
"""simple docstring"""
from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_torch_available():
			import torch

			from transformers.modeling_outputs import BaseModelOutput

			from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
@add_end_docstrings(A__ )
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				**snake_case_				)       ->  Optional[Any]:
													super().__init__(**snake_case_				)

													if self.framework == "tf":
																			raise ValueError(f"""The {self.__class__} is only available in PyTorch."""				)

													requires_backends(self    ,				"""vision"""				)
													self.check_model_type(snake_case_				)




							def __call__( self    ,				snake_case_    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  Tuple:
													if "text_queries" in kwargs:
																			__lowerCAmelCase     =  kwargs.pop("""text_queries"""				)

													if isinstance(snake_case_    ,				(str, Image.Image)				):
																			__lowerCAmelCase     =  {"""image""": image, """candidate_labels""": candidate_labels}
													else:
																			__lowerCAmelCase     =  image
													__lowerCAmelCase     =  super().__call__(snake_case_    ,				**snake_case_				)
													return results




							def    A__						( self    ,				**snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  {}
													if "threshold" in kwargs:
																			__lowerCAmelCase     =  kwargs["""threshold"""]
													if "top_k" in kwargs:
																			__lowerCAmelCase     =  kwargs["""top_k"""]
													return {}, {}, postprocess_params




							def    A__						( self    ,				snake_case_				)       ->  Any:
													__lowerCAmelCase     =  load_image(inputs["""image"""]				)
													__lowerCAmelCase     =  inputs["""candidate_labels"""]
													if isinstance(snake_case_    ,				snake_case_				):
																			__lowerCAmelCase     =  candidate_labels.split(""","""				)

													__lowerCAmelCase     =  torch.tensor([[image.height, image.width]]    ,				dtype=torch.intaa				)
													for i, candidate_label in enumerate(snake_case_				):
																			__lowerCAmelCase     =  self.tokenizer(snake_case_    ,				return_tensors=self.framework				)
																			__lowerCAmelCase     =  self.image_processor(snake_case_    ,				return_tensors=self.framework				)
																			yield {
																			    "is_last": i == len(snake_case_				) - 1,
																			    "target_size": target_size,
																			    "candidate_label": candidate_label,
																			    **text_inputs,
																			    **image_features,
																			}




							def    A__						( self    ,				snake_case_				)       ->  List[Any]:
													__lowerCAmelCase     =  model_inputs.pop("""target_size"""				)
													__lowerCAmelCase     =  model_inputs.pop("""candidate_label"""				)
													__lowerCAmelCase     =  model_inputs.pop("""is_last"""				)

													__lowerCAmelCase     =  self.model(**snake_case_				)

													__lowerCAmelCase     =  {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
													return model_outputs




							def    A__						( self    ,				snake_case_    ,				snake_case_=0.1    ,				snake_case_=None				)       ->  Tuple:
													__lowerCAmelCase     =  []
													for model_output in model_outputs:
																			__lowerCAmelCase     =  model_output["""candidate_label"""]
																			__lowerCAmelCase     =  BaseModelOutput(snake_case_				)
																			__lowerCAmelCase     =  self.image_processor.post_process_object_detection(
																			    outputs=snake_case_    ,				threshold=snake_case_    ,				target_sizes=model_output["""target_size"""]				)[0]

																			for index in outputs["scores"].nonzero():
																									__lowerCAmelCase     =  outputs["""scores"""][index].item()
																									__lowerCAmelCase     =  self._get_bounding_box(outputs["""boxes"""][index][0]				)

																									__lowerCAmelCase     =  {"""score""": score, """label""": label, """box""": box}
																									results.append(snake_case_				)

													__lowerCAmelCase     =  sorted(snake_case_    ,				key=lambda snake_case_				: x["score"]    ,				reverse=snake_case_				)
													if top_k:
																			__lowerCAmelCase     =  results[:top_k]

													return results




							def    A__						( self    ,				snake_case_				)       ->  Dict[str, int]:
													if self.framework != "pt":
																			raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""				)
													__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  box.int().tolist()
													__lowerCAmelCase     =  {
													    """xmin""": xmin,
													    """ymin""": ymin,
													    """xmax""": xmax,
													    """ymax""": ymax,
													}
													return bbox


"""simple docstring"""
from __future__ import annotations

import random

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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  len([g for position, g in enumerate(_lowerCAmelCase						) if g == main_target[position]]						)
						return (item, float(_lowerCAmelCase						))

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  random.randint(0 ,    len(_lowerCAmelCase						) - 1						)
						__lowerCAmelCase     =  parent_a[:random_slice] + parent_a[random_slice:]
						__lowerCAmelCase     =  parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  list(_lowerCAmelCase						)
						if random.uniform(0 ,    1						) < MUTATION_PROBABILITY:
												__lowerCAmelCase     =  random.choice(_lowerCAmelCase						)
						return "".join(_lowerCAmelCase						)

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

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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = True						):

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

						# Generate random starting population.
						__lowerCAmelCase     =  []
						for _ in range(_lowerCAmelCase						):
												population.append("""""".join([random.choice(_lowerCAmelCase						) for i in range(len(_lowerCAmelCase						)						)]						)						)

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

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

												# 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.
												__lowerCAmelCase     =  [evaluate(_lowerCAmelCase ,    _lowerCAmelCase						) for item in population]

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

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


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