




import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    SwinConfig()
							SCREAMING_SNAKE_CASE_   	=    swin_name.split('''_'''     )

							SCREAMING_SNAKE_CASE_   	=    name_split[1]
							SCREAMING_SNAKE_CASE_   	=    int(name_split[4]     )
							SCREAMING_SNAKE_CASE_   	=    int(name_split[3][-1]     )

							if model_size == "tiny":
														SCREAMING_SNAKE_CASE_   	=    9_6
														SCREAMING_SNAKE_CASE_   	=    (2, 2, 6, 2)
														SCREAMING_SNAKE_CASE_   	=    (3, 6, 1_2, 2_4)
							elif model_size == "small":
														SCREAMING_SNAKE_CASE_   	=    9_6
														SCREAMING_SNAKE_CASE_   	=    (2, 2, 1_8, 2)
														SCREAMING_SNAKE_CASE_   	=    (3, 6, 1_2, 2_4)
							elif model_size == "base":
														SCREAMING_SNAKE_CASE_   	=    1_2_8
														SCREAMING_SNAKE_CASE_   	=    (2, 2, 1_8, 2)
														SCREAMING_SNAKE_CASE_   	=    (4, 8, 1_6, 3_2)
							else:
														SCREAMING_SNAKE_CASE_   	=    1_9_2
														SCREAMING_SNAKE_CASE_   	=    (2, 2, 1_8, 2)
														SCREAMING_SNAKE_CASE_   	=    (6, 1_2, 2_4, 4_8)

							if "in22k" in swin_name:
														SCREAMING_SNAKE_CASE_   	=    2_1_8_4_1
							else:
														SCREAMING_SNAKE_CASE_   	=    1_0_0_0
														SCREAMING_SNAKE_CASE_   	=    '''huggingface/label-files'''
														SCREAMING_SNAKE_CASE_   	=    '''imagenet-1k-id2label.json'''
														SCREAMING_SNAKE_CASE_   	=    json.load(open(hf_hub_download(_lowerCAmelCase  ,    _lowerCAmelCase  ,    repo_type='''dataset'''     )  ,    '''r'''     )     )
														SCREAMING_SNAKE_CASE_   	=    {int(_lowerCAmelCase     ): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE_   	=    idalabel
														SCREAMING_SNAKE_CASE_   	=    {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE_   	=    img_size
							SCREAMING_SNAKE_CASE_   	=    num_classes
							SCREAMING_SNAKE_CASE_   	=    embed_dim
							SCREAMING_SNAKE_CASE_   	=    depths
							SCREAMING_SNAKE_CASE_   	=    num_heads
							SCREAMING_SNAKE_CASE_   	=    window_size

							return config

def 			a   (_lowerCAmelCase     ):
							if "patch_embed.proj" in name:
														SCREAMING_SNAKE_CASE_   	=    name.replace('''patch_embed.proj'''  ,    '''embeddings.patch_embeddings.projection'''     )
							if "patch_embed.norm" in name:
														SCREAMING_SNAKE_CASE_   	=    name.replace('''patch_embed.norm'''  ,    '''embeddings.norm'''     )
							if "layers" in name:
														SCREAMING_SNAKE_CASE_   	=    '''encoder.''' + name
							if "attn.proj" in name:
														SCREAMING_SNAKE_CASE_   	=    name.replace('''attn.proj'''  ,    '''attention.output.dense'''     )
							if "attn" in name:
														SCREAMING_SNAKE_CASE_   	=    name.replace('''attn'''  ,    '''attention.self'''     )
							if "norm1" in name:
														SCREAMING_SNAKE_CASE_   	=    name.replace('''norm1'''  ,    '''layernorm_before'''     )
							if "norm2" in name:
														SCREAMING_SNAKE_CASE_   	=    name.replace('''norm2'''  ,    '''layernorm_after'''     )
							if "mlp.fc1" in name:
														SCREAMING_SNAKE_CASE_   	=    name.replace('''mlp.fc1'''  ,    '''intermediate.dense'''     )
							if "mlp.fc2" in name:
														SCREAMING_SNAKE_CASE_   	=    name.replace('''mlp.fc2'''  ,    '''output.dense'''     )

							if name == "norm.weight":
														SCREAMING_SNAKE_CASE_   	=    '''layernorm.weight'''
							if name == "norm.bias":
														SCREAMING_SNAKE_CASE_   	=    '''layernorm.bias'''

							if "head" in name:
														SCREAMING_SNAKE_CASE_   	=    name.replace('''head'''  ,    '''classifier'''     )
							else:
														SCREAMING_SNAKE_CASE_   	=    '''swin.''' + name

							return name

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE_   	=    orig_state_dict.pop(_lowerCAmelCase     )

