


"""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    :    List[Any]				):


  '''simple docstring'''







  lowerCAmelCase     :     List[str]								=							checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     str								=							flatten_dict(SCREAMING_SNAKE_CASE				)
  return flax_params
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[str]				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							{}

  lowerCAmelCase     :     Optional[Any]								=							{
      "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     :     Union[str, 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
      lowerCAmelCase     :     int								=							".".join(key[1:]				)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        lowerCAmelCase     :     Tuple								=							new_key.replace(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          lowerCAmelCase     :     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
        lowerCAmelCase     :     Union[str, Any]								=							re.sub(r"layers_(\d+)"				,      r"layer.\1"				,      SCREAMING_SNAKE_CASE				)
        lowerCAmelCase     :     List[str]								=							new_key.replace("encoder"				,      "encoder.encoder"				)

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

      lowerCAmelCase     :     List[str]								=							flax_dict[key]

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

  return converted_torch_dict
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    Any=False				,      SCREAMING_SNAKE_CASE    :    Tuple=False				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							get_flax_param(SCREAMING_SNAKE_CASE				)

  if not use_large:
    lowerCAmelCase     :     Dict								=							PixaStructVisionConfig()
    lowerCAmelCase     :     List[Any]								=							PixaStructTextConfig()
  else:
    lowerCAmelCase     :     List[str]								=							PixaStructVisionConfig(
        hidden_size=1_5_3_6				,      d_ff=3_9_6_8				,      num_attention_heads=2_4				,      num_hidden_layers=1_8				)
    lowerCAmelCase     :     str								=							PixaStructTextConfig(hidden_size=1_5_3_6				,      d_ff=3_9_6_8				,      num_heads=2_4				,      num_layers=1_8				)
  lowerCAmelCase     :     int								=							PixaStructConfig(
      vision_config=encoder_config.to_dict()				,      text_config=decoder_config.to_dict()				,      is_vqa=SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     int								=							PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     Optional[int]								=							rename_and_convert_flax_params(SCREAMING_SNAKE_CASE				)
  model.load_state_dict(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     str								=							AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"				)
  lowerCAmelCase     :     Any								=							PixaStructImageProcessor()
  lowerCAmelCase     :     Optional[int]								=							PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE				,      tokenizer=SCREAMING_SNAKE_CASE				)

  if use_large:
    lowerCAmelCase     :     str								=							4_0_9_6

  lowerCAmelCase     :     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__":
     lowerCAmelCase__      				=   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__      				=   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 __future__ import annotations

from functools import lru_cache
from math import ceil

lowerCAmelCase__      				=   100

lowerCAmelCase__      				=   set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__      				=   42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
     if prime not in primes:
          continue
     primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_0_0				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  if number_to_partition < 0:
    return set()
  elif number_to_partition == 0:
    return {1}

  lowerCAmelCase     :     set[int]								=							set()
  lowerCAmelCase     :     int
  lowerCAmelCase     :     int

  for prime in primes:
    if prime > number_to_partition:
      continue
    for sub in partition(number_to_partition - prime				):
      ret.add(sub * prime				)

  return ret
def 					a__			(				SCREAMING_SNAKE_CASE    :    int = 5_0_0_0				):


  '''simple docstring'''







  for number_to_partition in range(1				,      SCREAMING_SNAKE_CASE				):
    if len(partition(SCREAMING_SNAKE_CASE				)				) > number_unique_partitions:
      return number_to_partition
  return None


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





