


"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification





def 				A_       ( snake_case__		)					->  List[str]:
			_UpperCamelCase					:List[Any]					     =	SwinvaConfig()
			_UpperCamelCase					:str					     =	swinva_name.split('''_'''		)

			_UpperCamelCase					:List[str]					     =	name_split[1]
			if "to" in name_split[3]:
						_UpperCamelCase					:List[Any]					     =	int(name_split[3][-3:]		)
			else:
						_UpperCamelCase					:int					     =	int(name_split[3]		)
			if "to" in name_split[2]:
						_UpperCamelCase					:List[Any]					     =	int(name_split[2][-2:]		)
			else:
						_UpperCamelCase					:str					     =	int(name_split[2][6:]		)

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

			if "to" in swinva_name:
						_UpperCamelCase					:Optional[Any]					     =	(12, 12, 12, 6)

			if ("22k" in swinva_name) and ("to" not in swinva_name):
						_UpperCamelCase					:int					     =	2_18_41
						_UpperCamelCase					:Union[str, Any]					     =	"huggingface/label-files"
						_UpperCamelCase					:Optional[Any]					     =	"imagenet-22k-id2label.json"
						_UpperCamelCase					:List[str]					     =	json.load(open(hf_hub_download(snake_case__       ,					snake_case__       ,					repo_type='''dataset'''		)       ,					'''r'''		)		)
						_UpperCamelCase					:List[Any]					     =	{int(snake_case__		): v for k, v in idalabel.items()}
						_UpperCamelCase					:Union[str, Any]					     =	idalabel
						_UpperCamelCase					:Any					     =	{v: k for k, v in idalabel.items()}

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

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

			return config





def 				A_       ( snake_case__		)					->  Tuple:





			if "patch_embed.proj" in name:
						_UpperCamelCase					:int					     =	name.replace('''patch_embed.proj'''       ,					'''embeddings.patch_embeddings.projection'''		)
			if "patch_embed.norm" in name:
						_UpperCamelCase					:Tuple					     =	name.replace('''patch_embed.norm'''       ,					'''embeddings.norm'''		)
			if "layers" in name:
						_UpperCamelCase					:Optional[Any]					     =	"encoder." + name
			if "attn.proj" in name:
						_UpperCamelCase					:List[str]					     =	name.replace('''attn.proj'''       ,					'''attention.output.dense'''		)
			if "attn" in name:
						_UpperCamelCase					:Optional[Any]					     =	name.replace('''attn'''       ,					'''attention.self'''		)
			if "norm1" in name:
						_UpperCamelCase					:Union[str, Any]					     =	name.replace('''norm1'''       ,					'''layernorm_before'''		)
			if "norm2" in name:
						_UpperCamelCase					:Union[str, Any]					     =	name.replace('''norm2'''       ,					'''layernorm_after'''		)
			if "mlp.fc1" in name:
						_UpperCamelCase					:List[Any]					     =	name.replace('''mlp.fc1'''       ,					'''intermediate.dense'''		)
			if "mlp.fc2" in name:
						_UpperCamelCase					:Union[str, Any]					     =	name.replace('''mlp.fc2'''       ,					'''output.dense'''		)
			if "q_bias" in name:
						_UpperCamelCase					:Union[str, Any]					     =	name.replace('''q_bias'''       ,					'''query.bias'''		)
			if "k_bias" in name:
						_UpperCamelCase					:Any					     =	name.replace('''k_bias'''       ,					'''key.bias'''		)
			if "v_bias" in name:
						_UpperCamelCase					:int					     =	name.replace('''v_bias'''       ,					'''value.bias'''		)
			if "cpb_mlp" in name:
						_UpperCamelCase					:Optional[Any]					     =	name.replace('''cpb_mlp'''       ,					'''continuous_position_bias_mlp'''		)
			if name == "norm.weight":
						_UpperCamelCase					:Optional[Any]					     =	"layernorm.weight"
			if name == "norm.bias":
						_UpperCamelCase					:Optional[Any]					     =	"layernorm.bias"

			if "head" in name:
						_UpperCamelCase					:Any					     =	name.replace('''head'''       ,					'''classifier'''		)
			else:
						_UpperCamelCase					:List[str]					     =	"swinv2." + name

			return name





def 				A_       ( snake_case__       ,					snake_case__		)					->  int:





			for key in orig_state_dict.copy().keys():
						_UpperCamelCase					:Union[str, Any]					     =	orig_state_dict.pop(snake_case__		)

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

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

			return orig_state_dict





def 				A_       ( snake_case__       ,					snake_case__		)					->  Optional[int]:
			_UpperCamelCase					:Optional[Any]					     =	timm.create_model(snake_case__       ,					pretrained=snake_case__		)
			timm_model.eval()

			_UpperCamelCase					:List[str]					     =	get_swinva_config(snake_case__		)
			_UpperCamelCase					:str					     =	SwinvaForImageClassification(snake_case__		)
			model.eval()

			_UpperCamelCase					:int					     =	convert_state_dict(timm_model.state_dict()       ,					snake_case__		)
			model.load_state_dict(snake_case__		)

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

			_UpperCamelCase					:Dict					     =	AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''       ,					'''-'''		)		)		)
			_UpperCamelCase					:Optional[int]					     =	Image.open(requests.get(snake_case__       ,					stream=snake_case__		).raw		)
			_UpperCamelCase					:List[Any]					     =	image_processor(images=snake_case__       ,					return_tensors='''pt'''		)

			_UpperCamelCase					:List[str]					     =	timm_model(inputs['''pixel_values''']		)
			_UpperCamelCase					:Optional[Any]					     =	model(**snake_case__		).logits

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

			print(f"Saving model {swinva_name} to {pytorch_dump_folder_path}"		)
			model.save_pretrained(snake_case__		)

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

			model.push_to_hub(
			    repo_path_or_name=Path(snake_case__       ,					snake_case__		)       ,					organization='''nandwalritik'''       ,					commit_message='''Add model'''       ,					)


if __name__ == "__main__":
					UpperCamelCase__				:Any			   =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--swinv2_name""",
					    default="""swinv2_tiny_patch4_window8_256""",
					    type=str,
					    help="""Name of the Swinv2 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__				:Tuple			   =	parser.parse_args()
					convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case       =       {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """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

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





