





'''simple docstring'''







# 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 					_UpperCAmelCase							(		TensorFormatter[Mapping, """tf.Tensor""", Mapping]     ):
		def __init__(						self,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					super().__init__(features=__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       tf_tensor_kwargs
					import tensorflow as tf  # noqa: import tf at initialization
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					import tensorflow as tf

					if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) and column:
								if all(
								    isinstance(__SCREAMING_SNAKE_CASE,tf.Tensor    ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column    ):
											return tf.stack(__SCREAMING_SNAKE_CASE    )
								elif all(
								    isinstance(__SCREAMING_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(__SCREAMING_SNAKE_CASE    )

					return column
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					import tensorflow as tf

					if value is None:
								return value

					__lowerCAmelCase      		=       {}

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

								if isinstance(__SCREAMING_SNAKE_CASE,PIL.Image.Image    ):
											__lowerCAmelCase      		=       np.asarray(__SCREAMING_SNAKE_CASE    )

					return tf.convert_to_tensor(__SCREAMING_SNAKE_CASE,**{**default_dtype, **self.tf_tensor_kwargs}    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''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(__SCREAMING_SNAKE_CASE,torch.Tensor    ):
											return self._tensorize(data_struct.detach().cpu().numpy()[()]    )
					if hasattr(__SCREAMING_SNAKE_CASE,"""__array__"""    ) and not isinstance(__SCREAMING_SNAKE_CASE,tf.Tensor    ):
								__lowerCAmelCase      		=       data_struct.__array__()
					# support for nested types like struct of list of struct
					if isinstance(__SCREAMING_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(__SCREAMING_SNAKE_CASE    ) for substruct in data_struct]    )
					elif isinstance(__SCREAMING_SNAKE_CASE,(list, tuple)    ):
								return self._consolidate([self.recursive_tensorize(__SCREAMING_SNAKE_CASE    ) for substruct in data_struct]    )
					return self._tensorize(__SCREAMING_SNAKE_CASE    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					return map_nested(self._recursive_tensorize,__SCREAMING_SNAKE_CASE,map_list=__SCREAMING_SNAKE_CASE    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       self.numpy_arrow_extractor().extract_row(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       self.python_features_decoder.decode_row(__SCREAMING_SNAKE_CASE    )
					return self.recursive_tensorize(__SCREAMING_SNAKE_CASE    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       self.numpy_arrow_extractor().extract_column(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       self.python_features_decoder.decode_column(__SCREAMING_SNAKE_CASE,pa_table.column_names[0]    )
					__lowerCAmelCase      		=       self.recursive_tensorize(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       self._consolidate(__SCREAMING_SNAKE_CASE    )
					return column







		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       self.numpy_arrow_extractor().extract_batch(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       self.python_features_decoder.decode_batch(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       self.recursive_tensorize(__SCREAMING_SNAKE_CASE    )
					for column_name in batch:
								__lowerCAmelCase      		=       self._consolidate(batch[column_name]    )
					return batch








'''simple docstring'''







from cva import destroyAllWindows, imread, imshow, waitKey





def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      int:
			__lowerCAmelCase		, __lowerCAmelCase      		=       img.shape[0], img.shape[1]

			def to_grayscale(lowercase      ,			lowercase      ,			lowercase			):
						return 0.21_26 * red + 0.5_87 * green + 0.1_14 * blue

			def normalize(lowercase			):
						return min(lowercase      ,			255			)

			for i in range(lowercase			):
						for j in range(lowercase			):
									__lowerCAmelCase      		=       int(to_grayscale(*img[i][j]			)			)
									__lowerCAmelCase      		=       [
									    normalize(lowercase			),
									    normalize(greyscale + factor			),
									    normalize(greyscale + 2 * factor			),
									]

			return img


if __name__ == "__main__":
	# read original image
	_a      :				Any											=					{
	    percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
	}

	for percentage, img in images.items():
		make_sepia(img, percentage)

	for percentage, img in images.items():
		imshow(f'Original image with sepia (factor: {percentage})', img)

	waitKey(0)
	destroyAllWindows()

