
"""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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__ :       Any						     =			logging.get_logger(__name__)





def __A						(   a_ :							str		,		a_ :							Dict=False     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
		for i in range(config.num_hidden_layers     ):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((F"blocks.{i}.norm1.weight", F"deit.encoder.layer.{i}.layernorm_before.weight")     )
				rename_keys.append((F"blocks.{i}.norm1.bias", F"deit.encoder.layer.{i}.layernorm_before.bias")     )
				rename_keys.append((F"blocks.{i}.attn.proj.weight", F"deit.encoder.layer.{i}.attention.output.dense.weight")     )
				rename_keys.append((F"blocks.{i}.attn.proj.bias", F"deit.encoder.layer.{i}.attention.output.dense.bias")     )
				rename_keys.append((F"blocks.{i}.norm2.weight", F"deit.encoder.layer.{i}.layernorm_after.weight")     )
				rename_keys.append((F"blocks.{i}.norm2.bias", F"deit.encoder.layer.{i}.layernorm_after.bias")     )
				rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"deit.encoder.layer.{i}.intermediate.dense.weight")     )
				rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"deit.encoder.layer.{i}.intermediate.dense.bias")     )
				rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"deit.encoder.layer.{i}.output.dense.weight")     )
				rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"deit.encoder.layer.{i}.output.dense.bias")     )

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ('''cls_token''', '''deit.embeddings.cls_token'''),
		        ('''dist_token''', '''deit.embeddings.distillation_token'''),
		        ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
		        ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
		        ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
		    ]     )

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ('''norm.weight''', '''layernorm.weight'''),
				        ('''norm.bias''', '''layernorm.bias'''),
				        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
				        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
				    ]     )

				# if just the base model, we should remove "deit" from all keys that start with "deit"
				SCREAMING_SNAKE_CASE       :     List[Any]				= [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''     ) else pair for pair in rename_keys]
		else:
				# layernorm + classification heads
				rename_keys.extend(
				    [
				        ('''norm.weight''', '''deit.layernorm.weight'''),
				        ('''norm.bias''', '''deit.layernorm.bias'''),
				        ('''head.weight''', '''cls_classifier.weight'''),
				        ('''head.bias''', '''cls_classifier.bias'''),
				        ('''head_dist.weight''', '''distillation_classifier.weight'''),
				        ('''head_dist.bias''', '''distillation_classifier.bias'''),
				    ]     )

		return rename_keys





def __A						(   a_ :							Dict		,		a_ :							Optional[Any]		,		a_ :							Tuple=False     )->							Optional[int]:




		'''simple docstring'''






		for i in range(config.num_hidden_layers     ):
				if base_model:
						SCREAMING_SNAKE_CASE       :     Tuple				= ''''''
				else:
						SCREAMING_SNAKE_CASE       :     int				= '''deit.'''
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				SCREAMING_SNAKE_CASE       :     Dict				= state_dict.pop(F"blocks.{i}.attn.qkv.weight"     )
				SCREAMING_SNAKE_CASE       :     Optional[int]				= state_dict.pop(F"blocks.{i}.attn.qkv.bias"     )
				# next, add query, keys and values (in that order) to the state dict
				SCREAMING_SNAKE_CASE       :     str				= in_proj_weight[
				    : config.hidden_size, :
				]
				SCREAMING_SNAKE_CASE       :     Dict				= in_proj_bias[: config.hidden_size]
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				SCREAMING_SNAKE_CASE       :     Any				= in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				SCREAMING_SNAKE_CASE       :     Any				= in_proj_weight[
				    -config.hidden_size :, :
				]
				SCREAMING_SNAKE_CASE       :     List[Any]				= in_proj_bias[-config.hidden_size :]





def __A						(   a_ :							Optional[int]		,		a_ :							List[str]		,		a_ :							List[str]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= dct.pop(a_     )
		SCREAMING_SNAKE_CASE       :     str				= val





def __A						(   )->							List[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE       :     str				= Image.open(requests.get(a_		,		stream=a_     ).raw     )
		return im





