

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     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]					)	->					Any:




				'''simple docstring'''





				A__						=	3_8_4
				A__						=	7
				if "tiny" in model_name:
								A__						=	9_6
								A__						=	(2, 2, 6, 2)
								A__						=	(3, 6, 1_2, 2_4)
				elif "small" in model_name:
								A__						=	9_6
								A__						=	(2, 2, 1_8, 2)
								A__						=	(3, 6, 1_2, 2_4)
				elif "base" in model_name:
								A__						=	1_2_8
								A__						=	(2, 2, 1_8, 2)
								A__						=	(4, 8, 1_6, 3_2)
								A__						=	1_2
								A__						=	5_1_2
				elif "large" in model_name:
								A__						=	1_9_2
								A__						=	(2, 2, 1_8, 2)
								A__						=	(6, 1_2, 2_4, 4_8)
								A__						=	1_2
								A__						=	7_6_8

				# set label information
				A__						=	1_5_0
				A__						=	"huggingface/label-files"
				A__						=	"ade20k-id2label.json"
				A__						=	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				repo_type="dataset"					)		,				"r"					)					)
				A__						=	{int(SCREAMING_SNAKE_CASE_					): v for k, v in idalabel.items()}
				A__						=	{v: k for k, v in idalabel.items()}

				A__						=	SwinConfig(
				    embed_dim=SCREAMING_SNAKE_CASE_		,				depths=SCREAMING_SNAKE_CASE_		,				num_heads=SCREAMING_SNAKE_CASE_		,				window_size=SCREAMING_SNAKE_CASE_		,				out_features=["stage1", "stage2", "stage3", "stage4"]		,				)
				A__						=	UperNetConfig(
				    backbone_config=SCREAMING_SNAKE_CASE_		,				auxiliary_in_channels=SCREAMING_SNAKE_CASE_		,				num_labels=SCREAMING_SNAKE_CASE_		,				idalabel=SCREAMING_SNAKE_CASE_		,				labelaid=SCREAMING_SNAKE_CASE_		,				)

				return config



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]					)	->					List[str]:




				'''simple docstring'''





				A__						=	[]

				# 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     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[int]		,				SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							int					)	->					List[Any]:




				'''simple docstring'''





				A__						=	dct.pop(SCREAMING_SNAKE_CASE_					)
				A__						=	val



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Any		,				SCREAMING_SNAKE_CASE_:							Dict					)	->					Union[str, Any]:




				'''simple docstring'''





				A__						=	[int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
				for i in range(len(backbone_config.depths					)					):
								A__						=	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)
												A__						=	state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'					)
												A__						=	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
												A__						=	in_proj_weight[:dim, :]
												A__						=	in_proj_bias[: dim]
												A__						=	in_proj_weight[
												    dim : dim * 2, :
												]
												A__						=	in_proj_bias[
												    dim : dim * 2
												]
												A__						=	in_proj_weight[
												    -dim :, :
												]
												A__						=	in_proj_bias[-dim :]
												# fmt: on



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Dict					)	->					List[str]:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]					)	->					Any:




				'''simple docstring'''





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

				return x



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple					)	->					int:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]					)	->					Any:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Any		,				SCREAMING_SNAKE_CASE_:							List[Any]		,				SCREAMING_SNAKE_CASE_:							Tuple					)	->					str:




				'''simple docstring'''





				A__						=	{
				    "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",
				}
				A__						=	model_name_to_url[model_name]
				A__						=	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_		,				map_location="cpu"		,				file_name=SCREAMING_SNAKE_CASE_					)[
				    "state_dict"
				]

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

				A__						=	get_upernet_config(SCREAMING_SNAKE_CASE_					)
				A__						=	UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE_					)
				model.eval()

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

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

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

				model.load_state_dict(SCREAMING_SNAKE_CASE_					)

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

				A__						=	SegformerImageProcessor()
				A__						=	processor(SCREAMING_SNAKE_CASE_		,				return_tensors="pt"					).pixel_values

				with torch.no_grad():
								A__						=	model(SCREAMING_SNAKE_CASE_					)
								A__						=	outputs.logits

				print(logits.shape					)
				print("First values of logits:"		,				logits[0, 0, :3, :3]					)
				# assert values
				if model_name == "upernet-swin-tiny":
								A__						=	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":
								A__						=	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":
								A__						=	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":
								A__						=	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]		,				SCREAMING_SNAKE_CASE_		,				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(SCREAMING_SNAKE_CASE_					)
								print(F'Saving processor to {pytorch_dump_folder_path}'					)
								processor.save_pretrained(SCREAMING_SNAKE_CASE_					)

				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__":
	lowerCAmelCase__		      =       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."""
	)

