




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__ (   lowercase					)  ->       Optional[Any]:
   snake_case				:    Dict           =	SwinConfig()
   snake_case				:    Any           =	swin_name.split("""_"""					)

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

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

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

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

   return config




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       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				:    Union[str, Any]           =	name.replace("""patch_embed.norm"""						,"""embeddings.norm"""					)
   if "layers" in name:
      snake_case				:    Optional[int]           =	"""encoder.""" + name
   if "attn.proj" in name:
      snake_case				:    Optional[int]           =	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				:    Optional[Any]           =	name.replace("""norm2"""						,"""layernorm_after"""					)
   if "mlp.fc1" in name:
      snake_case				:    str           =	name.replace("""mlp.fc1"""						,"""intermediate.dense"""					)
   if "mlp.fc2" in name:
      snake_case				:    Optional[Any]           =	name.replace("""mlp.fc2"""						,"""output.dense"""					)

   if name == "norm.weight":
      snake_case				:    Any           =	"""layernorm.weight"""
   if name == "norm.bias":
      snake_case				:    Any           =	"""layernorm.bias"""

   if "head" in name:
      snake_case				:    int           =	name.replace("""head"""						,"""classifier"""					)
   else:
      snake_case				:    Optional[int]           =	"""swin.""" + name

   return name




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[str]:
   for key in orig_state_dict.copy().keys():
      snake_case				:    List[str]           =	orig_state_dict.pop(lowercase					)

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

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

   return orig_state_dict




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       str:
   snake_case				:    Any           =	timm.create_model(lowercase						,pretrained=lowercase					)
   timm_model.eval()

   snake_case				:    Tuple           =	get_swin_config(lowercase					)
   snake_case				:    Dict           =	SwinForImageClassification(lowercase					)
   model.eval()

   snake_case				:    int           =	convert_state_dict(timm_model.state_dict()						,lowercase					)
   model.load_state_dict(lowercase					)

   snake_case				:    Optional[int]           =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""

   snake_case				:    int           =	AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""						,"""-"""					)					)					)
   snake_case				:    int           =	Image.open(requests.get(lowercase						,stream=lowercase					).raw					)
   snake_case				:    str           =	image_processor(images=lowercase						,return_tensors="""pt"""					)

   snake_case				:    Tuple           =	timm_model(inputs["""pixel_values"""]					)
   snake_case				:    Optional[Any]           =	model(**lowercase					).logits

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

   print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""					)
   model.save_pretrained(lowercase					)

   print(f"""Saving image processor to {pytorch_dump_folder_path}"""					)
   image_processor.save_pretrained(lowercase					)


if __name__ == "__main__":
       lowerCamelCase	:	Tuple				=   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	:	Dict				=   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_tokenizers_available,
    is_torch_available,
)


lowerCamelCase	:	Optional[Any]				=   {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[str]				=   [
           'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaForCausalLM',
           'RobertaForMaskedLM',
           'RobertaForMultipleChoice',
           'RobertaForQuestionAnswering',
           'RobertaForSequenceClassification',
           'RobertaForTokenClassification',
           'RobertaModel',
           'RobertaPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaForCausalLM',
           'TFRobertaForMaskedLM',
           'TFRobertaForMultipleChoice',
           'TFRobertaForQuestionAnswering',
           'TFRobertaForSequenceClassification',
           'TFRobertaForTokenClassification',
           'TFRobertaMainLayer',
           'TFRobertaModel',
           'TFRobertaPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'FlaxRobertaForCausalLM',
           'FlaxRobertaForMaskedLM',
           'FlaxRobertaForMultipleChoice',
           'FlaxRobertaForQuestionAnswering',
           'FlaxRobertaForSequenceClassification',
           'FlaxRobertaForTokenClassification',
           'FlaxRobertaModel',
           'FlaxRobertaPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase	:	str				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





