'''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__      ):


						'''simple docstring'''






						_lowerCAmelCase										=checkpoints.load_tax_checkpoint(a__      )
						_lowerCAmelCase										=flatten_dict(a__      )
						return flax_params



def 							UpperCamelCase__			(			a__      ):


						'''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(a__			,      a__      )

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

																		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'			,      a__      )
																								_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'			,      a__      )

																		_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 							UpperCamelCase__			(			a__			,      a__			,      a__=False			,      a__=False      ):


						'''simple docstring'''






						_lowerCAmelCase										=get_flax_param(a__      )

						if not use_large:
												_lowerCAmelCase										=PixaStructVisionConfig()
												_lowerCAmelCase										=PixaStructTextConfig()
						else:
												_lowerCAmelCase										=PixaStructVisionConfig(
												    hidden_size=1_5_3_6			,      d_ff=3_9_6_8			,      num_attention_heads=2_4			,      num_hidden_layers=1_8      )
												_lowerCAmelCase										=PixaStructTextConfig(hidden_size=1_5_3_6			,      d_ff=3_9_6_8			,      num_heads=2_4			,      num_layers=1_8      )
						_lowerCAmelCase										=PixaStructConfig(
						    vision_config=encoder_config.to_dict()			,      text_config=decoder_config.to_dict()			,      is_vqa=a__      )

						_lowerCAmelCase										=PixaStructForConditionalGeneration(a__      )

						_lowerCAmelCase										=rename_and_convert_flax_params(a__      )
						model.load_state_dict(a__      )

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

						if use_large:
												_lowerCAmelCase										=4_0_9_6

						_lowerCAmelCase										=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__":
							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
							)






'''simple docstring'''

from __future__ import annotations

from typing import Any
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A				) ->     None:
								_lowerCAmelCase										=num_of_nodes
								_lowerCAmelCase										=[]
								_lowerCAmelCase										={}






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:
								self.m_edges.append([u_node, v_node, weight]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:

								if self.m_component[u_node] == u_node:
														return u_node
								return self.find_component(self.m_component[u_node]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     None:

								if self.m_component[u_node] != u_node:
														for k in self.m_component:
																				_lowerCAmelCase										=self.find_component(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:

								if component_size[u_node] <= component_size[v_node]:
														_lowerCAmelCase										=v_node
														component_size[v_node] += component_size[u_node]
														self.set_component(__A				)

								elif component_size[u_node] >= component_size[v_node]:
														_lowerCAmelCase										=self.find_component(__A				)
														component_size[u_node] += component_size[v_node]
														self.set_component(__A				)






		def 		UpperCamelCase__       (   self				) ->     None:
								_lowerCAmelCase										=[]
								_lowerCAmelCase										=0

								_lowerCAmelCase										=[-1] * self.m_num_of_nodes

								# A list of components (initialized to all of the nodes)
								for node in range(self.m_num_of_nodes				):
														self.m_component.update({node: node}				)
														component_size.append(1				)

								_lowerCAmelCase										=self.m_num_of_nodes

								while num_of_components > 1:
														for edge in self.m_edges:
																				_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																				_lowerCAmelCase										=self.m_component[u]
																				_lowerCAmelCase										=self.m_component[v]

																				if u_component != v_component:

																										for component in (u_component, v_component):
																																if (
																																    minimum_weight_edge[component] == -1
																																    or minimum_weight_edge[component][2] > w
																																):
																																						_lowerCAmelCase										=[u, v, w]

														for edge in minimum_weight_edge:
																				if isinstance(__A		,						__A				):
																										_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																										_lowerCAmelCase										=self.m_component[u]
																										_lowerCAmelCase										=self.m_component[v]

																										if u_component != v_component:
																																mst_weight += w
																																self.union(__A		,						__A		,						__A				)
																																print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''				)
																																num_of_components -= 1

														_lowerCAmelCase										=[-1] * self.m_num_of_nodes
								print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''








if __name__ == "__main__":
							import doctest

							doctest.testmod()





