




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 __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  checkpoints.load_tax_checkpoint(_A  )
						lowerCAmelCase_      			=  flatten_dict(_A  )
						return flax_params





def __UpperCamelCase			(       _A  ):
						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(_A ,  _A  )

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

																		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''' ,  _A  )
																								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''' ,  _A  )

																		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 __UpperCamelCase			(       _A ,  _A ,  _A=False ,  _A=False  ):
						lowerCAmelCase_      			=  get_flax_param(_A  )

						if not use_large:
												lowerCAmelCase_      			=  PixaStructVisionConfig()
												lowerCAmelCase_      			=  PixaStructTextConfig()
						else:
												lowerCAmelCase_      			=  PixaStructVisionConfig(
												    hidden_size=1536 ,  d_ff=3968 ,  num_attention_heads=24 ,  num_hidden_layers=18  )
												lowerCAmelCase_      			=  PixaStructTextConfig(hidden_size=1536 ,  d_ff=3968 ,  num_heads=24 ,  num_layers=18  )
						lowerCAmelCase_      			=  PixaStructConfig(
						    vision_config=encoder_config.to_dict() ,  text_config=decoder_config.to_dict() ,  is_vqa=_A  )

						lowerCAmelCase_      			=  PixaStructForConditionalGeneration(_A  )

						lowerCAmelCase_      			=  rename_and_convert_flax_params(_A  )
						model.load_state_dict(_A  )

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

						if use_large:
												lowerCAmelCase_      			=  4096

						lowerCAmelCase_      			=  True

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

						model.save_pretrained(_A  )
						processor.save_pretrained(_A  )

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


if __name__ == "__main__":
						_A     =			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.''')
						_A     =			parser.parse_args()

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










from functools import lru_cache





@lru_cache
def __UpperCamelCase			(       _A  ):
						if num < 0:
												raise ValueError('''Number should not be negative.'''  )

						return 1 if num in (0, 1) else num * factorial(num - 1  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




