

'''simple docstring'''





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 DetrImageProcessor





class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def __init__(       self     :		Optional[int]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Optional[int]=7		,					UpperCAmelCase__     :		str=3		,					UpperCAmelCase__     :		str=30		,					UpperCAmelCase__     :		Tuple=400		,					UpperCAmelCase__     :		Tuple=True		,					UpperCAmelCase__     :		int=None		,					UpperCAmelCase__     :		Any=True		,					UpperCAmelCase__     :		Optional[int]=1 / 255		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		int=[0.5, 0.5, 0.5]		,					UpperCAmelCase__     :		Union[str, Any]=[0.5, 0.5, 0.5]		,					UpperCAmelCase__     :		Any=True		,					):
										'''simple docstring'''
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										lowercase :							Union[str, Any]    		=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
										lowercase :							List[Any]    		=parent
										lowercase :							List[str]    		=batch_size
										lowercase :							Any    		=num_channels
										lowercase :							Tuple    		=min_resolution
										lowercase :							Union[str, Any]    		=max_resolution
										lowercase :							Tuple    		=do_resize
										lowercase :							Optional[Any]    		=size
										lowercase :							List[Any]    		=do_rescale
										lowercase :							Union[str, Any]    		=rescale_factor
										lowercase :							Dict    		=do_normalize
										lowercase :							int    		=image_mean
										lowercase :							Any    		=image_std
										lowercase :							Union[str, Any]    		=do_pad







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_pad": self.do_pad,
										}







				def  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Any=False   ):
										'''simple docstring'''
										if not batched:
																lowercase :							List[Any]    		=image_inputs[0]
																if isinstance(UpperCAmelCase__		,					Image.Image   ):
																						lowercase		,	lowercase :							Dict    		=image.size
																else:
																						lowercase		,	lowercase :							Optional[int]    		=image.shape[1], image.shape[2]
																if w < h:
																						lowercase :							int    		=int(self.size['''shortest_edge'''] * h / w   )
																						lowercase :							Any    		=self.size['''shortest_edge''']
																elif w > h:
																						lowercase :							str    		=self.size['''shortest_edge''']
																						lowercase :							List[str]    		=int(self.size['''shortest_edge'''] * w / h   )
																else:
																						lowercase :							List[Any]    		=self.size['''shortest_edge''']
																						lowercase :							List[Any]    		=self.size['''shortest_edge''']

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

										return expected_height, expected_width





@require_torch
@require_vision
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    DetrImageProcessor if is_vision_available() else None







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							Any    		=DetrImageProcessingTester(self   )







				@property
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''image_mean'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''image_std'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_normalize'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_rescale'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''rescale_factor'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_resize'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''size'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_pad'''   )   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							str    		=self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size		,					{'''shortest_edge''': 18, '''longest_edge''': 1333}   )
										self.assertEqual(image_processor.do_pad		,					UpperCAmelCase__   )

										lowercase :							Optional[Any]    		=self.image_processing_class.from_dict(
										    self.image_processor_dict		,					size=42		,					max_size=84		,					pad_and_return_pixel_mask=UpperCAmelCase__   )
										self.assertEqual(image_processor.size		,					{'''shortest_edge''': 42, '''longest_edge''': 84}   )
										self.assertEqual(image_processor.do_pad		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							int    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowercase :							Any    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					Image.Image   )

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

										lowercase		,	lowercase :							List[Any]    		=self.image_processor_tester.get_expected_values(UpperCAmelCase__   )

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

										# Test batched
										lowercase		,	lowercase :							List[str]    		=self.image_processor_tester.get_expected_values(UpperCAmelCase__		,					batched=UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=image_processing(UpperCAmelCase__		,					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  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							str    		=self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowercase :							Tuple    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__		,					numpify=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					np.ndarray   )

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

										lowercase		,	lowercase :							Optional[int]    		=self.image_processor_tester.get_expected_values(UpperCAmelCase__   )

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

										# Test batched
										lowercase :							List[Any]    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values

										lowercase		,	lowercase :							Tuple    		=self.image_processor_tester.get_expected_values(UpperCAmelCase__		,					batched=UpperCAmelCase__   )

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







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							int    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowercase :							List[Any]    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__		,					torchify=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					torch.Tensor   )

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

										lowercase		,	lowercase :							Optional[Any]    		=self.image_processor_tester.get_expected_values(UpperCAmelCase__   )

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

										# Test batched
										lowercase :							Dict    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values

										lowercase		,	lowercase :							Optional[Any]    		=self.image_processor_tester.get_expected_values(UpperCAmelCase__		,					batched=UpperCAmelCase__   )

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







				@slow
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										# prepare image and target
										lowercase :							Union[str, Any]    		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
										with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		,					'''r'''   ) as f:
																lowercase :							str    		=json.loads(f.read()   )

										lowercase :							Dict    		={'''image_id''': 39769, '''annotations''': target}

										# encode them
										lowercase :							Union[str, Any]    		=DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''   )
										lowercase :							Optional[int]    		=image_processing(images=UpperCAmelCase__		,					annotations=UpperCAmelCase__		,					return_tensors='''pt'''   )