@torch.no_grad()
def __A						(   a_ :							Tuple		,		a_ :							int     )->							List[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= DeiTConfig()
		# all deit models have fine-tuned heads
		SCREAMING_SNAKE_CASE       :     Tuple				= False
		# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
		SCREAMING_SNAKE_CASE       :     List[Any]				= 10_00
		SCREAMING_SNAKE_CASE       :     Tuple				= '''huggingface/label-files'''
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= '''imagenet-1k-id2label.json'''
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= json.load(open(hf_hub_download(a_		,		a_		,		repo_type='''dataset'''     )		,		'''r'''     )     )
		SCREAMING_SNAKE_CASE       :     Dict				= {int(a_     ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= idalabel
		SCREAMING_SNAKE_CASE       :     Optional[int]				= {v: k for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE       :     List[str]				= int(deit_name[-6:-4]     )
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= int(deit_name[-3:]     )
		# size of the architecture
		if deit_name[9:].startswith('''tiny'''     ):
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 1_92
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 7_68
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 12
				SCREAMING_SNAKE_CASE       :     Dict				= 3
		elif deit_name[9:].startswith('''small'''     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= 3_84
				SCREAMING_SNAKE_CASE       :     Tuple				= 15_36
				SCREAMING_SNAKE_CASE       :     List[str]				= 12
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 6
		if deit_name[9:].startswith('''base'''     ):
				pass
		elif deit_name[4:].startswith('''large'''     ):
				SCREAMING_SNAKE_CASE       :     int				= 10_24
				SCREAMING_SNAKE_CASE       :     List[Any]				= 40_96
				SCREAMING_SNAKE_CASE       :     Optional[int]				= 24
				SCREAMING_SNAKE_CASE       :     Optional[int]				= 16

		# load original model from timm
		SCREAMING_SNAKE_CASE       :     Any				= timm.create_model(a_		,		pretrained=a_     )
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		SCREAMING_SNAKE_CASE       :     str				= timm_model.state_dict()
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= create_rename_keys(a_		,		a_     )
		for src, dest in rename_keys:
				rename_key(a_		,		a_		,		a_     )
		read_in_q_k_v(a_		,		a_		,		a_     )

		# load HuggingFace model
		SCREAMING_SNAKE_CASE       :     int				= DeiTForImageClassificationWithTeacher(a_     ).eval()
		model.load_state_dict(a_     )

		# Check outputs on an image, prepared by DeiTImageProcessor
		SCREAMING_SNAKE_CASE       :     List[str]				= int(
		    (2_56 / 2_24) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
		SCREAMING_SNAKE_CASE       :     int				= DeiTImageProcessor(size=a_		,		crop_size=config.image_size     )
		SCREAMING_SNAKE_CASE       :     Optional[int]				= image_processor(images=prepare_img()		,		return_tensors='''pt'''     )
		SCREAMING_SNAKE_CASE       :     Tuple				= encoding['''pixel_values''']
		SCREAMING_SNAKE_CASE       :     str				= model(a_     )

		SCREAMING_SNAKE_CASE       :     Dict				= timm_model(a_     )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(a_		,		outputs.logits		,		atol=1E-3     )

		Path(a_     ).mkdir(exist_ok=a_     )
		print(F"Saving model {deit_name} to {pytorch_dump_folder_path}"     )
		model.save_pretrained(a_     )
		print(F"Saving image processor to {pytorch_dump_folder_path}"     )
		image_processor.save_pretrained(a_     )


if __name__ == "__main__":
					lowerCamelCase__ :       List[Any]						     =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--deit_name",
					    default="vit_deit_base_distilled_patch16_224",
					    type=str,
					    help="Name of the DeiT 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__ :       Optional[Any]						     =			parser.parse_args()
					convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







"""simple docstring"""






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


lowerCamelCase__ :       int						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """maskformer-swin"""

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





	def __init__(						self    :Optional[int]		, lowerCamelCase_    :List[Any]=2_24		, lowerCamelCase_    :Tuple=4		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :List[str]=96		, lowerCamelCase_    :int=[2, 2, 6, 2]		, lowerCamelCase_    :Union[str, Any]=[3, 6, 12, 24]		, lowerCamelCase_    :Optional[int]=7		, lowerCamelCase_    :Tuple=4.0		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Dict=0.0		, lowerCamelCase_    :Any=0.0		, lowerCamelCase_    :List[Any]=0.1		, lowerCamelCase_    :Dict="gelu"		, lowerCamelCase_    :Optional[int]=False		, lowerCamelCase_    :List[str]=0.0_2		, lowerCamelCase_    :Any=1E-5		, lowerCamelCase_    :Union[str, Any]=None		, lowerCamelCase_    :List[str]=None		, **lowerCamelCase_    :Union[str, Any]		, )							->			Dict:







			'''simple docstring'''




			super().__init__(**lowerCamelCase_					)

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





