



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

lowercase_	       =			logging.get_logger(__name__)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    int				,   __lowerCamelCase	:    int	)  ->						Optional[int]:
					return [
					    int(1000 * (box[0] / width)	),
					    int(1000 * (box[1] / height)	),
					    int(1000 * (box[2] / width)	),
					    int(1000 * (box[3] / height)	),
					]






def 					__lowerCAmelCase		(  __lowerCamelCase	:    np.ndarray				,   __lowerCamelCase	:    Optional[str]				,   __lowerCamelCase	:    Optional[str]	)  ->						Optional[Any]:
					__lowerCAmelCase	   =to_pil_image(__A	)
					__lowerCAmelCase	   =pil_image.size
					__lowerCAmelCase	   =pytesseract.image_to_data(__A				,   lang=__A				,   output_type="""dict"""				,   config=__A	)
					__lowerCAmelCase	   =data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

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

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

					return words, normalized_boxes






class 						__a (     __A				):
	SCREAMING_SNAKE_CASE       =       ["pixel_values"]



	def __init__(						self							:    List[Any]				,	snake_case_							:    bool = True				,	snake_case_							:    Dict[str, int] = None				,	snake_case_							:    PILImageResampling = PILImageResampling.BILINEAR				,	snake_case_							:    bool = True				,	snake_case_							:    float = 1 / 2_55				,	snake_case_							:    bool = True				,	snake_case_							:    Union[float, Iterable[float]] = None				,	snake_case_							:    Union[float, Iterable[float]] = None				,	snake_case_							:    bool = True				,	snake_case_							:    Optional[str] = None				,	snake_case_							:    Optional[str] = ""				,	**snake_case_							:    Tuple				,	)->							None:
						super().__init__(**snake_case_)
						__lowerCAmelCase	   =size if size is not None else {'''height''': 2_24, '''width''': 2_24}
						__lowerCAmelCase	   =get_size_dict(snake_case_)

						__lowerCAmelCase	   =do_resize
						__lowerCAmelCase	   =size
						__lowerCAmelCase	   =resample
						__lowerCAmelCase	   =do_rescale
						__lowerCAmelCase	   =rescale_value
						__lowerCAmelCase	   =do_normalize
						__lowerCAmelCase	   =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase	   =image_std if image_std is not None else IMAGENET_STANDARD_STD
						__lowerCAmelCase	   =apply_ocr
						__lowerCAmelCase	   =ocr_lang
						__lowerCAmelCase	   =tesseract_config



	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    np.ndarray				,	snake_case_							:    Dict[str, int]				,	snake_case_							:    PILImageResampling = PILImageResampling.BILINEAR				,	snake_case_							:    Optional[Union[str, ChannelDimension]] = None				,	**snake_case_							:    Optional[Any]				,	)->							np.ndarray:
						__lowerCAmelCase	   =get_size_dict(snake_case_)
						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()}""")
						__lowerCAmelCase	   =(size['''height'''], size['''width'''])
						return resize(snake_case_				,	size=snake_case_				,	resample=snake_case_				,	data_format=snake_case_				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    np.ndarray				,	snake_case_							:    Union[int, float]				,	snake_case_							:    Optional[Union[str, ChannelDimension]] = None				,	**snake_case_							:    Optional[Any]				,	)->							np.ndarray:
						return rescale(snake_case_				,	scale=snake_case_				,	data_format=snake_case_				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    np.ndarray				,	snake_case_							:    Union[float, Iterable[float]]				,	snake_case_							:    Union[float, Iterable[float]]				,	snake_case_							:    Optional[Union[str, ChannelDimension]] = None				,	**snake_case_							:    Optional[int]				,	)->							np.ndarray:
						return normalize(snake_case_				,	mean=snake_case_				,	std=snake_case_				,	data_format=snake_case_				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    ImageInput				,	snake_case_							:    bool = None				,	snake_case_							:    Dict[str, int] = None				,	snake_case_							:    Union[str, Any]=None				,	snake_case_							:    bool = None				,	snake_case_							:    float = None				,	snake_case_							:    bool = None				,	snake_case_							:    Union[float, Iterable[float]] = None				,	snake_case_							:    Union[float, Iterable[float]] = None				,	snake_case_							:    bool = None				,	snake_case_							:    Optional[str] = None				,	snake_case_							:    Optional[str] = None				,	snake_case_							:    Optional[Union[str, TensorType]] = None				,	snake_case_							:    ChannelDimension = ChannelDimension.FIRST				,	**snake_case_							:    List[str]				,	)->							PIL.Image.Image:
						__lowerCAmelCase	   =do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase	   =size if size is not None else self.size
						__lowerCAmelCase	   =get_size_dict(snake_case_)
						__lowerCAmelCase	   =resample if resample is not None else self.resample
						__lowerCAmelCase	   =do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase	   =rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase	   =do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase	   =image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase	   =image_std if image_std is not None else self.image_std
						__lowerCAmelCase	   =apply_ocr if apply_ocr is not None else self.apply_ocr
						__lowerCAmelCase	   =ocr_lang if ocr_lang is not None else self.ocr_lang
						__lowerCAmelCase	   =tesseract_config if tesseract_config is not None else self.tesseract_config

