




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     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



      lowerCamelCase_	  =					checkpoints.load_tax_checkpoint(lowercase  )
      lowerCamelCase_	  =					flatten_dict(lowercase  )
      return flax_params

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{}

      lowerCamelCase_	  =					{
          '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_	  =					{
          '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_	  =					'.'.join(key[1:]  )

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

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

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

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

                  lowerCamelCase_	  =					flax_dict[key]

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

      return converted_torch_dict

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Optional[int]			,							lowercase			: Dict=False			,							lowercase			: Tuple=False  ):


      '''simple docstring'''



      lowerCamelCase_	  =					get_flax_param(lowercase  )

      if not use_large:
            lowerCamelCase_	  =					PixaStructVisionConfig()
            lowerCamelCase_	  =					PixaStructTextConfig()
      else:
            lowerCamelCase_	  =					PixaStructVisionConfig(
                hidden_size=15_36			,							d_ff=39_68			,							num_attention_heads=24			,							num_hidden_layers=18  )
            lowerCamelCase_	  =					PixaStructTextConfig(hidden_size=15_36			,							d_ff=39_68			,							num_heads=24			,							num_layers=18  )
      lowerCamelCase_	  =					PixaStructConfig(
          vision_config=encoder_config.to_dict()			,							text_config=decoder_config.to_dict()			,							is_vqa=lowercase  )

      lowerCamelCase_	  =					PixaStructForConditionalGeneration(lowercase  )

      lowerCamelCase_	  =					rename_and_convert_flax_params(lowercase  )
      model.load_state_dict(lowercase  )

      lowerCamelCase_	  =					AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'  )
      lowerCamelCase_	  =					PixaStructImageProcessor()
      lowerCamelCase_	  =					PixaStructProcessor(image_processor=lowercase			,							tokenizer=lowercase  )

      if use_large:
            lowerCamelCase_	  =					40_96

      lowerCamelCase_	  =					True

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

      model.save_pretrained(lowercase  )
      processor.save_pretrained(lowercase  )

      print('Model saved in {}'.format(lowercase  )  )


if __name__ == "__main__":
 lowerCamelCase       :	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.")
 lowerCamelCase       :	Optional[Any]									=     parser.parse_args()

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









from __future__ import annotations

from fractions import Fraction

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: int  ):


      '''simple docstring'''



      return (
          num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
      )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



      lowerCamelCase_	  =					[]
      lowerCamelCase_	  =					11
      lowerCamelCase_	  =					int('1' + '0' * digit_len  )
      for num in range(lowercase			,							lowercase  ):
            while den <= 99:
                  if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                        if is_digit_cancelling(lowercase			,							lowercase  ):
                              solutions.append(f"""{num}/{den}"""  )
                  den += 1
            num += 1
            lowerCamelCase_	  =					10
      return solutions

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int = 2  ):


      '''simple docstring'''



      lowerCamelCase_	  =					1.0
      for fraction in fraction_list(lowercase  ):
            lowerCamelCase_	  =					Fraction(lowercase  )
            result *= frac.denominator / frac.numerator
      return int(lowercase  )


if __name__ == "__main__":
 print(solution())



