



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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
   lowercase__					  =				SwinConfig()
   lowercase__					  =				swin_name.split("_"  )

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

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

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

   lowercase__					  =				img_size
   lowercase__					  =				num_classes
   lowercase__					  =				embed_dim
   lowercase__					  =				depths
   lowercase__					  =				num_heads
   lowercase__					  =				window_size

   return config



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):


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

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

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

   return name



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):


   for key in orig_state_dict.copy().keys():
      lowercase__					  =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_  )

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

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

   return orig_state_dict



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
   lowercase__					  =				timm.create_model(SCREAMING_SNAKE_CASE_						,  pretrained=SCREAMING_SNAKE_CASE_  )
   timm_model.eval()

   lowercase__					  =				get_swin_config(SCREAMING_SNAKE_CASE_  )
   lowercase__					  =				SwinForImageClassification(SCREAMING_SNAKE_CASE_  )
   model.eval()

   lowercase__					  =				convert_state_dict(timm_model.state_dict()						,  SCREAMING_SNAKE_CASE_  )
   model.load_state_dict(SCREAMING_SNAKE_CASE_  )

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

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

   lowercase__					  =				timm_model(inputs["pixel_values"]  )
   lowercase__					  =				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__":
       lowercase_								=     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."""
       )

       lowercase_								=     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_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_								=     {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RobertaPreLayerNormForCausalLM""",
							    """RobertaPreLayerNormForMaskedLM""",
							    """RobertaPreLayerNormForMultipleChoice""",
							    """RobertaPreLayerNormForQuestionAnswering""",
							    """RobertaPreLayerNormForSequenceClassification""",
							    """RobertaPreLayerNormForTokenClassification""",
							    """RobertaPreLayerNormModel""",
							    """RobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRobertaPreLayerNormForCausalLM""",
							    """TFRobertaPreLayerNormForMaskedLM""",
							    """TFRobertaPreLayerNormForMultipleChoice""",
							    """TFRobertaPreLayerNormForQuestionAnswering""",
							    """TFRobertaPreLayerNormForSequenceClassification""",
							    """TFRobertaPreLayerNormForTokenClassification""",
							    """TFRobertaPreLayerNormMainLayer""",
							    """TFRobertaPreLayerNormModel""",
							    """TFRobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """FlaxRobertaPreLayerNormForCausalLM""",
							    """FlaxRobertaPreLayerNormForMaskedLM""",
							    """FlaxRobertaPreLayerNormForMultipleChoice""",
							    """FlaxRobertaPreLayerNormForQuestionAnswering""",
							    """FlaxRobertaPreLayerNormForSequenceClassification""",
							    """FlaxRobertaPreLayerNormForTokenClassification""",
							    """FlaxRobertaPreLayerNormModel""",
							    """FlaxRobertaPreLayerNormPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roberta_prelayernorm import (
							    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    RobertaPreLayerNormConfig,
							    RobertaPreLayerNormOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta_prelayernorm import (
														    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaPreLayerNormForCausalLM,
														    RobertaPreLayerNormForMaskedLM,
														    RobertaPreLayerNormForMultipleChoice,
														    RobertaPreLayerNormForQuestionAnswering,
														    RobertaPreLayerNormForSequenceClassification,
														    RobertaPreLayerNormForTokenClassification,
														    RobertaPreLayerNormModel,
														    RobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta_prelayernorm import (
														    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaPreLayerNormForCausalLM,
														    TFRobertaPreLayerNormForMaskedLM,
														    TFRobertaPreLayerNormForMultipleChoice,
														    TFRobertaPreLayerNormForQuestionAnswering,
														    TFRobertaPreLayerNormForSequenceClassification,
														    TFRobertaPreLayerNormForTokenClassification,
														    TFRobertaPreLayerNormMainLayer,
														    TFRobertaPreLayerNormModel,
														    TFRobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta_prelayernorm import (
														    FlaxRobertaPreLayerNormForCausalLM,
														    FlaxRobertaPreLayerNormForMaskedLM,
														    FlaxRobertaPreLayerNormForMultipleChoice,
														    FlaxRobertaPreLayerNormForQuestionAnswering,
														    FlaxRobertaPreLayerNormForSequenceClassification,
														    FlaxRobertaPreLayerNormForTokenClassification,
														    FlaxRobertaPreLayerNormModel,
														    FlaxRobertaPreLayerNormPreTrainedModel,
														)

else:
							import sys

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




