




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      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[str]:
  lowerCAmelCase__				:  Optional[Any]		       =     checkpoints.load_tax_checkpoint(UpperCamelCase      )
  lowerCAmelCase__				:  int		       =     flatten_dict(UpperCamelCase      )
  return flax_params



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  Dict		       =     {}

  lowerCAmelCase__				:  Any		       =     {
      '''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''',
  }

  lowerCAmelCase__				:  List[str]		       =     {
      '''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
      lowerCAmelCase__				:  Optional[Any]		       =     '''.'''.join(key[1:]      )

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        lowerCAmelCase__				:  List[str]		       =     new_key.replace(UpperCamelCase	,      UpperCamelCase      )

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          lowerCAmelCase__				:  List[Any]		       =     new_key.replace(UpperCamelCase	,      UpperCamelCase      )

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

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

      lowerCAmelCase__				:  str		       =     flax_dict[key]

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

  return converted_torch_dict



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase=False	,      UpperCamelCase=False      )     ->	List[Any]:
  lowerCAmelCase__				:  Union[str, Any]		       =     get_flax_param(UpperCamelCase      )

  if not use_large:
    lowerCAmelCase__				:  List[str]		       =     PixaStructVisionConfig()
    lowerCAmelCase__				:  List[str]		       =     PixaStructTextConfig()
  else:
    lowerCAmelCase__				:  Optional[int]		       =     PixaStructVisionConfig(
        hidden_size=1536	,      d_ff=3968	,      num_attention_heads=24	,      num_hidden_layers=18      )
    lowerCAmelCase__				:  int		       =     PixaStructTextConfig(hidden_size=1536	,      d_ff=3968	,      num_heads=24	,      num_layers=18      )
  lowerCAmelCase__				:  Tuple		       =     PixaStructConfig(
      vision_config=encoder_config.to_dict()	,      text_config=decoder_config.to_dict()	,      is_vqa=UpperCamelCase      )

  lowerCAmelCase__				:  Tuple		       =     PixaStructForConditionalGeneration(UpperCamelCase      )

  lowerCAmelCase__				:  List[str]		       =     rename_and_convert_flax_params(UpperCamelCase      )
  model.load_state_dict(UpperCamelCase      )

  lowerCAmelCase__				:  int		       =     AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''      )
  lowerCAmelCase__				:  str		       =     PixaStructImageProcessor()
  lowerCAmelCase__				:  List[Any]		       =     PixaStructProcessor(image_processor=UpperCamelCase	,      tokenizer=UpperCamelCase      )

  if use_large:
    lowerCAmelCase__				:  Tuple		       =     4096

  lowerCAmelCase__				:  Union[str, Any]		       =     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__":
     lowerCAmelCase_					    = 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.""")
     lowerCAmelCase_					    = parser.parse_args()

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







import numpy as np






class  _lowerCAmelCase     :

      def __init__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     (0, 0)
        lowerCAmelCase__				:  Optional[int]		       =     None
        lowerCAmelCase__				:  Optional[Any]		       =     0
        lowerCAmelCase__				:  Optional[int]		       =     0
        lowerCAmelCase__				:  Tuple		       =     0

      def __eq__(   self      ,				__UpperCAmelCase		):
        return self.position == cell.position







      def 	__magic_name__(   self		):
        print(self.position		)






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase=(5, 5)		):
        lowerCAmelCase__				:  List[str]		       =     np.zeros(__UpperCAmelCase		)
        lowerCAmelCase__				:  List[Any]		       =     world_size[0]
        lowerCAmelCase__				:  str		       =     world_size[1]

      def 	__magic_name__(   self		):
        print(self.w		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        lowerCAmelCase__				:  Tuple		       =     cell.position[0]
        lowerCAmelCase__				:  Optional[int]		       =     cell.position[1]
        lowerCAmelCase__				:  Any		       =     []
        for n in neughbour_cord:
          lowerCAmelCase__				:  int		       =     current_x + n[0]
          lowerCAmelCase__				:  Dict		       =     current_y + n[1]
          if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            lowerCAmelCase__				:  Optional[int]		       =     Cell()
            lowerCAmelCase__				:  Optional[int]		       =     (x, y)
            lowerCAmelCase__				:  Any		       =     cell
            neighbours.append(__UpperCAmelCase		)
        return neighbours



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Union[str, Any]:
  lowerCAmelCase__				:  int		       =     []
  lowerCAmelCase__				:  int		       =     []
  _open.append(UpperCamelCase      )

  while _open:
    lowerCAmelCase__				:  Dict		       =     np.argmin([n.f for n in _open]      )
    lowerCAmelCase__				:  Dict		       =     _open[min_f]
    _closed.append(_open.pop(UpperCamelCase      )      )
    if current == goal:
      break
    for n in world.get_neigbours(UpperCamelCase      ):
      for c in _closed:
        if c == n:
          continue
      lowerCAmelCase__				:  Any		       =     current.g + 1
      lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     n.position
      lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     goal.position
      lowerCAmelCase__				:  Tuple		       =     (ya - ya) ** 2 + (xa - xa) ** 2
      lowerCAmelCase__				:  Optional[Any]		       =     n.h + n.g

      for c in _open:
        if c == n and c.f < n.f:
          continue
      _open.append(UpperCamelCase      )
  lowerCAmelCase__				:  List[Any]		       =     []
  while current.parent is not None:
    path.append(current.position      )
    lowerCAmelCase__				:  List[Any]		       =     current.parent
  path.append(current.position      )
  return path[::-1]


if __name__ == "__main__":
     lowerCAmelCase_					    = Gridworld()
     # Start position and goal
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (0, 0)
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (4, 4)
     print(F"""path from {start.position} to {goal.position}""")
     lowerCAmelCase_					    = astar(world, start, goal)
     #   Just for visual reasons.
     for i in s:
          lowerCAmelCase_					    = 1
     print(world.w)

