


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_(   lowerCamelCase_				)					->		int:
    _lowercase :							Optional[int]   	=	checkpoints.load_tax_checkpoint(lowerCamelCase_				)
    _lowercase :							Optional[Any]   	=	flatten_dict(lowerCamelCase_				)
    return flax_params



def        UpperCamelCase_(   lowerCamelCase_				)					->		Optional[int]:
    _lowercase :							Optional[Any]   	=	{}

    _lowercase :							Optional[int]   	=	{
        '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 :							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
            _lowercase :							Any   	=	'.'.join(key[1:]				)

            # rename the key
            for old, new in CONVERSION_MAPPING.items():
                _lowercase :							Optional[Any]   	=	new_key.replace(lowerCamelCase_							,				lowerCamelCase_				)

            if "decoder" in new_key:
                for old, new in DECODER_CONVERSION_MAPPING.items():
                    _lowercase :							int   	=	new_key.replace(lowerCamelCase_							,				lowerCamelCase_				)

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                _lowercase :							Optional[int]   	=	re.sub(R'layers_(\d+)'							,				R'layer.\1'							,				lowerCamelCase_				)
                _lowercase :							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
                _lowercase :							int   	=	re.sub(R'layers_(\d+)'							,				R'layer.\1'							,				lowerCamelCase_				)

            _lowercase :							List[Any]   	=	flax_dict[key]

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

    return converted_torch_dict



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_=False							,				lowerCamelCase_=False				)					->		List[str]:
    _lowercase :							List[Any]   	=	get_flax_param(lowerCamelCase_				)

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

    _lowercase :							Tuple   	=	PixaStructForConditionalGeneration(lowerCamelCase_				)

    _lowercase :							Optional[Any]   	=	rename_and_convert_flax_params(lowerCamelCase_				)
    model.load_state_dict(lowerCamelCase_				)

    _lowercase :							Any   	=	AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'				)
    _lowercase :							Tuple   	=	PixaStructImageProcessor()
    _lowercase :							List[str]   	=	PixaStructProcessor(image_processor=lowerCamelCase_							,				tokenizer=lowerCamelCase_				)

    if use_large:
        _lowercase :							List[Any]   	=	4096

    _lowercase :							Optional[Any]   	=	True

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

    model.save_pretrained(lowerCamelCase_				)
    processor.save_pretrained(lowerCamelCase_				)

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE :   Tuple							      =				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.")
 SCREAMING_SNAKE_CASE :   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_(   lowerCamelCase_							,				lowerCamelCase_				)					->		bool:
    return numa ^ numa < 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()


