import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ConditionalDetrImageProcessor


class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase=7 , __lowercase=3 , __lowercase=30 , __lowercase=400 , __lowercase=True , __lowercase=None , __lowercase=True , __lowercase=[0.5, 0.5, 0.5] , __lowercase=[0.5, 0.5, 0.5] , __lowercase=True , __lowercase=1 / 255 , __lowercase=True , )       ->	List[str]:
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        __UpperCamelCase     :Any			  =		size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
        __UpperCamelCase     :List[Any]			  =		parent
        __UpperCamelCase     :Tuple			  =		batch_size
        __UpperCamelCase     :int			  =		num_channels
        __UpperCamelCase     :Union[str, Any]			  =		min_resolution
        __UpperCamelCase     :Any			  =		max_resolution
        __UpperCamelCase     :Tuple			  =		do_resize
        __UpperCamelCase     :int			  =		size
        __UpperCamelCase     :int			  =		do_normalize
        __UpperCamelCase     :Dict			  =		image_mean
        __UpperCamelCase     :List[Any]			  =		image_std
        __UpperCamelCase     :Any			  =		do_rescale
        __UpperCamelCase     :List[Any]			  =		rescale_factor
        __UpperCamelCase     :List[Any]			  =		do_pad







    def 			UpperCamelCase__   (	self)       ->	str:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
        }







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=False)       ->	Tuple:
        if not batched:
            __UpperCamelCase     :Optional[int]			  =		image_inputs[0]
            if isinstance(__lowercase , Image.Image):
                __UpperCamelCase  ,       __UpperCamelCase     :int			  =		image.size
            else:
                __UpperCamelCase  ,       __UpperCamelCase     :str			  =		image.shape[1], image.shape[2]
            if w < h:
                __UpperCamelCase     :List[str]			  =		int(self.size['''shortest_edge'''] * h / w)
                __UpperCamelCase     :str			  =		self.size['''shortest_edge''']
            elif w > h:
                __UpperCamelCase     :List[Any]			  =		self.size['''shortest_edge''']
                __UpperCamelCase     :Optional[int]			  =		int(self.size['''shortest_edge'''] * w / h)
            else:
                __UpperCamelCase     :Union[str, Any]			  =		self.size['''shortest_edge''']
                __UpperCamelCase     :Union[str, Any]			  =		self.size['''shortest_edge''']

        else:
            __UpperCamelCase     :str			  =		[]
            for image in image_inputs:
                __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		self.get_expected_values([image])
                expected_values.append((expected_height, expected_width))
            __UpperCamelCase     :Tuple			  =		max(__lowercase , key=lambda __lowercase: item[0])[0]
            __UpperCamelCase     :List[Any]			  =		max(__lowercase , key=lambda __lowercase: item[1])[1]

        return expected_height, expected_width


@require_torch
@require_vision
class 	lowerCamelCase_     (   UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						Tuple						=      ConditionalDetrImageProcessor if is_vision_available() else None







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Optional[Any]			  =		ConditionalDetrImageProcessingTester(self)







    @property
    def 			UpperCamelCase__   (	self)       ->	str:
        return self.image_processor_tester.prepare_image_processor_dict()







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :Any			  =		self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(__lowercase , '''image_mean'''))
        self.assertTrue(hasattr(__lowercase , '''image_std'''))
        self.assertTrue(hasattr(__lowercase , '''do_normalize'''))
        self.assertTrue(hasattr(__lowercase , '''do_resize'''))
        self.assertTrue(hasattr(__lowercase , '''size'''))







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        __UpperCamelCase     :int			  =		self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1_333})
        self.assertEqual(image_processor.do_pad , __lowercase)

        __UpperCamelCase     :str			  =		self.image_processing_class.from_dict(
            self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__lowercase)
        self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84})
        self.assertEqual(image_processor.do_pad , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	str:
        pass







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        # Initialize image_processing
        __UpperCamelCase     :Optional[int]			  =		self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        __UpperCamelCase     :Optional[Any]			  =		prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase , Image.Image)

        # Test not batched input
        __UpperCamelCase     :Optional[int]			  =		image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values

        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		self.image_processor_tester.get_expected_values(__lowercase)

        self.assertEqual(
            encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

