






import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
		import textwrap

		from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
		import torch

		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		_a      	=  False


_a      	=  logging.get_logger(__name__)
_a      	=  '''ybelkada/fonts'''


def __A    (      )->  str:





						"""simple docstring"""



						if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
												raise ImportError(
												    F"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
												    'Pix2StructImageProcessor. Please upgrade torch.'	)


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





						"""simple docstring"""



						requires_backends(_UpperCAmelCase ,		['torch']	)
						_check_torch_version()

						_UpperCAmelCase									=			image_tensor.unsqueeze(0	)
						_UpperCAmelCase									=			torch.nn.functional.unfold(_UpperCAmelCase ,		(patch_height, patch_width) ,		stride=(patch_height, patch_width)	)
						_UpperCAmelCase									=			patches.reshape(image_tensor.size(0	) ,		image_tensor.size(1	) ,		_UpperCAmelCase ,		_UpperCAmelCase ,		-1	)
						_UpperCAmelCase									=			patches.permute(0 ,		4 ,		2 ,		3 ,		1	).reshape(
						    image_tensor.size(2	) // patch_height ,		image_tensor.size(3	) // patch_width ,		image_tensor.size(1	) * patch_height * patch_width ,		)
						return patches.unsqueeze(0	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase = 36 ,		__lowerCAmelCase = "black" ,		__lowerCAmelCase = "white" ,		__lowerCAmelCase = 5 ,		__lowerCAmelCase = 5 ,		__lowerCAmelCase = 5 ,		__lowerCAmelCase = 5 ,		__lowerCAmelCase = None ,		__lowerCAmelCase = None ,		)->  Union[str, Any]:





						"""simple docstring"""



						requires_backends(_UpperCAmelCase ,		'vision'	)
						# Add new lines so that each line is no more than 80 characters.

						_UpperCAmelCase									=			textwrap.TextWrapper(width=80	)
						_UpperCAmelCase									=			wrapper.wrap(text=_UpperCAmelCase	)
						_UpperCAmelCase									=			'\n'.join(_UpperCAmelCase	)

						if font_bytes is not None and font_path is None:
												_UpperCAmelCase									=			io.BytesIO(_UpperCAmelCase	)
						elif font_path is not None:
												_UpperCAmelCase									=			font_path
						else:
												_UpperCAmelCase									=			hf_hub_download(_UpperCAmelCase ,		'Arial.TTF'	)
						_UpperCAmelCase									=			ImageFont.truetype(_UpperCAmelCase ,		encoding='UTF-8' ,		size=_UpperCAmelCase	)

						# Use a temporary canvas to determine the width and height in pixels when
						# rendering the text.
						_UpperCAmelCase									=			ImageDraw.Draw(Image.new('RGB' ,		(1, 1) ,		_UpperCAmelCase	)	)
						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			temp_draw.textbbox((0, 0) ,		_UpperCAmelCase ,		_UpperCAmelCase	)

						# Create the actual image with a bit of padding around the text.
						_UpperCAmelCase									=			text_width + left_padding + right_padding
						_UpperCAmelCase									=			text_height + top_padding + bottom_padding
						_UpperCAmelCase									=			Image.new('RGB' ,		(image_width, image_height) ,		_UpperCAmelCase	)
						_UpperCAmelCase									=			ImageDraw.Draw(_UpperCAmelCase	)
						draw.text(xy=(left_padding, top_padding) ,		text=_UpperCAmelCase ,		fill=_UpperCAmelCase ,		font=_UpperCAmelCase	)
						return image


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





						"""simple docstring"""



						requires_backends(_UpperCAmelCase ,		'vision'	)

						# Convert to PIL image if necessary
						_UpperCAmelCase									=			to_pil_image(_UpperCAmelCase	)

						_UpperCAmelCase									=			render_text(_UpperCAmelCase ,		**_UpperCAmelCase	)
						_UpperCAmelCase									=			max(header_image.width ,		image.width	)

						_UpperCAmelCase									=			int(image.height * (new_width / image.width)	)
						_UpperCAmelCase									=			int(header_image.height * (new_width / header_image.width)	)

