

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__ (					_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       checkpoints.load_tax_checkpoint(_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       flatten_dict(_a)
	return flax_params

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       {}

	SCREAMING_SNAKE_CASE   :      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",
	}

	SCREAMING_SNAKE_CASE   :      List[str]				     =       {
	    "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   :      Union[str, Any]				     =       ".".join(key[1:])

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

			if "decoder" in new_key:
				for old, new in DECODER_CONVERSION_MAPPING.items():
					SCREAMING_SNAKE_CASE   :      str				     =       new_key.replace(_a							,			_a)

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

			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       flax_dict[key]

	SCREAMING_SNAKE_CASE   :      Tuple				     =       {}
	# 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   :      Optional[Any]				     =       torch.from_numpy(converted_dict[key])

	return converted_torch_dict

def  lowerCamelCase__ (					_a							,			_a							,			_a=False							,			_a=False):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       get_flax_param(_a)

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

	SCREAMING_SNAKE_CASE   :      List[str]				     =       PixaStructForConditionalGeneration(_a)

	SCREAMING_SNAKE_CASE   :      str				     =       rename_and_convert_flax_params(_a)
	model.load_state_dict(_a)

	SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer")
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       PixaStructImageProcessor()
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       PixaStructProcessor(image_processor=_a							,			tokenizer=_a)

	if use_large:
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       4096

	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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
		)


def  lowerCamelCase__ (					_a = 50):
	SCREAMING_SNAKE_CASE   :      int				     =       [1] * (length + 1)

	for row_length in range(length + 1):
		for tile_length in range(2							,			5):
			for tile_start in range(row_length - tile_length + 1):
				ways_number[row_length] += ways_number[
				    row_length - tile_start - tile_length
				]

	return ways_number[length]


if __name__ == "__main__":
			print(F'''{solution() = }''')