

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(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			checkpoints.load_tax_checkpoint(lowercase__						)
	snake_case_   		=			flatten_dict(lowercase__						)
	return flax_params





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			{}

	snake_case_   		=			{
	    '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',
	}

	snake_case_   		=			{
	    '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
			snake_case_   		=			'.'.join(key[1:]						)

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

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

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

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

			snake_case_   		=			flax_dict[key]

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

	return converted_torch_dict





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





	'''simple docstring'''
	snake_case_   		=			get_flax_param(lowercase__						)

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

	snake_case_   		=			PixaStructForConditionalGeneration(lowercase__						)

	snake_case_   		=			rename_and_convert_flax_params(lowercase__						)
	model.load_state_dict(lowercase__						)

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

	if use_large:
		snake_case_   		=			4096

	snake_case_   		=			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__":
		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
		)



A									=  [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
