





"""simple docstring"""




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_    (							_lowercase       ):
						_UpperCamelCase      : Dict         =   SwinConfig()
						_UpperCamelCase      : List[str]         =   swin_name.split('''_'''       )

						_UpperCamelCase      : List[Any]         =   name_split[1]
						_UpperCamelCase      : Optional[int]         =   int(name_split[4]       )
						_UpperCamelCase      : Any         =   int(name_split[3][-1]       )

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

						if "in22k" in swin_name:
												_UpperCamelCase      : List[str]         =   2_1841
						else:
												_UpperCamelCase      : Dict         =   1000
												_UpperCamelCase      : Optional[Any]         =   '''huggingface/label-files'''
												_UpperCamelCase      : Any         =   '''imagenet-1k-id2label.json'''
												_UpperCamelCase      : Optional[Any]         =   json.load(open(hf_hub_download(A_	,						A_	,						repo_type='''dataset'''       )	,						'''r'''       )       )
												_UpperCamelCase      : Any         =   {int(A_       ): v for k, v in idalabel.items()}
												_UpperCamelCase      : str         =   idalabel
												_UpperCamelCase      : List[str]         =   {v: k for k, v in idalabel.items()}

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

						return config





def 	a_    (							_lowercase       ):
						if "patch_embed.proj" in name:
												_UpperCamelCase      : str         =   name.replace('''patch_embed.proj'''	,						'''embeddings.patch_embeddings.projection'''       )
						if "patch_embed.norm" in name:
												_UpperCamelCase      : str         =   name.replace('''patch_embed.norm'''	,						'''embeddings.norm'''       )
						if "layers" in name:
												_UpperCamelCase      : Any         =   '''encoder.''' + name
						if "attn.proj" in name:
												_UpperCamelCase      : List[str]         =   name.replace('''attn.proj'''	,						'''attention.output.dense'''       )
						if "attn" in name:
												_UpperCamelCase      : str         =   name.replace('''attn'''	,						'''attention.self'''       )
						if "norm1" in name:
												_UpperCamelCase      : Tuple         =   name.replace('''norm1'''	,						'''layernorm_before'''       )
						if "norm2" in name:
												_UpperCamelCase      : Tuple         =   name.replace('''norm2'''	,						'''layernorm_after'''       )
						if "mlp.fc1" in name:
												_UpperCamelCase      : int         =   name.replace('''mlp.fc1'''	,						'''intermediate.dense'''       )
						if "mlp.fc2" in name:
												_UpperCamelCase      : Dict         =   name.replace('''mlp.fc2'''	,						'''output.dense'''       )

						if name == "norm.weight":
												_UpperCamelCase      : Optional[Any]         =   '''layernorm.weight'''
						if name == "norm.bias":
												_UpperCamelCase      : str         =   '''layernorm.bias'''

						if "head" in name:
												_UpperCamelCase      : List[str]         =   name.replace('''head'''	,						'''classifier'''       )
						else:
												_UpperCamelCase      : List[Any]         =   '''swin.''' + name

						return name





def 	a_    (							_lowercase	,						_lowercase       ):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase      : Any         =   orig_state_dict.pop(A_       )

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

																		if "weight" in key:
																								_UpperCamelCase      : Union[str, Any]         =   val[:dim, :]
																								_UpperCamelCase      : Optional[Any]         =   val[
																								    dim : dim * 2, :
																								]
																								_UpperCamelCase      : Any         =   val[-dim:, :]
																		else:
																								_UpperCamelCase      : int         =   val[
																								    :dim
																								]
																								_UpperCamelCase      : Any         =   val[
																								    dim : dim * 2
																								]
																								_UpperCamelCase      : Union[str, Any]         =   val[
																								    -dim:
																								]
												else:
																		_UpperCamelCase      : Dict         =   val

						return orig_state_dict





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : int         =   timm.create_model(A_	,						pretrained=A_       )
						timm_model.eval()

						_UpperCamelCase      : Tuple         =   get_swin_config(A_       )
						_UpperCamelCase      : str         =   SwinForImageClassification(A_       )
						model.eval()

						_UpperCamelCase      : List[Any]         =   convert_state_dict(timm_model.state_dict()	,						A_       )
						model.load_state_dict(A_       )

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

						_UpperCamelCase      : Any         =   AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''	,						'''-'''       )       )       )
						_UpperCamelCase      : str         =   Image.open(requests.get(A_	,						stream=A_       ).raw       )
						_UpperCamelCase      : Any         =   image_processor(images=A_	,						return_tensors='''pt'''       )