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



'''simple docstring'''







import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    str ,	__a      :    Optional[Any]=None ):






   """simple docstring"""

   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   _a				:	str			=   nn.Parameter(__a )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      _a				:	Any			=   nn.Parameter(__a )





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Optional[Any] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Tuple			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	Dict			=   np.asarray(weights[2] )

   set_param(
       torch_layer.self_attention.query_key ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.value ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.output.dense ,	torch.tensor(__a ).view(-1 ,	__a ).contiguous().transpose(0 ,	1 ) ,	)





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    Optional[int] ,	__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	str			=   np.asarray(weights[2] )
   _a				:	int			=   np.asarray(weights[3] )

   set_param(
       torch_layer.self_attention.query ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.key ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.value ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.output.dense ,	torch.tensor(__a ).view(-1 ,	__a ).contiguous().transpose(0 ,	1 ) ,	)





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Any ,	__a      :    Optional[Any] ):






   """simple docstring"""

   _a				:	List[str]			=   weights[0][0][0]
   _a				:	List[Any]			=   np.asarray(layer_norm_a[0] )
   _a				:	List[str]			=   np.asarray(layer_norm_a[1] )
   set_param(
       torch_block.attention.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # lsh weights + output
   _a				:	List[str]			=   weights[0][1]
   if len(__a ) < 4:
      set_layer_weights_in_torch_lsh(__a ,	torch_block.attention ,	__a )
   else:
      set_layer_weights_in_torch_local(__a ,	torch_block.attention ,	__a )

   # intermediate weighs
   _a				:	Optional[Any]			=   weights[2][0][1][2]

   # Chunked Feed Forward
   if len(__a ) == 4:
      _a				:	Union[str, Any]			=   intermediate_weights[2]

   # layernorm 2
   _a				:	Any			=   np.asarray(intermediate_weights[0][0] )
   _a				:	List[Any]			=   np.asarray(intermediate_weights[0][1] )
   set_param(
       torch_block.feed_forward.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # intermediate dense
   _a				:	Any			=   np.asarray(intermediate_weights[1][0] )
   _a				:	Any			=   np.asarray(intermediate_weights[1][1] )
   set_param(
       torch_block.feed_forward.dense.dense ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)

   # intermediate out
   _a				:	Optional[int]			=   np.asarray(intermediate_weights[4][0] )
   _a				:	int			=   np.asarray(intermediate_weights[4][1] )
   set_param(
       torch_block.feed_forward.output.dense ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Dict ,	__a      :    Dict ,	__a      :    List[Any] ):






   """simple docstring"""

   _a				:	Optional[int]			=   torch_model.reformer

   # word embeds
   _a				:	Tuple			=   np.asarray(weights[1] )
   set_param(
       torch_model_reformer.embeddings.word_embeddings ,	torch.tensor(__a ) ,	)

   if isinstance(weights[3] ,	__a ):
      _a				:	Any			=   torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights ) ):
         _a				:	List[Any]			=   np.asarray(weights[3][emb_idx][0] )
         assert (
             position_embeddings.weights[emb_idx].shape == emb_weights.shape
         ), f"""{position_embeddings[emb_idx]} emb does not match"""
         _a				:	Any			=   nn.Parameter(torch.tensor(__a ) )

   _a				:	List[str]			=   weights[5]
   assert len(torch_model_reformer.encoder.layers ) * 4 == len(
       __a ), "HF and trax model do not have the same number of layers"
   for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
      _a				:	Tuple			=   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(__a ,	__a ,	__a )

   # output layer norm
   _a				:	Optional[Any]			=   np.asarray(weights[7][0] )
   _a				:	int			=   np.asarray(weights[7][1] )
   set_param(
       torch_model_reformer.encoder.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # output embeddings
   _a				:	List[str]			=   np.asarray(weights[9][0] )
   _a				:	int			=   np.asarray(weights[9][1] )
   set_param(
       torch_model.lm_head.decoder ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Tuple ,	__a      :    Optional[Any] ,	__a      :    Dict ):






   """simple docstring"""

   _a				:	List[Any]			=   ReformerConfig.from_json_file(__a )
   print(f"""Building PyTorch model from configuration: {config}""" )
   _a				:	int			=   ReformerModelWithLMHead(__a )

   with open(__a ,	'rb' ) as f:
      _a				:	Optional[Any]			=   pickle.load(__a )['weights']

   set_model_weights_in_torch(__a ,	__a ,	config.hidden_size )

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""" )
   torch.save(model.state_dict() ,	__a )


if __name__ == "__main__":
  __lowerCAmelCase         =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained Reformer model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __lowerCAmelCase         =    parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



