
"""simple docstring"""



from __future__ import annotations

from typing import Any

class       lowerCAmelCase__				:







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			int			):
       _snake_case					     =       num_of_nodes
       _snake_case					     =       []
       _snake_case					     =       {}







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int			):
       self.m_edges.append([u_node, v_node, weight]			)







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			int			):

       if self.m_component[u_node] == u_node:
         return u_node
       return self.find_component(self.m_component[u_node]			)







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			int			):

       if self.m_component[u_node] != u_node:
         for k in self.m_component:
           _snake_case					     =       self.find_component(_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			list[int]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int			):

       if component_size[u_node] <= component_size[v_node]:
         _snake_case					     =       v_node
         component_size[v_node] += component_size[u_node]
         self.set_component(_lowerCamelCase			)

       elif component_size[u_node] >= component_size[v_node]:
         _snake_case					     =       self.find_component(_lowerCamelCase			)
         component_size[u_node] += component_size[v_node]
         self.set_component(_lowerCamelCase			)







     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       []
       _snake_case					     =       0

       _snake_case					     =       [-1] * self.m_num_of_nodes

       # A list of components (initialized to all of the nodes)
       for node in range(self.m_num_of_nodes			):
         self.m_component.update({node: node}			)
         component_size.append(1			)

       _snake_case					     =       self.m_num_of_nodes

       while num_of_components > 1:
         for edge in self.m_edges:
           _snake_case     ,					_snake_case     ,					_snake_case					     =       edge

           _snake_case					     =       self.m_component[u]
           _snake_case					     =       self.m_component[v]

           if u_component != v_component:

             for component in (u_component, v_component):
               if (
                   minimum_weight_edge[component] == -1
                   or minimum_weight_edge[component][2] > w
               ):
                 _snake_case					     =       [u, v, w]

         for edge in minimum_weight_edge:
           if isinstance(_lowerCamelCase    ,      _lowerCamelCase			):
             _snake_case     ,					_snake_case     ,					_snake_case					     =       edge

             _snake_case					     =       self.m_component[u]
             _snake_case					     =       self.m_component[v]

             if u_component != v_component:
               mst_weight += w
               self.union(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)
               print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''			)
               num_of_components -= 1

         _snake_case					     =       [-1] * self.m_num_of_nodes
       print(f'''The total weight of the minimal spanning tree is: {mst_weight}'''			)






def 					_UpperCAmelCase      ( )     ->					None:
  pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()





"""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
 )



