






'''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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Union[str, Any]:
       lowerCamelCase__      :			Dict 						= SwinConfig()
       lowerCamelCase__      :			str 						= swin_name.split("""_"""   )

       lowerCamelCase__      :			str 						= name_split[1]
       lowerCamelCase__      :			Optional[int] 						= int(name_split[4]   )
       lowerCamelCase__      :			Dict 						= int(name_split[3][-1]   )

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

       if "in22k" in swin_name:
              lowerCamelCase__      :			Any 						= 21841
       else:
              lowerCamelCase__      :			Optional[Any] 						= 1000
              lowerCamelCase__      :			Optional[int] 						= """huggingface/label-files"""
              lowerCamelCase__      :			Union[str, Any] 						= """imagenet-1k-id2label.json"""
              lowerCamelCase__      :			Optional[Any] 						= json.load(open(hf_hub_download(UpperCamelCase       ,    UpperCamelCase       ,    repo_type="""dataset"""   )       ,    """r"""   )   )
              lowerCamelCase__      :			int 						= {int(UpperCamelCase   ): v for k, v in idalabel.items()}
              lowerCamelCase__      :			Optional[Any] 						= idalabel
              lowerCamelCase__      :			Dict 						= {v: k for k, v in idalabel.items()}

       lowerCamelCase__      :			List[Any] 						= img_size
       lowerCamelCase__      :			List[Any] 						= num_classes
       lowerCamelCase__      :			Optional[Any] 						= embed_dim
       lowerCamelCase__      :			Optional[int] 						= depths
       lowerCamelCase__      :			Any 						= num_heads
       lowerCamelCase__      :			List[str] 						= window_size

       return config





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Any:
       if "patch_embed.proj" in name:
              lowerCamelCase__      :			int 						= name.replace("""patch_embed.proj"""       ,    """embeddings.patch_embeddings.projection"""   )
       if "patch_embed.norm" in name:
              lowerCamelCase__      :			List[str] 						= name.replace("""patch_embed.norm"""       ,    """embeddings.norm"""   )
       if "layers" in name:
              lowerCamelCase__      :			Optional[int] 						= """encoder.""" + name
       if "attn.proj" in name:
              lowerCamelCase__      :			int 						= name.replace("""attn.proj"""       ,    """attention.output.dense"""   )
       if "attn" in name:
              lowerCamelCase__      :			List[Any] 						= name.replace("""attn"""       ,    """attention.self"""   )
       if "norm1" in name:
              lowerCamelCase__      :			Optional[int] 						= name.replace("""norm1"""       ,    """layernorm_before"""   )
       if "norm2" in name:
              lowerCamelCase__      :			int 						= name.replace("""norm2"""       ,    """layernorm_after"""   )
       if "mlp.fc1" in name:
              lowerCamelCase__      :			Dict 						= name.replace("""mlp.fc1"""       ,    """intermediate.dense"""   )
       if "mlp.fc2" in name:
              lowerCamelCase__      :			Dict 						= name.replace("""mlp.fc2"""       ,    """output.dense"""   )

       if name == "norm.weight":
              lowerCamelCase__      :			int 						= """layernorm.weight"""
       if name == "norm.bias":
              lowerCamelCase__      :			Any 						= """layernorm.bias"""

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

       return name





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> List[str]:
       for key in orig_state_dict.copy().keys():
              lowerCamelCase__      :			Optional[int] 						= orig_state_dict.pop(UpperCamelCase   )

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

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

       return orig_state_dict





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> int:
       lowerCamelCase__      :			Optional[int] 						= timm.create_model(UpperCamelCase       ,    pretrained=UpperCamelCase   )
       timm_model.eval()

       lowerCamelCase__      :			Optional[Any] 						= get_swin_config(UpperCamelCase   )
       lowerCamelCase__      :			Dict 						= SwinForImageClassification(UpperCamelCase   )
       model.eval()

       lowerCamelCase__      :			Tuple 						= convert_state_dict(timm_model.state_dict()       ,    UpperCamelCase   )
       model.load_state_dict(UpperCamelCase   )

       lowerCamelCase__      :			List[str] 						= """http://images.cocodataset.org/val2017/000000039769.jpg"""

       lowerCamelCase__      :			Union[str, Any] 						= AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""       ,    """-"""   )   )   )
       lowerCamelCase__      :			List[str] 						= Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )
       lowerCamelCase__      :			List[str] 						= image_processor(images=UpperCamelCase       ,    return_tensors="""pt"""   )

       lowerCamelCase__      :			Optional[int] 						= timm_model(inputs["""pixel_values"""]   )
       lowerCamelCase__      :			Any 						= model(**UpperCamelCase   ).logits

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

       print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''   )
       model.save_pretrained(UpperCamelCase   )

       print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
       image_processor.save_pretrained(UpperCamelCase   )


if __name__ == "__main__":
   _A			:    str					  =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.'''
   )

   _A			:    List[str]					  =parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A			:    Any					  ={
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A			:    str					  =[
			    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TrOCRForCausalLM''',
			    '''TrOCRPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
			from .processing_trocr import TrOCRProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
			import sys

			_A			:    Dict					  =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

