
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			Any							)    ->   Optional[Any]:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							Dict											=SwinConfig(image_size=192							)

							if "base" in model_name:
														SCREAMING_SNAKE_CASE_	:							Any											=6
														SCREAMING_SNAKE_CASE_	:							Dict											=128
														SCREAMING_SNAKE_CASE_	:							List[Any]											=(2, 2, 18, 2)
														SCREAMING_SNAKE_CASE_	:							Optional[int]											=(4, 8, 16, 32)
							elif "large" in model_name:
														SCREAMING_SNAKE_CASE_	:							int											=12
														SCREAMING_SNAKE_CASE_	:							str											=192
														SCREAMING_SNAKE_CASE_	:							int											=(2, 2, 18, 2)
														SCREAMING_SNAKE_CASE_	:							Optional[int]											=(6, 12, 24, 48)
							else:
														raise ValueError('Model not supported, only supports base and large variants'							)

							SCREAMING_SNAKE_CASE_	:							List[str]											=window_size
							SCREAMING_SNAKE_CASE_	:							Dict											=embed_dim
							SCREAMING_SNAKE_CASE_	:							Optional[int]											=depths
							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=num_heads

							return config
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			int							)    ->   Dict:







							"""simple docstring"""






							if "encoder.mask_token" in name:
														SCREAMING_SNAKE_CASE_	:							Any											=name.replace('encoder.mask_token'       ,'embeddings.mask_token'							)
							if "encoder.patch_embed.proj" in name:
														SCREAMING_SNAKE_CASE_	:							Dict											=name.replace('encoder.patch_embed.proj'       ,'embeddings.patch_embeddings.projection'							)
							if "encoder.patch_embed.norm" in name:
														SCREAMING_SNAKE_CASE_	:							Dict											=name.replace('encoder.patch_embed.norm'       ,'embeddings.norm'							)
							if "attn.proj" in name:
														SCREAMING_SNAKE_CASE_	:							Any											=name.replace('attn.proj'       ,'attention.output.dense'							)
							if "attn" in name:
														SCREAMING_SNAKE_CASE_	:							List[str]											=name.replace('attn'       ,'attention.self'							)
							if "norm1" in name:
														SCREAMING_SNAKE_CASE_	:							Optional[Any]											=name.replace('norm1'       ,'layernorm_before'							)
							if "norm2" in name:
														SCREAMING_SNAKE_CASE_	:							Dict											=name.replace('norm2'       ,'layernorm_after'							)
							if "mlp.fc1" in name:
														SCREAMING_SNAKE_CASE_	:							List[Any]											=name.replace('mlp.fc1'       ,'intermediate.dense'							)
							if "mlp.fc2" in name:
														SCREAMING_SNAKE_CASE_	:							Optional[Any]											=name.replace('mlp.fc2'       ,'output.dense'							)

							if name == "encoder.norm.weight":
														SCREAMING_SNAKE_CASE_	:							List[str]											='''layernorm.weight'''
							if name == "encoder.norm.bias":
														SCREAMING_SNAKE_CASE_	:							List[str]											='''layernorm.bias'''

							if "decoder" in name:
														pass
							else:
														SCREAMING_SNAKE_CASE_	:							Any											='''swin.''' + name

							return name
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			Optional[int]       ,lowerCAmelCase_				:			Any							)    ->   Optional[int]:







							"""simple docstring"""






							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE_	:							str											=orig_state_dict.pop(lowerCAmelCase_							)

														if "attn_mask" in key:
																					pass
														elif "qkv" in key:
																					SCREAMING_SNAKE_CASE_	:							List[str]											=key.split('.'							)
																					SCREAMING_SNAKE_CASE_	:							Any											=int(key_split[2]							)
																					SCREAMING_SNAKE_CASE_	:							List[str]											=int(key_split[4]							)
																					SCREAMING_SNAKE_CASE_	:							int											=model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												SCREAMING_SNAKE_CASE_	:							Dict											=val[:dim, :]
																												SCREAMING_SNAKE_CASE_	:							Optional[int]											=val[
																												    dim : dim * 2, :
																												]
																												SCREAMING_SNAKE_CASE_	:							str											=val[-dim:, :]
																					else:
																												SCREAMING_SNAKE_CASE_	:							Any											=val[
																												    :dim
																												]
																												SCREAMING_SNAKE_CASE_	:							Optional[int]											=val[
																												    dim : dim * 2
																												]
																												SCREAMING_SNAKE_CASE_	:							Any											=val[
																												    -dim:
																												]
														else:
																					SCREAMING_SNAKE_CASE_	:							Any											=val

							return orig_state_dict
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			List[Any]       ,lowerCAmelCase_				:			Any       ,lowerCAmelCase_				:			Optional[int]       ,lowerCAmelCase_				:			int							)    ->   Tuple:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							Tuple											=torch.load(lowerCAmelCase_       ,map_location='cpu'							)['''model''']

							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=get_swin_config(lowerCAmelCase_							)
							SCREAMING_SNAKE_CASE_	:							int											=SwinForMaskedImageModeling(lowerCAmelCase_							)
							model.eval()

							SCREAMING_SNAKE_CASE_	:							List[Any]											=convert_state_dict(lowerCAmelCase_       ,lowerCAmelCase_							)
							model.load_state_dict(lowerCAmelCase_							)

							SCREAMING_SNAKE_CASE_	:							List[Any]											='''http://images.cocodataset.org/val2017/000000039769.jpg'''

							SCREAMING_SNAKE_CASE_	:							Optional[Any]											=ViTImageProcessor(size={'height': 192, 'width': 192}							)
							SCREAMING_SNAKE_CASE_	:							List[str]											=Image.open(requests.get(lowerCAmelCase_       ,stream=lowerCAmelCase_							).raw							)
							SCREAMING_SNAKE_CASE_	:							str											=image_processor(images=lowerCAmelCase_       ,return_tensors='pt'							)

							with torch.no_grad():
														SCREAMING_SNAKE_CASE_	:							List[str]											=model(**lowerCAmelCase_							).logits

							print(outputs.keys()							)
							print('Looks ok!'							)

							if pytorch_dump_folder_path is not None:
														print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""							)
														model.save_pretrained(lowerCAmelCase_							)

														print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
														image_processor.save_pretrained(lowerCAmelCase_							)

							if push_to_hub:
														print(F"""Pushing model and image processor for {model_name} to hub"""							)
														model.push_to_hub(F"""microsoft/{model_name}"""							)
														image_processor.push_to_hub(F"""microsoft/{model_name}"""							)


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE						       =						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='swin-base-simmim-window6-192',
							    type=str,
							    choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
							    help='Name of the Swin SimMIM model you\'d like to convert.',
							)
							parser.add_argument(
							    '--checkpoint_path',
							    default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
							    type=str,
							    help='Path to the original PyTorch checkpoint (.pth file).',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)

							__SCREAMING_SNAKE_CASE						       =						parser.parse_args()
							convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








'''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}'''
							)





