

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__      (   SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''simple docstring'''





     lowercase__   :       Optional[Any]				=	checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Optional[int]				=	flatten_dict(SCREAMING_SNAKE_CASE_      )
     return flax_params

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	{}

     lowercase__   :       Dict				=	{
         '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',
     }

     lowercase__   :       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
               lowercase__   :       Union[str, Any]				=	'.'.join(key[1:]      )

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

               if "decoder" in new_key:
                    for old, new in DECODER_CONVERSION_MAPPING.items():
                         lowercase__   :       Union[str, 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
                    lowercase__   :       Tuple				=	re.sub(R'layers_(\d+)'     ,					R'layer.\1'     ,					SCREAMING_SNAKE_CASE_      )
                    lowercase__   :       Tuple				=	new_key.replace('encoder'     ,					'encoder.encoder'      )

               elif "layers" in new_key and "decoder" in new_key:
                    # use regex to replace the layer number
                    lowercase__   :       Optional[int]				=	re.sub(R'layers_(\d+)'     ,					R'layer.\1'     ,					SCREAMING_SNAKE_CASE_      )

               lowercase__   :       int				=	flax_dict[key]

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

     return converted_torch_dict

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Any=False     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]=False      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	get_flax_param(SCREAMING_SNAKE_CASE_      )

     if not use_large:
          lowercase__   :       Optional[Any]				=	PixaStructVisionConfig()
          lowercase__   :       List[str]				=	PixaStructTextConfig()
     else:
          lowercase__   :       Optional[int]				=	PixaStructVisionConfig(
              hidden_size=1_536     ,					d_ff=3_968     ,					num_attention_heads=24     ,					num_hidden_layers=18      )
          lowercase__   :       List[str]				=	PixaStructTextConfig(hidden_size=1_536     ,					d_ff=3_968     ,					num_heads=24     ,					num_layers=18      )
     lowercase__   :       Any				=	PixaStructConfig(
         vision_config=encoder_config.to_dict()     ,					text_config=decoder_config.to_dict()     ,					is_vqa=SCREAMING_SNAKE_CASE_      )

     lowercase__   :       Tuple				=	PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	rename_and_convert_flax_params(SCREAMING_SNAKE_CASE_      )
     model.load_state_dict(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'      )
     lowercase__   :       Optional[Any]				=	PixaStructImageProcessor()
     lowercase__   :       Union[str, Any]				=	PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE_     ,					tokenizer=SCREAMING_SNAKE_CASE_      )

     if use_large:
          lowercase__   :       Any				=	4_096

     lowercase__   :       int				=	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__":
       snake_case_        =			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.''')
       snake_case_        =			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

import math

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5     ,					int(math.sqrt(SCREAMING_SNAKE_CASE_      ) + 1      )     ,					6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       int				=	str(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Union[str, Any]				=	[n]
     for i in range(1     ,					len(SCREAMING_SNAKE_CASE_      )      ):
          list_nums.append(int(str_num[i:]      )      )
          list_nums.append(int(str_num[:-i]      )      )
     return list_nums

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     if len(str(SCREAMING_SNAKE_CASE_      )      ) > 3:
          if not is_prime(int(str(SCREAMING_SNAKE_CASE_      )[-3:]      )      ) or not is_prime(int(str(SCREAMING_SNAKE_CASE_      )[:3]      )      ):
               return False
     return True

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int = 11      ):



     '''simple docstring'''





     lowercase__   :       list[int]				=	[]
     lowercase__   :       Tuple				=	13
     while len(SCREAMING_SNAKE_CASE_      ) != count:
          if validate(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Optional[int]				=	list_truncated_nums(SCREAMING_SNAKE_CASE_      )
               if all(is_prime(SCREAMING_SNAKE_CASE_      ) for i in list_nums      ):
                    list_truncated_primes.append(SCREAMING_SNAKE_CASE_      )
          num += 2
     return list_truncated_primes

def      snake_case__      (   ):



     '''simple docstring'''





     return sum(compute_truncated_primes(11      )      )


if __name__ == "__main__":
       print(F'''{sum(compute_truncated_primes(11)) = }''')

