



"""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 						UpperCAmelCase  (		A						:    List[Any]							):

		'''simple docstring'''


		_UpperCAmelCase							=		checkpoints.load_tax_checkpoint(lowercase__							)
		_UpperCAmelCase							=		flatten_dict(lowercase__							)
		return flax_params




def 						UpperCAmelCase  (		A						:    str							):

		'''simple docstring'''


		_UpperCAmelCase							=		{}

		_UpperCAmelCase							=		{
		    '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',
		}

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

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

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

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

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

						_UpperCAmelCase							=		flax_dict[key]

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

		return converted_torch_dict




def 						UpperCAmelCase  (		A						:    Optional[int]  ,					A						:    Union[str, Any]  ,					A						:    Optional[Any]=False  ,					A						:    Any=False							):

		'''simple docstring'''


		_UpperCAmelCase							=		get_flax_param(lowercase__							)

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

		_UpperCAmelCase							=		PixaStructForConditionalGeneration(lowercase__							)

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

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

		if use_large:
				_UpperCAmelCase							=		4096

		_UpperCAmelCase							=		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__":
					lowercase					=       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.''')
					lowercase					=       parser.parse_args()

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





class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			val
				snake_case_   		=			None
				snake_case_   		=			None







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if self.val:
					if val < self.val:
						if self.left is None:
							snake_case_   		=			Node(__UpperCamelCase				)
						else:
							self.left.insert(__UpperCamelCase				)
					elif val > self.val:
						if self.right is None:
							snake_case_   		=			Node(__UpperCamelCase				)
						else:
							self.right.insert(__UpperCamelCase				)
				else:
					snake_case_   		=			val







def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	# Recursive traversal
	if root:
		inorder(root.left      ,			lowercase__						)
		res.append(root.val						)
		inorder(root.right      ,			lowercase__						)





def a(lowercase__						):





	'''simple docstring'''
	# Build BST
	if len(lowercase__						) == 0:
		return arr
	snake_case_   		=			Node(arr[0]						)
	for i in range(1      ,			len(lowercase__						)						):
		root.insert(arr[i]						)
	# Traverse BST in order.
	snake_case_   		=			[]
	inorder(lowercase__      ,			lowercase__						)
	return res


if __name__ == "__main__":
		print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
