


'''simple docstring'''

import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a_  :				Any						= {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

a_  :				Union[str, Any]						= logging.get_logger(__name__)





class      snake_case    (    A_			):





  """simple docstring"""




  _lowerCamelCase		    =  "mask2former"
  _lowerCamelCase		    =  ["swin"]
  _lowerCamelCase		    =  {"hidden_size": "hidden_dim"}






  def __init__(    self			,  UpperCamelCase = None			,  UpperCamelCase = 256			,  UpperCamelCase = 256			,  UpperCamelCase = 256			,  UpperCamelCase = 1024			,  UpperCamelCase = "relu"			,  UpperCamelCase = 6			,  UpperCamelCase = 10			,  UpperCamelCase = 8			,  UpperCamelCase = 0.0			,  UpperCamelCase = 2048			,  UpperCamelCase = False			,  UpperCamelCase = False			,  UpperCamelCase = 4			,  UpperCamelCase = 255			,  UpperCamelCase = 100			,  UpperCamelCase = 0.1			,  UpperCamelCase = 2.0			,  UpperCamelCase = 5.0			,  UpperCamelCase = 5.0			,  UpperCamelCase = 1_2544			,  UpperCamelCase = 3.0			,  UpperCamelCase = 0.75			,  UpperCamelCase = 0.02			,  UpperCamelCase = 1.0			,  UpperCamelCase = True			,  UpperCamelCase = [4, 8, 16, 32]			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   if backbone_config is None:
    logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."     )
    lowerCamelCase_      =  CONFIG_MAPPING["swin"](
        image_size=224			,  in_channels=3			,  patch_size=4			,  embed_dim=96			,  depths=[2, 2, 18, 2]			,  num_heads=[3, 6, 12, 24]			,  window_size=7			,  drop_path_rate=0.3			,  use_absolute_embeddings=_lowerCamelCase			,  out_features=["stage1", "stage2", "stage3", "stage4"]			,  )

   if isinstance(_lowerCamelCase			,  _lowerCamelCase     ):
    lowerCamelCase_      =  backbone_config.pop("model_type"     )
    lowerCamelCase_      =  CONFIG_MAPPING[backbone_model_type]
    lowerCamelCase_      =  config_class.from_dict(_lowerCamelCase     )

   # verify that the backbone is supported
   if backbone_config.model_type not in self.backbones_supported:
    logger.warning_once(
        f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
        f'''Supported model types: {",".join(self.backbones_supported     )}'''     )

   lowerCamelCase_      =  backbone_config
   lowerCamelCase_      =  feature_size
   lowerCamelCase_      =  mask_feature_size
   lowerCamelCase_      =  hidden_dim
   lowerCamelCase_      =  encoder_feedforward_dim
   lowerCamelCase_      =  activation_function
   lowerCamelCase_      =  encoder_layers
   lowerCamelCase_      =  decoder_layers
   lowerCamelCase_      =  num_attention_heads
   lowerCamelCase_      =  dropout
   lowerCamelCase_      =  dim_feedforward
   lowerCamelCase_      =  pre_norm
   lowerCamelCase_      =  enforce_input_projection
   lowerCamelCase_      =  common_stride
   lowerCamelCase_      =  ignore_value
   lowerCamelCase_      =  num_queries
   lowerCamelCase_      =  no_object_weight
   lowerCamelCase_      =  class_weight
   lowerCamelCase_      =  mask_weight
   lowerCamelCase_      =  dice_weight
   lowerCamelCase_      =  train_num_points
   lowerCamelCase_      =  oversample_ratio
   lowerCamelCase_      =  importance_sample_ratio
   lowerCamelCase_      =  init_std
   lowerCamelCase_      =  init_xavier_std
   lowerCamelCase_      =  use_auxiliary_loss
   lowerCamelCase_      =  feature_strides
   lowerCamelCase_      =  output_auxiliary_logits
   lowerCamelCase_      =  decoder_layers

   super().__init__(**_lowerCamelCase     )






  @classmethod
  def   snake_case		(    cls			,  UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   return cls(
       backbone_config=_lowerCamelCase			,  **_lowerCamelCase			,  )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  copy.deepcopy(self.__dict__     )
   lowerCamelCase_      =  self.backbone_config.to_dict()
   lowerCamelCase_      =  self.__class__.model_type
   return output





"""simple docstring"""



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 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Optional[int]:
  _snake_case					     =       checkpoints.load_tax_checkpoint(__lowerCamelCase		)
  _snake_case					     =       flatten_dict(__lowerCamelCase		)
  return flax_params
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					Optional[int]:
  _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(__lowerCamelCase ,					__lowerCamelCase		)

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

      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''' ,					__lowerCamelCase		)
        _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''' ,					__lowerCamelCase		)

      _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 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Any=False ,					__lowerCamelCase   :       Optional[int]=False		)     ->					int:
  _snake_case					     =       get_flax_param(__lowerCamelCase		)

  if not use_large:
    _snake_case					     =       PixaStructVisionConfig()
    _snake_case					     =       PixaStructTextConfig()
  else:
    _snake_case					     =       PixaStructVisionConfig(
        hidden_size=15_36 ,					d_ff=39_68 ,					num_attention_heads=24 ,					num_hidden_layers=18		)
    _snake_case					     =       PixaStructTextConfig(hidden_size=15_36 ,					d_ff=39_68 ,					num_heads=24 ,					num_layers=18		)
  _snake_case					     =       PixaStructConfig(
      vision_config=encoder_config.to_dict() ,					text_config=decoder_config.to_dict() ,					is_vqa=__lowerCamelCase		)

  _snake_case					     =       PixaStructForConditionalGeneration(__lowerCamelCase		)

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

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

  if use_large:
    _snake_case					     =       40_96

  _snake_case					     =       True

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

  model.save_pretrained(__lowerCamelCase		)
  processor.save_pretrained(__lowerCamelCase		)

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


if __name__ == "__main__":
 UpperCAmelCase__          = 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.')
 UpperCAmelCase__          = parser.parse_args()

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