						_UpperCamelCase      : str         =   timm_model(inputs['''pixel_values''']       )
						_UpperCamelCase      : Any         =   model(**A_       ).logits

						assert torch.allclose(A_	,						A_	,						atol=1E-3       )

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

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


if __name__ == "__main__":
						UpperCamelCase_									=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."""
						)

						UpperCamelCase_									=parser.parse_args()
						convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)










"""simple docstring"""




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   ['''image_processor''', '''tokenizer''']
				UpperCamelCase								=   '''BlipImageProcessor'''
				UpperCamelCase								=   '''AutoTokenizer'''






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




										'''simple docstring'''



										_UpperCamelCase      : Any         =   False
										super().__init__(lowerCAmelCase__,			lowerCAmelCase__							)
										_UpperCamelCase      : Tuple         =   self.image_processor






				def __call__(			self		:   str,			lowerCAmelCase__		:   ImageInput = None,			lowerCAmelCase__		:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,			lowerCAmelCase__		:   bool = True,			lowerCAmelCase__		:   Union[bool, str, PaddingStrategy] = False,			lowerCAmelCase__		:   Union[bool, str, TruncationStrategy] = None,			lowerCAmelCase__		:   Optional[int] = None,			lowerCAmelCase__		:   int = 0,			lowerCAmelCase__		:   Optional[int] = None,			lowerCAmelCase__		:   Optional[bool] = None,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = True,			lowerCAmelCase__		:   Optional[Union[str, TensorType]] = None,			**lowerCAmelCase__		:   Optional[Any],			)     ->				BatchEncoding:




										'''simple docstring'''



										if images is None and text is None:
																raise ValueError('''You have to specify either images or text.'''							)

										# Get only text
										if images is None:
																_UpperCamelCase      : int         =   self.tokenizer
																_UpperCamelCase      : List[str]         =   self.tokenizer(
																    text=lowerCAmelCase__,			add_special_tokens=lowerCAmelCase__,			padding=lowerCAmelCase__,			truncation=lowerCAmelCase__,			max_length=lowerCAmelCase__,			stride=lowerCAmelCase__,			pad_to_multiple_of=lowerCAmelCase__,			return_attention_mask=lowerCAmelCase__,			return_overflowing_tokens=lowerCAmelCase__,			return_special_tokens_mask=lowerCAmelCase__,			return_offsets_mapping=lowerCAmelCase__,			return_token_type_ids=lowerCAmelCase__,			return_length=lowerCAmelCase__,			verbose=lowerCAmelCase__,			return_tensors=lowerCAmelCase__,			**lowerCAmelCase__,			)
																return text_encoding

										# add pixel_values
										_UpperCamelCase      : List[str]         =   self.image_processor(lowerCAmelCase__,			return_tensors=lowerCAmelCase__							)

										if text is not None:
																_UpperCamelCase      : Any         =   self.tokenizer(
																    text=lowerCAmelCase__,			add_special_tokens=lowerCAmelCase__,			padding=lowerCAmelCase__,			truncation=lowerCAmelCase__,			max_length=lowerCAmelCase__,			stride=lowerCAmelCase__,			pad_to_multiple_of=lowerCAmelCase__,			return_attention_mask=lowerCAmelCase__,			return_overflowing_tokens=lowerCAmelCase__,			return_special_tokens_mask=lowerCAmelCase__,			return_offsets_mapping=lowerCAmelCase__,			return_token_type_ids=lowerCAmelCase__,			return_length=lowerCAmelCase__,			verbose=lowerCAmelCase__,			return_tensors=lowerCAmelCase__,			**lowerCAmelCase__,			)
										else:
																_UpperCamelCase      : List[Any]         =   None

										if text_encoding is not None:
																encoding_image_processor.update(lowerCAmelCase__							)

										return encoding_image_processor






				def     snake_case     (			self		:   List[Any],			*lowerCAmelCase__		:   List[str],			**lowerCAmelCase__		:   str							)     ->				Optional[int]:




										'''simple docstring'''



										return self.tokenizer.batch_decode(*lowerCAmelCase__,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   List[Any],			*lowerCAmelCase__		:   Dict,			**lowerCAmelCase__		:   Any							)     ->				List[str]:




										'''simple docstring'''



										return self.tokenizer.decode(*lowerCAmelCase__,			**lowerCAmelCase__							)






				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def     snake_case     (			self		:   Any							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.tokenizer.model_input_names
										_UpperCamelCase      : Union[str, Any]         =   self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



