


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCAmelCase							=						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.
lowerCAmelCase							=						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.
lowerCAmelCase							=						0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    tuple[str, float]:




						'''simple docstring'''







						__UpperCAmelCase :       str  						=  len([g for position, g in enumerate(lowercase_			) if g == main_target[position]]			)
						return (item, float(lowercase_			))




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    tuple[str, str]:




						'''simple docstring'''







						__UpperCAmelCase :       List[str]  						=  random.randint(0			,					len(lowercase_			) - 1			)
						__UpperCAmelCase :       Optional[Any]  						=  parent_a[:random_slice] + parent_a[random_slice:]
						__UpperCAmelCase :       Optional[Any]  						=  parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







						__UpperCAmelCase :       List[str]  						=  list(lowercase_			)
						if random.uniform(0			,					1			) < MUTATION_PROBABILITY:
												__UpperCAmelCase :       Union[str, Any]  						=  random.choice(lowercase_			)
						return "".join(lowercase_			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					)		->    list[str]:




						'''simple docstring'''







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

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




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_ = True			)		->    tuple[int, int, str]:




						'''simple docstring'''







						if N_POPULATION < N_SELECTED:
												__UpperCAmelCase :       Optional[Any]  						=  f"{N_POPULATION} must be bigger than {N_SELECTED}"
												raise ValueError(lowercase_			)
						# 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 :       Dict  						=  f"{not_in_genes_list} is not in genes list, evolution cannot converge"
												raise ValueError(lowercase_			)

						# Generate random starting population.
						__UpperCAmelCase :       List[Any]  						=  []
						for _ in range(lowercase_			):
												population.append(''''''.join([random.choice(lowercase_			) for i in range(len(lowercase_			)			)]			)			)

						# Just some logs to know what the algorithms is doing.
						__UpperCAmelCase						,			__UpperCAmelCase :       str  						=  0, 0

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

												# 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(lowercase_			,					lowercase_			) for item in population]

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

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


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





import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor




class        lowerCamelCase      (     unittest.TestCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__=7							,    lowercase__=3							,    lowercase__=1_8							,    lowercase__=3_0							,    lowercase__=4_0_0							,    lowercase__=True							,    lowercase__=None							,    lowercase__=True							,    ):
							__UpperCAmelCase :       Union[str, Any]  						=  size if size is not None else {'''height''': 1_8, '''width''': 1_8}
							__UpperCAmelCase :       Any  						=  parent
							__UpperCAmelCase :       Dict  						=  batch_size
							__UpperCAmelCase :       List[str]  						=  num_channels
							__UpperCAmelCase :       int  						=  image_size
							__UpperCAmelCase :       Tuple  						=  min_resolution
							__UpperCAmelCase :       str  						=  max_resolution
							__UpperCAmelCase :       Optional[int]  						=  do_resize
							__UpperCAmelCase :       Tuple  						=  size
							__UpperCAmelCase :       Union[str, Any]  						=  do_normalize


	def    A(						self):
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
							            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
							        ]),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}






@require_torch
@require_vision
class        lowerCamelCase      (     _UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		Dict        =	ImageGPTImageProcessor if is_vision_available() else None


	def    A(						self):
							__UpperCAmelCase :       Optional[Any]  						=  ImageGPTImageProcessingTester(self)


	@property
	def    A(						self):
							return self.image_processor_tester.prepare_image_processor_dict()


	def    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class(**self.image_processor_dict)
							self.assertTrue(hasattr(lowercase__							,    '''clusters'''))
							self.assertTrue(hasattr(lowercase__							,    '''do_resize'''))
							self.assertTrue(hasattr(lowercase__							,    '''size'''))
							self.assertTrue(hasattr(lowercase__							,    '''do_normalize'''))


	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.image_processing_class.from_dict(self.image_processor_dict)
							self.assertEqual(image_processor.size							,    {'''height''': 1_8, '''width''': 1_8})

							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class.from_dict(self.image_processor_dict							,    size=4_2)
							self.assertEqual(image_processor.size							,    {'''height''': 4_2, '''width''': 4_2})


	def    A(						self):
							__UpperCAmelCase :       Any  						=  self.image_processing_class(**self.image_processor_dict)
							__UpperCAmelCase :       Any  						=  json.loads(image_processor.to_json_string())
							for key, value in self.image_processor_dict.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowercase__							,    obj[key]))
													else:
																			self.assertEqual(obj[key]							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.image_processing_class(**self.image_processor_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase :       Dict  						=  os.path.join(lowercase__							,    '''image_processor.json''')
													image_processor_first.to_json_file(lowercase__)
													__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class.from_json_file(lowercase__).to_dict()

							__UpperCAmelCase :       Any  						=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowercase__							,    image_processor_second[key]))
													else:
																			self.assertEqual(image_processor_first[key]							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class(**self.image_processor_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													image_processor_first.save_pretrained(lowercase__)
													__UpperCAmelCase :       Dict  						=  self.image_processing_class.from_pretrained(lowercase__).to_dict()

							__UpperCAmelCase :       Optional[Any]  						=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowercase__							,    image_processor_second[key]))
													else:
																			self.assertEqual(image_processor_first[key]							,    lowercase__)


	@unittest.skip('''ImageGPT requires clusters at initialization''')
	def    A(						self):
							pass






def 						__SCREAMING_SNAKE_CASE       (					)		->    int:




						'''simple docstring'''







						__UpperCAmelCase :       List[str]  						=  load_dataset('''hf-internal-testing/fixtures_image_utils'''			,					split='''test'''			)

						__UpperCAmelCase :       Optional[Any]  						=  Image.open(dataset[4]['''file''']			)
						__UpperCAmelCase :       Optional[int]  						=  Image.open(dataset[5]['''file''']			)

						__UpperCAmelCase :       int  						=  [imagea, imagea]

						return images





@require_vision
@require_torch
class        lowerCamelCase      (     unittest.TestCase   ):


	@slow
	def    A(						self):
							__UpperCAmelCase :       int  						=  ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small''')

							__UpperCAmelCase :       Any  						=  prepare_images()

							# test non-batched
							__UpperCAmelCase :       int  						=  image_processing(images[0]							,    return_tensors='''pt''')

							self.assertIsInstance(encoding.input_ids							,    torch.LongTensor)
							self.assertEqual(encoding.input_ids.shape							,    (1, 1_0_2_4))

							__UpperCAmelCase :       int  						=  [3_0_6, 1_9_1, 1_9_1]
							self.assertEqual(encoding.input_ids[0, :3].tolist()							,    lowercase__)

							# test batched
							__UpperCAmelCase :       int  						=  image_processing(lowercase__							,    return_tensors='''pt''')

							self.assertIsInstance(encoding.input_ids							,    torch.LongTensor)
							self.assertEqual(encoding.input_ids.shape							,    (2, 1_0_2_4))

							__UpperCAmelCase :       Any  						=  [3_0_3, 1_3, 1_3]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()							,    lowercase__)

