
"""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		(       snake_case   :Any	)   ->			str:
 __UpperCamelCase       						= checkpoints.load_tax_checkpoint(snake_case	)
 __UpperCamelCase       						= flatten_dict(snake_case	)
 return flax_params






def 							A		(       snake_case   :List[str]	)   ->			str:
 __UpperCamelCase       						= {}

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

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

   if "decoder" in new_key:
    for old, new in DECODER_CONVERSION_MAPPING.items():
     __UpperCamelCase       						= 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       						= re.sub(r'layers_(\d+)'       ,      r'layer.\1'       ,      snake_case	)
    __UpperCamelCase       						= new_key.replace('encoder'       ,      'encoder.encoder'	)

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

   __UpperCamelCase       						= flax_dict[key]

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

 return converted_torch_dict






def 							A		(       snake_case   :Tuple       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[int]=False       ,      snake_case   :str=False	)   ->			Optional[int]:
 __UpperCamelCase       						= get_flax_param(snake_case	)

 if not use_large:
  __UpperCamelCase       						= PixaStructVisionConfig()
  __UpperCamelCase       						= PixaStructTextConfig()
 else:
  __UpperCamelCase       						= PixaStructVisionConfig(
      hidden_size=1_5_3_6       ,      d_ff=3_9_6_8       ,      num_attention_heads=2_4       ,      num_hidden_layers=1_8	)
  __UpperCamelCase       						= PixaStructTextConfig(hidden_size=1_5_3_6       ,      d_ff=3_9_6_8       ,      num_heads=2_4       ,      num_layers=1_8	)
 __UpperCamelCase       						= PixaStructConfig(
     vision_config=encoder_config.to_dict()       ,      text_config=decoder_config.to_dict()       ,      is_vqa=snake_case	)

 __UpperCamelCase       						= PixaStructForConditionalGeneration(snake_case	)

 __UpperCamelCase       						= rename_and_convert_flax_params(snake_case	)
 model.load_state_dict(snake_case	)

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

 if use_large:
  __UpperCamelCase       						= 4_0_9_6

 __UpperCamelCase       						= 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__":
      UpperCamelCase			:       Optional[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.")
      UpperCamelCase			:       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
      )





"""simple docstring"""

def 							A		(       snake_case   :int	)   ->			bool:
 return str(snake_case	) == str(snake_case	)[::-1]






def 							A		(       snake_case   :int	)   ->			int:
 return int(snake_case	) + int(str(snake_case	)[::-1]	)






def 							A		(       snake_case   :int = 1_0_0_0_0	)   ->			int:
 __UpperCamelCase       						= []
 for num in range(1       ,      snake_case	):
  __UpperCamelCase       						= 0
  __UpperCamelCase       						= num
  while iterations < 5_0:
   __UpperCamelCase       						= sum_reverse(snake_case	)
   iterations += 1
   if is_palindrome(snake_case	):
    break
  else:
   lychrel_nums.append(snake_case	)
 return len(snake_case	)


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