						_UpperCAmelCase									=			Image.new('RGB' ,		(new_width, new_height + new_header_height) ,		'white'	)
						new_image.paste(header_image.resize((new_width, new_header_height)	) ,		(0, 0)	)
						new_image.paste(image.resize((new_width, new_height)	) ,		(0, new_header_height)	)

						# Convert back to the original framework if necessary
						_UpperCAmelCase									=			to_numpy_array(_UpperCAmelCase	)

						if infer_channel_dimension_format(_UpperCAmelCase	) == ChannelDimension.LAST:
												_UpperCAmelCase									=			to_channel_dimension_format(_UpperCAmelCase ,		ChannelDimension.LAST	)

						return new_image





class __lowerCamelCase (      __UpperCAmelCase):





	"""simple docstring"""






	UpperCamelCase__								=      ['''flattened_patches''']

	def __init__(							self       ,    UpperCAmelCase = True       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 2048       ,    UpperCAmelCase = False       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							super().__init__(**__SCREAMING_SNAKE_CASE    )
							_UpperCAmelCase									=			patch_size if patch_size is not None else {'height': 16, 'width': 16}
							_UpperCAmelCase									=			do_normalize
							_UpperCAmelCase									=			do_convert_rgb
							_UpperCAmelCase									=			max_patches
							_UpperCAmelCase									=			is_vqa

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							requires_backends(self.extract_flattened_patches       ,    'torch'    )
							_check_torch_version()

							# convert to torch
							_UpperCAmelCase									=			to_channel_dimension_format(__SCREAMING_SNAKE_CASE       ,    ChannelDimension.FIRST    )
							_UpperCAmelCase									=			torch.from_numpy(__SCREAMING_SNAKE_CASE    )

							_UpperCAmelCase					, _UpperCAmelCase									=			patch_size['height'], patch_size['width']
							_UpperCAmelCase					, _UpperCAmelCase									=			get_image_size(__SCREAMING_SNAKE_CASE    )

							# maximize scale s.t.
							_UpperCAmelCase									=			math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)    )
							_UpperCAmelCase									=			max(min(math.floor(scale * image_height / patch_height    )       ,    __SCREAMING_SNAKE_CASE    )       ,    1    )
							_UpperCAmelCase									=			max(min(math.floor(scale * image_width / patch_width    )       ,    __SCREAMING_SNAKE_CASE    )       ,    1    )
							_UpperCAmelCase									=			max(num_feasible_rows * patch_height       ,    1    )
							_UpperCAmelCase									=			max(num_feasible_cols * patch_width       ,    1    )

							_UpperCAmelCase									=			torch.nn.functional.interpolate(
							    image.unsqueeze(0    )       ,    size=(resized_height, resized_width)       ,    mode='bilinear'       ,    align_corners=__SCREAMING_SNAKE_CASE       ,    antialias=__SCREAMING_SNAKE_CASE       ,    ).squeeze(0    )

							# [1, rows, columns, patch_height * patch_width * image_channels]
							_UpperCAmelCase									=			torch_extract_patches(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE    )

							_UpperCAmelCase									=			patches.shape
							_UpperCAmelCase									=			patches_shape[1]
							_UpperCAmelCase									=			patches_shape[2]
							_UpperCAmelCase									=			patches_shape[3]

							# [rows * columns, patch_height * patch_width * image_channels]
							_UpperCAmelCase									=			patches.reshape([rows * columns, depth]    )

							# [rows * columns, 1]
							_UpperCAmelCase									=			torch.arange(__SCREAMING_SNAKE_CASE    ).reshape([rows, 1]    ).repeat(1       ,    __SCREAMING_SNAKE_CASE    ).reshape([rows * columns, 1]    )
							_UpperCAmelCase									=			torch.arange(__SCREAMING_SNAKE_CASE    ).reshape([1, columns]    ).repeat(__SCREAMING_SNAKE_CASE       ,    1    ).reshape([rows * columns, 1]    )

							# Offset by 1 so the ids do not contain zeros, which represent padding.
							row_ids += 1
							col_ids += 1

							# Prepare additional patch features.
							# [rows * columns, 1]
							_UpperCAmelCase									=			row_ids.to(torch.floataa    )
							_UpperCAmelCase									=			col_ids.to(torch.floataa    )

							# [rows * columns, 2 + patch_height * patch_width * image_channels]
							_UpperCAmelCase									=			torch.cat([row_ids, col_ids, patches]       ,    -1    )

