



'''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_   (		_UpperCAmelCase    :      int				)    ->  Union[str, Any]:
      __snake_case :   Tuple													=    SwinConfig()
      __snake_case :   Optional[Any]													=    swin_name.split('_'				)

      __snake_case :   Dict													=    name_split[1]
      __snake_case :   Union[str, Any]													=    int(name_split[4]				)
      __snake_case :   Dict													=    int(name_split[3][-1]				)

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

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

      __snake_case :   Any													=    img_size
      __snake_case :   int													=    num_classes
      __snake_case :   List[str]													=    embed_dim
      __snake_case :   str													=    depths
      __snake_case :   Union[str, Any]													=    num_heads
      __snake_case :   str													=    window_size

      return config





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

      if name == "norm.weight":
            __snake_case :   Tuple													=    'layernorm.weight'
      if name == "norm.bias":
            __snake_case :   Dict													=    'layernorm.bias'

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

      return name





def   a_   (		_UpperCAmelCase    :      Dict ,_UpperCAmelCase    :      int				)    ->  List[Any]:
      for key in orig_state_dict.copy().keys():
            __snake_case :   List[str]													=    orig_state_dict.pop(_UpperCAmelCase				)

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

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

      return orig_state_dict





def   a_   (		_UpperCAmelCase    :      List[str] ,_UpperCAmelCase    :      Union[str, Any]				)    ->  Dict:
      __snake_case :   Tuple													=    timm.create_model(_UpperCAmelCase ,pretrained=_UpperCAmelCase				)
      timm_model.eval()

      __snake_case :   Optional[int]													=    get_swin_config(_UpperCAmelCase				)
      __snake_case :   List[Any]													=    SwinForImageClassification(_UpperCAmelCase				)
      model.eval()

      __snake_case :   Optional[Any]													=    convert_state_dict(timm_model.state_dict() ,_UpperCAmelCase				)
      model.load_state_dict(_UpperCAmelCase				)

      __snake_case :   List[Any]													=    'http://images.cocodataset.org/val2017/000000039769.jpg'

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

      __snake_case :   Optional[Any]													=    timm_model(inputs['pixel_values']				)
      __snake_case :   int													=    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__    :   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.'''
    )

    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_tokenizers_available, is_torch_available


A__    :   Optional[Any]           =					{
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Tuple           =					['''SqueezeBertTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   str           =					[
        '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''SqueezeBertForMaskedLM''',
        '''SqueezeBertForMultipleChoice''',
        '''SqueezeBertForQuestionAnswering''',
        '''SqueezeBertForSequenceClassification''',
        '''SqueezeBertForTokenClassification''',
        '''SqueezeBertModel''',
        '''SqueezeBertModule''',
        '''SqueezeBertPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_squeezebert import (
        SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SqueezeBertConfig,
        SqueezeBertOnnxConfig,
    )
    from .tokenization_squeezebert import SqueezeBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_squeezebert import (
            SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            SqueezeBertForMaskedLM,
            SqueezeBertForMultipleChoice,
            SqueezeBertForQuestionAnswering,
            SqueezeBertForSequenceClassification,
            SqueezeBertForTokenClassification,
            SqueezeBertModel,
            SqueezeBertModule,
            SqueezeBertPreTrainedModel,
        )

else:
    import sys

    A__    :   List[Any]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



