

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  UpperCamelCase_(							_snake_case     :						List[Any]			):




			"""simple docstring"""






			__a						   =tmp_path / 'text.txt'
			__a						   =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(_snake_case ,  'w' ,  encoding='utf-8'			) as f:
						f.write(_snake_case			)
			return str(_snake_case			)






@pytest.fixture
def  UpperCamelCase_(							_snake_case     :						Optional[int] ,  _snake_case     :						List[str]			):




			"""simple docstring"""






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






@pytest.mark.parametrize('keep_linebreaks' ,  [True, False]			)
def  UpperCamelCase_(							_snake_case     :						Any ,  _snake_case     :						Dict			):




			"""simple docstring"""






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






@require_pil
def  UpperCamelCase_(							_snake_case     :						int			):




			"""simple docstring"""






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






@pytest.mark.parametrize('sample_by' ,  ['line', 'paragraph', 'document']			)
def  UpperCamelCase_(							_snake_case     :						Optional[Any] ,  _snake_case     :						Tuple			):




			"""simple docstring"""






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









# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
						import tensorflow as tf






class 			__magic_name__					(    TensorFormatter[Mapping, 'tf.Tensor', Mapping]   ):





			def __init__(	self      ,						__snake_case=None      ,						**__snake_case      )     ->				Union[str, Any]:




						'''simple docstring'''
						super().__init__(features=__snake_case      )
						__a						   =tf_tensor_kwargs
						import tensorflow as tf  # noqa: import tf at initialization





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				int:




						'''simple docstring'''
						import tensorflow as tf

						if isinstance(__snake_case      ,						__snake_case      ) and column:
									if all(
									    isinstance(__snake_case      ,						tf.Tensor      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
												return tf.stack(__snake_case      )
									elif all(
									    isinstance(__snake_case      ,						(tf.Tensor, tf.RaggedTensor)      ) and x.ndim == 1 and x.dtype == column[0].dtype
									    for x in column      ):
												# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
												return tf.ragged.stack(__snake_case      )

						return column





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Any:




						'''simple docstring'''
						import tensorflow as tf

						if value is None:
									return value

						__a						   ={}

						if isinstance(__snake_case      ,						(np.number, np.ndarray)      ) and np.issubdtype(value.dtype      ,						np.integer      ):
									__a						   ={'dtype': tf.intaa}
						elif isinstance(__snake_case      ,						(np.number, np.ndarray)      ) and np.issubdtype(value.dtype      ,						np.floating      ):
									__a						   ={'dtype': tf.floataa}
						elif config.PIL_AVAILABLE and "PIL" in sys.modules:
									import PIL.Image

									if isinstance(__snake_case      ,						PIL.Image.Image      ):
												__a						   =np.asarray(__snake_case      )

						return tf.convert_to_tensor(__snake_case      ,						**{**default_dtype, **self.tf_tensor_kwargs}      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Dict:




						'''simple docstring'''
						import tensorflow as tf

						# support for torch, tf, jax etc.
						if config.TORCH_AVAILABLE and "torch" in sys.modules:
									import torch

									if isinstance(__snake_case      ,						torch.Tensor      ):
												return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
						if hasattr(__snake_case      ,						'__array__'      ) and not isinstance(__snake_case      ,						tf.Tensor      ):
									__a						   =data_struct.__array__()
						# support for nested types like struct of list of struct
						if isinstance(__snake_case      ,						np.ndarray      ):
									if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
												return self._consolidate([self.recursive_tensorize(__snake_case      ) for substruct in data_struct]      )
						elif isinstance(__snake_case      ,						(list, tuple)      ):
									return self._consolidate([self.recursive_tensorize(__snake_case      ) for substruct in data_struct]      )
						return self._tensorize(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Optional[Any]:




						'''simple docstring'''
						return map_nested(self._recursive_tensorize      ,						__snake_case      ,						map_list=__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Mapping:




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_row(__snake_case      )
						__a						   =self.python_features_decoder.decode_row(__snake_case      )
						return self.recursive_tensorize(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				"tf.Tensor":




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_column(__snake_case      )
						__a						   =self.python_features_decoder.decode_column(__snake_case      ,						pa_table.column_names[0]      )
						__a						   =self.recursive_tensorize(__snake_case      )
						__a						   =self._consolidate(__snake_case      )
						return column





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Mapping:




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_batch(__snake_case      )
						__a						   =self.python_features_decoder.decode_batch(__snake_case      )
						__a						   =self.recursive_tensorize(__snake_case      )
						for column_name in batch:
									__a						   =self._consolidate(batch[column_name]      )
						return batch