							# [max_patches, 2 + patch_height * patch_width * image_channels]
							_UpperCAmelCase									=			torch.nn.functional.pad(__SCREAMING_SNAKE_CASE       ,    [0, 0, 0, max_patches - (rows * columns)]    ).float()

							_UpperCAmelCase									=			to_numpy_array(__SCREAMING_SNAKE_CASE    )

							return result

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							if image.dtype == np.uinta:
													_UpperCAmelCase									=			image.astype(np.floataa    )

							# take mean across the whole `image`
							_UpperCAmelCase									=			np.mean(__SCREAMING_SNAKE_CASE    )
							_UpperCAmelCase									=			np.std(__SCREAMING_SNAKE_CASE    )
							_UpperCAmelCase									=			max(__SCREAMING_SNAKE_CASE       ,    1.0 / math.sqrt(np.prod(image.shape    )    )    )

							return normalize(__SCREAMING_SNAKE_CASE       ,    mean=__SCREAMING_SNAKE_CASE       ,    std=__SCREAMING_SNAKE_CASE       ,    **__SCREAMING_SNAKE_CASE    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = ChannelDimension.FIRST       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			do_normalize if do_normalize is not None else self.do_normalize
							_UpperCAmelCase									=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							_UpperCAmelCase									=			patch_size if patch_size is not None else self.patch_size
							_UpperCAmelCase									=			max_patches if max_patches is not None else self.max_patches
							_UpperCAmelCase									=			self.is_vqa

							if kwargs.get('data_format'       ,    __SCREAMING_SNAKE_CASE    ) is not None:
													raise ValueError('data_format is not an accepted input as the outputs are '    )

							_UpperCAmelCase									=			make_list_of_images(__SCREAMING_SNAKE_CASE    )

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

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
													_UpperCAmelCase									=			[convert_to_rgb(__SCREAMING_SNAKE_CASE    ) for image in images]

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

							if is_vqa:
													if header_text is None:
																			raise ValueError('A header text must be provided for VQA models.'    )
													_UpperCAmelCase									=			kwargs.pop('font_bytes'       ,    __SCREAMING_SNAKE_CASE    )
													_UpperCAmelCase									=			kwargs.pop('font_path'       ,    __SCREAMING_SNAKE_CASE    )

													if isinstance(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE    ):
																			_UpperCAmelCase									=			[header_text] * len(__SCREAMING_SNAKE_CASE    )

													_UpperCAmelCase									=			[
													    render_header(__SCREAMING_SNAKE_CASE       ,    header_text[i]       ,    font_bytes=__SCREAMING_SNAKE_CASE       ,    font_path=__SCREAMING_SNAKE_CASE    )
													    for i, image in enumerate(__SCREAMING_SNAKE_CASE    )
													]

							if do_normalize:
													_UpperCAmelCase									=			[self.normalize(image=__SCREAMING_SNAKE_CASE    ) for image in images]

							# convert to torch tensor and permute
							_UpperCAmelCase									=			[
							    self.extract_flattened_patches(image=__SCREAMING_SNAKE_CASE       ,    max_patches=__SCREAMING_SNAKE_CASE       ,    patch_size=__SCREAMING_SNAKE_CASE    )
							    for image in images
							]

							# create attention mask in numpy
							_UpperCAmelCase									=			[(image.sum(axis=-1    ) != 0).astype(np.floataa    ) for image in images]

							_UpperCAmelCase									=			BatchFeature(
							    data={'flattened_patches': images, 'attention_mask': attention_masks}       ,    tensor_type=__SCREAMING_SNAKE_CASE    )

							return encoded_outputs





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__snake_case						:Optional[int]      		=     200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__snake_case						:List[str]      		=     50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__snake_case						:List[Any]      		=     0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				len([g for position, g in enumerate(_UpperCAmelCase				) if g == main_target[position]]				)
    return (item, float(_UpperCAmelCase				))