										# verify pixel values
										lowercase :							int    		=torch.Size([1, 3, 800, 1066]   )
										self.assertEqual(encoding['''pixel_values'''].shape		,					UpperCAmelCase__   )

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

										# verify area
										lowercase :							Any    		=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''']		,					UpperCAmelCase__   )   )
										# verify boxes
										lowercase :							Any    		=torch.Size([6, 4]   )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,					UpperCAmelCase__   )
										lowercase :							List[str]    		=torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,					UpperCAmelCase__		,					atol=1E-3   )   )
										# verify image_id
										lowercase :							int    		=torch.tensor([39769]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,					UpperCAmelCase__   )   )
										# verify is_crowd
										lowercase :							Optional[Any]    		=torch.tensor([0, 0, 0, 0, 0, 0]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,					UpperCAmelCase__   )   )
										# verify class_labels
										lowercase :							Union[str, Any]    		=torch.tensor([75, 75, 63, 65, 17, 17]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,					UpperCAmelCase__   )   )
										# verify orig_size
										lowercase :							Optional[int]    		=torch.tensor([480, 640]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,					UpperCAmelCase__   )   )
										# verify size
										lowercase :							Any    		=torch.tensor([800, 1066]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,					UpperCAmelCase__   )   )







				@slow
				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# prepare image, target and masks_path
										lowercase :							List[str]    		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
										with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''		,					'''r'''   ) as f:
																lowercase :							Tuple    		=json.loads(f.read()   )

										lowercase :							Dict    		={'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

										lowercase :							Any    		=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''   )

										# encode them
										lowercase :							Optional[Any]    		=DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''   )
										lowercase :							List[str]    		=image_processing(images=UpperCAmelCase__		,					annotations=UpperCAmelCase__		,					masks_path=UpperCAmelCase__		,					return_tensors='''pt'''   )

										# verify pixel values
										lowercase :							Union[str, Any]    		=torch.Size([1, 3, 800, 1066]   )
										self.assertEqual(encoding['''pixel_values'''].shape		,					UpperCAmelCase__   )

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

										# verify area
										lowercase :							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''']		,					UpperCAmelCase__   )   )
										# verify boxes
										lowercase :							int    		=torch.Size([6, 4]   )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,					UpperCAmelCase__   )
										lowercase :							int    		=torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,					UpperCAmelCase__		,					atol=1E-3   )   )
										# verify image_id
										lowercase :							List[Any]    		=torch.tensor([39769]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,					UpperCAmelCase__   )   )
										# verify is_crowd
										lowercase :							Optional[int]    		=torch.tensor([0, 0, 0, 0, 0, 0]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,					UpperCAmelCase__   )   )
										# verify class_labels
										lowercase :							Optional[Any]    		=torch.tensor([17, 17, 63, 75, 75, 93]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,					UpperCAmelCase__   )   )
										# verify masks
										lowercase :							str    		=822873
										self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		,					UpperCAmelCase__   )
										# verify orig_size
										lowercase :							Any    		=torch.tensor([480, 640]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,					UpperCAmelCase__   )   )
										# verify size
										lowercase :							str    		=torch.tensor([800, 1066]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,					UpperCAmelCase__   )   )








'''simple docstring'''





from __future__ import annotations

import random

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






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  tuple[str, float]:
						lowercase :							int    		=len([g for position, g in enumerate(__magic_name__						) if g == main_target[position]]						)
						return (item, float(__magic_name__						))






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  tuple[str, str]:
						lowercase :							Any    		=random.randint(0   , len(__magic_name__						) - 1						)
						lowercase :							Tuple    		=parent_a[:random_slice] + parent_a[random_slice:]
						lowercase :							List[str]    		=parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     list[str]						)				->  str:
						lowercase :							Union[str, Any]    		=list(__magic_name__						)
						if random.uniform(0   , 1						) < MUTATION_PROBABILITY:
												lowercase :							Dict    		=random.choice(__magic_name__						)
						return "".join(__magic_name__						)






def 				_lowerCAmelCase			(   __magic_name__						:     tuple[str, float]   , __magic_name__						:     list[tuple[str, float]]   , __magic_name__						:     list[str]   , )				->  list[str]:
						lowercase :							Any    		=[]
						# Generate more children proportionally to the fitness score.
						lowercase :							Dict    		=int(parent_a[1] * 100						) + 1
						lowercase :							List[str]    		=10 if child_n >= 10 else child_n
						for _ in range(__magic_name__						):
												lowercase :							List[str]    		=population_score[random.randint(0   , __magic_name__						)][0]

												lowercase		,	lowercase :							Dict    		=crossover(parent_a[0]   , __magic_name__						)
												# Append new string to the population list.
												pop.append(mutate(__magic_name__   , __magic_name__						)						)
												pop.append(mutate(__magic_name__   , __magic_name__						)						)
						return pop






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     list[str]   , __magic_name__						:     bool = True						)				->  tuple[int, int, str]:

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

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

						# Just some logs to know what the algorithms is doing.
						lowercase		,	lowercase :							Optional[int]    		=0, 0

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

												# 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.
												lowercase :							List[str]    		=[evaluate(__magic_name__   , __magic_name__						) for item in population]

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

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


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





