


'''simple docstring'''



import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[int]       ):
					__a      :	Tuple    =  SwinvaConfig()
					__a      :	Union[str, Any]    =  swinva_name.split('_'       )

					__a      :	Tuple    =  name_split[1]
					if "to" in name_split[3]:
										__a      :	List[str]    =  int(name_split[3][-3:]       )
					else:
										__a      :	Union[str, Any]    =  int(name_split[3]       )
					if "to" in name_split[2]:
										__a      :	Optional[int]    =  int(name_split[2][-2:]       )
					else:
										__a      :	int    =  int(name_split[2][6:]       )

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

					if "to" in swinva_name:
										__a      :	Any    =  (12, 12, 12, 6)

					if ("22k" in swinva_name) and ("to" not in swinva_name):
										__a      :	Tuple    =  21_841
										__a      :	Optional[int]    =  'huggingface/label-files'
										__a      :	List[Any]    =  'imagenet-22k-id2label.json'
										__a      :	Any    =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				repo_type='dataset'       )						,				'r'       )       )
										__a      :	Dict    =  {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
										__a      :	str    =  idalabel
										__a      :	List[str]    =  {v: k for k, v in idalabel.items()}

					else:
										__a      :	List[str]    =  1_000
										__a      :	Tuple    =  'huggingface/label-files'
										__a      :	Union[str, Any]    =  'imagenet-1k-id2label.json'
										__a      :	Optional[int]    =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				repo_type='dataset'       )						,				'r'       )       )
										__a      :	Optional[Any]    =  {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
										__a      :	Union[str, Any]    =  idalabel
										__a      :	List[str]    =  {v: k for k, v in idalabel.items()}

					__a      :	Union[str, Any]    =  img_size
					__a      :	str    =  num_classes
					__a      :	List[Any]    =  embed_dim
					__a      :	str    =  depths
					__a      :	Optional[int]    =  num_heads
					__a      :	Optional[Any]    =  window_size

					return config






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Union[str, Any]       ):
					if "patch_embed.proj" in name:
										__a      :	int    =  name.replace('patch_embed.proj'						,				'embeddings.patch_embeddings.projection'       )
					if "patch_embed.norm" in name:
										__a      :	Tuple    =  name.replace('patch_embed.norm'						,				'embeddings.norm'       )
					if "layers" in name:
										__a      :	Any    =  'encoder.' + name
					if "attn.proj" in name:
										__a      :	str    =  name.replace('attn.proj'						,				'attention.output.dense'       )
					if "attn" in name:
										__a      :	Optional[int]    =  name.replace('attn'						,				'attention.self'       )
					if "norm1" in name:
										__a      :	Optional[int]    =  name.replace('norm1'						,				'layernorm_before'       )
					if "norm2" in name:
										__a      :	int    =  name.replace('norm2'						,				'layernorm_after'       )
					if "mlp.fc1" in name:
										__a      :	List[Any]    =  name.replace('mlp.fc1'						,				'intermediate.dense'       )
					if "mlp.fc2" in name:
										__a      :	Dict    =  name.replace('mlp.fc2'						,				'output.dense'       )
					if "q_bias" in name:
										__a      :	Union[str, Any]    =  name.replace('q_bias'						,				'query.bias'       )
					if "k_bias" in name:
										__a      :	str    =  name.replace('k_bias'						,				'key.bias'       )
					if "v_bias" in name:
										__a      :	Dict    =  name.replace('v_bias'						,				'value.bias'       )
					if "cpb_mlp" in name:
										__a      :	Dict    =  name.replace('cpb_mlp'						,				'continuous_position_bias_mlp'       )
					if name == "norm.weight":
										__a      :	str    =  'layernorm.weight'
					if name == "norm.bias":
										__a      :	List[Any]    =  'layernorm.bias'

					if "head" in name:
										__a      :	str    =  name.replace('head'						,				'classifier'       )
					else:
										__a      :	Optional[Any]    =  'swinv2.' + name

					return name






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       Any       ):
					for key in orig_state_dict.copy().keys():
										__a      :	List[Any]    =  orig_state_dict.pop(_SCREAMING_SNAKE_CASE       )

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

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

					return orig_state_dict






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]       ):
					__a      :	Optional[int]    =  timm.create_model(_SCREAMING_SNAKE_CASE						,				pretrained=_SCREAMING_SNAKE_CASE       )
					timm_model.eval()

					__a      :	Optional[int]    =  get_swinva_config(_SCREAMING_SNAKE_CASE       )
					__a      :	Tuple    =  SwinvaForImageClassification(_SCREAMING_SNAKE_CASE       )
					model.eval()

					__a      :	Any    =  convert_state_dict(timm_model.state_dict()						,				_SCREAMING_SNAKE_CASE       )
					model.load_state_dict(_SCREAMING_SNAKE_CASE       )

					__a      :	Any    =  'http://images.cocodataset.org/val2017/000000039769.jpg'

					__a      :	Any    =  AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'						,				'-'       )       )       )
					__a      :	Optional[int]    =  Image.open(requests.get(_SCREAMING_SNAKE_CASE						,				stream=_SCREAMING_SNAKE_CASE       ).raw       )
					__a      :	Dict    =  image_processor(images=_SCREAMING_SNAKE_CASE						,				return_tensors='pt'       )

					__a      :	List[str]    =  timm_model(inputs['pixel_values']       )
					__a      :	int    =  model(**_SCREAMING_SNAKE_CASE       ).logits

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

					print(F"""Saving model {swinva_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       )

					model.push_to_hub(
					    repo_path_or_name=Path(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )						,				organization='nandwalritik'						,				commit_message='Add model'						,				)


if __name__ == "__main__":
	__lowercase     :						Dict	=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--swinv2_name',
	    default='swinv2_tiny_patch4_window8_256',
	    type=str,
	    help='Name of the Swinv2 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     :						Any	=     parser.parse_args()
	convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







'''simple docstring'''



from __future__ import annotations

from dataclasses import dataclass





@dataclass
class 							__UpperCamelCase    :
	A_           =   42
	A_           =   None
	A_           =   None






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       TreeNode | None       ):

					# Validation
					def is_valid_tree(_SCREAMING_SNAKE_CASE				:       TreeNode | None       ) -> bool:
										if node is None:
															return True

										if not isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
															return False

										try:
															float(node.data       )
										except (TypeError, ValueError):
															return False

										return is_valid_tree(node.left       ) and is_valid_tree(node.right       )

					if not is_valid_tree(_SCREAMING_SNAKE_CASE       ):
										raise ValueError(
										    'Each node should be type of TreeNode and data should be float.'       )

					def is_binary_search_tree_recursive_check(
					    _SCREAMING_SNAKE_CASE				:       TreeNode | None						,				_SCREAMING_SNAKE_CASE				:       float						,				_SCREAMING_SNAKE_CASE				:       float       ) -> bool:

										if node is None:
															return True

										return (
										    left_bound < node.data < right_bound
										    and is_binary_search_tree_recursive_check(node.left						,				_SCREAMING_SNAKE_CASE						,				node.data       )
										    and is_binary_search_tree_recursive_check(
										        node.right						,				node.data						,				_SCREAMING_SNAKE_CASE       )
										)

					return is_binary_search_tree_recursive_check(_SCREAMING_SNAKE_CASE						,				-float('inf'       )						,				float('inf'       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



