




'''simple docstring'''

from __future__ import annotations

import random

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


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   tuple[str, float]:

					'''simple docstring'''

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


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   tuple[str, str]:

					'''simple docstring'''

					snake_case_		     =							random.randint(0, len(__UpperCAmelCase      ) - 1      )
					snake_case_		     =							parent_a[:random_slice] + parent_a[random_slice:]
					snake_case_		     =							parent_a[:random_slice] + parent_a[random_slice:]
					return (child_a, child_a)


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   str:

					'''simple docstring'''

					snake_case_		     =							list(__UpperCAmelCase      )
					if random.uniform(0, 1      ) < MUTATION_PROBABILITY:
										snake_case_		     =							random.choice(__UpperCAmelCase      )
					return "".join(__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )						->   list[str]:

					'''simple docstring'''

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

										snake_case_     ,snake_case_		     =							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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = True      )						->   tuple[int, int, str]:

					'''simple docstring'''

					if N_POPULATION < N_SELECTED:
										snake_case_		     =							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.
					snake_case_		     =							sorted({c for c in target if c not in genes}      )
					if not_in_genes_list:
										snake_case_		     =							F"{not_in_genes_list} is not in genes list, evolution cannot converge"
										raise ValueError(__UpperCAmelCase      )

					# Generate random starting population.
					snake_case_		     =							[]
					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.
					snake_case_     ,snake_case_		     =							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.
										snake_case_		     =							[evaluate(__UpperCAmelCase, __UpperCAmelCase      ) for item in population]

										# Check if there is a matching evolution.
										snake_case_		     =							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.
										snake_case_		     =							population[: int(N_POPULATION / 3      )]
										population.clear()
										population.extend(__UpperCAmelCase      )
										# Normalize population score to be between 0 and 1.
										snake_case_		     =							[
										    (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__":
							a    :							Optional[int]										=    (
							    'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
							)
							a    :							str										=    list(
							    ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
							    'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
							)
							a						,a						,a    :							List[Any]										=    basic(target_str, genes_list)
							print(
							    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
							)







'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def 						__magic_name__       (      __UpperCAmelCase      )						->   str:

					'''simple docstring'''

					if "img_encoder.pos_embed" in name:
										snake_case_		     =							name.replace('''img_encoder.pos_embed''', '''vision_model.embeddings.position_embeddings'''      )
					if "img_encoder.patch_embed.proj" in name:
										snake_case_		     =							name.replace('''img_encoder.patch_embed.proj''', '''vision_model.embeddings.patch_embeddings.projection'''      )
					if "img_encoder.patch_embed.norm" in name:
										snake_case_		     =							name.replace('''img_encoder.patch_embed.norm''', '''vision_model.embeddings.layernorm'''      )
					if "img_encoder.layers" in name:
										snake_case_		     =							name.replace('''img_encoder.layers''', '''vision_model.encoder.stages'''      )
					if "blocks" in name and "res" not in name:
										snake_case_		     =							name.replace('''blocks''', '''layers'''      )
					if "attn" in name and "pre_assign" not in name:
										snake_case_		     =							name.replace('''attn''', '''self_attn'''      )
					if "proj" in name and "self_attn" in name and "text" not in name:
										snake_case_		     =							name.replace('''proj''', '''out_proj'''      )
					if "pre_assign_attn.attn.proj" in name:
										snake_case_		     =							name.replace('''pre_assign_attn.attn.proj''', '''pre_assign_attn.attn.out_proj'''      )
					if "norm1" in name:
										snake_case_		     =							name.replace('''norm1''', '''layer_norm1'''      )
					if "norm2" in name and "pre_assign" not in name:
										snake_case_		     =							name.replace('''norm2''', '''layer_norm2'''      )
					if "img_encoder.norm" in name:
										snake_case_		     =							name.replace('''img_encoder.norm''', '''vision_model.layernorm'''      )
					# text encoder
					if "text_encoder.token_embedding" in name:
										snake_case_		     =							name.replace('''text_encoder.token_embedding''', '''text_model.embeddings.token_embedding'''      )
					if "text_encoder.positional_embedding" in name:
										snake_case_		     =							name.replace('''text_encoder.positional_embedding''', '''text_model.embeddings.position_embedding.weight'''      )
					if "text_encoder.transformer.resblocks." in name:
										snake_case_		     =							name.replace('''text_encoder.transformer.resblocks.''', '''text_model.encoder.layers.'''      )
					if "ln_1" in name:
										snake_case_		     =							name.replace('''ln_1''', '''layer_norm1'''      )
					if "ln_2" in name:
										snake_case_		     =							name.replace('''ln_2''', '''layer_norm2'''      )
					if "c_fc" in name:
										snake_case_		     =							name.replace('''c_fc''', '''fc1'''      )
					if "c_proj" in name:
										snake_case_		     =							name.replace('''c_proj''', '''fc2'''      )
					if "text_encoder" in name:
										snake_case_		     =							name.replace('''text_encoder''', '''text_model'''      )
					if "ln_final" in name:
										snake_case_		     =							name.replace('''ln_final''', '''final_layer_norm'''      )
					# projection layers
					if "img_projector.linear_hidden." in name:
										snake_case_		     =							name.replace('''img_projector.linear_hidden.''', '''visual_projection.'''      )
					if "img_projector.linear_out." in name:
										snake_case_		     =							name.replace('''img_projector.linear_out.''', '''visual_projection.3.'''      )
					if "text_projector.linear_hidden" in name:
										snake_case_		     =							name.replace('''text_projector.linear_hidden''', '''text_projection'''      )
					if "text_projector.linear_out" in name:
										snake_case_		     =							name.replace('''text_projector.linear_out''', '''text_projection.3'''      )

