
'''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        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       List[Any]:





  """simple docstring"""

  __A		:     Any												=		checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE )
  __A		:     Optional[Any]												=		flatten_dict(__SCREAMING_SNAKE_CASE )
  return flax_params







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       str:





  """simple docstring"""

  __A		:     Any												=		{}

  __A		:     str												=		{
      """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""",
  }

  __A		:     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
      __A		:     Optional[Any]												=		""".""".join(key[1:] )

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        __A		:     Optional[int]												=		new_key.replace(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          __A		:     Optional[Any]												=		new_key.replace(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        __A		:     List[Any]												=		re.sub(R"""layers_(\d+)"""			,     R"""layer.\1"""			,     __SCREAMING_SNAKE_CASE )
        __A		:     Tuple												=		new_key.replace("""encoder"""			,     """encoder.encoder""" )

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        __A		:     Union[str, Any]												=		re.sub(R"""layers_(\d+)"""			,     R"""layer.\1"""			,     __SCREAMING_SNAKE_CASE )

      __A		:     Optional[Any]												=		flax_dict[key]

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

  return converted_torch_dict







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  List[Any]=False			,     __SCREAMING_SNAKE_CASE						:  List[str]=False )				->       str:





  """simple docstring"""

  __A		:     Any												=		get_flax_param(__SCREAMING_SNAKE_CASE )

  if not use_large:
    __A		:     List[Any]												=		PixaStructVisionConfig()
    __A		:     Tuple												=		PixaStructTextConfig()
  else:
    __A		:     Any												=		PixaStructVisionConfig(
        hidden_size=1536			,     d_ff=3968			,     num_attention_heads=24			,     num_hidden_layers=18 )
    __A		:     Any												=		PixaStructTextConfig(hidden_size=1536			,     d_ff=3968			,     num_heads=24			,     num_layers=18 )
  __A		:     Dict												=		PixaStructConfig(
      vision_config=encoder_config.to_dict()			,     text_config=decoder_config.to_dict()			,     is_vqa=__SCREAMING_SNAKE_CASE )

  __A		:     Tuple												=		PixaStructForConditionalGeneration(__SCREAMING_SNAKE_CASE )

  __A		:     Optional[int]												=		rename_and_convert_flax_params(__SCREAMING_SNAKE_CASE )
  model.load_state_dict(__SCREAMING_SNAKE_CASE )

  __A		:     Dict												=		AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer""" )
  __A		:     Union[str, Any]												=		PixaStructImageProcessor()
  __A		:     Dict												=		PixaStructProcessor(image_processor=__SCREAMING_SNAKE_CASE			,     tokenizer=__SCREAMING_SNAKE_CASE )

  if use_large:
    __A		:     List[Any]												=		4096

  __A		:     Optional[Any]												=		True

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

  model.save_pretrained(__SCREAMING_SNAKE_CASE )
  processor.save_pretrained(__SCREAMING_SNAKE_CASE )

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


if __name__ == "__main__":
 A__   :		Dict 		=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__   :		str 		=parser.parse_args()

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





'''simple docstring'''
from collections.abc import Sequence







def        A_     (						__SCREAMING_SNAKE_CASE						:  Sequence[float]			,     __SCREAMING_SNAKE_CASE						:  bool = False )				->       float:





  """simple docstring"""

  if not arr:
    return 0

  __A		:     Any												=		0 if allow_empty_subarrays else float("""-inf""" )
  __A		:     List[Any]												=		0.0
  for num in arr:
    __A		:     Tuple												=		max(0 if allow_empty_subarrays else num			,     curr_sum + num )
    __A		:     Optional[int]												=		max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  return max_sum


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     A__   :		Any 		=[-2, 1, -3, 4, -1, 2, 1, -5, 4]
     print(F"{max_subarray_sum(nums) = }")



