




from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
		import PIL

# soft dependency
if is_pytesseract_available():
		import pytesseract

__UpperCamelCase					:       Optional[Any]    					=      logging.get_logger(__name__)





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Tuple:
	return [
	    int(1000 * (box[0] / width)    ),
	    int(1000 * (box[1] / height)    ),
	    int(1000 * (box[2] / width)    ),
	    int(1000 * (box[3] / height)    ),
	]





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       List[Any]:
	a										=							to_pil_image(__lowerCamelCase    )
	a				, a										=							pil_image.size
	a										=							pytesseract.image_to_data(__lowerCamelCase							,			lang=__lowerCamelCase							,			output_type="""dict"""							,			config=__lowerCamelCase    )
	a				, a				, a				, a				, a										=							data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

	# filter empty words and corresponding coordinates
	a										=							[idx for idx, word in enumerate(__lowerCamelCase    ) if not word.strip()]
	a										=							[word for idx, word in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
	a										=							[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
	a										=							[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
	a										=							[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
	a										=							[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]

	# turn coordinates into (left, top, left+width, top+height) format
	a										=							[]
	for x, y, w, h in zip(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    ):
		a										=							[x, y, x + w, y + h]
		actual_boxes.append(__lowerCamelCase    )

	# finally, normalize the bounding boxes
	a										=							[]
	for box in actual_boxes:
		normalized_boxes.append(normalize_box(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )    )

	assert len(__lowerCamelCase    ) == len(__lowerCamelCase    ), "Not as many words as there are bounding boxes"

	return words, normalized_boxes



class 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  ['''pixel_values''']
							def __init__(						self						:str		,      __magic_name__						:bool = True		,      __magic_name__						:Dict[str, int] = None		,      __magic_name__						:PILImageResampling = PILImageResampling.BILINEAR		,      __magic_name__						:bool = True		,      __magic_name__						:float = 1 / 255		,      __magic_name__						:bool = True		,      __magic_name__						:Union[float, Iterable[float]] = None		,      __magic_name__						:Union[float, Iterable[float]] = None		,      __magic_name__						:bool = True		,      __magic_name__						:Optional[str] = None		,      __magic_name__						:Optional[str] = ""		,      **__magic_name__						:Optional[Any]		,      ):







								'''simple docstring'''







								super().__init__(**__magic_name__				)
								a										=							size if size is not None else {"""height""": 224, """width""": 224}
								a										=							get_size_dict(__magic_name__				)

								a										=							do_resize
								a										=							size
								a										=							resample
								a										=							do_rescale
								a										=							rescale_value
								a										=							do_normalize
								a										=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								a										=							image_std if image_std is not None else IMAGENET_STANDARD_STD
								a										=							apply_ocr
								a										=							ocr_lang
								a										=							tesseract_config
							def       lowerCamelCase__							(						self						:str		,      __magic_name__						:np.ndarray		,      __magic_name__						:Dict[str, int]		,      __magic_name__						:PILImageResampling = PILImageResampling.BILINEAR		,      __magic_name__						:Optional[Union[str, ChannelDimension]] = None		,      **__magic_name__						:List[str]		,      ):







								'''simple docstring'''







								a										=							get_size_dict(__magic_name__				)
								if "height" not in size or "width" not in size:
									raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'				)
								a										=							(size["""height"""], size["""width"""])
								return resize(__magic_name__		,      size=__magic_name__		,      resample=__magic_name__		,      data_format=__magic_name__		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:Optional[int]		,      __magic_name__						:np.ndarray		,      __magic_name__						:Union[int, float]		,      __magic_name__						:Optional[Union[str, ChannelDimension]] = None		,      **__magic_name__						:Optional[int]		,      ):







								'''simple docstring'''







								return rescale(__magic_name__		,      scale=__magic_name__		,      data_format=__magic_name__		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:Tuple		,      __magic_name__						:np.ndarray		,      __magic_name__						:Union[float, Iterable[float]]		,      __magic_name__						:Union[float, Iterable[float]]		,      __magic_name__						:Optional[Union[str, ChannelDimension]] = None		,      **__magic_name__						:Optional[int]		,      ):







								'''simple docstring'''







								return normalize(__magic_name__		,      mean=__magic_name__		,      std=__magic_name__		,      data_format=__magic_name__		,      **__magic_name__				)





							def       lowerCamelCase__							(						self						:int		,      __magic_name__						:ImageInput		,      __magic_name__						:bool = None		,      __magic_name__						:Dict[str, int] = None		,      __magic_name__						:Optional[int]=None		,      __magic_name__						:bool = None		,      __magic_name__						:float = None		,      __magic_name__						:bool = None		,      __magic_name__						:Union[float, Iterable[float]] = None		,      __magic_name__						:Union[float, Iterable[float]] = None		,      __magic_name__						:bool = None		,      __magic_name__						:Optional[str] = None		,      __magic_name__						:Optional[str] = None		,      __magic_name__						:Optional[Union[str, TensorType]] = None		,      __magic_name__						:ChannelDimension = ChannelDimension.FIRST		,      **__magic_name__						:Union[str, Any]		,      ):