						__lowerCAmelCase	   =make_list_of_images(snake_case_)

						if not valid_images(snake_case_):
											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.
						__lowerCAmelCase	   =[to_numpy_array(snake_case_) for image in images]

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

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

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

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

						__lowerCAmelCase	   =[to_channel_dimension_format(snake_case_				,	snake_case_) for image in images]

						__lowerCAmelCase	   =BatchFeature(data={"""pixel_values""": images}				,	tensor_type=snake_case_)

						if apply_ocr:
											__lowerCAmelCase	   =words_batch
											__lowerCAmelCase	   =boxes_batch
						return data









def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						None:
					__lowerCAmelCase	   =generate_pascal_triangle(__lowerCamelCase	)
					for row_idx in range(__lowerCamelCase	):
										# Print left spaces
										for _ in range(num_rows - row_idx - 1	):
															print(end=""" """	)
										# Print row values
										for col_idx in range(row_idx + 1	):
															if col_idx != row_idx:
																				print(triangle[row_idx][col_idx]				,   end=""" """	)
															else:
																				print(triangle[row_idx][col_idx]				,   end=""""""	)
										print()






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						list[list[int]]:

					if not isinstance(__lowerCamelCase				,   __lowerCamelCase	):
										raise TypeError("""The input value of 'num_rows' should be 'int'"""	)

					if num_rows == 0:
										return []
					elif num_rows < 0:
										raise ValueError(
										    """The input value of 'num_rows' should be greater than or equal to 0"""	)

					__lowerCAmelCase	   =[]
					for current_row_idx in range(__lowerCamelCase	):
										__lowerCAmelCase	   =populate_current_row(__lowerCamelCase				,   __lowerCamelCase	)
										triangle.append(__lowerCamelCase	)
					return triangle






def 					__lowerCAmelCase		(  __lowerCamelCase	:    list[list[int]]				,   __lowerCamelCase	:    int	)  ->						list[int]:
					__lowerCAmelCase	   =[-1] * (current_row_idx + 1)
					# first and last elements of current row are equal to 1
					__lowerCAmelCase   ,	__lowerCAmelCase	   =1, 1
					for current_col_idx in range(1				,   __lowerCamelCase	):
										calculate_current_element(
										    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)
					return current_row






def 					__lowerCAmelCase		(  __lowerCamelCase	:    list[list[int]]				,   __lowerCamelCase	:    list[int]				,   __lowerCamelCase	:    int				,   __lowerCamelCase	:    int				,   )  ->						None:
					__lowerCAmelCase	   =triangle[current_row_idx - 1][current_col_idx - 1]
					__lowerCAmelCase	   =triangle[current_row_idx - 1][current_col_idx]
					__lowerCAmelCase	   =above_to_left_elt + above_to_right_elt






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						list[list[int]]:

					if not isinstance(__lowerCamelCase				,   __lowerCamelCase	):
										raise TypeError("""The input value of 'num_rows' should be 'int'"""	)

					if num_rows == 0:
										return []
					elif num_rows < 0:
										raise ValueError(
										    """The input value of 'num_rows' should be greater than or equal to 0"""	)

					__lowerCAmelCase	   =[[1]]

					for row_index in range(1				,   __lowerCamelCase	):
										__lowerCAmelCase	   =[0] + result[-1] + [0]
										__lowerCAmelCase	   =row_index + 1
										# Calculate the number of distinct elements in a row
										__lowerCAmelCase	   =sum(divmod(__lowerCamelCase				,   2	)	)
										__lowerCAmelCase	   =[
										    temp_row[i - 1] + temp_row[i] for i in range(1				,   distinct_elements + 1	)
										]
										__lowerCAmelCase	   =row_first_half[: (row_index + 1) // 2]
										row_second_half.reverse()
										__lowerCAmelCase	   =row_first_half + row_second_half
										result.append(__lowerCamelCase	)

					return result






def 					__lowerCAmelCase		(  )  ->						None:
					from collections.abc import Callable
					from timeit import timeit

					def benchmark_a_function(__lowerCamelCase	:    Callable				,   __lowerCamelCase	:    int	) -> None:
										__lowerCAmelCase	   =f"""{func.__name__}({value})"""
										__lowerCAmelCase	   =timeit(f"""__main__.{call}"""				,   setup="""import __main__"""	)
										# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
										print(f"""{call:38} -- {timing:.4f} seconds"""	)

					for value in range(15	):  # (1, 7, 14):
										for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
															benchmark_a_function(__lowerCamelCase				,   __lowerCamelCase	)
										print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()




