
"""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 ( __a    ):
		SCREAMING_SNAKE_CASE_						 = checkpoints.load_tax_checkpoint(A_    )
		SCREAMING_SNAKE_CASE_						 = flatten_dict(A_    )
		return flax_params



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = {}

		SCREAMING_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''',
		}

		SCREAMING_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
						SCREAMING_SNAKE_CASE_						 = '''.'''.join(key[1:]    )

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

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

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

						SCREAMING_SNAKE_CASE_						 = flax_dict[key]

		SCREAMING_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):
						SCREAMING_SNAKE_CASE_						 = torch.from_numpy(converted_dict[key].T    )
				else:
						SCREAMING_SNAKE_CASE_						 = torch.from_numpy(converted_dict[key]    )

		return converted_torch_dict



def        _lowerCamelCase ( __a,							__a,							__a=False,							__a=False    ):
		SCREAMING_SNAKE_CASE_						 = get_flax_param(A_    )

		if not use_large:
				SCREAMING_SNAKE_CASE_						 = PixaStructVisionConfig()
				SCREAMING_SNAKE_CASE_						 = PixaStructTextConfig()
		else:
				SCREAMING_SNAKE_CASE_						 = PixaStructVisionConfig(
				    hidden_size=1_536,							d_ff=3_968,							num_attention_heads=24,							num_hidden_layers=18    )
				SCREAMING_SNAKE_CASE_						 = PixaStructTextConfig(hidden_size=1_536,							d_ff=3_968,							num_heads=24,							num_layers=18    )
		SCREAMING_SNAKE_CASE_						 = PixaStructConfig(
		    vision_config=encoder_config.to_dict(),							text_config=decoder_config.to_dict(),							is_vqa=A_    )

		SCREAMING_SNAKE_CASE_						 = PixaStructForConditionalGeneration(A_    )

		SCREAMING_SNAKE_CASE_						 = rename_and_convert_flax_params(A_    )
		model.load_state_dict(A_    )

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

		if use_large:
				SCREAMING_SNAKE_CASE_						 = 4_096

		SCREAMING_SNAKE_CASE_						 = 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__":
						lowerCAmelCase__							=				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.')
						lowerCAmelCase__							=				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 collections.abc import Sequence
from queue import Queue





class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=None    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = start
									SCREAMING_SNAKE_CASE_						 = end
									SCREAMING_SNAKE_CASE_						 = val
									SCREAMING_SNAKE_CASE_						 = (start + end) // 2
									SCREAMING_SNAKE_CASE_						 = left
									SCREAMING_SNAKE_CASE_						 = right







							def __repr__(self    ):

									"""simple docstring"""







									return f'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'







class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = collection
									SCREAMING_SNAKE_CASE_						 = function
									if self.collection:
											SCREAMING_SNAKE_CASE_						 = self._build_tree(0       ,       len(SCREAMING_SNAKE_CASE_    ) - 1    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									self._update_tree(self.root       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return self._query_range(self.root       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if start == end:
											return SegmentTreeNode(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       self.collection[start]    )
									SCREAMING_SNAKE_CASE_						 = (start + end) // 2
									SCREAMING_SNAKE_CASE_						 = self._build_tree(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = self._build_tree(mid + 1       ,       SCREAMING_SNAKE_CASE_    )
									return SegmentTreeNode(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       self.fn(left.val       ,       right.val    )       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if node.start == i and node.end == i:
											SCREAMING_SNAKE_CASE_						 = val
											return
									if i <= node.mid:
											self._update_tree(node.left       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									else:
											self._update_tree(node.right       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = self.fn(node.left.val       ,       node.right.val    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if node.start == i and node.end == j:
											return node.val

									if i <= node.mid:
											if j <= node.mid:
													# range in left child tree
													return self._query_range(node.left       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											else:
													# range in left child tree and right child tree
													return self.fn(
													    self._query_range(node.left       ,       SCREAMING_SNAKE_CASE_       ,       node.mid    )       ,       self._query_range(node.right       ,       node.mid + 1       ,       SCREAMING_SNAKE_CASE_    )       ,       )
									else:
											# range in right child tree
											return self._query_range(node.right       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									if self.root is not None:
											SCREAMING_SNAKE_CASE_						 = Queue()
											queue.put(self.root    )
											while not queue.empty():
													SCREAMING_SNAKE_CASE_						 = queue.get()
													yield node

													if node.left is not None:
															queue.put(node.left    )

													if node.right is not None:
															queue.put(node.right    )


if __name__ == "__main__":
						import operator

						for fn in [operator.add, max, min]:
												print('*' * 50)
												lowerCAmelCase__							=				SegmentTree([2, 1, 5, 3, 4], fn)
												for node in arr.traverse():
																		print(node)
												print()

												arr.update(1, 5)
												for node in arr.traverse():
																		print(node)
												print()

												print(arr.query_range(3, 4))  # 7
												print(arr.query_range(2, 2))  # 5
												print(arr.query_range(1, 3))  # 13
												print()