



'''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  (					_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		List[str]							     =		checkpoints.load_tax_checkpoint(_lowercase		)
							UpperCAmelCase     :		Union[str, Any]							     =		flatten_dict(_lowercase		)
							return flax_params



def       __lowerCamelCase  (					_lowercase		)      ->			str:
							UpperCAmelCase     :		List[Any]							     =		{}

							UpperCAmelCase     :		Optional[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""",
							}

							UpperCAmelCase     :		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
																					UpperCAmelCase     :		str							     =		""".""".join(key[1:]		)

																					# rename the key
																					for old, new in CONVERSION_MAPPING.items():
																												UpperCAmelCase     :		Tuple							     =		new_key.replace(_lowercase	,				_lowercase		)

																					if "decoder" in new_key:
																												for old, new in DECODER_CONVERSION_MAPPING.items():
																																			UpperCAmelCase     :		Dict							     =		new_key.replace(_lowercase	,				_lowercase		)

																					if "layers" in new_key and "decoder" not in new_key:
																												# use regex to replace the layer number
																												UpperCAmelCase     :		Dict							     =		re.sub(R"""layers_(\d+)"""	,				R"""layer.\1"""	,				_lowercase		)
																												UpperCAmelCase     :		Tuple							     =		new_key.replace("""encoder"""	,				"""encoder.encoder"""		)

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

																					UpperCAmelCase     :		Optional[int]							     =		flax_dict[key]

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

							return converted_torch_dict



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase=False	,				_lowercase=False		)      ->			Any:
							UpperCAmelCase     :		Optional[Any]							     =		get_flax_param(_lowercase		)

							if not use_large:
														UpperCAmelCase     :		Dict							     =		PixaStructVisionConfig()
														UpperCAmelCase     :		Any							     =		PixaStructTextConfig()
							else:
														UpperCAmelCase     :		Union[str, Any]							     =		PixaStructVisionConfig(
														    hidden_size=1_5_3_6	,				d_ff=3_9_6_8	,				num_attention_heads=2_4	,				num_hidden_layers=1_8		)
														UpperCAmelCase     :		str							     =		PixaStructTextConfig(hidden_size=1_5_3_6	,				d_ff=3_9_6_8	,				num_heads=2_4	,				num_layers=1_8		)
							UpperCAmelCase     :		int							     =		PixaStructConfig(
							    vision_config=encoder_config.to_dict()	,				text_config=decoder_config.to_dict()	,				is_vqa=_lowercase		)

							UpperCAmelCase     :		Union[str, Any]							     =		PixaStructForConditionalGeneration(_lowercase		)

							UpperCAmelCase     :		Union[str, Any]							     =		rename_and_convert_flax_params(_lowercase		)
							model.load_state_dict(_lowercase		)

							UpperCAmelCase     :		Any							     =		AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""		)
							UpperCAmelCase     :		str							     =		PixaStructImageProcessor()
							UpperCAmelCase     :		Any							     =		PixaStructProcessor(image_processor=_lowercase	,				tokenizer=_lowercase		)

							if use_large:
														UpperCAmelCase     :		List[str]							     =		4_0_9_6

							UpperCAmelCase     :		Tuple							     =		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__":
							a   :			Tuple					  =    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   :			Tuple					  =    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

import math

class 			UpperCamelCase_ :





	def __init__(  self		,		A    )			->			None:
								UpperCAmelCase     :		Optional[int]							     =		size
								# approximate the overall size of segment tree with given value
								UpperCAmelCase     :		Optional[int]							     =		[0 for i in range(0		,		4 * size    )]
								# create array to store lazy update
								UpperCAmelCase     :		Any							     =		[0 for i in range(0		,		4 * size    )]
								UpperCAmelCase     :		Tuple							     =		[0 for i in range(0		,		4 * size    )]  # flag for lazy update





	def  _lowercase(  self		,		A    )			->			int:
								return idx * 2





	def  _lowercase(  self		,		A    )			->			int:
								return idx * 2 + 1





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			None:
								if left_element == right_element:
															UpperCAmelCase     :		str							     =		a[left_element - 1]
								else:
															UpperCAmelCase     :		Tuple							     =		(left_element + right_element) // 2
															self.build(self.left(A    )		,		A		,		A		,		A    )
															self.build(self.right(A    )		,		mid + 1		,		A		,		A    )
															UpperCAmelCase     :		str							     =		max(
															    self.segment_tree[self.left(A    )]		,		self.segment_tree[self.right(A    )]    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A		,		A		,		A    )			->			bool:
								if self.flag[idx] is True:
															UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
															UpperCAmelCase     :		int							     =		False
															if left_element != right_element:
																						UpperCAmelCase     :		List[str]							     =		self.lazy[idx]
																						UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
																						UpperCAmelCase     :		List[str]							     =		True
																						UpperCAmelCase     :		int							     =		True

								if right_element < a or left_element > b:
															return True
								if left_element >= a and right_element <= b:
															UpperCAmelCase     :		Optional[Any]							     =		val
															if left_element != right_element:
																						UpperCAmelCase     :		Tuple							     =		val
																						UpperCAmelCase     :		int							     =		val
																						UpperCAmelCase     :		Any							     =		True
																						UpperCAmelCase     :		str							     =		True
															return True
								UpperCAmelCase     :		str							     =		(left_element + right_element) // 2
								self.update(self.left(A    )		,		A		,		A		,		A		,		A		,		A    )
								self.update(self.right(A    )		,		mid + 1		,		A		,		A		,		A		,		A    )
								UpperCAmelCase     :		List[str]							     =		max(
								    self.segment_tree[self.left(A    )]		,		self.segment_tree[self.right(A    )]    )
								return True





	def  _lowercase(  self		,		A		,		A		,		A		,		A		,		A    )			->			int | float:
								if self.flag[idx] is True:
															UpperCAmelCase     :		Any							     =		self.lazy[idx]
															UpperCAmelCase     :		Any							     =		False
															if left_element != right_element:
																						UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
																						UpperCAmelCase     :		Tuple							     =		self.lazy[idx]
																						UpperCAmelCase     :		List[str]							     =		True
																						UpperCAmelCase     :		Tuple							     =		True
								if right_element < a or left_element > b:
															return -math.inf
								if left_element >= a and right_element <= b:
															return self.segment_tree[idx]
								UpperCAmelCase     :		Dict							     =		(left_element + right_element) // 2
								UpperCAmelCase     :		List[Any]							     =		self.query(self.left(A    )		,		A		,		A		,		A		,		A    )
								UpperCAmelCase     :		str							     =		self.query(self.right(A    )		,		mid + 1		,		A		,		A		,		A    )
								return max(A		,		A    )






	def __str__(  self    )			->			str:
								return str([self.query(1		,		1		,		self.size		,		A		,		A    ) for i in range(1		,		self.size + 1    )]    )


if __name__ == "__main__":
							a   :			Optional[int]					  =    [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
							a   :			Optional[Any]					  =    1_5
							a   :			Union[str, Any]					  =    SegmentTree(size)
							segt.build(1, 1, size, A)
							print(segt.query(1, 1, size, 4, 6))
							print(segt.query(1, 1, size, 7, 1_1))
							print(segt.query(1, 1, size, 7, 1_2))
							segt.update(1, 1, size, 1, 3, 1_1_1)
							print(segt.query(1, 1, size, 1, 1_5))
							segt.update(1, 1, size, 7, 8, 2_3_5)
							print(segt)

