



"""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        snake_case_  (      A_					:		Union[str, Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				Dict    			= SwinConfig()
       _lowerCamelCase						:				str    			= swin_name.split('''_'''						)

       _lowerCamelCase						:				List[str]    			= name_split[1]
       _lowerCamelCase						:				str    			= int(name_split[4]						)
       _lowerCamelCase						:				str    			= int(name_split[3][-1]						)

       if model_size == "tiny":
              _lowerCamelCase						:				int    			= 96
              _lowerCamelCase						:				List[Any]    			= (2, 2, 6, 2)
              _lowerCamelCase						:				Tuple    			= (3, 6, 12, 24)
       elif model_size == "small":
              _lowerCamelCase						:				Union[str, Any]    			= 96
              _lowerCamelCase						:				Tuple    			= (2, 2, 18, 2)
              _lowerCamelCase						:				List[Any]    			= (3, 6, 12, 24)
       elif model_size == "base":
              _lowerCamelCase						:				Union[str, Any]    			= 1_28
              _lowerCamelCase						:				List[Any]    			= (2, 2, 18, 2)
              _lowerCamelCase						:				Tuple    			= (4, 8, 16, 32)
       else:
              _lowerCamelCase						:				Union[str, Any]    			= 1_92
              _lowerCamelCase						:				int    			= (2, 2, 18, 2)
              _lowerCamelCase						:				List[str]    			= (6, 12, 24, 48)

       if "in22k" in swin_name:
              _lowerCamelCase						:				Optional[int]    			= 2_18_41
       else:
              _lowerCamelCase						:				str    			= 10_00
              _lowerCamelCase						:				Optional[int]    			= '''huggingface/label-files'''
              _lowerCamelCase						:				List[str]    			= '''imagenet-1k-id2label.json'''
              _lowerCamelCase						:				Dict    			= json.load(open(hf_hub_download(A_,				A_,				repo_type='''dataset'''						),				'''r'''						)						)
              _lowerCamelCase						:				List[str]    			= {int(A_						): v for k, v in idalabel.items()}
              _lowerCamelCase						:				List[str]    			= idalabel
              _lowerCamelCase						:				Optional[Any]    			= {v: k for k, v in idalabel.items()}

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

       return config






def        snake_case_  (      A_					:		List[str]						):


       '''simple docstring'''





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

       if name == "norm.weight":
              _lowerCamelCase						:				Optional[int]    			= '''layernorm.weight'''
       if name == "norm.bias":
              _lowerCamelCase						:				int    			= '''layernorm.bias'''

       if "head" in name:
              _lowerCamelCase						:				Optional[Any]    			= name.replace('''head''',				'''classifier'''						)
       else:
              _lowerCamelCase						:				Tuple    			= '''swin.''' + name

       return name






def        snake_case_  (      A_					:		Optional[int],				A_					:		int						):


       '''simple docstring'''





       for key in orig_state_dict.copy().keys():
              _lowerCamelCase						:				Optional[Any]    			= orig_state_dict.pop(A_						)

              if "mask" in key:
                     continue
              elif "qkv" in key:
                     _lowerCamelCase						:				Any    			= key.split('''.'''						)
                     _lowerCamelCase						:				Optional[int]    			= int(key_split[1]						)
                     _lowerCamelCase						:				List[Any]    			= 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						:				List[Any]    			= val[:dim, :]
                            _lowerCamelCase						:				Dict    			= val[
                                dim : dim * 2, :
                            ]
                            _lowerCamelCase						:				List[str]    			= val[-dim:, :]
                     else:
                            _lowerCamelCase						:				List[Any]    			= val[
                                :dim
                            ]
                            _lowerCamelCase						:				List[Any]    			= val[
                                dim : dim * 2
                            ]
                            _lowerCamelCase						:				List[Any]    			= val[
                                -dim:
                            ]
              else:
                     _lowerCamelCase						:				Optional[Any]    			= val

       return orig_state_dict






def        snake_case_  (      A_					:		Dict,				A_					:		Optional[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				int    			= timm.create_model(A_,				pretrained=A_						)
       timm_model.eval()

       _lowerCamelCase						:				List[str]    			= get_swin_config(A_						)
       _lowerCamelCase						:				List[str]    			= SwinForImageClassification(A_						)
       model.eval()

       _lowerCamelCase						:				Union[str, Any]    			= convert_state_dict(timm_model.state_dict(),				A_						)
       model.load_state_dict(A_						)

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

       _lowerCamelCase						:				List[Any]    			= AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_''',				'''-'''						)						)						)
       _lowerCamelCase						:				Optional[int]    			= Image.open(requests.get(A_,				stream=A_						).raw						)
       _lowerCamelCase						:				Tuple    			= image_processor(images=A_,				return_tensors='''pt'''						)

       _lowerCamelCase						:				List[str]    			= timm_model(inputs['''pixel_values''']						)
       _lowerCamelCase						:				str    			= 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__        =				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__        =				parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__A        =    {
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    [
					    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ASTForAudioClassification",
					    "ASTModel",
					    "ASTPreTrainedModel",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    ["ASTFeatureExtractor"]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

					__A        =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


