

import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



		"""simple docstring"""






		_snake_case						=							384
		_snake_case						=							7
		if "tiny" in model_name:
				_snake_case						=							96
				_snake_case						=							(2, 2, 6, 2)
				_snake_case						=							(3, 6, 12, 24)
		elif "small" in model_name:
				_snake_case						=							96
				_snake_case						=							(2, 2, 18, 2)
				_snake_case						=							(3, 6, 12, 24)
		elif "base" in model_name:
				_snake_case						=							128
				_snake_case						=							(2, 2, 18, 2)
				_snake_case						=							(4, 8, 16, 32)
				_snake_case						=							12
				_snake_case						=							512
		elif "large" in model_name:
				_snake_case						=							192
				_snake_case						=							(2, 2, 18, 2)
				_snake_case						=							(6, 12, 24, 48)
				_snake_case						=							12
				_snake_case						=							768

		# set label information
		_snake_case						=							150
		_snake_case						=							'''huggingface/label-files'''
		_snake_case						=							'''ade20k-id2label.json'''
		_snake_case						=							json.load(open(hf_hub_download(_UpperCamelCase			,	_UpperCamelCase			,	repo_type='''dataset'''					)			,	'''r'''					)					)
		_snake_case						=							{int(_UpperCamelCase					): v for k, v in idalabel.items()}
		_snake_case						=							{v: k for k, v in idalabel.items()}

		_snake_case						=							SwinConfig(
		    embed_dim=_UpperCamelCase			,	depths=_UpperCamelCase			,	num_heads=_UpperCamelCase			,	window_size=_UpperCamelCase			,	out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			,	)
		_snake_case						=							UperNetConfig(
		    backbone_config=_UpperCamelCase			,	auxiliary_in_channels=_UpperCamelCase			,	num_labels=_UpperCamelCase			,	idalabel=_UpperCamelCase			,	labelaid=_UpperCamelCase			,	)

		return config



def 				snake_case_(_UpperCamelCase					)							->     Any:



		"""simple docstring"""






		_snake_case						=							[]

		# fmt: off
		# stem
		rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''')					)
		rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''')					)
		rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''')					)
		rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''')					)
		# stages
		for i in range(len(config.backbone_config.depths					)					):
				for j in range(config.backbone_config.depths[i]					):
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")					)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")					)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")					)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")					)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")					)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")					)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")					)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")					)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")					)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")					)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")					)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")					)

				if i < 3:
						rename_keys.append((F"""backbone.stages.{i}.downsample.reduction.weight""", F"""backbone.encoder.layers.{i}.downsample.reduction.weight""")					)
						rename_keys.append((F"""backbone.stages.{i}.downsample.norm.weight""", F"""backbone.encoder.layers.{i}.downsample.norm.weight""")					)
						rename_keys.append((F"""backbone.stages.{i}.downsample.norm.bias""", F"""backbone.encoder.layers.{i}.downsample.norm.bias""")					)
				rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""")					)
				rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""")					)

		# decode head
		rename_keys.extend(
		    [
		        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
		        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
		        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
		        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
		    ]					)
		# fmt: on

		return rename_keys



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Any:



		"""simple docstring"""






		_snake_case						=							dct.pop(_UpperCamelCase					)
		_snake_case						=							val



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     int:



		"""simple docstring"""






		_snake_case						=							[int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
		for i in range(len(backbone_config.depths					)					):
				_snake_case						=							num_features[i]
				for j in range(backbone_config.depths[i]					):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						_snake_case						=							state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""					)
						_snake_case						=							state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""					)
						# next, add query, keys and values (in that order) to the state dict
						_snake_case						=							in_proj_weight[:dim, :]
						_snake_case						=							in_proj_bias[: dim]
						_snake_case						=							in_proj_weight[
						    dim : dim * 2, :
						]
						_snake_case						=							in_proj_bias[
						    dim : dim * 2
						]
						_snake_case						=							in_proj_weight[
						    -dim :, :
						]
						_snake_case						=							in_proj_bias[-dim :]
						# fmt: on



def 				snake_case_(_UpperCamelCase					)							->     Union[str, Any]:



		"""simple docstring"""






		_snake_case,					_snake_case						=							x.shape
		_snake_case						=							x.reshape(_UpperCamelCase			,	4			,	in_channel // 4					)
		_snake_case						=							x[:, [0, 2, 1, 3], :].transpose(1			,	2					).reshape(_UpperCamelCase			,	_UpperCamelCase					)
		return x



def 				snake_case_(_UpperCamelCase					)							->     Optional[Any]:



		"""simple docstring"""






		_snake_case,					_snake_case						=							x.shape
		_snake_case						=							x.reshape(_UpperCamelCase			,	in_channel // 4			,	4					)
		_snake_case						=							x[:, :, [0, 2, 1, 3]].transpose(1			,	2					).reshape(_UpperCamelCase			,	_UpperCamelCase					)

		return x



def 				snake_case_(_UpperCamelCase					)							->     Any:



