



from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser






@dataclass
class     __a			:
	SCREAMING_SNAKE_CASE  	=							field(
	    metadata={"help": "The output directory where the model will be written."}						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    metadata={
	        "help": (
	            "The encoder model checkpoint for weights initialization."
	            "Don't set if you want to train an encoder model from scratch."
	        )
	    }						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    metadata={
	        "help": (
	            "The decoder model checkpoint for weights initialization."
	            "Don't set if you want to train a decoder model from scratch."
	        )
	    }						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}					)
	SCREAMING_SNAKE_CASE  	=							field(
	    default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}					)






def        __lowerCAmelCase		(	)	->    Optional[Any]:
					__lowerCAmelCase						 =HfArgumentParser((ModelArguments,)						)
					((__lowerCAmelCase) ,						)						 =parser.parse_args_into_dataclasses()

					# Load pretrained model and tokenizer

					# Use explicit specified encoder config
					if model_args.encoder_config_name:
										__lowerCAmelCase						 =AutoConfig.from_pretrained(model_args.encoder_config_name						)
					# Use pretrained encoder model's config
					else:
										__lowerCAmelCase						 =AutoConfig.from_pretrained(model_args.encoder_model_name_or_path						)

					# Use explicit specified decoder config
					if model_args.decoder_config_name:
										__lowerCAmelCase						 =AutoConfig.from_pretrained(model_args.decoder_config_name						)
					# Use pretrained decoder model's config
					else:
										__lowerCAmelCase						 =AutoConfig.from_pretrained(model_args.decoder_model_name_or_path						)

					# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
					__lowerCAmelCase						 =True
					__lowerCAmelCase						 =True

					__lowerCAmelCase						 =FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
					    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path					, decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path					, encoder_config=__lowerCamelCase					, decoder_config=__lowerCamelCase					, )

					# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
					__lowerCAmelCase						 =decoder_config.decoder_start_token_id
					__lowerCAmelCase						 =decoder_config.pad_token_id
					if decoder_start_token_id is None:
										__lowerCAmelCase						 =decoder_config.bos_token_id
					if pad_token_id is None:
										__lowerCAmelCase						 =decoder_config.eos_token_id

					# This is necessary to make Flax's generate() work
					__lowerCAmelCase						 =decoder_config.eos_token_id
					__lowerCAmelCase						 =decoder_start_token_id
					__lowerCAmelCase						 =pad_token_id

					__lowerCAmelCase						 =AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path						)

					__lowerCAmelCase						 =AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path						)
					__lowerCAmelCase						 =tokenizer.convert_ids_to_tokens(model.config.pad_token_id						)

					model.save_pretrained(model_args.output_dir						)
					image_processor.save_pretrained(model_args.output_dir						)
					tokenizer.save_pretrained(model_args.output_dir						)


if __name__ == "__main__":
				main()









from collections.abc import Sequence
from queue import Queue






class     __a			:



	def __init__(    self				:     str					,						snake_case_				:     List[str]					,						snake_case_				:     Tuple					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[Any]=None					,						snake_case_				:     Dict=None)->				Optional[int]:
						__lowerCAmelCase						 =start
						__lowerCAmelCase						 =end
						__lowerCAmelCase						 =val
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =left
						__lowerCAmelCase						 =right



	def __repr__(    self				:     List[Any])->				Dict:
						return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""






class     __a			:



	def __init__(    self				:     List[str]					,						snake_case_				:     Sequence					,						snake_case_				:     int)->				str:
						__lowerCAmelCase						 =collection
						__lowerCAmelCase						 =function
						if self.collection:
											__lowerCAmelCase						 =self._build_tree(0					,						len(snake_case_) - 1)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[int])->				Optional[int]:
						self._update_tree(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     Any					,						snake_case_				:     Union[str, Any])->				Union[str, Any]:
						return self._query_range(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     List[str]					,						snake_case_				:     List[str])->				List[Any]:
						if start == end:
											return SegmentTreeNode(snake_case_					,						snake_case_					,						self.collection[start])
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =self._build_tree(snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self._build_tree(mid + 1					,						snake_case_)
						return SegmentTreeNode(snake_case_					,						snake_case_					,						self.fn(left.val					,						right.val)					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     int					,						snake_case_				:     int)->				List[Any]:
						if node.start == i and node.end == i:
											__lowerCAmelCase						 =val
											return
						if i <= node.mid:
											self._update_tree(node.left					,						snake_case_					,						snake_case_)
						else:
											self._update_tree(node.right					,						snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self.fn(node.left.val					,						node.right.val)



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     str					,						snake_case_				:     List[Any])->				Optional[int]:
						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					,						snake_case_					,						snake_case_)
											else:
																# range in left child tree and right child tree
																return self.fn(
																    self._query_range(node.left					,						snake_case_					,						node.mid)					,						self._query_range(node.right					,						node.mid + 1					,						snake_case_)					,						)
						else:
											# range in right child tree
											return self._query_range(node.right					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str])->				Tuple:
						if self.root is not None:
											__lowerCAmelCase						 =Queue()
											queue.put(self.root)
											while not queue.empty():
																__lowerCAmelCase						 =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)
								lowercase_													=      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()




