
'''simple docstring'''







import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowercase    :							Optional[int]      =					logging.get_logger(__name__)







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[int]:
	# initialize config
	if "resnet-50" in model_name:
		_snake_case   						=		ResNetConfig.from_pretrained('microsoft/resnet-50'	)
	elif "resnet-101" in model_name:
		_snake_case   						=		ResNetConfig.from_pretrained('microsoft/resnet-101'	)
	else:
		raise ValueError('Model name should include either resnet50 or resnet101'	)

	_snake_case   						=		DetrConfig(use_timm_backbone=__A			,				backbone_config=__A	)

	# set label attributes
	_snake_case   						=		'panoptic' in model_name
	if is_panoptic:
		_snake_case   						=		250
	else:
		_snake_case   						=		91
		_snake_case   						=		'huggingface/label-files'
		_snake_case   						=		'coco-detection-id2label.json'
		_snake_case   						=		json.load(open(hf_hub_download(__A			,				__A			,				repo_type='dataset'	)			,				'r'	)	)
		_snake_case   						=		{int(__A	): v for k, v in idalabel.items()}
		_snake_case   						=		idalabel
		_snake_case   						=		{v: k for k, v in idalabel.items()}

	return config, is_panoptic







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Tuple:
	# here we list all keys to be renamed (original name on the left, our name on the right)
	_snake_case   						=		[]

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')	)
	rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')	)
	rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')	)
	rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')	)
	rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')	)
	# stages
	for stage_idx in range(len(config.backbone_config.depths	)	):
		for layer_idx in range(config.backbone_config.depths[stage_idx]	):
			# shortcut
			if layer_idx == 0:
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
				    )	)
			# 3 convs
			for i in range(3	):
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
				    )	)
				rename_keys.append(
				    (
				        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
				        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
				    )	)
    # fmt: on

	for i in range(config.encoder_layers	):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (
		        F'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
		        F'encoder.layers.{i}.self_attn.out_proj.weight',
		    )	)
		rename_keys.append(
		    (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')	)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight')	)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias')	)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight')	)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias')	)
		rename_keys.append(
		    (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')	)
		rename_keys.append(
		    (F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias')	)
		rename_keys.append(
		    (F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight')	)
		rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias')	)
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (
		        F'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
		        F'decoder.layers.{i}.self_attn.out_proj.weight',
		    )	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')	)
		rename_keys.append(
		    (
		        F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
		        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
		    )	)
		rename_keys.append(
		    (
		        F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
		        F'decoder.layers.{i}.encoder_attn.out_proj.bias',
		    )	)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight')	)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias')	)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight')	)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias')	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias')	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')	)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight')	)
		rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias')	)

	# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
	rename_keys.extend(
	    [
	        ('input_proj.weight', 'input_projection.weight'),
	        ('input_proj.bias', 'input_projection.bias'),
	        ('query_embed.weight', 'query_position_embeddings.weight'),
	        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
	        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
	        ('class_embed.weight', 'class_labels_classifier.weight'),
	        ('class_embed.bias', 'class_labels_classifier.bias'),
	        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
	        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
	        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
	        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
	        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
	        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
	    ]	)

	return rename_keys







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Union[str, Any]:
	_snake_case   						=		state_dict.pop(__A	)
	_snake_case   						=		val







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=False	)       ->	int:
	_snake_case   						=		''
	if is_panoptic:
		_snake_case   						=		'detr.'

	# first: transformer encoder
	for i in range(6	):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		_snake_case   						=		state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'	)
		_snake_case   						=		state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'	)
		# next, add query, keys and values (in that order) to the state dict
		_snake_case   						=		in_proj_weight[:256, :]
		_snake_case   						=		in_proj_bias[:256]
		_snake_case   						=		in_proj_weight[256:512, :]
		_snake_case   						=		in_proj_bias[256:512]
		_snake_case   						=		in_proj_weight[-256:, :]
		_snake_case   						=		in_proj_bias[-256:]
	# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
	for i in range(6	):
		# read in weights + bias of input projection layer of self-attention
		_snake_case   						=		state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'	)
		_snake_case   						=		state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'	)
		# next, add query, keys and values (in that order) to the state dict
		_snake_case   						=		in_proj_weight[:256, :]
		_snake_case   						=		in_proj_bias[:256]
		_snake_case   						=		in_proj_weight[256:512, :]
		_snake_case   						=		in_proj_bias[256:512]
		_snake_case   						=		in_proj_weight[-256:, :]
		_snake_case   						=		in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		_snake_case   						=		state_dict.pop(
		    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'	)
		_snake_case   						=		state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'	)
		# next, add query, keys and values (in that order) of cross-attention to the state dict
		_snake_case   						=		in_proj_weight_cross_attn[:256, :]
		_snake_case   						=		in_proj_bias_cross_attn[:256]
		_snake_case   						=		in_proj_weight_cross_attn[256:512, :]
		_snake_case   						=		in_proj_bias_cross_attn[256:512]
		_snake_case   						=		in_proj_weight_cross_attn[-256:, :]
		_snake_case   						=		in_proj_bias_cross_attn[-256:]







def 				SCREAMING_SNAKE_CASE__	(   )       ->	Optional[int]:
	_snake_case   						=		'http://images.cocodataset.org/val2017/000000039769.jpg'
	_snake_case   						=		Image.open(requests.get(__A			,				stream=__A	).raw	)

	return im







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=None			,				__A=False	)       ->	Optional[int]:
	_snake_case						, _snake_case   						=		get_detr_config(__A	)

