





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]:
  __snake_case:						List[str]   =						checkpoints.load_tax_checkpoint(__UpperCAmelCase)
  __snake_case:						Optional[Any]   =						flatten_dict(__UpperCAmelCase)
  return flax_params







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Union[str, Any]:
  __snake_case:						List[str]   =						{}

  __snake_case:						List[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''',
  }

  __snake_case:						Any   =						{
      '''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
      __snake_case:						str   =						'''.'''.join(key[1:])

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        __snake_case:						int   =						new_key.replace(__UpperCAmelCase					,					__UpperCAmelCase)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          __snake_case:						Union[str, Any]   =						new_key.replace(__UpperCAmelCase					,					__UpperCAmelCase)

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        __snake_case:						Optional[Any]   =						re.sub(r"""layers_(\d+)"""					,					r"""layer.\1"""					,					__UpperCAmelCase)
        __snake_case:						Optional[int]   =						new_key.replace("""encoder"""					,					"""encoder.encoder""")

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

      __snake_case:						Dict   =						flax_dict[key]

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

  return converted_torch_dict







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False					,					SCREAMING_SNAKE_CASE__=False)   ->    Tuple:
  __snake_case:						Union[str, Any]   =						get_flax_param(__UpperCAmelCase)

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

  __snake_case:						Optional[Any]   =						PixaStructForConditionalGeneration(__UpperCAmelCase)

  __snake_case:						Optional[int]   =						rename_and_convert_flax_params(__UpperCAmelCase)
  model.load_state_dict(__UpperCAmelCase)

  __snake_case:						Any   =						AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer""")
  __snake_case:						Any   =						PixaStructImageProcessor()
  __snake_case:						str   =						PixaStructProcessor(image_processor=__UpperCAmelCase					,					tokenizer=__UpperCAmelCase)

  if use_large:
    __snake_case:						str   =						4096

  __snake_case:						str   =						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__":
      __UpperCAmelCase       :   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       :   Dict				       =							parser.parse_args()

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













def 		lowerCAmelCase_    (  __UpperCAmelCase:			int = 100_0000      )      ->	int:
    UpperCamelCase__							:				str          =      limit + 1
    UpperCamelCase__							:				List[str]          =      [0] * limit
    for first_term in range(1		,   __UpperCAmelCase      ):
        for n in range(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      ):
            UpperCamelCase__							:				str          =      first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    UpperCamelCase__							:				Any          =      sum(1 for x in frequency[1:limit] if x == 10      )

    return count


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

