

'''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	(       __snake_case					:		Optional[Any]	)	->							Optional[Any]:






						"""simple docstring"""





						A__					:       List[str]     =checkpoints.load_tax_checkpoint(__snake_case	)
						A__					:       Tuple     =flatten_dict(__snake_case	)
						return flax_params
def 						__lowerCamelCase	(       __snake_case					:		int	)	->							Optional[int]:






						"""simple docstring"""





						A__					:       Optional[Any]     ={}

						A__					:       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""",
						}

						A__					:       Any     ={
						    """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
																		A__					:       Tuple     =""".""".join(key[1:]	)

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								A__					:       str     =new_key.replace(__snake_case,		__snake_case	)

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														A__					:       List[str]     =new_key.replace(__snake_case,		__snake_case	)

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								A__					:       str     =re.sub(r"""layers_(\d+)""",		r"""layer.\1""",		__snake_case	)
																								A__					:       List[Any]     =new_key.replace("""encoder""",		"""encoder.encoder"""	)

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

																		A__					:       List[str]     =flax_dict[key]

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

						return converted_torch_dict
def 						__lowerCamelCase	(       __snake_case					:		str,		__snake_case					:		Optional[int],		__snake_case					:		Any=False,		__snake_case					:		Tuple=False	)	->							Optional[int]:






						"""simple docstring"""





						A__					:       List[Any]     =get_flax_param(__snake_case	)

						if not use_large:
												A__					:       Optional[int]     =PixaStructVisionConfig()
												A__					:       List[str]     =PixaStructTextConfig()
						else:
												A__					:       List[Any]     =PixaStructVisionConfig(
												    hidden_size=1_536,		d_ff=3_968,		num_attention_heads=24,		num_hidden_layers=18	)
												A__					:       Optional[Any]     =PixaStructTextConfig(hidden_size=1_536,		d_ff=3_968,		num_heads=24,		num_layers=18	)
						A__					:       List[str]     =PixaStructConfig(
						    vision_config=encoder_config.to_dict(),		text_config=decoder_config.to_dict(),		is_vqa=__snake_case	)

						A__					:       Dict     =PixaStructForConditionalGeneration(__snake_case	)

						A__					:       Union[str, Any]     =rename_and_convert_flax_params(__snake_case	)
						model.load_state_dict(__snake_case	)

						A__					:       Union[str, Any]     =AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""	)
						A__					:       int     =PixaStructImageProcessor()
						A__					:       Union[str, Any]     =PixaStructProcessor(image_processor=__snake_case,		tokenizer=__snake_case	)

						if use_large:
												A__					:       Tuple     =4_096

						A__					:       Union[str, Any]     =True

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

						model.save_pretrained(__snake_case	)
						processor.save_pretrained(__snake_case	)

						print("""Model saved in {}""".format(__snake_case	)	)


if __name__ == "__main__":
		__snake_case			:     int										=      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.')
		__snake_case			:     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'''



from __future__ import annotations

from typing import Any



class lowerCamelCase			:



			'''simple docstring'''






			def __init__(				self	:						List[str]		,      lowerCAmelCase_	:						int			)      ->		None:





									'''simple docstring'''
									A__					:       Any     =num_of_nodes
									A__					:       list[list[int]]     =[]
									A__					:       dict[int, int]     ={}


			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int			)      ->		None:





									'''simple docstring'''
									self.m_edges.append([u_node, v_node, weight]			)


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						int			)      ->		int:





									'''simple docstring'''

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


			def lowercase__						(				self	:						Optional[int]		,      lowerCAmelCase_	:						int			)      ->		None:





									'''simple docstring'''

									if self.m_component[u_node] != u_node:
															for k in self.m_component:
																					A__					:       str     =self.find_component(lowerCAmelCase_			)


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						list[int]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int			)      ->		None:





									'''simple docstring'''

									if component_size[u_node] <= component_size[v_node]:
															A__					:       int     =v_node
															component_size[v_node] += component_size[u_node]
															self.set_component(lowerCAmelCase_			)

									elif component_size[u_node] >= component_size[v_node]:
															A__					:       List[str]     =self.find_component(lowerCAmelCase_			)
															component_size[u_node] += component_size[v_node]
															self.set_component(lowerCAmelCase_			)



			def lowercase__						(				self	:						str			)      ->		None:





									'''simple docstring'''
									A__					:       Union[str, Any]     =[]
									A__					:       List[str]     =0

									A__					:       list[Any]     =[-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			)

									A__					:       List[str]     =self.m_num_of_nodes

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

																					A__					:       Tuple     =self.m_component[u]
																					A__					:       Optional[Any]     =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
																																	):
																																							A__					:       Optional[int]     =[u, v, w]

															for edge in minimum_weight_edge:
																					if isinstance(lowerCAmelCase_		,      lowerCAmelCase_			):
																											A__   ,						A__   ,						A__					:       Tuple     =edge

																											A__					:       Any     =self.m_component[u]
																											A__					:       Optional[Any]     =self.m_component[v]

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

															A__					:       int     =[-1] * self.m_num_of_nodes
									print(f"The total weight of the minimal spanning tree is: {mst_weight}"			)


def 						__lowerCamelCase	(       )	->							None:






						"""simple docstring"""







if __name__ == "__main__":
		import doctest

		doctest.testmod()

