

"""simple docstring"""




import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification

def 						UpperCamelCase (lowercase_:			Union[str, Any] )					->				Dict:
	A__							:   int					       =		[]
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
	        f"""stage{idx}.patch_embed.proj.weight""",
	    ) )
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
	        f"""stage{idx}.patch_embed.proj.bias""",
	    ) )
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
	        f"""stage{idx}.patch_embed.norm.weight""",
	    ) )
	embed.append(
	    (
	        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
	        f"""stage{idx}.patch_embed.norm.bias""",
	    ) )
	return embed

def 						UpperCamelCase (lowercase_:			Union[str, Any]   ,				lowercase_:			Union[str, Any] )					->				List[Any]:
	A__							:   Optional[int]					       =		[]
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
	        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
	    ) )
	attention_weights.append(
	    (
	        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
	        f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
	    ) )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""") )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""") )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""") )
	attention_weights.append(
	    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""") )
	return attention_weights

def 						UpperCamelCase (lowercase_:			Any )					->				Optional[Any]:
	A__							:   int					       =		[]
	token.append((f"""cvt.encoder.stages.{idx}.cls_token""", """stage2.cls_token""") )
	return token

def 						UpperCamelCase ()					->				Any:
	A__							:   List[Any]					       =		[]
	head.append(("""layernorm.weight""", """norm.weight""") )
	head.append(("""layernorm.bias""", """norm.bias""") )
	head.append(("""classifier.weight""", """head.weight""") )
	head.append(("""classifier.bias""", """head.bias""") )
	return head

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			List[Any]   ,				lowercase_:			List[str]   ,				lowercase_:			str )					->				Optional[int]:
	A__							:   Dict					       =		"""imagenet-1k-id2label.json"""
	A__							:   List[Any]					       =		1000

	A__							:   Optional[int]					       =		"""huggingface/label-files"""
	A__							:   Dict					       =		num_labels
	A__							:   Optional[int]					       =		json.load(open(cached_download(hf_hub_url(__lowerCAmelCase   ,				__lowerCAmelCase   ,				repo_type="""dataset""" ) )   ,				"""r""" ) )
	A__							:   Dict					       =		{int(__lowerCAmelCase ): v for k, v in idalabel.items()}

	A__							:   Any					       =		idalabel
	A__							:   Tuple					       =		{v: k for k, v in idalabel.items()}

	A__							:   Any					       =		CvtConfig(num_labels=__lowerCAmelCase   ,				idalabel=__lowerCAmelCase   ,				labelaid=__lowerCAmelCase )

	# For depth size 13 (13 = 1+2+10)
	if cvt_model.rsplit("""/"""   ,				1 )[-1][4:6] == "13":
		A__							:   int					       =		[1, 2, 10]

	# For depth size 21 (21 = 1+4+16)
	elif cvt_model.rsplit("""/"""   ,				1 )[-1][4:6] == "21":
		A__							:   int					       =		[1, 4, 16]

	# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
	else:
		A__							:   Any					       =		[2, 2, 20]
		A__							:   Any					       =		[3, 12, 16]
		A__							:   Optional[int]					       =		[192, 768, 1024]

	A__							:   Tuple					       =		CvtForImageClassification(__lowerCAmelCase )
	A__							:   str					       =		AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
	A__							:   str					       =		image_size
	A__							:   str					       =		torch.load(__lowerCAmelCase   ,				map_location=torch.device("""cpu""" ) )

	A__							:   Optional[int]					       =		OrderedDict()
	A__							:   str					       =		[]

	for idx in range(len(config.depth ) ):
		if config.cls_token[idx]:
			A__							:   Union[str, Any]					       =		list_of_state_dict + cls_token(__lowerCAmelCase )
		A__							:   Any					       =		list_of_state_dict + embeddings(__lowerCAmelCase )
		for cnt in range(config.depth[idx] ):
			A__							:   Optional[int]					       =		list_of_state_dict + attention(__lowerCAmelCase   ,				__lowerCAmelCase )

	A__							:   Optional[int]					       =		list_of_state_dict + final()
	for gg in list_of_state_dict:
		print(__lowerCAmelCase )
	for i in range(len(__lowerCAmelCase ) ):
		A__							:   List[Any]					       =		original_weights[list_of_state_dict[i][1]]

	model.load_state_dict(__lowerCAmelCase )
	model.save_pretrained(__lowerCAmelCase )
	image_processor.save_pretrained(__lowerCAmelCase )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
	A_	:			Tuple  	=      argparse.ArgumentParser()
	parser.add_argument(
	    '--cvt_model',
	    default='cvt-w24',
	    type=str,
	    help='Name of the cvt model you\'d like to convert.',
	)
	parser.add_argument(
	    '--image_size',
	    default=384,
	    type=int,
	    help='Input Image Size',
	)
	parser.add_argument(
	    '--cvt_file_name',
	    default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
	    type=str,
	    help='Input Image Size',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)

	A_	:			List[Any]  	=      parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






from queue import PriorityQueue
from typing import Any

import numpy as np

def 						UpperCamelCase (lowercase_:			dict   ,				lowercase_:			str   ,				lowercase_:			set   ,				lowercase_:			set   ,				lowercase_:			dict   ,				lowercase_:			dict   ,				lowercase_:			PriorityQueue   ,				lowercase_:			dict   ,				lowercase_:			float | int   ,				)					->				float | int:
	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A__							:   Any					       =		cst_fwd.get(lowercase_   ,				np.inf )
		A__							:   List[Any]					       =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt) )
			A__							:   Tuple					       =		new_cost_f
			A__							:   Any					       =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A__							:   Optional[int]					       =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			dict   ,				lowercase_:			dict )					->				int:
	A__							:   Dict					       =		-1

	A__							:   List[Any]					       =		set()
	A__							:   Union[str, Any]					       =		set()
	A__							:   Optional[Any]					       =		{source: 0}
	A__							:   int					       =		{destination: 0}
	A__							:   Optional[Any]					       =		{source: None}
	A__							:   Union[str, Any]					       =		{destination: None}
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()

	A__							:   List[Any]					       =		np.inf

	queue_forward.put((0, source) )
	queue_backward.put((0, destination) )

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		A__       ,       A__							:   Tuple					       =		queue_forward.get()
		visited_forward.add(lowercase_ )

		A__       ,       A__							:   Optional[Any]					       =		queue_backward.get()
		visited_backward.add(lowercase_ )

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		A__							:   int					       =		shortest_distance
	return shortest_path_distance


A_	:			List[Any]  	=      {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A_	:			Optional[int]  	=      {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
	import doctest

	doctest.testmod()



