






"""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 						lowercase__(A						)    ->Dict:




						"""simple docstring"""



						lowercase__    :				Dict=	checkpoints.load_tax_checkpoint(A						)
						lowercase__    :				List[Any]=	flatten_dict(A						)
						return flax_params





def 						lowercase__(A						)    ->Tuple:




						"""simple docstring"""



						lowercase__    :				Union[str, Any]=	{}

						lowercase__    :				Any=	{
						    "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",
						}

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

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								lowercase__    :				List[Any]=	new_key.replace(A ,		A						)

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

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

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

																		lowercase__    :				Dict=	flax_dict[key]

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

						return converted_torch_dict





def 						lowercase__(A ,		A ,		A=False ,		A=False						)    ->Union[str, Any]:




						"""simple docstring"""



						lowercase__    :				Tuple=	get_flax_param(A						)

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

						lowercase__    :				Optional[Any]=	PixaStructForConditionalGeneration(A						)

						lowercase__    :				Tuple=	rename_and_convert_flax_params(A						)
						model.load_state_dict(A						)

						lowercase__    :				List[str]=	AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"						)
						lowercase__    :				List[str]=	PixaStructImageProcessor()
						lowercase__    :				Dict=	PixaStructProcessor(image_processor=A ,		tokenizer=A						)

						if use_large:
												lowercase__    :				Tuple=	4_096

						lowercase__    :				List[Any]=	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			:    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.""")
			a			:    Optional[int]				 =		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 collections.abc import Iterator
from typing import Any


class    __UpperCAmelCase:




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	data
												lowercase__    :				Node | None=	None


class    __UpperCAmelCase:




						"""simple docstring"""




						def __init__(       self      ):
												'''simple docstring'''
												lowercase__    :				List[str]=	None
												lowercase__    :				int=	None




						def __iter__(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	self.head
												while self.head:
																		yield node.data
																		lowercase__    :				str=	node.next
																		if node == self.head:
																								break




						def __len__(       self      ):
												'''simple docstring'''
												return sum(1 for _ in self      )




						def __repr__(       self      ):
												'''simple docstring'''
												return "->".join(str(snake_case__      ) for item in iter(self      )      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												self.insert_nth(len(self      )	,  snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												self.insert_nth(0	,  snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												if index < 0 or index > len(self      ):
																		raise IndexError("list index out of range."      )
												lowercase__    :				Optional[Any]=	Node(snake_case__      )
												if self.head is None:
																		lowercase__    :				Tuple=	new_node  # first node points itself
																		lowercase__    :				str=	new_node
												elif index == 0:  # insert at head
																		lowercase__    :				Dict=	self.head
																		lowercase__    :				int=	new_node
												else:
																		lowercase__    :				Dict=	self.head
																		for _ in range(index - 1      ):
																								lowercase__    :				Any=	temp.next
																		lowercase__    :				str=	temp.next
																		lowercase__    :				Dict=	new_node
																		if index == len(self      ) - 1:  # insert at tail
																								lowercase__    :				int=	new_node




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return self.delete_nth(0      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return self.delete_nth(len(self      ) - 1      )




						def 		UpperCAmelCase_	(       self	,  snake_case__ = 0      ):
												'''simple docstring'''
												if not 0 <= index < len(self      ):
																		raise IndexError("list index out of range."      )
												lowercase__    :				int=	self.head
												if self.head == self.tail:  # just one node
																		lowercase__    :				Union[str, Any]=	None
												elif index == 0:  # delete head node
																		lowercase__    :				Tuple=	self.tail.next.next
																		lowercase__    :				Union[str, Any]=	self.head.next
												else:
																		lowercase__    :				Union[str, Any]=	self.head
																		for _ in range(index - 1      ):
																								lowercase__    :				Dict=	temp.next
																		lowercase__    :				List[str]=	temp.next
																		lowercase__    :				Optional[Any]=	temp.next.next
																		if index == len(self      ) - 1:  # delete at tail
																								lowercase__    :				Optional[Any]=	temp
												return delete_node.data




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return len(self      ) == 0





def 						lowercase__()    ->None:




						"""simple docstring"""



						lowercase__    :				Dict=	CircularLinkedList()
						assert len(A						) == 0
						assert circular_linked_list.is_empty() is True
						assert str(A						) == ""

						try:
												circular_linked_list.delete_front()
												raise AssertionError  # This should not happen
						except IndexError:
												assert True  # This should happen

						try:
												circular_linked_list.delete_tail()
												raise AssertionError  # This should not happen
						except IndexError:
												assert True  # This should happen

						try:
												circular_linked_list.delete_nth(-1						)
												raise AssertionError
						except IndexError:
												assert True

						try:
												circular_linked_list.delete_nth(0						)
												raise AssertionError
						except IndexError:
												assert True

						assert circular_linked_list.is_empty() is True
						for i in range(5						):
												assert len(A						) == i
												circular_linked_list.insert_nth(A ,		i + 1						)
						assert str(A						) == "->".join(str(A						) for i in range(1 ,		6						)						)

						circular_linked_list.insert_tail(6						)
						assert str(A						) == "->".join(str(A						) for i in range(1 ,		7						)						)
						circular_linked_list.insert_head(0						)
						assert str(A						) == "->".join(str(A						) for i in range(0 ,		7						)						)

						assert circular_linked_list.delete_front() == 0
						assert circular_linked_list.delete_tail() == 6
						assert str(A						) == "->".join(str(A						) for i in range(1 ,		6						)						)
						assert circular_linked_list.delete_nth(2						) == 3

						circular_linked_list.insert_nth(2 ,		3						)
						assert str(A						) == "->".join(str(A						) for i in range(1 ,		6						)						)

						assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
			import doctest

			doctest.testmod()






