import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Any			  =		SwinConfig()
    __UpperCamelCase     :Tuple			  =		swin_name.split('''_'''	)

    __UpperCamelCase     :Union[str, Any]			  =		name_split[1]
    __UpperCamelCase     :List[Any]			  =		int(name_split[4]	)
    __UpperCamelCase     :Tuple			  =		int(name_split[3][-1]	)

    if model_size == "tiny":
        __UpperCamelCase     :List[str]			  =		96
        __UpperCamelCase     :int			  =		(2, 2, 6, 2)
        __UpperCamelCase     :str			  =		(3, 6, 12, 24)
    elif model_size == "small":
        __UpperCamelCase     :str			  =		96
        __UpperCamelCase     :int			  =		(2, 2, 18, 2)
        __UpperCamelCase     :int			  =		(3, 6, 12, 24)
    elif model_size == "base":
        __UpperCamelCase     :List[Any]			  =		128
        __UpperCamelCase     :List[str]			  =		(2, 2, 18, 2)
        __UpperCamelCase     :List[str]			  =		(4, 8, 16, 32)
    else:
        __UpperCamelCase     :str			  =		192
        __UpperCamelCase     :Union[str, Any]			  =		(2, 2, 18, 2)
        __UpperCamelCase     :Optional[Any]			  =		(6, 12, 24, 48)

    if "in22k" in swin_name:
        __UpperCamelCase     :Dict			  =		21_841
    else:
        __UpperCamelCase     :List[str]			  =		1_000
        __UpperCamelCase     :str			  =		'''huggingface/label-files'''
        __UpperCamelCase     :Optional[int]			  =		'''imagenet-1k-id2label.json'''
        __UpperCamelCase     :str			  =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			repo_type='''dataset'''	)  ,			'''r'''	)	)
        __UpperCamelCase     :Dict			  =		{int(SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
        __UpperCamelCase     :Optional[Any]			  =		idalabel
        __UpperCamelCase     :Optional[int]			  =		{v: k for k, v in idalabel.items()}

    __UpperCamelCase     :Union[str, Any]			  =		img_size
    __UpperCamelCase     :Any			  =		num_classes
    __UpperCamelCase     :Union[str, Any]			  =		embed_dim
    __UpperCamelCase     :Optional[Any]			  =		depths
    __UpperCamelCase     :Any			  =		num_heads
    __UpperCamelCase     :str			  =		window_size

    return config



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

    if name == "norm.weight":
        __UpperCamelCase     :Optional[Any]			  =		'''layernorm.weight'''
    if name == "norm.bias":
        __UpperCamelCase     :Optional[Any]			  =		'''layernorm.bias'''

    if "head" in name:
        __UpperCamelCase     :Dict			  =		name.replace('''head'''  ,			'''classifier'''	)
    else:
        __UpperCamelCase     :Dict			  =		'''swin.''' + name

    return name



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for key in orig_state_dict.copy().keys():
        __UpperCamelCase     :Optional[int]			  =		orig_state_dict.pop(SCREAMING_SNAKE_CASE	)

        if "mask" in key:
            continue
        elif "qkv" in key:
            __UpperCamelCase     :Tuple			  =		key.split('''.'''	)
            __UpperCamelCase     :Union[str, Any]			  =		int(key_split[1]	)
            __UpperCamelCase     :str			  =		int(key_split[3]	)
            __UpperCamelCase     :Any			  =		model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

    return orig_state_dict



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :str			  =		timm.create_model(SCREAMING_SNAKE_CASE  ,			pretrained=SCREAMING_SNAKE_CASE	)
    timm_model.eval()

    __UpperCamelCase     :List[Any]			  =		get_swin_config(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :int			  =		SwinForImageClassification(SCREAMING_SNAKE_CASE	)
    model.eval()

    __UpperCamelCase     :Union[str, Any]			  =		convert_state_dict(timm_model.state_dict()  ,			SCREAMING_SNAKE_CASE	)
    model.load_state_dict(SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :Optional[Any]			  =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''

    __UpperCamelCase     :List[str]			  =		AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''  ,			'''-'''	)	)	)
    __UpperCamelCase     :Any			  =		Image.open(requests.get(SCREAMING_SNAKE_CASE  ,			stream=SCREAMING_SNAKE_CASE	).raw	)
    __UpperCamelCase     :Tuple			  =		image_processor(images=SCREAMING_SNAKE_CASE  ,			return_tensors='''pt'''	)

    __UpperCamelCase     :Any			  =		timm_model(inputs['''pixel_values''']	)
    __UpperCamelCase     :List[Any]			  =		model(**SCREAMING_SNAKE_CASE	).logits

    assert torch.allclose(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			atol=1e-3	)

    print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""	)
    model.save_pretrained(SCREAMING_SNAKE_CASE	)

    print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--swin_name''',
        default='''swin_tiny_patch4_window7_224''',
        type=str,
        help='''Name of the Swin timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    __lowercase							 =					parser.parse_args()
    convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






from __future__ import annotations

import random

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Any			  =		len([g for position, g in enumerate(SCREAMING_SNAKE_CASE	) if g == main_target[position]]	)
    return (item, float(SCREAMING_SNAKE_CASE	))



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		random.randint(0  ,			len(SCREAMING_SNAKE_CASE	) - 1	)
    __UpperCamelCase     :Tuple			  =		parent_a[:random_slice] + parent_a[random_slice:]
    __UpperCamelCase     :Union[str, Any]			  =		parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		list(SCREAMING_SNAKE_CASE	)
    if random.uniform(0  ,			1	) < MUTATION_PROBABILITY:
        __UpperCamelCase     :str			  =		random.choice(SCREAMING_SNAKE_CASE	)
    return "".join(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			):
    '''simple docstring'''







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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = True	):
    '''simple docstring'''







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

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

    # Just some logs to know what the algorithms is doing.
    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		0, 0

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

        # 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(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	) for item in population]

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

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


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





