





'''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			(	UpperCAmelCase_							)   ->Dict:
	snake_case__	       =   checkpoints.load_tax_checkpoint(UpperCAmelCase_							)
	snake_case__	       =   flatten_dict(UpperCAmelCase_							)
	return flax_params




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Optional[int]:
	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(UpperCAmelCase_					,				UpperCAmelCase_							)

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

			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'					,				UpperCAmelCase_							)
				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'					,				UpperCAmelCase_							)

			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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=False					,				UpperCAmelCase_=False							)   ->str:
	snake_case__	       =   get_flax_param(UpperCAmelCase_							)

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

	snake_case__	       =   PixaStructForConditionalGeneration(UpperCAmelCase_							)

	snake_case__	       =   rename_and_convert_flax_params(UpperCAmelCase_							)
	model.load_state_dict(UpperCAmelCase_							)

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

	if use_large:
		snake_case__	       =   40_96

	snake_case__	       =   True

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

	model.save_pretrained(UpperCAmelCase_							)
	processor.save_pretrained(UpperCAmelCase_							)

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


if __name__ == "__main__":
			a__					:			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.''')
			a__					:			List[str]          =     parser.parse_args()

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










'''simple docstring'''




import os




def 						__lowerCamelCase			(	UpperCAmelCase_ = "input.txt"							)   ->int:

	with open(os.path.join(os.path.dirname(UpperCAmelCase_							)					,				UpperCAmelCase_							)							) as input_file:
		snake_case__	       =   [
		    [int(UpperCAmelCase_							) for element in line.split(','							)]
		    for line in input_file.readlines()
		]

	snake_case__	       =   len(UpperCAmelCase_							)
	snake_case__	       =   len(matrix[0]							)

	snake_case__	       =   [[-1 for _ in range(UpperCAmelCase_							)] for _ in range(UpperCAmelCase_							)]
	for i in range(UpperCAmelCase_							):
		snake_case__	       =   matrix[i][0]

	for j in range(1					,				UpperCAmelCase_							):
		for i in range(UpperCAmelCase_							):
			snake_case__	       =   minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1					,				UpperCAmelCase_							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i - 1][j] + matrix[i][j]							)

		for i in range(rows - 2					,				-1					,				-1							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i + 1][j] + matrix[i][j]							)

	return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums							)


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



