
"""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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Dict:







							"""simple docstring"""


							__snake_case     			=       SwinvaConfig()
							__snake_case     			=       swinva_name.split("_" )

							__snake_case     			=       name_split[1]
							if "to" in name_split[3]:
														__snake_case     			=       int(name_split[3][-3:] )
							else:
														__snake_case     			=       int(name_split[3] )
							if "to" in name_split[2]:
														__snake_case     			=       int(name_split[2][-2:] )
							else:
														__snake_case     			=       int(name_split[2][6:] )

							if model_size == "tiny":
														__snake_case     			=       96
														__snake_case     			=       (2, 2, 6, 2)
														__snake_case     			=       (3, 6, 12, 24)
							elif model_size == "small":
														__snake_case     			=       96
														__snake_case     			=       (2, 2, 18, 2)
														__snake_case     			=       (3, 6, 12, 24)
							elif model_size == "base":
														__snake_case     			=       1_28
														__snake_case     			=       (2, 2, 18, 2)
														__snake_case     			=       (4, 8, 16, 32)
							else:
														__snake_case     			=       1_92
														__snake_case     			=       (2, 2, 18, 2)
														__snake_case     			=       (6, 12, 24, 48)

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

							if ("22k" in swinva_name) and ("to" not in swinva_name):
														__snake_case     			=       2_18_41
														__snake_case     			=       "huggingface/label-files"
														__snake_case     			=       "imagenet-22k-id2label.json"
														__snake_case     			=       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    repo_type="dataset" )							,    "r" ) )
														__snake_case     			=       {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
														__snake_case     			=       idalabel
														__snake_case     			=       {v: k for k, v in idalabel.items()}

							else:
														__snake_case     			=       10_00
														__snake_case     			=       "huggingface/label-files"
														__snake_case     			=       "imagenet-1k-id2label.json"
														__snake_case     			=       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    repo_type="dataset" )							,    "r" ) )
														__snake_case     			=       {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
														__snake_case     			=       idalabel
														__snake_case     			=       {v: k for k, v in idalabel.items()}

							__snake_case     			=       img_size
							__snake_case     			=       num_classes
							__snake_case     			=       embed_dim
							__snake_case     			=       depths
							__snake_case     			=       num_heads
							__snake_case     			=       window_size

							return config
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							if "patch_embed.proj" in name:
														__snake_case     			=       name.replace("patch_embed.proj"							,    "embeddings.patch_embeddings.projection" )
							if "patch_embed.norm" in name:
														__snake_case     			=       name.replace("patch_embed.norm"							,    "embeddings.norm" )
							if "layers" in name:
														__snake_case     			=       "encoder." + name
							if "attn.proj" in name:
														__snake_case     			=       name.replace("attn.proj"							,    "attention.output.dense" )
							if "attn" in name:
														__snake_case     			=       name.replace("attn"							,    "attention.self" )
							if "norm1" in name:
														__snake_case     			=       name.replace("norm1"							,    "layernorm_before" )
							if "norm2" in name:
														__snake_case     			=       name.replace("norm2"							,    "layernorm_after" )
							if "mlp.fc1" in name:
														__snake_case     			=       name.replace("mlp.fc1"							,    "intermediate.dense" )
							if "mlp.fc2" in name:
														__snake_case     			=       name.replace("mlp.fc2"							,    "output.dense" )
							if "q_bias" in name:
														__snake_case     			=       name.replace("q_bias"							,    "query.bias" )
							if "k_bias" in name:
														__snake_case     			=       name.replace("k_bias"							,    "key.bias" )
							if "v_bias" in name:
														__snake_case     			=       name.replace("v_bias"							,    "value.bias" )
							if "cpb_mlp" in name:
														__snake_case     			=       name.replace("cpb_mlp"							,    "continuous_position_bias_mlp" )
							if name == "norm.weight":
														__snake_case     			=       "layernorm.weight"
							if name == "norm.bias":
														__snake_case     			=       "layernorm.bias"

							if "head" in name:
														__snake_case     			=       name.replace("head"							,    "classifier" )
							else:
														__snake_case     			=       "swinv2." + name

							return name
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Any:







							"""simple docstring"""


							for key in orig_state_dict.copy().keys():
														__snake_case     			=       orig_state_dict.pop(SCREAMING_SNAKE_CASE )

														if "mask" in key:
																					continue
														elif "qkv" in key:
																					__snake_case     			=       key.split("." )
																					__snake_case     			=       int(key_split[1] )
																					__snake_case     			=       int(key_split[3] )
																					__snake_case     			=       model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												__snake_case     			=       val[:dim, :]
																												__snake_case     			=       val[dim : dim * 2, :]
																												__snake_case     			=       val[-dim:, :]
																					else:
																												__snake_case     			=       val[:dim]
																												__snake_case     			=       val[
																												    dim : dim * 2
																												]
																												__snake_case     			=       val[-dim:]
														else:
																					__snake_case     			=       val

							return orig_state_dict
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[str]:







							"""simple docstring"""


							__snake_case     			=       timm.create_model(SCREAMING_SNAKE_CASE							,    pretrained=SCREAMING_SNAKE_CASE )
							timm_model.eval()

							__snake_case     			=       get_swinva_config(SCREAMING_SNAKE_CASE )
							__snake_case     			=       SwinvaForImageClassification(SCREAMING_SNAKE_CASE )
							model.eval()

							__snake_case     			=       convert_state_dict(timm_model.state_dict()							,    SCREAMING_SNAKE_CASE )
							model.load_state_dict(SCREAMING_SNAKE_CASE )

							__snake_case     			=       "http://images.cocodataset.org/val2017/000000039769.jpg"

							__snake_case     			=       AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"							,    "-" ) ) )
							__snake_case     			=       Image.open(requests.get(SCREAMING_SNAKE_CASE							,    stream=SCREAMING_SNAKE_CASE ).raw )
							__snake_case     			=       image_processor(images=SCREAMING_SNAKE_CASE							,    return_tensors="pt" )

							__snake_case     			=       timm_model(inputs["pixel_values"] )
							__snake_case     			=       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__":
		_SCREAMING_SNAKE_CASE	       =      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."""
		)

		_SCREAMING_SNAKE_CASE	       =      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 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					float
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     bool:







							"""simple docstring"""


							def is_valid_tree(SCREAMING_SNAKE_CASE ) -> 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							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) -> 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()
