
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a		       =							{
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a		       =							[
        'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BigBirdPegasusForCausalLM',
        'BigBirdPegasusForConditionalGeneration',
        'BigBirdPegasusForQuestionAnswering',
        'BigBirdPegasusForSequenceClassification',
        'BigBirdPegasusModel',
        'BigBirdPegasusPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

    _a		       =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









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   (      _SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						SwinConfig()
      SCREAMING_SNAKE_CASE		=						swin_name.split("""_"""	)

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

      if model_size == "tiny":
            SCREAMING_SNAKE_CASE		=						96
            SCREAMING_SNAKE_CASE		=						(2, 2, 6, 2)
            SCREAMING_SNAKE_CASE		=						(3, 6, 12, 24)
      elif model_size == "small":
            SCREAMING_SNAKE_CASE		=						96
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(3, 6, 12, 24)
      elif model_size == "base":
            SCREAMING_SNAKE_CASE		=						1_28
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(4, 8, 16, 32)
      else:
            SCREAMING_SNAKE_CASE		=						1_92
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(6, 12, 24, 48)

      if "in22k" in swin_name:
            SCREAMING_SNAKE_CASE		=						2_18_41
      else:
            SCREAMING_SNAKE_CASE		=						10_00
            SCREAMING_SNAKE_CASE		=						"""huggingface/label-files"""
            SCREAMING_SNAKE_CASE		=						"""imagenet-1k-id2label.json"""
            SCREAMING_SNAKE_CASE		=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						repo_type="""dataset"""	) ,						"""r"""	)	)
            SCREAMING_SNAKE_CASE		=						{int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE		=						idalabel
            SCREAMING_SNAKE_CASE		=						{v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE		=						img_size
      SCREAMING_SNAKE_CASE		=						num_classes
      SCREAMING_SNAKE_CASE		=						embed_dim
      SCREAMING_SNAKE_CASE		=						depths
      SCREAMING_SNAKE_CASE		=						num_heads
      SCREAMING_SNAKE_CASE		=						window_size

      return config



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Dict:


      '''simple docstring'''


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

      if name == "norm.weight":
            SCREAMING_SNAKE_CASE		=						"""layernorm.weight"""
      if name == "norm.bias":
            SCREAMING_SNAKE_CASE		=						"""layernorm.bias"""

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

      return name



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Any:


      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE		=						orig_state_dict.pop(_SCREAMING_SNAKE_CASE	)

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

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

      return orig_state_dict



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						timm.create_model(_SCREAMING_SNAKE_CASE ,						pretrained=_SCREAMING_SNAKE_CASE	)
      timm_model.eval()

      SCREAMING_SNAKE_CASE		=						get_swin_config(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						SwinForImageClassification(_SCREAMING_SNAKE_CASE	)
      model.eval()

      SCREAMING_SNAKE_CASE		=						convert_state_dict(timm_model.state_dict() ,						_SCREAMING_SNAKE_CASE	)
      model.load_state_dict(_SCREAMING_SNAKE_CASE	)

      SCREAMING_SNAKE_CASE		=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""

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

      SCREAMING_SNAKE_CASE		=						timm_model(inputs["""pixel_values"""]	)
      SCREAMING_SNAKE_CASE		=						model(**_SCREAMING_SNAKE_CASE	).logits

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

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

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


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

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