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				random.randint(0   , len(_UpperCAmelCase				) - 1				)
    __a						       =				parent_a[:random_slice] + parent_a[random_slice:]
    __a						       =				parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				list(_UpperCAmelCase				)
    if random.uniform(0   , 1				) < MUTATION_PROBABILITY:
        __a						       =				random.choice(_UpperCAmelCase				)
    return "".join(_UpperCAmelCase				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , ):
    __a						       =				[]
    # Generate more children proportionally to the fitness score.
    __a						       =				int(parent_a[1] * 100				) + 1
    __a						       =				10 if child_n >= 10 else child_n
    for _ in range(_UpperCAmelCase				):
        __a						       =				population_score[random.randint(0   , _UpperCAmelCase				)][0]

        __a       ,   __a						       =				crossover(parent_a[0]   , _UpperCAmelCase				)
        # Append new string to the population list.
        pop.append(mutate(_UpperCAmelCase   , _UpperCAmelCase				)				)
        pop.append(mutate(_UpperCAmelCase   , _UpperCAmelCase				)				)
    return pop





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase = True				):

    # Verify if N_POPULATION is bigger than N_SELECTED
    if N_POPULATION < N_SELECTED:
        __a						       =				f'{N_POPULATION} must be bigger than {N_SELECTED}'
        raise ValueError(_UpperCAmelCase				)
    # Verify that the target contains no genes besides the ones inside genes variable.
    __a						       =				sorted({c for c in target if c not in genes}				)
    if not_in_genes_list:
        __a						       =				f'{not_in_genes_list} is not in genes list, evolution cannot converge'
        raise ValueError(_UpperCAmelCase				)

    # Generate random starting population.
    __a						       =				[]
    for _ in range(_UpperCAmelCase				):
        population.append(''''''.join([random.choice(_UpperCAmelCase				) for i in range(len(_UpperCAmelCase				)				)]				)				)

    # Just some logs to know what the algorithms is doing.
    __a       ,   __a						       =				0, 0

    # This loop will end when we find a perfect match for our target.
    while True:
        generation += 1
        total_population += len(_UpperCAmelCase				)

        # Random population created. Now it's time to evaluate.

        # Adding a bit of concurrency can make everything faster,
        #
        # import concurrent.futures
        # population_score: list[tuple[str, float]] = []
        # with concurrent.futures.ThreadPoolExecutor(
        #                                   max_workers=NUM_WORKERS) as executor:
        #     futures = {executor.submit(evaluate, item) for item in population}
        #     concurrent.futures.wait(futures)
        #     population_score = [item.result() for item in futures]
        #
        # but with a simple algorithm like this, it will probably be slower.
        # We just need to call evaluate for every item inside the population.
        __a						       =				[evaluate(_UpperCAmelCase   , _UpperCAmelCase				) for item in population]

        # Check if there is a matching evolution.
        __a						       =				sorted(_UpperCAmelCase   , key=lambda _UpperCAmelCase				: x[1]   , reverse=_UpperCAmelCase				)
        if population_score[0][0] == target:
            return (generation, total_population, population_score[0][0])

        # Print the best result every 10 generation.
        # Just to know that the algorithm is working.
        if debug and generation % 10 == 0:
            print(
                f'\nGeneration: {generation}'
                f'\nTotal Population:{total_population}'
                f'\nBest score: {population_score[0][1]}'
                f'\nBest string: {population_score[0][0]}'				)

        # Flush the old population, keeping some of the best evolutions.
        # Keeping this avoid regression of evolution.
        __a						       =				population[: int(N_POPULATION / 3				)]
        population.clear()
        population.extend(_UpperCAmelCase				)
        # Normalize population score to be between 0 and 1.
        __a						       =				[
            (item, score / len(_UpperCAmelCase				)) for item, score in population_score
        ]

        # This is selection
        for i in range(_UpperCAmelCase				):
            population.extend(select(population_score[int(_UpperCAmelCase				)]   , _UpperCAmelCase   , _UpperCAmelCase				)				)
            # Check if the population has already reached the maximum value and if so,
            # break the cycle.  If this check is disabled, the algorithm will take
            # forever to compute large strings, but will also calculate small strings in
            # a far fewer generations.
            if len(_UpperCAmelCase				) > N_POPULATION:
                break


if __name__ == "__main__":
 __snake_case						:Optional[int]      		=     (
     '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
 )
 __snake_case						:List[Any]      		=     list(
     ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
     '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
 )
 __snake_case		,__snake_case		,__snake_case						:Dict      		=     basic(target_str, genes_list)
 print(
     f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
 )