		"""simple docstring"""






		_snake_case						=							x.shape[0]
		_snake_case						=							x.reshape(4			,	in_channel // 4					)
		_snake_case						=							x[[0, 2, 1, 3], :].transpose(0			,	1					).reshape(_UpperCamelCase					)
		return x



def 				snake_case_(_UpperCamelCase					)							->     List[Any]:



		"""simple docstring"""






		_snake_case						=							x.shape[0]
		_snake_case						=							x.reshape(in_channel // 4			,	4					)
		_snake_case						=							x[:, [0, 2, 1, 3]].transpose(0			,	1					).reshape(_UpperCamelCase					)
		return x



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[int]:



		"""simple docstring"""






		_snake_case						=							{
		    '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
		    '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
		    '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
		    '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
		}
		_snake_case						=							model_name_to_url[model_name]
		_snake_case						=							torch.hub.load_state_dict_from_url(_UpperCamelCase			,	map_location='''cpu'''			,	file_name=_UpperCamelCase					)[
		    '''state_dict'''
		]

		for name, param in state_dict.items():
				print(_UpperCamelCase			,	param.shape					)

		_snake_case						=							get_upernet_config(_UpperCamelCase					)
		_snake_case						=							UperNetForSemanticSegmentation(_UpperCamelCase					)
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				_snake_case						=							state_dict.pop(_UpperCamelCase					)
				if "bn" in key:
						_snake_case						=							key.replace('''bn'''			,	'''batch_norm'''					)
				_snake_case						=							val

		# rename keys
		_snake_case						=							create_rename_keys(_UpperCamelCase					)
		for src, dest in rename_keys:
				rename_key(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)
		read_in_q_k_v(_UpperCamelCase			,	config.backbone_config					)

		# fix downsample parameters
		for key, value in state_dict.items():
				if "downsample" in key:
						if "reduction" in key:
								_snake_case						=							reverse_correct_unfold_reduction_order(_UpperCamelCase					)
						if "norm" in key:
								_snake_case						=							reverse_correct_unfold_norm_order(_UpperCamelCase					)

		model.load_state_dict(_UpperCamelCase					)

		# verify on image
		_snake_case						=							'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
		_snake_case						=							Image.open(requests.get(_UpperCamelCase			,	stream=_UpperCamelCase					).raw					).convert('''RGB'''					)

		_snake_case						=							SegformerImageProcessor()
		_snake_case						=							processor(_UpperCamelCase			,	return_tensors='''pt'''					).pixel_values

		with torch.no_grad():
				_snake_case						=							model(_UpperCamelCase					)
				_snake_case						=							outputs.logits

		print(logits.shape					)
		print('''First values of logits:'''			,	logits[0, 0, :3, :3]					)
		# assert values
		if model_name == "upernet-swin-tiny":
				_snake_case						=							torch.tensor(
				    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]					)
		elif model_name == "upernet-swin-small":
				_snake_case						=							torch.tensor(
				    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]					)
		elif model_name == "upernet-swin-base":
				_snake_case						=							torch.tensor(
				    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]					)
		elif model_name == "upernet-swin-large":
				_snake_case						=							torch.tensor(
				    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]					)
		print('''Logits:'''			,	outputs.logits[0, 0, :3, :3]					)
		assert torch.allclose(outputs.logits[0, 0, :3, :3]			,	_UpperCamelCase			,	atol=1E-4					)
		print('''Looks ok!'''					)

		if pytorch_dump_folder_path is not None:
				print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""					)
				model.save_pretrained(_UpperCamelCase					)
				print(F"""Saving processor to {pytorch_dump_folder_path}"""					)
				processor.save_pretrained(_UpperCamelCase					)

		if push_to_hub:
				print(F"""Pushing model and processor for {model_name} to hub"""					)
				model.push_to_hub(F"""openmmlab/{model_name}"""					)
				processor.push_to_hub(F"""openmmlab/{model_name}"""					)


if __name__ == "__main__":
	__A      		=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--model_name''',
	    default='''upernet-swin-tiny''',
	    type=str,
	    choices=[f'''upernet-swin-{size}''' for size in ['''tiny''', '''small''', '''base''', '''large''']],
	    help='''Name of the Swin + UperNet 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.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
	)

	__A      		=     parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




from ..utils import DummyObject, requires_backends



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[int]										=    ["speech"]



      def __init__(      self				:    str      , *A__				:    List[str]      , **A__				:    Tuple		)   ->					Optional[Any]:
        requires_backends(self      , ['''speech''']		)



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[Any]										=    ["speech"]



      def __init__(      self				:    Dict      , *A__				:    int      , **A__				:    int		)   ->					Tuple:
        requires_backends(self      , ['''speech''']		)

