

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)




import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)



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



  """simple docstring"""






  _snake_case						=							checkpoints.load_tax_checkpoint(_UpperCamelCase					)
  _snake_case						=							flatten_dict(_UpperCamelCase					)
  return flax_params



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



  """simple docstring"""






  _snake_case						=							{}

  _snake_case						=							{
      '''token_embedder''': '''embeddings''',
      '''encoder_norm''': '''layernorm''',
      '''kernel''': '''weight''',
      '''.out''': '''.output''',
      '''scale''': '''weight''',
      '''embedders_0.pos_embedding''': '''row_embedder.weight''',
      '''embedders_1.pos_embedding''': '''column_embedder.weight''',
  }

  _snake_case						=							{
      '''query''': '''attention.query''',
      '''key''': '''attention.key''',
      '''value''': '''attention.value''',
      '''output.dense''': '''output''',
      '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
      '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
      '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
      '''mlp.''': '''mlp.DenseReluDense.''',
      '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
      '''self_attention.o''': '''self_attention.attention.o''',
      '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
      '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
      '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
      '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
  }

  for key in flax_dict.keys():
    if "target" in key:
      # remove the first prefix from the key
      _snake_case						=							'''.'''.join(key[1:]					)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        _snake_case						=							new_key.replace(_UpperCamelCase			,	_UpperCamelCase					)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          _snake_case						=							new_key.replace(_UpperCamelCase			,	_UpperCamelCase					)

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        _snake_case						=							re.sub(R'''layers_(\d+)'''			,	R'''layer.\1'''			,	_UpperCamelCase					)
        _snake_case						=							new_key.replace('''encoder'''			,	'''encoder.encoder'''					)

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        _snake_case						=							re.sub(R'''layers_(\d+)'''			,	R'''layer.\1'''			,	_UpperCamelCase					)

      _snake_case						=							flax_dict[key]

  _snake_case						=							{}
  # convert converted_dict into torch format
  for key in converted_dict.keys():
    if ("embed_tokens" not in key) and ("embedder" not in key):
      _snake_case						=							torch.from_numpy(converted_dict[key].T					)
    else:
      _snake_case						=							torch.from_numpy(converted_dict[key]					)

  return converted_torch_dict



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



  """simple docstring"""






  _snake_case						=							get_flax_param(_UpperCamelCase					)

  if not use_large:
    _snake_case						=							PixaStructVisionConfig()
    _snake_case						=							PixaStructTextConfig()
  else:
    _snake_case						=							PixaStructVisionConfig(
        hidden_size=1_536			,	d_ff=3_968			,	num_attention_heads=24			,	num_hidden_layers=18					)
    _snake_case						=							PixaStructTextConfig(hidden_size=1_536			,	d_ff=3_968			,	num_heads=24			,	num_layers=18					)
  _snake_case						=							PixaStructConfig(
      vision_config=encoder_config.to_dict()			,	text_config=decoder_config.to_dict()			,	is_vqa=_UpperCamelCase					)

  _snake_case						=							PixaStructForConditionalGeneration(_UpperCamelCase					)

  _snake_case						=							rename_and_convert_flax_params(_UpperCamelCase					)
  model.load_state_dict(_UpperCamelCase					)

  _snake_case						=							AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''					)
  _snake_case						=							PixaStructImageProcessor()
  _snake_case						=							PixaStructProcessor(image_processor=_UpperCamelCase			,	tokenizer=_UpperCamelCase					)

  if use_large:
    _snake_case						=							4_096

  _snake_case						=							True

  # mkdir if needed
  os.makedirs(_UpperCamelCase			,	exist_ok=_UpperCamelCase					)

  model.save_pretrained(_UpperCamelCase					)
  processor.save_pretrained(_UpperCamelCase					)

  print('''Model saved in {}'''.format(_UpperCamelCase					)					)


if __name__ == "__main__":
 __A      		=     argparse.ArgumentParser()
 parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
 parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
 __A      		=     parser.parse_args()

 convert_pixastruct_original_pytorch_checkpoint_to_hf(
     args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
 )

