



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    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			[]
	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    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			[]
	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    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			[]
	token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")   )
	return token



def    snake_case_				(    ):
	__lowercase		       =			[]
	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    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			"imagenet-1k-id2label.json"
	__lowercase		       =			1_0_0_0

	__lowercase		       =			"huggingface/label-files"
	__lowercase		       =			num_labels
	__lowercase		       =			json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )   )  ,	"r"   )   )
	__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}

	__lowercase		       =			idalabel
	__lowercase		       =			{v: k for k, v in idalabel.items()}

	__lowercase		       =			__lowercase		       =			CvtConfig(num_labels=_SCREAMING_SNAKE_CASE  ,	idalabel=_SCREAMING_SNAKE_CASE  ,	labelaid=_SCREAMING_SNAKE_CASE   )

	# For depth size 13 (13 = 1+2+10)
	if cvt_model.rsplit("/"  ,	1   )[-1][4:6] == "13":
		__lowercase		       =			[1, 2, 1_0]

	# For depth size 21 (21 = 1+4+16)
	elif cvt_model.rsplit("/"  ,	1   )[-1][4:6] == "21":
		__lowercase		       =			[1, 4, 1_6]

	# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
	else:
		__lowercase		       =			[2, 2, 2_0]
		__lowercase		       =			[3, 1_2, 1_6]
		__lowercase		       =			[1_9_2, 7_6_8, 1_0_2_4]

	__lowercase		       =			CvtForImageClassification(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"   )
	__lowercase		       =			image_size
	__lowercase		       =			torch.load(_SCREAMING_SNAKE_CASE  ,	map_location=torch.device("cpu"   )   )

	__lowercase		       =			OrderedDict()
	__lowercase		       =			[]

	for idx in range(len(config.depth   )   ):
		if config.cls_token[idx]:
			__lowercase		       =			list_of_state_dict + cls_token(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			list_of_state_dict + embeddings(_SCREAMING_SNAKE_CASE   )
		for cnt in range(config.depth[idx]   ):
			__lowercase		       =			list_of_state_dict + attention(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

	__lowercase		       =			list_of_state_dict + final()
	for gg in list_of_state_dict:
		print(_SCREAMING_SNAKE_CASE   )
	for i in range(len(_SCREAMING_SNAKE_CASE   )   ):
		__lowercase		       =			original_weights[list_of_state_dict[i][1]]

	model.load_state_dict(_SCREAMING_SNAKE_CASE   )
	model.save_pretrained(_SCREAMING_SNAKE_CASE   )
	image_processor.save_pretrained(_SCREAMING_SNAKE_CASE   )


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

if __name__ == "__main__":
	snake_case__					:     Optional[int]    		=   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=3_84,
	    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."""
	)

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








from __future__ import annotations

import bisect



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	if hi < 0:
		__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )

	while lo < hi:
		__lowercase		       =			lo + (hi - lo) // 2
		if sorted_collection[mid] < item:
			__lowercase		       =			mid + 1
		else:
			__lowercase		       =			mid

	return lo



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	if hi < 0:
		__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )

	while lo < hi:
		__lowercase		       =			lo + (hi - lo) // 2
		if sorted_collection[mid] <= item:
			__lowercase		       =			mid + 1
		else:
			__lowercase		       =			mid

	return lo



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			0
	__lowercase		       =			len(_SCREAMING_SNAKE_CASE   ) - 1

	while left <= right:
		__lowercase		       =			left + (right - left) // 2
		__lowercase		       =			sorted_collection[midpoint]
		if current_item == item:
			return midpoint
		elif item < current_item:
			__lowercase		       =			midpoint - 1
		else:
			__lowercase		       =			midpoint + 1
	return None



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			bisect.bisect_left(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	if index != len(_SCREAMING_SNAKE_CASE   ) and sorted_collection[index] == item:
		return index
	return None



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if right < left:
		return None

	__lowercase		       =			left + (right - left) // 2

	if sorted_collection[midpoint] == item:
		return midpoint
	elif sorted_collection[midpoint] > item:
		return binary_search_by_recursion(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	midpoint - 1   )
	else:
		return binary_search_by_recursion(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	midpoint + 1  ,	_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	snake_case__					:     Optional[Any]    		=   input("""Enter numbers separated by comma:\n""").strip()
	snake_case__					:     Any    		=   sorted(int(item) for item in user_input.split(""","""))
	snake_case__					:     Any    		=   int(input("""Enter a single number to be found in the list:\n"""))
	snake_case__					:     List[Any]    		=   binary_search(collection, target)
	if result is None:
		print(F'''{target} was not found in {collection}.''')
	else:
		print(F'''{target} was found at position {result} in {collection}.''')