        # Test batched
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		self.image_processor_tester.get_expected_values(__lowercase , batched=__lowercase)

        __UpperCamelCase     :List[str]			  =		image_processing(__lowercase , return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ) , )







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        # Initialize image_processing
        __UpperCamelCase     :str			  =		self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        __UpperCamelCase     :List[Any]			  =		prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , numpify=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase , np.ndarray)

        # Test not batched input
        __UpperCamelCase     :Union[str, Any]			  =		image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values

        __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		self.image_processor_tester.get_expected_values(__lowercase)

        self.assertEqual(
            encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

        # Test batched
        __UpperCamelCase     :Dict			  =		image_processing(__lowercase , return_tensors='''pt''').pixel_values

        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.image_processor_tester.get_expected_values(__lowercase , batched=__lowercase)

        self.assertEqual(
            encoded_images.shape , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ) , )







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        # Initialize image_processing
        __UpperCamelCase     :Optional[Any]			  =		self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        __UpperCamelCase     :Union[str, Any]			  =		prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , torchify=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase , torch.Tensor)

        # Test not batched input
        __UpperCamelCase     :Optional[Any]			  =		image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values

        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		self.image_processor_tester.get_expected_values(__lowercase)

        self.assertEqual(
            encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

        # Test batched
        __UpperCamelCase     :Tuple			  =		image_processing(__lowercase , return_tensors='''pt''').pixel_values

        __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		self.image_processor_tester.get_expected_values(__lowercase , batched=__lowercase)

        self.assertEqual(
            encoded_images.shape , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ) , )







    @slow
    def 			UpperCamelCase__   (	self)       ->	Any:
        # prepare image and target
        __UpperCamelCase     :Optional[Any]			  =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
        with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''') as f:
            __UpperCamelCase     :int			  =		json.loads(f.read())

        __UpperCamelCase     :int			  =		{'''image_id''': 39_769, '''annotations''': target}

        # encode them
        __UpperCamelCase     :int			  =		ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''')
        __UpperCamelCase     :Optional[int]			  =		image_processing(images=__lowercase , annotations=__lowercase , return_tensors='''pt''')

        # verify pixel values
        __UpperCamelCase     :Union[str, Any]			  =		torch.Size([1, 3, 800, 1_066])
        self.assertEqual(encoding['''pixel_values'''].shape , __lowercase)

        __UpperCamelCase     :Optional[Any]			  =		torch.tensor([0.27_96, 0.31_38, 0.34_81])
        self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowercase , atol=1E-4))

        # verify area
        __UpperCamelCase     :str			  =		torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowercase))
        # verify boxes
        __UpperCamelCase     :str			  =		torch.Size([6, 4])
        self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowercase)
        __UpperCamelCase     :Dict			  =		torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowercase , atol=1E-3))
        # verify image_id
        __UpperCamelCase     :List[Any]			  =		torch.tensor([39_769])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowercase))
        # verify is_crowd
        __UpperCamelCase     :Optional[int]			  =		torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowercase))
        # verify class_labels
        __UpperCamelCase     :Tuple			  =		torch.tensor([75, 75, 63, 65, 17, 17])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowercase))
        # verify orig_size
        __UpperCamelCase     :Union[str, Any]			  =		torch.tensor([480, 640])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowercase))
        # verify size
        __UpperCamelCase     :Dict			  =		torch.tensor([800, 1_066])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowercase))







    @slow
    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        # prepare image, target and masks_path
        __UpperCamelCase     :Any			  =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
        with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''') as f:
            __UpperCamelCase     :str			  =		json.loads(f.read())

        __UpperCamelCase     :Dict			  =		{'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

        __UpperCamelCase     :Tuple			  =		pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

        # encode them
        __UpperCamelCase     :Tuple			  =		ConditionalDetrImageProcessor(format='''coco_panoptic''')
        __UpperCamelCase     :Union[str, Any]			  =		image_processing(images=__lowercase , annotations=__lowercase , masks_path=__lowercase , return_tensors='''pt''')

        # verify pixel values
        __UpperCamelCase     :int			  =		torch.Size([1, 3, 800, 1_066])
        self.assertEqual(encoding['''pixel_values'''].shape , __lowercase)

