



'''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        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int]      ):
  __SCREAMING_SNAKE_CASE :	List[str]					 =	SwinConfig()
  __SCREAMING_SNAKE_CASE :	Optional[int]					 =	swin_name.split("""_"""      )

  __SCREAMING_SNAKE_CASE :	Optional[int]					 =	name_split[1]
  __SCREAMING_SNAKE_CASE :	Optional[int]					 =	int(name_split[4]      )
  __SCREAMING_SNAKE_CASE :	str					 =	int(name_split[3][-1]      )

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

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

  __SCREAMING_SNAKE_CASE :	Tuple					 =	img_size
  __SCREAMING_SNAKE_CASE :	Dict					 =	num_classes
  __SCREAMING_SNAKE_CASE :	Any					 =	embed_dim
  __SCREAMING_SNAKE_CASE :	Optional[int]					 =	depths
  __SCREAMING_SNAKE_CASE :	int					 =	num_heads
  __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	window_size

  return config


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

  if name == "norm.weight":
    __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	"""layernorm.weight"""
  if name == "norm.bias":
    __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	"""layernorm.bias"""

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

  return name


def        lowerCAmelCase_     (			_lowerCamelCase:     Union[str, Any] ,   _lowerCamelCase:     Union[str, Any]      ):
  for key in orig_state_dict.copy().keys():
    __SCREAMING_SNAKE_CASE :	List[str]					 =	orig_state_dict.pop(_lowerCamelCase      )

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

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

  return orig_state_dict


def        lowerCAmelCase_     (			_lowerCamelCase:     str ,   _lowerCamelCase:     List[Any]      ):
  __SCREAMING_SNAKE_CASE :	Dict					 =	timm.create_model(_lowerCamelCase ,   pretrained=_lowerCamelCase      )
  timm_model.eval()

  __SCREAMING_SNAKE_CASE :	Optional[int]					 =	get_swin_config(_lowerCamelCase      )
  __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	SwinForImageClassification(_lowerCamelCase      )
  model.eval()

  __SCREAMING_SNAKE_CASE :	str					 =	convert_state_dict(timm_model.state_dict() ,   _lowerCamelCase      )
  model.load_state_dict(_lowerCamelCase      )

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

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

  __SCREAMING_SNAKE_CASE :	List[str]					 =	timm_model(inputs["""pixel_values"""]      )
  __SCREAMING_SNAKE_CASE :	List[Any]					 =	model(**_lowerCamelCase      ).logits

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

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

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


if __name__ == "__main__":
      UpperCamelCase__ :			str												=				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.'''
      )

      UpperCamelCase__ :			Dict												=				parser.parse_args()
      convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




'''simple docstring'''




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__ :			Tuple												=				logging.get_logger(__name__)

UpperCamelCase__ :			List[str]												=				{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Union[str, Any]    					=	'''t5'''
			_A   :	Optional[Any]    					=	['''past_key_values''']
			_A   :	Any    					=	{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

			def __init__(							self  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]=3_2_1_2_8							,  lowerCAmelCase__  :				Dict=5_1_2							,  lowerCAmelCase__  :				str=6_4							,  lowerCAmelCase__  :				Tuple=2_0_4_8							,  lowerCAmelCase__  :				Optional[Any]=6							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Any=8							,  lowerCAmelCase__  :				Dict=3_2							,  lowerCAmelCase__  :				Dict=1_2_8							,  lowerCAmelCase__  :				Any=0.1							,  lowerCAmelCase__  :				str=1E-6							,  lowerCAmelCase__  :				str=1.0							,  lowerCAmelCase__  :				Optional[Any]="relu"							,  lowerCAmelCase__  :				List[str]=True							,  lowerCAmelCase__  :				Union[str, Any]=True							,  lowerCAmelCase__  :				Optional[Any]=0							,  lowerCAmelCase__  :				List[Any]=1							,  **lowerCAmelCase__  :				Dict							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	vocab_size
					__SCREAMING_SNAKE_CASE :	str					 =	d_model
					__SCREAMING_SNAKE_CASE :	str					 =	d_kv
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	d_ff
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	num_layers
					__SCREAMING_SNAKE_CASE :	List[str]					 =	(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	num_heads
					__SCREAMING_SNAKE_CASE :	Any					 =	relative_attention_num_buckets
					__SCREAMING_SNAKE_CASE :	int					 =	relative_attention_max_distance
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dropout_rate
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	layer_norm_epsilon
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	initializer_factor
					__SCREAMING_SNAKE_CASE :	Any					 =	feed_forward_proj
					__SCREAMING_SNAKE_CASE :	str					 =	use_cache

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.feed_forward_proj.split("""-"""		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	act_info[-1]
					__SCREAMING_SNAKE_CASE :	int					 =	act_info[0] == """gated"""

					if len(lowerCAmelCase__		) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__		) > 2:
							raise ValueError(
							    F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
							    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
							    """'gated-gelu' or 'relu'"""		)

					# for backwards compatibility
					if feed_forward_proj == "gated-gelu":
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	"""gelu_new"""

					super().__init__(
					    pad_token_id=lowerCAmelCase__							,  eos_token_id=lowerCAmelCase__							,  is_encoder_decoder=lowerCAmelCase__							,  **lowerCAmelCase__							,  )






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			@property
			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	{
					    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
					    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
					}
					if self.use_past:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	"""past_encoder_sequence + sequence"""
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{0: """batch"""}
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
					else:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	{0: """batch""", 1: """decoder_sequence"""}
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{0: """batch""", 1: """decoder_sequence"""}

					if self.use_past:
							self.fill_with_past_key_values_(lowerCAmelCase__							,  direction="""inputs"""		)

					return common_inputs



			@property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					return 1_3