					return name


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					for key in orig_state_dict.copy().keys():
										snake_case_		     =							orig_state_dict.pop(__UpperCAmelCase      )

										if "qkv" in key:
															# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															snake_case_		     =							key.split('''.'''      )
															snake_case_     ,snake_case_		     =							int(key_split[2]      ), int(key_split[4]      )
															snake_case_		     =							config.vision_config.hidden_size
															if "weight" in key:
																				snake_case_		     =							val[:dim, :]
																				snake_case_		     =							val[dim : dim * 2, :]
																				snake_case_		     =							val[-dim:, :]
															else:
																				snake_case_		     =							val[:dim]
																				snake_case_		     =							val[dim : dim * 2]
																				snake_case_		     =							val[-dim:]
										elif "in_proj" in key:
															# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															snake_case_		     =							key.split('''.'''      )
															snake_case_		     =							int(key_split[3]      )
															snake_case_		     =							config.text_config.hidden_size
															if "weight" in key:
																				snake_case_		     =							val[:dim, :]
																				snake_case_		     =							val[
																				    dim : dim * 2, :
																				]
																				snake_case_		     =							val[-dim:, :]
															else:
																				snake_case_		     =							val[:dim]
																				snake_case_		     =							val[dim : dim * 2]
																				snake_case_		     =							val[-dim:]
										else:
															snake_case_		     =							rename_key(__UpperCAmelCase      )
															# squeeze if necessary
															if (
															    "text_projection.0" in new_name
															    or "text_projection.3" in new_name
															    or "visual_projection.0" in new_name
															    or "visual_projection.3" in new_name
															):
																				snake_case_		     =							val.squeeze_()
															else:
																				snake_case_		     =							val

					return orig_state_dict


def 						__magic_name__       (      )						->   Union[str, Any]:

					'''simple docstring'''

					snake_case_		     =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					snake_case_		     =							Image.open(requests.get(__UpperCAmelCase, stream=__UpperCAmelCase      ).raw      )
					return im


@torch.no_grad()
def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase="groupvit-gcc-yfcc", __UpperCAmelCase=False      )						->   List[str]:

					'''simple docstring'''

					snake_case_		     =							GroupViTConfig()
					snake_case_		     =							GroupViTModel(__UpperCAmelCase      ).eval()

					snake_case_		     =							torch.load(__UpperCAmelCase, map_location='''cpu'''      )['''model''']
					snake_case_		     =							convert_state_dict(__UpperCAmelCase, __UpperCAmelCase      )
					snake_case_     ,snake_case_		     =							model.load_state_dict(__UpperCAmelCase, strict=__UpperCAmelCase      )
					assert missing_keys == ["text_model.embeddings.position_ids"]
					assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__UpperCAmelCase      ) == 0)

					# verify result
					snake_case_		     =							CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''      )
					snake_case_		     =							prepare_img()
					snake_case_		     =							processor(text=['''a photo of a cat''', '''a photo of a dog'''], images=__UpperCAmelCase, padding=__UpperCAmelCase, return_tensors='''pt'''      )

					with torch.no_grad():
										snake_case_		     =							model(**__UpperCAmelCase      )

					if model_name == "groupvit-gcc-yfcc":
										snake_case_		     =							torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]      )
					elif model_name == "groupvit-gcc-redcaps":
										snake_case_		     =							torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]      )
					else:
										raise ValueError(F"Model name {model_name} not supported."      )
					assert torch.allclose(outputs.logits_per_image, __UpperCAmelCase, atol=1e-3      )

					processor.save_pretrained(__UpperCAmelCase      )
					model.save_pretrained(__UpperCAmelCase      )
					print('''Successfully saved processor and model to''', __UpperCAmelCase      )

					if push_to_hub:
										print('''Pushing to the hub...'''      )
										processor.push_to_hub(__UpperCAmelCase, organization='''nielsr'''      )
										model.push_to_hub(__UpperCAmelCase, organization='''nielsr'''      )


if __name__ == "__main__":
							a    :							Optional[int]										=    argparse.ArgumentParser()
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
							)
							parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
							parser.add_argument(
							    '--model_name',
							    default='groupvit-gccy-fcc',
							    type=str,
							    help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
							)
							parser.add_argument(
							    '--push_to_hub',
							    action='store_true',
							    help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
							)
							a    :							str										=    parser.parse_args()

							convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