	# load original model from torch hub
	_snake_case   						=		{
	    'detr-resnet-50': 'detr_resnet50',
	    'detr-resnet-101': 'detr_resnet101',
	}
	logger.info(F'Converting model {model_name}...'	)
	_snake_case   						=		torch.hub.load('facebookresearch/detr'			,				model_name_to_original_name[model_name]			,				pretrained=__A	).eval()
	_snake_case   						=		detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(__A	):
		if is_panoptic:
			_snake_case   						=		'detr.' + src
		rename_key(__A			,				__A			,				__A	)
	# query, key and value matrices need special treatment
	read_in_q_k_v(__A			,				is_panoptic=__A	)
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	_snake_case   						=		'detr.model.' if is_panoptic else 'model.'
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith('detr'	)
			    and not key.startswith('class_labels_classifier'	)
			    and not key.startswith('bbox_predictor'	)
			):
				_snake_case   						=		state_dict.pop(__A	)
				_snake_case   						=		val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				_snake_case   						=		state_dict.pop(__A	)
				_snake_case   						=		val
			elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
				continue
			else:
				_snake_case   						=		state_dict.pop(__A	)
				_snake_case   						=		val
		else:
			if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
				_snake_case   						=		state_dict.pop(__A	)
				_snake_case   						=		val

    # finally, create HuggingFace model and load state dict
	_snake_case   						=		DetrForSegmentation(__A	) if is_panoptic else DetrForObjectDetection(__A	)
	model.load_state_dict(__A	)
	model.eval()

	# verify our conversion on an image
	_snake_case   						=		'coco_panoptic' if is_panoptic else 'coco_detection'
	_snake_case   						=		DetrImageProcessor(format=__A	)

	_snake_case   						=		processor(images=prepare_img()			,				return_tensors='pt'	)
	_snake_case   						=		encoding['pixel_values']

	_snake_case   						=		detr(__A	)
	_snake_case   						=		model(__A	)

	assert torch.allclose(outputs.logits			,				original_outputs['pred_logits']			,				atol=1e-3	)
	assert torch.allclose(outputs.pred_boxes			,				original_outputs['pred_boxes']			,				atol=1e-3	)
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks			,				original_outputs['pred_masks']			,				atol=1e-4	)
	print('Looks ok!'	)

	if pytorch_dump_folder_path is not None:
		# Save model and image processor
		logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'	)
		Path(__A	).mkdir(exist_ok=__A	)
		model.save_pretrained(__A	)
		processor.save_pretrained(__A	)

	if push_to_hub:
		# Upload model and image processor to the hub
		logger.info('Uploading PyTorch model and image processor to the hub...'	)
		model.push_to_hub(F'nielsr/{model_name}'	)
		processor.push_to_hub(F'nielsr/{model_name}'	)


if __name__ == "__main__":
					lowercase    :							Optional[int]      =					argparse.ArgumentParser()

					parser.add_argument(
					    "--model_name",
					    default="detr-resnet-50",
					    type=str,
					    choices=["detr-resnet-50", "detr-resnet-101"],
					    help="Name of the DETR model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
					)
					parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
					lowercase    :							Any      =					parser.parse_args()
					convert_detr_checkpoint(args.model_name, 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.
lowercase    :							Dict      =					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    :							Optional[int]      =					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    :							Optional[Any]      =					0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[str, float]:
	_snake_case   						=		len([g for position, g in enumerate(__A	) if g == main_target[position]]	)
	return (item, float(__A	))







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[str, str]:
	_snake_case   						=		random.randint(0			,				len(__A	) - 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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	_snake_case   						=		list(__A	)
	if random.uniform(0			,				1	) < MUTATION_PROBABILITY:
		_snake_case   						=		random.choice(__A	)
	return "".join(__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				)       ->	list[str]:
	_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(__A	):
		_snake_case   						=		population_score[random.randint(0			,				__A	)][0]

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A = True	)       ->	tuple[int, int, str]:

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		_snake_case   						=		F'{N_POPULATION} must be bigger than {N_SELECTED}'
		raise ValueError(__A	)
	# 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(__A	)

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

	# 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(__A	)

		# 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(__A			,				__A	) for item in population]

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

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


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

