


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							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     int					=		checkpoints.load_tax_checkpoint(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		flatten_dict(lowercase__      )
						return flax_params


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		{}

						__SCREAMING_SNAKE_CASE						:     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''',
						}

						__SCREAMING_SNAKE_CASE						:     Optional[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
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		'''.'''.join(key[1:]      )

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

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														__SCREAMING_SNAKE_CASE						:     List[str]					=		new_key.replace(lowercase__    ,       lowercase__      )

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		re.sub(R'''layers_(\d+)'''    ,       R'''layer.\1'''    ,       lowercase__      )
																								__SCREAMING_SNAKE_CASE						:     Any					=		new_key.replace('''encoder'''    ,       '''encoder.encoder'''      )

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

																		__SCREAMING_SNAKE_CASE						:     Any					=		flax_dict[key]

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

						return converted_torch_dict


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__=False    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     int					=		get_flax_param(lowercase__      )

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

						__SCREAMING_SNAKE_CASE						:     int					=		PixaStructForConditionalGeneration(lowercase__      )

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		rename_and_convert_flax_params(lowercase__      )
						model.load_state_dict(lowercase__      )

						__SCREAMING_SNAKE_CASE						:     List[Any]					=		AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		PixaStructImageProcessor()
						__SCREAMING_SNAKE_CASE						:     Dict					=		PixaStructProcessor(image_processor=lowercase__    ,       tokenizer=lowercase__      )

						if use_large:
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		4096

						__SCREAMING_SNAKE_CASE						:     Tuple					=		True

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

						model.save_pretrained(lowercase__      )
						processor.save_pretrained(lowercase__      )

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


if __name__ == "__main__":
						__lowerCAmelCase	: Dict  				=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	: List[Any]  				=parser.parse_args()

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




def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__=False      ):

						if isinstance(lowercase__    ,       lowercase__      ) and isinstance(lowercase__    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     List[str]					=		len(set_a.intersection(lowercase__      )      )

												if alternative_union:
																		__SCREAMING_SNAKE_CASE						:     int					=		len(lowercase__      ) + len(lowercase__      )
												else:
																		__SCREAMING_SNAKE_CASE						:     int					=		len(set_a.union(lowercase__      )      )

												return intersection / union

						if isinstance(lowercase__    ,       (list, tuple)      ) and isinstance(lowercase__    ,       (list, tuple)      ):
												__SCREAMING_SNAKE_CASE						:     Dict					=		[element for element in set_a if element in set_b]

												if alternative_union:
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		len(lowercase__      ) + len(lowercase__      )
																		return len(lowercase__      ) / union
												else:
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		set_a + [element for element in set_b if element not in set_a]
																		return len(lowercase__      ) / len(lowercase__      )

												return len(lowercase__      ) / len(lowercase__      )
						return None


if __name__ == "__main__":
						__lowerCAmelCase	: List[Any]  				={'a', 'b', 'c', 'd', 'e'}
						__lowerCAmelCase	: Optional[Any]  				={'c', 'd', 'e', 'f', 'h', 'i'}
						print(jaccard_similarity(set_a, set_b))
