
"""simple docstring"""







import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase="attention"  ):
	__lowerCAmelCase    :		Tuple		    =   params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
	__lowerCAmelCase    :		str		    =   params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
	__lowerCAmelCase    :		Optional[int]		    =   params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
	__lowerCAmelCase    :		Dict		    =   params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
	return k, o, q, v
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False  ):
	if split_mlp_wi:
		__lowerCAmelCase    :		Dict		    =   params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
		__lowerCAmelCase    :		Union[str, Any]		    =   params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
		__lowerCAmelCase    :		int		    =   (wi_a, wi_a)
	else:
		__lowerCAmelCase    :		str		    =   params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

	__lowerCAmelCase    :		Tuple		    =   params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
	return wi, wo
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase  ):
	return params[F"{prefix}/layers_{i}/{layer_name}/scale"]
def 					__lowerCAmelCase       (_UpperCamelCase ,						*, _UpperCamelCase ,						_UpperCamelCase  ):
	__lowerCAmelCase    :		List[Any]		    =   traverse_util.flatten_dict(variables['target']  )
	__lowerCAmelCase    :		str		    =   {'/'.join(_UpperCamelCase  ): v for k, v in old.items()}

	# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
	__lowerCAmelCase    :		List[Any]		    =   'encoder/layers_0/mlp/wi_0/kernel' in old
	print('Split MLP:' ,						_UpperCamelCase  )

	__lowerCAmelCase    :		List[Any]		    =   collections.OrderedDict()

	# Shared embeddings.
	__lowerCAmelCase    :		Any		    =   old['token_embedder/embedding']

	# Encoder.
	for i in range(_UpperCamelCase  ):
		# Block i, layer 0 (Self Attention).
		__lowerCAmelCase    :		Union[str, Any]		    =   tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'encoder' ,						'pre_attention_layer_norm'  )
		__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		List[str]		    =   tax_attention_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'encoder' ,						'attention'  )
		__lowerCAmelCase    :		Union[str, Any]		    =   layer_norm
		__lowerCAmelCase    :		List[str]		    =   k.T
		__lowerCAmelCase    :		Union[str, Any]		    =   o.T
		__lowerCAmelCase    :		Optional[Any]		    =   q.T
		__lowerCAmelCase    :		List[str]		    =   v.T

		# Block i, layer 1 (MLP).
		__lowerCAmelCase    :		str		    =   tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'encoder' ,						'pre_mlp_layer_norm'  )
		__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[Any]		    =   tax_mlp_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'encoder' ,						_UpperCamelCase  )
		__lowerCAmelCase    :		Union[str, Any]		    =   layer_norm
		if split_mlp_wi:
			__lowerCAmelCase    :		int		    =   wi[0].T
			__lowerCAmelCase    :		Union[str, Any]		    =   wi[1].T
		else:
			__lowerCAmelCase    :		Any		    =   wi.T
		__lowerCAmelCase    :		Any		    =   wo.T

	__lowerCAmelCase    :		int		    =   old[
	    'encoder/relpos_bias/rel_embedding'
	].T
	__lowerCAmelCase    :		Union[str, Any]		    =   old['encoder/encoder_norm/scale']

	if not is_encoder_only:
		# Decoder.
		for i in range(_UpperCamelCase  ):
			# Block i, layer 0 (Self Attention).
			__lowerCAmelCase    :		int		    =   tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						'pre_self_attention_layer_norm'  )
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		int		    =   tax_attention_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						'self_attention'  )
			__lowerCAmelCase    :		str		    =   layer_norm
			__lowerCAmelCase    :		Optional[Any]		    =   k.T
			__lowerCAmelCase    :		List[Any]		    =   o.T
			__lowerCAmelCase    :		Tuple		    =   q.T
			__lowerCAmelCase    :		str		    =   v.T

			# Block i, layer 1 (Cross Attention).
			__lowerCAmelCase    :		Tuple		    =   tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						'pre_cross_attention_layer_norm'  )
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   tax_attention_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						'encoder_decoder_attention'  )
			__lowerCAmelCase    :		Dict		    =   layer_norm
			__lowerCAmelCase    :		str		    =   k.T
			__lowerCAmelCase    :		Dict		    =   o.T
			__lowerCAmelCase    :		Any		    =   q.T
			__lowerCAmelCase    :		Optional[Any]		    =   v.T