								'''simple docstring'''







								a										=							do_resize if do_resize is not None else self.do_resize
								a										=							size if size is not None else self.size
								a										=							get_size_dict(__magic_name__				)
								a										=							resample if resample is not None else self.resample
								a										=							do_rescale if do_rescale is not None else self.do_rescale
								a										=							rescale_factor if rescale_factor is not None else self.rescale_factor
								a										=							do_normalize if do_normalize is not None else self.do_normalize
								a										=							image_mean if image_mean is not None else self.image_mean
								a										=							image_std if image_std is not None else self.image_std
								a										=							apply_ocr if apply_ocr is not None else self.apply_ocr
								a										=							ocr_lang if ocr_lang is not None else self.ocr_lang
								a										=							tesseract_config if tesseract_config is not None else self.tesseract_config

								a										=							make_list_of_images(__magic_name__				)

								if not valid_images(__magic_name__				):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""				)

								if do_resize and size is None:
									raise ValueError("""Size must be specified if do_resize is True."""				)

								if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

								if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""If do_normalize is True, image_mean and image_std must be specified."""				)

								# All transformations expect numpy arrays.
								a										=							[to_numpy_array(__magic_name__				) for image in images]

								# Tesseract OCR to get words + normalized bounding boxes
								if apply_ocr:
									requires_backends(self		,      """pytesseract"""				)
									a										=							[]
									a										=							[]
									for image in images:
										a				, a										=							apply_tesseract(__magic_name__		,      __magic_name__		,      __magic_name__				)
										words_batch.append(__magic_name__				)
										boxes_batch.append(__magic_name__				)

								if do_resize:
									a										=							[self.resize(image=__magic_name__		,      size=__magic_name__		,      resample=__magic_name__				) for image in images]

								if do_rescale:
									a										=							[self.rescale(image=__magic_name__		,      scale=__magic_name__				) for image in images]

								if do_normalize:
									a										=							[self.normalize(image=__magic_name__		,      mean=__magic_name__		,      std=__magic_name__				) for image in images]

								a										=							[to_channel_dimension_format(__magic_name__		,      __magic_name__				) for image in images]

								a										=							BatchFeature(data={"""pixel_values""": images}		,      tensor_type=__magic_name__				)

								if apply_ocr:
									a										=							words_batch
									a										=							boxes_batch
								return data







# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__UpperCamelCase					:       List[str]    					=      logging.get_logger(__name__)

__UpperCamelCase					:       Dict[Optional[str], Type[Formatter]]    					=      {}
__UpperCamelCase					:       Dict[Optional[str], str]    					=      {}
__UpperCamelCase					:       Dict[Optional[str], Exception]    					=      {}





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None							,			)      ->       Optional[int]:
	a										=							aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f'Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'    )
	a										=							formatter_cls
	for alias in set(aliases + [format_type]    ):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f'Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'    )
		a										=							format_type





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None    )      ->       List[str]:
	a										=							aliases if aliases is not None else []
	for alias in set(aliases + [format_type]    ):
		a										=							unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["python"])
_register_formatter(ArrowFormatter, "arrow", aliases=["pa", "pyarrow"])
_register_formatter(NumpyFormatter, "numpy", aliases=["np"])
_register_formatter(PandasFormatter, "pandas", aliases=["pd"])
_register_formatter(CustomFormatter, "custom")

if config.TORCH_AVAILABLE:
		from .torch_formatter import TorchFormatter

		_register_formatter(TorchFormatter, "torch", aliases=["pt", "pytorch"])
else:
		__UpperCamelCase					:       str    					=      ValueError("PyTorch needs to be installed to be able to return PyTorch tensors.")
		_register_unavailable_formatter(_torch_error, "torch", aliases=["pt", "pytorch"])

if config.TF_AVAILABLE:
		from .tf_formatter import TFFormatter

		_register_formatter(TFFormatter, "tensorflow", aliases=["tf"])
else:
		__UpperCamelCase					:       List[str]    					=      ValueError("Tensorflow needs to be installed to be able to return Tensorflow tensors.")
		_register_unavailable_formatter(_tf_error, "tensorflow", aliases=["tf"])

if config.JAX_AVAILABLE:
		from .jax_formatter import JaxFormatter

		_register_formatter(JaxFormatter, "jax", aliases=[])
else:
		__UpperCamelCase					:       List[str]    					=      ValueError("JAX needs to be installed to be able to return JAX arrays.")
		_register_unavailable_formatter(_jax_error, "jax", aliases=[])





def 		__A		(	__lowerCamelCase    )      ->       Optional[str]:
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type





def 		__A		(	__lowerCamelCase							,			**__lowerCamelCase    )      ->       Formatter:
	a										=							get_format_type_from_alias(__lowerCamelCase    )
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**__lowerCamelCase    )
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f'Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None    )}, but got \'{format_type}\''    )

