

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCAmelCase			=					logging.get_logger(__name__)

__UpperCAmelCase			=					{
    'shi-labs/dinat-mini-in1k-224': 'https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}







class   __lowercase      (     __lowerCamelCase						,      __lowerCamelCase       ):
  snake_case_												=							"""dinat"""

  snake_case_												=							{
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(	self		:      Union[str, Any]       ,A		:      Tuple=4       ,A		:      Dict=3       ,A		:      Union[str, Any]=64       ,A		:      List[str]=[3, 4, 6, 5]       ,A		:      Any=[2, 4, 8, 16]       ,A		:      Optional[Any]=7       ,A		:      Optional[Any]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]       ,A		:      List[Any]=3.0       ,A		:      int=True       ,A		:      int=0.0       ,A		:      List[str]=0.0       ,A		:      Any=0.1       ,A		:      List[str]="gelu"       ,A		:      Dict=0.0_2       ,A		:      Optional[int]=1e-5       ,A		:      Dict=0.0       ,A		:      str=None       ,A		:      List[Any]=None       ,**A		:      int       ,):






   '''simple docstring'''
   super().__init__(**A     )

   UpperCAmelCase__       :	str        =       patch_size
   UpperCAmelCase__       :	Tuple        =       num_channels
   UpperCAmelCase__       :	Optional[int]        =       embed_dim
   UpperCAmelCase__       :	int        =       depths
   UpperCAmelCase__       :	str        =       len(A     )
   UpperCAmelCase__       :	List[str]        =       num_heads
   UpperCAmelCase__       :	Optional[Any]        =       kernel_size
   UpperCAmelCase__       :	str        =       dilations
   UpperCAmelCase__       :	Union[str, Any]        =       mlp_ratio
   UpperCAmelCase__       :	Optional[Any]        =       qkv_bias
   UpperCAmelCase__       :	int        =       hidden_dropout_prob
   UpperCAmelCase__       :	int        =       attention_probs_dropout_prob
   UpperCAmelCase__       :	Tuple        =       drop_path_rate
   UpperCAmelCase__       :	Any        =       hidden_act
   UpperCAmelCase__       :	Optional[int]        =       layer_norm_eps
   UpperCAmelCase__       :	str        =       initializer_range
   # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   UpperCAmelCase__       :	Optional[int]        =       int(embed_dim * 2 ** (len(A     ) - 1)     )
   UpperCAmelCase__       :	Optional[Any]        =       layer_scale_init_value
   UpperCAmelCase__       :	Optional[Any]        =       ["""stem"""] + [f"stage{idx}" for idx in range(1       ,len(A     ) + 1     )]
   UpperCAmelCase__ ,   UpperCAmelCase__       :	int        =       get_aligned_output_features_output_indices(
       out_features=A       ,out_indices=A       ,stage_names=self.stage_names     )










"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase     =							logging.get_logger()

def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       str,	UpperCAmelCase_ :       LevitConfig,	UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       bool = True     )	-> int:






	"""simple docstring"""





	print(F"""Converting {name}..."""     )

	with torch.no_grad():
		if hidden_sizes == 128:
			if name[-1] == "S":
				A__											=					timm.create_model("levit_128s",	pretrained=UpperCAmelCase_     )
			else:
				A__											=					timm.create_model("levit_128",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 192:
			A__											=					timm.create_model("levit_192",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 256:
			A__											=					timm.create_model("levit_256",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 384:
			A__											=					timm.create_model("levit_384",	pretrained=UpperCAmelCase_     )

		from_model.eval()
		A__											=					LevitForImageClassificationWithTeacher(UpperCAmelCase_     ).eval()
		A__											=					OrderedDict()

		A__											=					from_model.state_dict()
		A__											=					list(from_model.state_dict().keys()     )
		A__											=					list(our_model.state_dict().keys()     )
		print(len(UpperCAmelCase_     ),	len(UpperCAmelCase_     )     )
		for i in range(len(UpperCAmelCase_     )     ):
			A__											=					weights[og_keys[i]]
		our_model.load_state_dict(UpperCAmelCase_     )

		A__											=					torch.randn((2, 3, 224, 224)     )
		A__											=					from_model(UpperCAmelCase_     )
		A__											=					our_model(UpperCAmelCase_     ).logits

	assert torch.allclose(UpperCAmelCase_,	UpperCAmelCase_     ), "The model logits don't match the original one."

	A__											=					name
	print(UpperCAmelCase_     )

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name     )
		A__											=					LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name     )

		print(F"""Pushed {checkpoint_name}"""     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       str = None,	UpperCAmelCase_ :       bool = True     )	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					"imagenet-1k-id2label.json"
	A__											=					1000
	A__											=					(1, num_labels)

	A__											=					"huggingface/label-files"
	A__											=					num_labels
	A__											=					json.load(open(hf_hub_download(UpperCAmelCase_,	UpperCAmelCase_,	repo_type="dataset"     ),	"r"     )     )
	A__											=					{int(UpperCAmelCase_     ): v for k, v in idalabel.items()}

	A__											=					idalabel
	A__											=					{v: k for k, v in idalabel.items()}

	A__											=					partial(UpperCAmelCase_,	num_labels=UpperCAmelCase_,	idalabel=UpperCAmelCase_,	labelaid=UpperCAmelCase_     )

	A__											=					{
	    "levit-128S": 128,
	    "levit-128": 128,
	    "levit-192": 192,
	    "levit-256": 256,
	    "levit-384": 384,
	}

	A__											=					{
	    "levit-128S": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384],	num_attention_heads=[4, 6, 8],	depths=[2, 3, 4],	key_dim=[16, 16, 16],	drop_path_rate=0,	),
	    "levit-128": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384],	num_attention_heads=[4, 8, 12],	depths=[4, 4, 4],	key_dim=[16, 16, 16],	drop_path_rate=0,	),
	    "levit-192": ImageNetPreTrainedConfig(
	        hidden_sizes=[192, 288, 384],	num_attention_heads=[3, 5, 6],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0,	),
	    "levit-256": ImageNetPreTrainedConfig(
	        hidden_sizes=[256, 384, 512],	num_attention_heads=[4, 6, 8],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0,	),
	    "levit-384": ImageNetPreTrainedConfig(
	        hidden_sizes=[384, 512, 768],	num_attention_heads=[6, 9, 12],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0.1,	),
	}

	if model_name:
		convert_weight_and_push(
		    names_to_hidden_sizes[model_name],	UpperCAmelCase_,	names_to_config[model_name],	UpperCAmelCase_,	UpperCAmelCase_     )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name],	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
	return config, expected_shape


if __name__ == "__main__":
			UpperCamelCase     =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--model_name""",
			    default=None,
			    type=str,
			    help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""",
			    default="""levit-dump-folder/""",
			    type=Path,
			    required=False,
			    help="""Path to the output PyTorch model directory.""",
			)
			parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
			parser.add_argument(
			    """--no-push_to_hub""",
			    dest="""push_to_hub""",
			    action="""store_false""",
			    help="""Do not push model and image processor to the hub""",
			)

			UpperCamelCase     =							parser.parse_args()
			UpperCamelCase     =							args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




