

"""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  _snake_case     (    UpperCAmelCase_  :					Tuple			):
     A__										=  checkpoints.load_tax_checkpoint(UpperCAmelCase_			)
     A__										=  flatten_dict(UpperCAmelCase_			)
     return flax_params





def  _snake_case     (    UpperCAmelCase_  :					Dict			):
     A__										=  {}

     A__										=  {
         """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__										=  {
         """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__										=  """.""".join(key[1:]			)

               # rename the key
               for old, new in CONVERSION_MAPPING.items():
                    A__										=  new_key.replace(UpperCAmelCase_					,   UpperCAmelCase_			)

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

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

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

               A__										=  flax_dict[key]

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

     return converted_torch_dict





def  _snake_case     (    UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Tuple=False					,   UpperCAmelCase_  :					str=False			):
     A__										=  get_flax_param(UpperCAmelCase_			)

     if not use_large:
          A__										=  PixaStructVisionConfig()
          A__										=  PixaStructTextConfig()
     else:
          A__										=  PixaStructVisionConfig(
              hidden_size=1536					,   d_ff=3968					,   num_attention_heads=24					,   num_hidden_layers=18			)
          A__										=  PixaStructTextConfig(hidden_size=1536					,   d_ff=3968					,   num_heads=24					,   num_layers=18			)
     A__										=  PixaStructConfig(
         vision_config=encoder_config.to_dict()					,   text_config=decoder_config.to_dict()					,   is_vqa=UpperCAmelCase_			)

     A__										=  PixaStructForConditionalGeneration(UpperCAmelCase_			)

     A__										=  rename_and_convert_flax_params(UpperCAmelCase_			)
     model.load_state_dict(UpperCAmelCase_			)

     A__										=  AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""			)
     A__										=  PixaStructImageProcessor()
     A__										=  PixaStructProcessor(image_processor=UpperCAmelCase_					,   tokenizer=UpperCAmelCase_			)

     if use_large:
          A__										=  4096

     A__										=  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__":
 SCREAMING_SNAKE_CASE_					:	List[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_					:	List[Any]							  =						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 itertools import count





def  _snake_case     (    UpperCAmelCase_  :					int = 50			):
     A__										=  [1] * min_block_length

     for n in count(UpperCAmelCase_			):
          fill_count_functions.append(1			)

          for block_length in range(UpperCAmelCase_					,   n + 1			):
               for block_start in range(n - block_length			):
                    fill_count_functions[n] += fill_count_functions[
                        n - block_start - block_length - 1
                    ]

               fill_count_functions[n] += 1

          if fill_count_functions[n] > 100_0000:
               break

     return n


if __name__ == "__main__":
 print(f"""{solution() = }""")






