


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    a__    (		snake_case__					:							Optional[int]     ):
  _UpperCAmelCase		:							str							=     checkpoints.load_tax_checkpoint(snake_case__     )
  _UpperCAmelCase		:							List[str]							=     flatten_dict(snake_case__     )
  return flax_params




def    a__    (		snake_case__					:							int     ):
  _UpperCAmelCase		:							List[Any]							=     {}

  _UpperCAmelCase		:							Union[str, 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""",
  }

  _UpperCAmelCase		:							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
      _UpperCAmelCase		:							Union[str, Any]							=     """.""".join(key[1:]     )

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        _UpperCAmelCase		:							List[Any]							=     new_key.replace(snake_case__						,   snake_case__     )

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          _UpperCAmelCase		:							Dict							=     new_key.replace(snake_case__						,   snake_case__     )

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        _UpperCAmelCase		:							str							=     re.sub(r"""layers_(\d+)"""						,   r"""layer.\1"""						,   snake_case__     )
        _UpperCAmelCase		:							Any							=     new_key.replace("""encoder"""						,   """encoder.encoder"""     )

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

      _UpperCAmelCase		:							int							=     flax_dict[key]

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

  return converted_torch_dict




def    a__    (		snake_case__					:							List[Any]						,   snake_case__					:							str						,   snake_case__					:							Optional[int]=False						,   snake_case__					:							str=False     ):
  _UpperCAmelCase		:							str							=     get_flax_param(snake_case__     )

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

  _UpperCAmelCase		:							Any							=     PixaStructForConditionalGeneration(snake_case__     )

  _UpperCAmelCase		:							Optional[int]							=     rename_and_convert_flax_params(snake_case__     )
  model.load_state_dict(snake_case__     )

  _UpperCAmelCase		:							str							=     AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""     )
  _UpperCAmelCase		:							str							=     PixaStructImageProcessor()
  _UpperCAmelCase		:							List[str]							=     PixaStructProcessor(image_processor=snake_case__						,   tokenizer=snake_case__     )

  if use_large:
    _UpperCAmelCase		:							Any							=     4096

  _UpperCAmelCase		:							List[Any]							=     True

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

  model.save_pretrained(snake_case__     )
  processor.save_pretrained(snake_case__     )

  print("""Model saved in {}""".format(snake_case__     )     )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     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.')
 SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     parser.parse_args()

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









def    a__    (		snake_case__					:							Tuple     ):  # noqa: E741
  _UpperCAmelCase		:							Dict							=     len(snake_case__     )
  _UpperCAmelCase		:							Tuple							=     0
  _UpperCAmelCase		:							Union[str, Any]							=     [0] * n
  _UpperCAmelCase		:							Union[str, Any]							=     [False] * n
  _UpperCAmelCase		:							Tuple							=     [False] * n

  def dfs(snake_case__					:							List[str]						,   snake_case__					:							int						,   snake_case__					:							str						,   snake_case__					:							int     ):
    if parent == root:
      out_edge_count += 1
    _UpperCAmelCase		:							List[Any]							=     True
    _UpperCAmelCase		:							List[str]							=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _UpperCAmelCase		:							Optional[Any]							=     dfs(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__     )
        _UpperCAmelCase		:							Optional[Any]							=     min(low[at]						,   low[to]     )

        # AP found via bridge
        if at < low[to]:
          _UpperCAmelCase		:							List[str]							=     True
        # AP found via cycle
        if at == low[to]:
          _UpperCAmelCase		:							Optional[Any]							=     True
      else:
        _UpperCAmelCase		:							str							=     min(low[at]						,   snake_case__     )
    return out_edge_count

  for i in range(snake_case__     ):
    if not visited[i]:
      _UpperCAmelCase		:							List[Any]							=     0
      _UpperCAmelCase		:							List[str]							=     dfs(snake_case__						,   snake_case__						,   -1						,   snake_case__     )
      _UpperCAmelCase		:							Optional[Any]							=     out_edge_count > 1

  for x in range(len(snake_case__     )     ):
    if is_art[x] is True:
      print(snake_case__     )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}





compute_ap(data)