			# Block i, layer 2 (MLP).
			__lowerCAmelCase    :		List[Any]		    =   tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						'pre_mlp_layer_norm'  )
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   tax_mlp_lookup(_UpperCamelCase ,						_UpperCamelCase ,						'decoder' ,						_UpperCamelCase  )
			__lowerCAmelCase    :		Any		    =   layer_norm
			if split_mlp_wi:
				__lowerCAmelCase    :		Union[str, Any]		    =   wi[0].T
				__lowerCAmelCase    :		Dict		    =   wi[1].T
			else:
				__lowerCAmelCase    :		Tuple		    =   wi.T
			__lowerCAmelCase    :		Dict		    =   wo.T

		__lowerCAmelCase    :		Dict		    =   old['decoder/decoder_norm/scale']
		__lowerCAmelCase    :		Any		    =   old[
		    'decoder/relpos_bias/rel_embedding'
		].T

		# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
		if "decoder/logits_dense/kernel" in old:
			__lowerCAmelCase    :		int		    =   old['decoder/logits_dense/kernel'].T

	return new
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase  ):
	__lowerCAmelCase    :		Optional[int]		    =   collections.OrderedDict([(k, torch.from_numpy(v.copy()  )) for (k, v) in converted_params.items()]  )

	# Add what is missing.
	if "encoder.embed_tokens.weight" not in state_dict:
		__lowerCAmelCase    :		Tuple		    =   state_dict['shared.weight']

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			__lowerCAmelCase    :		List[Any]		    =   state_dict['shared.weight']

		if "lm_head.weight" not in state_dict:  # For old 1.0 models.
			print('Using shared word embeddings as lm_head.'  )
			__lowerCAmelCase    :		Dict		    =   state_dict['shared.weight']

	return state_dict
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase  ):
	__lowerCAmelCase    :		Any		    =   checkpoints.load_tax_checkpoint(_UpperCamelCase  )
	__lowerCAmelCase    :		int		    =   convert_tax_to_pytorch(_UpperCamelCase ,						num_layers=config.num_layers ,						is_encoder_only=_UpperCamelCase  )
	__lowerCAmelCase    :		List[Any]		    =   make_state_dict(_UpperCamelCase ,						_UpperCamelCase  )
	model.load_state_dict(_UpperCamelCase ,						strict=_UpperCamelCase  )
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase = False  ):
	__lowerCAmelCase    :		Any		    =   TaConfig.from_json_file(_UpperCamelCase  )
	print(F"Building PyTorch model from configuration: {config}"  )
	# Non-v1.1 checkpoints could also use T5Model, but this works for all.
	# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
	if is_encoder_only:
		__lowerCAmelCase    :		int		    =   TaEncoderModel(_UpperCamelCase  )
	else:
		__lowerCAmelCase    :		Optional[int]		    =   TaForConditionalGeneration(_UpperCamelCase  )

	# Load weights from tf checkpoint
	load_tax_weights_in_ta(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase  )

	# Save pytorch-model
	print(F"Save PyTorch model to {pytorch_dump_path}"  )
	model.save_pretrained(_UpperCamelCase  )

	# Verify that we can load the checkpoint.
	model.from_pretrained(_UpperCamelCase  )
	print('Done'  )


if __name__ == "__main__":
							lowerCamelCase__           =	argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
							# Required parameters
							parser.add_argument(
							    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
							)
							lowerCamelCase__           =	parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
							)

"""simple docstring"""







from collections.abc import Sequence
from queue import Queue




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			__lowerCAmelCase    :		List[str]		    =   start
			__lowerCAmelCase    :		Union[str, Any]		    =   end
			__lowerCAmelCase    :		Union[str, Any]		    =   val
			__lowerCAmelCase    :		Dict		    =   (start + end) // 2
			__lowerCAmelCase    :		Dict		    =   left
			__lowerCAmelCase    :		List[Any]		    =   right







		def __repr__(   self				):
			return f"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   collection
			__lowerCAmelCase    :		Optional[int]		    =   function
			if self.collection:
				__lowerCAmelCase    :		str		    =   self._build_tree(0 ,	len(_SCREAMING_SNAKE_CASE				) - 1				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			self._update_tree(self.root ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			return self._query_range(self.root ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if start == end:
				return SegmentTreeNode(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	self.collection[start]				)
			__lowerCAmelCase    :		Tuple		    =   (start + end) // 2
			__lowerCAmelCase    :		List[str]		    =   self._build_tree(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if node.start == i and node.end == i:
				__lowerCAmelCase    :		int		    =   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				)
			__lowerCAmelCase    :		Optional[Any]		    =   self.fn(node.left.val ,	node.right.val				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			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 							__lowerCamelCase							(   self				):
			if self.root is not None:
				__lowerCAmelCase    :		Optional[Any]		    =   Queue()
				queue.put(self.root				)
				while not queue.empty():
					__lowerCAmelCase    :		str		    =   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()