        __UpperCamelCase     :Dict			  =		torch.tensor([0.27_96, 0.31_38, 0.34_81])
        self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowercase , atol=1E-4))

        # verify area
        __UpperCamelCase     :Optional[Any]			  =		torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowercase))
        # verify boxes
        __UpperCamelCase     :Any			  =		torch.Size([6, 4])
        self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowercase)
        __UpperCamelCase     :Dict			  =		torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowercase , atol=1E-3))
        # verify image_id
        __UpperCamelCase     :List[Any]			  =		torch.tensor([39_769])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowercase))
        # verify is_crowd
        __UpperCamelCase     :Optional[Any]			  =		torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowercase))
        # verify class_labels
        __UpperCamelCase     :List[Any]			  =		torch.tensor([17, 17, 63, 75, 75, 93])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowercase))
        # verify masks
        __UpperCamelCase     :Dict			  =		822_873
        self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , __lowercase)
        # verify orig_size
        __UpperCamelCase     :str			  =		torch.tensor([480, 640])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowercase))
        # verify size
        __UpperCamelCase     :Any			  =		torch.tensor([800, 1_066])
        self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowercase))






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowercase							 =					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.
__lowercase							 =					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.
__lowercase							 =					0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Any			  =		len([g for position, g in enumerate(SCREAMING_SNAKE_CASE	) if g == main_target[position]]	)
    return (item, float(SCREAMING_SNAKE_CASE	))



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		random.randint(0  ,			len(SCREAMING_SNAKE_CASE	) - 1	)
    __UpperCamelCase     :Tuple			  =		parent_a[:random_slice] + parent_a[random_slice:]
    __UpperCamelCase     :Union[str, Any]			  =		parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		list(SCREAMING_SNAKE_CASE	)
    if random.uniform(0  ,			1	) < MUTATION_PROBABILITY:
        __UpperCamelCase     :str			  =		random.choice(SCREAMING_SNAKE_CASE	)
    return "".join(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		[]
    # Generate more children proportionally to the fitness score.
    __UpperCamelCase     :int			  =		int(parent_a[1] * 100	) + 1
    __UpperCamelCase     :List[str]			  =		10 if child_n >= 10 else child_n
    for _ in range(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		population_score[random.randint(0  ,			SCREAMING_SNAKE_CASE	)][0]

        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		crossover(parent_a[0]  ,			SCREAMING_SNAKE_CASE	)
        # Append new string to the population list.
        pop.append(mutate(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)	)
        pop.append(mutate(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)	)
    return pop



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = True	):
    '''simple docstring'''







    if N_POPULATION < N_SELECTED:
        __UpperCamelCase     :List[Any]			  =		f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
        raise ValueError(SCREAMING_SNAKE_CASE	)
    # Verify that the target contains no genes besides the ones inside genes variable.
    __UpperCamelCase     :List[str]			  =		sorted({c for c in target if c not in genes}	)
    if not_in_genes_list:
        __UpperCamelCase     :Optional[int]			  =		f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
        raise ValueError(SCREAMING_SNAKE_CASE	)

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

    # Just some logs to know what the algorithms is doing.
    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		0, 0

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

        # 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.
        __UpperCamelCase     :Tuple			  =		[evaluate(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	) for item in population]

        # Check if there is a matching evolution.
        __UpperCamelCase     :Tuple			  =		sorted(SCREAMING_SNAKE_CASE  ,			key=lambda SCREAMING_SNAKE_CASE	: x[1]  ,			reverse=SCREAMING_SNAKE_CASE	)
        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.
        __UpperCamelCase     :str			  =		population[: int(N_POPULATION / 3	)]
        population.clear()
        population.extend(SCREAMING_SNAKE_CASE	)
        # Normalize population score to be between 0 and 1.
        __UpperCamelCase     :Union[str, Any]			  =		[
            (item, score / len(SCREAMING_SNAKE_CASE	)) for item, score in population_score
        ]

        # This is selection
        for i in range(SCREAMING_SNAKE_CASE	):
            population.extend(select(population_score[int(SCREAMING_SNAKE_CASE	)]  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)	)
            # 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(SCREAMING_SNAKE_CASE	) > N_POPULATION:
                break


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





