


'''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   _a		(lowercase__			:   List[str]						)			->			Optional[Any]:






     """simple docstring"""
     __snake_case           =						[]
     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   _a		(lowercase__			:   int							,   lowercase__			:   List[Any]						)			->			List[str]:






     """simple docstring"""
     __snake_case           =						[]
     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   _a		(lowercase__			:   int						)			->			int:






     """simple docstring"""
     __snake_case           =						[]
     token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')						)
     return token





def   _a		()			->			Union[str, Any]:






     """simple docstring"""
     __snake_case           =						[]
     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   _a		(lowercase__			:   List[str]							,   lowercase__			:   Any							,   lowercase__			:   List[Any]							,   lowercase__			:   Optional[int]						)			->			Optional[Any]:






     """simple docstring"""
     __snake_case           =						'imagenet-1k-id2label.json'
     __snake_case           =						1_0_0_0

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

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

     __snake_case           =						__snake_case           =						CvtConfig(num_labels=lowercase__							,   idalabel=lowercase__							,   labelaid=lowercase__						)

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

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

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

     __snake_case           =						CvtForImageClassification(lowercase__						)
     __snake_case           =						AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						)
     __snake_case           =						image_size
     __snake_case           =						torch.load(lowercase__							,   map_location=torch.device('cpu'						)						)

     __snake_case           =						OrderedDict()
     __snake_case           =						[]

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

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

     model.load_state_dict(lowercase__						)
     model.save_pretrained(lowercase__						)
     image_processor.save_pretrained(lowercase__						)


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

if __name__ == "__main__":
       _a   :   List[str]												=   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   :   Optional[Any]												=   parser.parse_args()
       convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)











'''simple docstring'''

from __future__ import annotations

import queue
class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			data
            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			None







def    a__							(							)						->						TreeNode:





     """simple docstring"""

     print('''\n********Press N to stop entering at any point of time********\n''' )
     _UpperCamelCase							       =			input('''Enter the value of the root node: ''' ).strip().lower()
     _UpperCamelCase							       =			queue.Queue()
     _UpperCamelCase							       =			TreeNode(int(lowercase ) )
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          _UpperCamelCase							       =			F"""Enter the left node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			left_node
          q.put(lowercase )
          _UpperCamelCase							       =			F"""Enter the right node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			right_node
          q.put(lowercase )
     raise







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     print(node.data,   end=''',''' )
     pre_order(node.left )
     pre_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     in_order(node.left )
     print(node.data,   end=''',''' )
     in_order(node.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     post_order(node.left )
     post_order(node.right )
     print(node.data,   end=''',''' )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          print(node_dequeued.data,   end=''',''' )
          if node_dequeued.left:
               q.put(node_dequeued.left )
          if node_dequeued.right:
               q.put(node_dequeued.right )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			[]
          while not q.empty():
               _UpperCamelCase							       =			q.get()
               print(node_dequeued.data,   end=''',''' )
               if node_dequeued.left:
                    list_.append(node_dequeued.left )
               if node_dequeued.right:
                    list_.append(node_dequeued.right )
          print()
          for node in list_:
               q.put(lowercase )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data,   end=''',''' )
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          # end of while means current node doesn't have left child
          _UpperCamelCase							       =			stack.pop()
          # start to traverse its right child
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          _UpperCamelCase							       =			stack.pop()
          print(n.data,   end=''',''' )
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase       ,							_UpperCamelCase							       =			[], []
     _UpperCamelCase							       =			node
     stacka.append(lowercase )
     while stacka:  # to find the reversed order of post order, store it in stack2
          _UpperCamelCase							       =			stacka.pop()
          if n.left:
               stacka.append(n.left )
          if n.right:
               stacka.append(n.right )
          stacka.append(lowercase )
     while stacka:  # pop up from stack2 will be the post order
          print(stacka.pop().data,   end=''',''' )







def    a__							(							lowercase   :	str = "",   lowercase   :	List[str]=50,   lowercase   :	List[str]="*" )						->						str:





     """simple docstring"""

     if not s:
          return "\n" + width * char
     _UpperCamelCase       ,							_UpperCamelCase							       =			divmod(width - len(lowercase ) - 2,   2 )
     return F"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(prompt('Binary Tree Traversals'))

   lowercase__		:     TreeNode			=							build_tree()
   print(prompt('Pre Order Traversal'))
   pre_order(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal'))
   in_order(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal'))
   post_order(node)
   print(prompt() + '\n')

   print(prompt('Level Order Traversal'))
   level_order(node)
   print(prompt() + '\n')

   print(prompt('Actual Level Order Traversal'))
   level_order_actual(node)
   print('*' * 50 + '\n')

   print(prompt('Pre Order Traversal - Iteration Version'))
   pre_order_iter(node)
   print(prompt() + '\n')

   print(prompt('In Order Traversal - Iteration Version'))
   in_order_iter(node)
   print(prompt() + '\n')

   print(prompt('Post Order Traversal - Iteration Version'))
   post_order_iter(node)
   print(prompt())



