


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   _lowercase(					__a				:						Dict     ):
							a__      =SwinConfig()
							a__      =swin_name.split('_'     )

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

							if model_size == "tiny":
														a__      =96
														a__      =(2, 2, 6, 2)
														a__      =(3, 6, 12, 24)
							elif model_size == "small":
														a__      =96
														a__      =(2, 2, 18, 2)
														a__      =(3, 6, 12, 24)
							elif model_size == "base":
														a__      =128
														a__      =(2, 2, 18, 2)
														a__      =(4, 8, 16, 32)
							else:
														a__      =192
														a__      =(2, 2, 18, 2)
														a__      =(6, 12, 24, 48)

							if "in22k" in swin_name:
														a__      =2_1841
							else:
														a__      =1000
														a__      ='huggingface/label-files'
														a__      ='imagenet-1k-id2label.json'
														a__      =json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type='dataset'     )    ,     'r'     )     )
														a__      ={int(__a     ): v for k, v in idalabel.items()}
														a__      =idalabel
														a__      ={v: k for k, v in idalabel.items()}

							a__      =img_size
							a__      =num_classes
							a__      =embed_dim
							a__      =depths
							a__      =num_heads
							a__      =window_size

							return config






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

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

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

							return name






def   _lowercase(					__a				:						List[str]    ,     __a				:						List[str]     ):
							for key in orig_state_dict.copy().keys():
														a__      =orig_state_dict.pop(__a     )

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

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

							return orig_state_dict






def   _lowercase(					__a				:						List[str]    ,     __a				:						List[Any]     ):
							a__      =timm.create_model(__a    ,     pretrained=__a     )
							timm_model.eval()

							a__      =get_swin_config(__a     )
							a__      =SwinForImageClassification(__a     )
							model.eval()

							a__      =convert_state_dict(timm_model.state_dict()    ,     __a     )
							model.load_state_dict(__a     )

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

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

							a__      =timm_model(inputs['pixel_values']     )
							a__      =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__":
	_lowerCAmelCase:						Any   				=       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.'
	)

	_lowerCAmelCase:						Tuple   				=       parser.parse_args()
	convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







'''simple docstring'''



from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


UpperCamelCase_    	=				logging.get_logger(__name__)


UpperCamelCase_    	=				TypeVar("DatasetType", Dataset, IterableDataset)




def   lowercase__(			__UpperCamelCase:					List[DatasetType]      ,__UpperCamelCase:					Optional[List[float]] = None      ,__UpperCamelCase:					Optional[int] = None      ,__UpperCamelCase:					Optional[DatasetInfo] = None      ,__UpperCamelCase:					Optional[NamedSplit] = None      ,__UpperCamelCase:					Literal["first_exhausted", "all_exhausted"] = "first_exhausted"      ,):

      """simple docstring"""






      from .arrow_dataset import Dataset
      from .iterable_dataset import IterableDataset

      if not datasets:
            raise ValueError('Unable to interleave an empty list of datasets.'    )
      for i, dataset in enumerate(__UpperCamelCase    ):
            if not isinstance(__UpperCamelCase      ,(Dataset, IterableDataset)    ):
                  if isinstance(__UpperCamelCase      ,(DatasetDict, IterableDatasetDict)    ):
                        if not dataset:
                              raise ValueError(
                                  f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                                  'is an empty dataset dictionary.'    )
                        raise ValueError(
                            f"Dataset at position {i} has at least one split: {list(__UpperCamelCase    )}\n"
                            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCamelCase    )    )}']"    )
                  raise ValueError(
                      f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCamelCase    ).__name__}."    )
            if i == 0:
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						(
                      (Dataset, IterableDataset) if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(__UpperCamelCase      ,__UpperCamelCase    ):
                  raise ValueError(
                      f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."    )
      if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
            raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."    )
      if dataset_type is Dataset:
            return _interleave_map_style_datasets(
                __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,stopping_strategy=__UpperCamelCase    )
      else:
            return _interleave_iterable_datasets(
                __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,stopping_strategy=__UpperCamelCase    )





def   lowercase__(			__UpperCamelCase:					List[DatasetType]      ,__UpperCamelCase:					Optional[DatasetInfo] = None      ,__UpperCamelCase:					Optional[NamedSplit] = None      ,__UpperCamelCase:					int = 0      ,):

      """simple docstring"""






      if not dsets:
            raise ValueError('Unable to concatenate an empty list of datasets.'    )
      for i, dataset in enumerate(__UpperCamelCase    ):
            if not isinstance(__UpperCamelCase      ,(Dataset, IterableDataset)    ):
                  if isinstance(__UpperCamelCase      ,(DatasetDict, IterableDatasetDict)    ):
                        if not dataset:
                              raise ValueError(
                                  f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                                  'is an empty dataset dictionary.'    )
                        raise ValueError(
                            f"Dataset at position {i} has at least one split: {list(__UpperCamelCase    )}\n"
                            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCamelCase    )    )}']"    )
                  raise ValueError(
                      f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCamelCase    ).__name__}."    )
            if i == 0:
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						(
                      (Dataset, IterableDataset) if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(__UpperCamelCase      ,__UpperCamelCase    ):
                  raise ValueError(
                      f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."    )
      if dataset_type is Dataset:
            return _concatenate_map_style_datasets(__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,axis=__UpperCamelCase    )
      else:
            return _concatenate_iterable_datasets(__UpperCamelCase      ,info=__UpperCamelCase      ,split=__UpperCamelCase      ,axis=__UpperCamelCase    )


