




import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil


@pytest.fixture
def __A   (				__lowerCAmelCase     )->  str:





						"""simple docstring"""



						_UpperCAmelCase     =      tmp_path / """text.txt"""
						_UpperCAmelCase     =      textwrap.dedent(
						    '\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        '     )
						with open(__lowerCAmelCase			,		'w'			,		encoding='utf-8'     ) as f:
												f.write(__lowerCAmelCase     )
						return str(__lowerCAmelCase     )


@pytest.fixture
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						_UpperCAmelCase     =      tmp_path / """text_with_image.txt"""
						with open(__lowerCAmelCase			,		'w'			,		encoding='utf-8'     ) as f:
												f.write(__lowerCAmelCase     )
						return str(__lowerCAmelCase     )


@pytest.mark.parametrize('keep_linebreaks'			,		[True, False]     )
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  Dict:





						"""simple docstring"""



						with open(__lowerCAmelCase			,		encoding='utf-8'     ) as f:
												_UpperCAmelCase     =      f.read().splitlines(keepends=__lowerCAmelCase     )
						_UpperCAmelCase     =      Text(keep_linebreaks=__lowerCAmelCase			,		encoding='utf-8'     )
						_UpperCAmelCase     =      text._generate_tables([[text_file]]     )
						_UpperCAmelCase     =      pa.concat_tables([table for _, table in generator]     ).to_pydict()["""text"""]
						assert generated_content == expected_content


@require_pil
def __A   (				__lowerCAmelCase     )->  Any:





						"""simple docstring"""



						with open(__lowerCAmelCase			,		encoding='utf-8'     ) as f:
												_UpperCAmelCase     =      f.read().splitlines()[0]
						_UpperCAmelCase     =      Text(encoding='utf-8'			,		features=Features({'image': Image()}     )     )
						_UpperCAmelCase     =      text._generate_tables([[text_file_with_image]]     )
						_UpperCAmelCase     =      pa.concat_tables([table for _, table in generator]     )
						assert pa_table.schema.field('image'     ).type == Image()()
						_UpperCAmelCase     =      pa_table.to_pydict()["""image"""]
						assert generated_content == [{"path": image_file, "bytes": None}]


@pytest.mark.parametrize('sample_by'			,		['line', 'paragraph', 'document']     )
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



						with open(__lowerCAmelCase			,		encoding='utf-8'     ) as f:
												_UpperCAmelCase     =      f.read()
						if sample_by == "line":
												_UpperCAmelCase     =      expected_content.splitlines()
						elif sample_by == "paragraph":
												_UpperCAmelCase     =      expected_content.split('\n\n'     )
						elif sample_by == "document":
												_UpperCAmelCase     =      [expected_content]
						_UpperCAmelCase     =      Text(sample_by=__lowerCAmelCase			,		encoding='utf-8'			,		chunksize=100     )
						_UpperCAmelCase     =      text._generate_tables([[text_file]]     )
						_UpperCAmelCase     =      pa.concat_tables([table for _, table in generator]     ).to_pydict()["""text"""]
						assert generated_content == expected_content








from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						if not inplace:
												_UpperCAmelCase     =      ma + ma
						else:
												ma += ma

						return ma


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      -1 * len(__lowerCAmelCase     )
						_UpperCAmelCase     =      list(range(len(tensor.shape[:zero_index]     )     )     )
						return tensor.permute(first_inds + [zero_index + i for i in inds]     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						return t.reshape(t.shape[:-no_dims] + (-1,)     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase = 1E-4     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      mask.expand(*value.shape     )
						return torch.sum(mask * value			,		dim=__lowerCAmelCase     ) / (eps + torch.sum(__lowerCAmelCase			,		dim=__lowerCAmelCase     ))


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase = 2.31_25			,		__lowerCAmelCase = 21.68_75			,		__lowerCAmelCase = 64     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      torch.linspace(__lowerCAmelCase			,		__lowerCAmelCase			,		no_bins - 1			,		device=pts.device     )
						_UpperCAmelCase     =      torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2			,		dim=-1     )     )
						return torch.bucketize(__lowerCAmelCase			,		__lowerCAmelCase     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  dict:





						"""simple docstring"""



						_UpperCAmelCase     =      dicts[0]
						_UpperCAmelCase     =      {}
						for k, v in first.items():
												_UpperCAmelCase     =      [d[k] for d in dicts]
												if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
																		_UpperCAmelCase     =      dict_multimap(__lowerCAmelCase			,		__lowerCAmelCase     )
												else:
																		_UpperCAmelCase     =      fn(__lowerCAmelCase     )

						return new_dict


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      v_bins.view(((1,) * len(x.shape     )) + (len(__lowerCAmelCase     ),)     )
						_UpperCAmelCase     =      x[..., None] - reshaped_bins
						_UpperCAmelCase     =      torch.argmin(torch.abs(__lowerCAmelCase     )			,		dim=-1     )
						return nn.functional.one_hot(__lowerCAmelCase			,		num_classes=len(__lowerCAmelCase     )     ).float()


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase = 0			,		__lowerCAmelCase = 0     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      []
						for i, s in enumerate(data.shape[:no_batch_dims]     ):
												_UpperCAmelCase     =      torch.arange(__lowerCAmelCase     )
												_UpperCAmelCase     =      r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
												ranges.append(__lowerCAmelCase     )

						_UpperCAmelCase     =      [slice(__lowerCAmelCase     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
						_UpperCAmelCase     =      inds
						ranges.extend(__lowerCAmelCase     )
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(__lowerCAmelCase     )]


_a				     =  TypeVar('''T''')


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Dict[Any, Union[dict, list, tuple, Any]]:





						"""simple docstring"""



						_UpperCAmelCase     =      {}
						for k, v in dic.items():
												if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
																		_UpperCAmelCase     =      dict_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
												else:
																		_UpperCAmelCase     =      tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )

						return new_dict


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Any:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  dict:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  list:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  tuple:





						"""simple docstring"""



						...


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



						if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return dict_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return [tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     ) for x in tree]
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return tuple(tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     ) for x in tree     )
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return fn(__lowerCAmelCase     )
						else:
												print(type(__lowerCAmelCase     )     )
												raise ValueError('Not supported'     )

_a				     =  partial(tree_map, leaf_type=torch.Tensor)
