



'''simple docstring'''



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   __lowerCAmelCase					(							lowerCamelCase   :						Optional[int]					):






	'''simple docstring'''




	__lowerCAmelCase												=  checkpoints.load_tax_checkpoint(lowerCamelCase					)
	__lowerCAmelCase												=  flatten_dict(lowerCamelCase					)
	return flax_params



def   __lowerCAmelCase					(							lowerCamelCase   :						int					):






	'''simple docstring'''




	__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(lowerCamelCase				,		lowerCamelCase					)

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

			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"				,		lowerCamelCase					)
				__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"				,		lowerCamelCase					)

			__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   __lowerCAmelCase					(							lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						str				,		lowerCamelCase   :						Union[str, Any]=False				,		lowerCamelCase   :						int=False					):






	'''simple docstring'''




	__lowerCAmelCase												=  get_flax_param(lowerCamelCase					)

	if not use_large:
		__lowerCAmelCase												=  PixaStructVisionConfig()
		__lowerCAmelCase												=  PixaStructTextConfig()
	else:
		__lowerCAmelCase												=  PixaStructVisionConfig(
		    hidden_size=15_36				,		d_ff=39_68				,		num_attention_heads=24				,		num_hidden_layers=18					)
		__lowerCAmelCase												=  PixaStructTextConfig(hidden_size=15_36				,		d_ff=39_68				,		num_heads=24				,		num_layers=18					)
	__lowerCAmelCase												=  PixaStructConfig(
	    vision_config=encoder_config.to_dict()				,		text_config=decoder_config.to_dict()				,		is_vqa=lowerCamelCase					)

	__lowerCAmelCase												=  PixaStructForConditionalGeneration(lowerCamelCase					)

	__lowerCAmelCase												=  rename_and_convert_flax_params(lowerCamelCase					)
	model.load_state_dict(lowerCamelCase					)

	__lowerCAmelCase												=  AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"					)
	__lowerCAmelCase												=  PixaStructImageProcessor()
	__lowerCAmelCase												=  PixaStructProcessor(image_processor=lowerCamelCase				,		tokenizer=lowerCamelCase					)

	if use_large:
		__lowerCAmelCase												=  40_96

	__lowerCAmelCase												=  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__":
	lowerCAmelCase	:		List[Any]				=   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	:		Optional[Any]				=   parser.parse_args()

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




'''simple docstring'''



def   __lowerCAmelCase					(							lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  1
	__lowerCAmelCase												=  2
	while i * i <= n:
		__lowerCAmelCase												=  0
		while n % i == 0:
			n //= i
			multiplicity += 1
		n_divisors *= multiplicity + 1
		i += 1
	if n > 1:
		n_divisors *= 2
	return n_divisors



def   __lowerCAmelCase					(							):






	'''simple docstring'''




	__lowerCAmelCase												=  1
	__lowerCAmelCase												=  1

	while True:
		i += 1
		t_num += i

		if count_divisors(lowerCamelCase					) > 5_00:
			break

	return t_num


if __name__ == "__main__":
	print(solution())