : str = giphy_api_key ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = '+'.join(query.split() )... | 85 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[... | 85 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, tor... | 85 | def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | 1 |
from scipy.stats import spearmanr
import datasets
SCREAMING_SNAKE_CASE__ : List[str] = "\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 correla... | 85 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_t... | 85 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'Speech2TextFeatureExtractor'
lowercase_ = 'Speech2TextTokenizer'
def __init__( self : Optional[Any] , a_ : O... | 85 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 1 |
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 snake_case ( UpperCamelCase_ ):
lowercase_ = ['i... | 85 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 1 |
from math import pow
def _a ( lowercase__ : int , lowercase__ : int , lowercase__ : int , lowercase__ : int , lowercase__ : int , ):
'''simple docstring'''
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then w... | 85 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 1 |
def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 1 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 85 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 1 |
SCREAMING_SNAKE_CASE__ : Optional[int] = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader... | 85 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 1 |
def _a ( lowercase__ : str ):
'''simple docstring'''
return "".join(chr(ord(lowercase__ ) - 32 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 85 | 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,
Stable... | 85 | 1 |
def _a ( lowercase__ : list ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = len(lowercase__ )
for _ in range(lowercase__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
SCREAMING_S... | 85 | import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | 1 |
from __future__ import annotations
def _a ( lowercase__ : list[float] ):
'''simple docstring'''
if len(lowercase__ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise Value... | 85 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'AutoImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self ... | 85 | 1 |
import torch
from diffusers import DiffusionPipeline
class snake_case ( UpperCamelCase_ ):
def __init__( self : Any , a_ : Optional[int] , a_ : Dict )-> List[str]:
"""simple docstring"""
super().__init__()
self.register_modules(unet=a_ ... | 85 | import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( lowercase__ : int = 3 ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('number of qubits... | 85 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-bas... | 85 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | 1 |
from __future__ import annotations
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [True] * limit
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
SCREAMING_SNAKE_CASE__ : Dict = ... | 85 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Opti... | 85 | 1 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"linear": PIL.Image.Resampling.BILINEAR,
"bili... | 85 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 85 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( lowercase__ : List[Any] , lowercase__ : List[Any] , lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = 0
if start < en... | 85 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 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
logging.set_verbosity_... | 85 | def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_... | 85 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_t... | 85 | 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 TokenizerTesterMixin
SCREAMING_SNAKE_CAS... | 85 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils i... | 85 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | 1 |
def _a ( lowercase__ : str ):
'''simple docstring'''
if n_term == "":
return []
SCREAMING_SNAKE_CASE__ : list = []
for temp in range(int(lowercase__ ) ):
series.append(f'''1/{temp + 1}''' if series else '1' )
return series
if ... | 85 | from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 85 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def _a ( lowercase__ : int ):
'''simple docstring'''
if "cls_token" in name:
SCREAMING_SNAKE_CASE__ : List[... | 85 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[... | 85 | 1 |
from __future__ import annotations
SCREAMING_SNAKE_CASE__ : str = list[list[int]]
# assigning initial values to the grid
SCREAMING_SNAKE_CASE__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3... | 85 | def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | 1 |
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,
Stable... | 85 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
SCREAMING_SNAKE_CASE__ : Any = 4
SCREAMING_SNAKE_CASE__ : Optional[Any] = ... | 85 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 1 |
def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_... | 85 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] ... | 85 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
fr... | 85 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 1 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _a ( lowercase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = [
'encoder.version',
'decoder.version... | 85 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 1 |
import qiskit
def _a ( lowercase__ : int = 2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = qubits
# Using Aer's simulator
SCREAMING_SNAKE_CASE__ : Optional[Any] = qiskit.Aer.get_backend('aer_simulator' )
# Cr... | 85 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 1 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n... | 85 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Any = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/con... | 85 | 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,
Stable... | 85 | 1 |
from ....utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
class snake_case ( UpperCamelCase_ ):
def __init__( self : Tuple , a_ : str , a_ : str=None , a_ : int=2048 )-> Union[str, Any]:
... | 85 | import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | 1 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have ... | 85 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'AutoImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self ... | 85 | 1 |
def _a ( lowercase__ : int = 2_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = [1, 2, 5, 10, 20, 50, 1_00, 2_00]
SCREAMING_SNAKE_CASE__ : int = [0] * (pence + 1)
SCREAMING_SNAKE_CASE__ : Dict = 1 # base case: 1 way to... | 85 | import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( lowercase__ : int = 3 ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('number of qubits... | 85 | 1 |
from __future__ import annotations
import bisect
def _a ( lowercase__ : list[int] , lowercase__ : int , lowercase__ : int = 0 , lowercase__ : int = -1 ):
'''simple docstring'''
if hi < 0:
SCREAMING_SNAKE_CASE__ : str = len(lowercase_... | 85 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/... | 85 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Opti... | 85 | 1 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 85 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 85 | 1 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
def _a ( lowercase__ : Union[tf.Tensor, np.ndarray] ):
'''simple docstring'''
... | 85 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 1 |
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_... | 85 | 1 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
SCREAMING_SNAKE_CASE__ : List[str] = HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__ : Tuple = pa... | 85 | 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 TokenizerTesterMixin
SCREAMING_SNAKE_CAS... | 85 | 1 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_p... | 85 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ : Any = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 85 | 1 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"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-builder": "hf-doc-builder>=0.3.0... | 85 | from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_ima... | 85 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"ut/deta": "https://huggin... | 85 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[... | 85 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case ( UpperCamel... | 85 | def _a ( lowercase__ : int = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Dict = int(lowercase__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
... | 85 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(lowercase__ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Tuple = ValueError('a should be a positive number' )
raise my_error
... | 85 | 1 |
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = [1]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = 0, 0, 0
SCREAMING_SNAKE_CASE__ : Any = ugly_... | 85 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 | 1 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class snake_case ( UpperCamelCase_ ):
def __init__( self : Dict , a_ : str , a_ : Optional[int]=None , a_ : s... | 85 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_... | 85 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
... | 85 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 1 |
from __future__ import annotations
import requests
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(lowercase__ ... | 85 | import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
SCREAMING_SNAKE_CASE__ : Any ... | 85 | 1 |
from __future__ import annotations
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = get_failure_array(lowercase__ )
# 2) Step through text searching for pattern
SCREAMING_SNAKE_CASE__ ... | 85 | import os
import pytest
from attr import dataclass
SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
lowercase_ = {
'... | 85 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snake_case ( UpperCamelCase_ , ... | 85 | import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snak... | 85 | 1 |
def _a ( lowercase__ : int = 10 , lowercase__ : int = 10_00 , lowercase__ : bool = True ):
'''simple docstring'''
assert (
isinstance(lowercase__ , lowercase__ )
and isinstance(lowercase__ , lowercase__ )
and isinstance(lowercase__ , low... | 85 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 85 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, rand... | 85 | 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,
Stable... | 85 | 1 |
from __future__ import annotations
from math import pi
def _a ( lowercase__ : float , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('One and only one argument... | 85 | import math
from collections.abc import Callable
def _a ( lowercase__ : Callable[[float], float] , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : float = xa
SCREAMING_SNAKE_CASE__ : float ... | 85 | 1 |
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