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

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

							return orig_state_dict

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    timm.create_model(_lowerCAmelCase  ,    pretrained=_lowerCAmelCase     )
							timm_model.eval()

							SCREAMING_SNAKE_CASE_   	=    get_swin_config(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    SwinForImageClassification(_lowerCAmelCase     )
							model.eval()

							SCREAMING_SNAKE_CASE_   	=    convert_state_dict(timm_model.state_dict()  ,    _lowerCAmelCase     )
							model.load_state_dict(_lowerCAmelCase     )

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

							SCREAMING_SNAKE_CASE_   	=    AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''  ,    '''-'''     )     )     )
							SCREAMING_SNAKE_CASE_   	=    Image.open(requests.get(_lowerCAmelCase  ,    stream=_lowerCAmelCase     ).raw     )
							SCREAMING_SNAKE_CASE_   	=    image_processor(images=_lowerCAmelCase  ,    return_tensors='''pt'''     )

							SCREAMING_SNAKE_CASE_   	=    timm_model(inputs['''pixel_values''']     )
							SCREAMING_SNAKE_CASE_   	=    model(**_lowerCAmelCase     ).logits

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

							print(F"Saving model {swin_name} to {pytorch_dump_folder_path}"     )
							model.save_pretrained(_lowerCAmelCase     )

							print(F"Saving image processor to {pytorch_dump_folder_path}"     )
							image_processor.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--swin_name""",
			    default="""swin_tiny_patch4_window7_224""",
			    type=str,
			    help="""Name of the Swin 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_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
			import torch






class  __magic_name__						(  TensorFormatter[Mapping, "torch.Tensor", Mapping]):
				'''simple docstring'''







				def __init__(							self:   int		,					_lowerCamelCase:   int=None		,					**_lowerCamelCase:   List[Any]		):
											super().__init__(features=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch_tensor_kwargs
											import torch  # noqa import torch at initialization


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											import torch

											if isinstance(_lowerCamelCase		,					_lowerCamelCase		) and column:
																		if all(
																		    isinstance(_lowerCamelCase		,					torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
																		    for x in column		):
																									return torch.stack(_lowerCamelCase		)
											return column


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   List[str]		):
											import torch

											if isinstance(_lowerCamelCase		,					(str, bytes, type(_lowerCamelCase		))		):
																		return value
											elif isinstance(_lowerCamelCase		,					(np.character, np.ndarray)		) and np.issubdtype(value.dtype		,					np.character		):
																		return value.tolist()

											SCREAMING_SNAKE_CASE_   	=    {}

											if isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.integer		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.intaa}
											elif isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.floating		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																		import PIL.Image

																		if isinstance(_lowerCamelCase		,					PIL.Image.Image		):
																									SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
											return torch.tensor(_lowerCamelCase		,					**{**default_dtype, **self.torch_tensor_kwargs}		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Dict		):
											import torch

											# support for torch, tf, jax etc.
											if hasattr(_lowerCamelCase		,					'''__array__'''		) and not isinstance(_lowerCamelCase		,					torch.Tensor		):
																		SCREAMING_SNAKE_CASE_   	=    data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(_lowerCamelCase		,					np.ndarray		):
																		if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																									return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											elif isinstance(_lowerCamelCase		,					(list, tuple)		):
																		return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											return self._tensorize(_lowerCamelCase		)


				def     _A							(							self:   str		,					_lowerCamelCase:   dict		):
											return map_nested(self._recursive_tensorize		,					_lowerCamelCase		,					map_list=_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_row(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_row(_lowerCamelCase		)
											return self.recursive_tensorize(_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_column(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_column(_lowerCamelCase		,					pa_table.column_names[0]		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self._consolidate(_lowerCamelCase		)
											return column





				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											for column_name in batch:
																		SCREAMING_SNAKE_CASE_   	=    self._consolidate(batch[column_name]		)
											return batch


