
'''simple docstring'''

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase						)			->   List[str]:
		_UpperCamelCase							:			List[Any]     =   AutoConfig.from_pretrained(a_						)
		_UpperCamelCase							:			Union[str, Any]     =   FlaxAutoModelForSeqaSeqLM.from_config(config=a_						)
		_UpperCamelCase							:			Optional[Any]     =   checkpoints.load_tax_checkpoint(a_						)

		_UpperCamelCase							:			str     =   '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

		if config.model_type == "t5":
				_UpperCamelCase							:			List[Any]     =   '''SelfAttention'''
		if config.model_type == "longt5" and config.encoder_attention_type == "local":
				_UpperCamelCase							:			Tuple     =   '''LocalSelfAttention'''
		elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				_UpperCamelCase							:			List[str]     =   '''TransientGlobalSelfAttention'''
		else:
				raise ValueError(
				    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
				    ''' attribute with a value from [\'local\', \'transient-global].'''						)

		# Encoder
		for layer_index in range(config.num_layers						):
				_UpperCamelCase							:			str     =   f'''layers_{str(a_						)}'''

				# Self-Attention
				_UpperCamelCase							:			Tuple     =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
				_UpperCamelCase							:			List[Any]     =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
				_UpperCamelCase							:			Tuple     =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
				_UpperCamelCase							:			Tuple     =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						_UpperCamelCase							:			Optional[Any]     =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

				# Layer Normalization
				_UpperCamelCase							:			str     =   tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

				if split_mlp_wi:
						_UpperCamelCase							:			str     =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
						_UpperCamelCase							:			Tuple     =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
				else:
						_UpperCamelCase							:			Any     =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

				_UpperCamelCase							:			int     =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

				# Layer Normalization
				_UpperCamelCase							:			List[Any]     =   tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

				# Assigning
				_UpperCamelCase							:			List[Any]     =   flax_model.params['''encoder''']['''block'''][str(a_						)]['''layer''']
				_UpperCamelCase							:			Union[str, Any]     =   tax_attention_key
				_UpperCamelCase							:			Any     =   tax_attention_out
				_UpperCamelCase							:			Optional[int]     =   tax_attention_query
				_UpperCamelCase							:			Any     =   tax_attention_value

				_UpperCamelCase							:			Union[str, Any]     =   tax_attention_layer_norm

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						_UpperCamelCase							:			Optional[Any]     =   tax_global_layer_norm

				if split_mlp_wi:
						_UpperCamelCase							:			str     =   tax_mlp_wi_a
						_UpperCamelCase							:			Any     =   tax_mlp_wi_a
				else:
						_UpperCamelCase							:			List[Any]     =   tax_mlp_wi

				_UpperCamelCase							:			Any     =   tax_mlp_wo
				_UpperCamelCase							:			List[Any]     =   tax_mlp_layer_norm

				_UpperCamelCase							:			Tuple     =   flax_model_encoder_layer_block

		# Only for layer 0:
		_UpperCamelCase							:			Optional[int]     =   tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
		_UpperCamelCase							:			List[Any]     =   tax_encoder_rel_embedding

		# Side/global relative position_bias + layer norm
		if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				_UpperCamelCase							:			Optional[Any]     =   tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
				_UpperCamelCase							:			Dict     =   tax_encoder_global_rel_embedding

		# Assigning
		_UpperCamelCase							:			Tuple     =   tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
		_UpperCamelCase							:			int     =   tax_encoder_norm

		# Decoder
		for layer_index in range(config.num_layers						):
				_UpperCamelCase							:			Tuple     =   f'''layers_{str(a_						)}'''

				# Self-Attention
				_UpperCamelCase							:			int     =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
				_UpperCamelCase							:			Union[str, Any]     =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
				_UpperCamelCase							:			Union[str, Any]     =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
				_UpperCamelCase							:			Union[str, Any]     =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

				# Layer Normalization
				_UpperCamelCase							:			int     =   tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
				    '''scale'''
				]

				# Encoder-Decoder-Attention
				_UpperCamelCase							:			Any     =   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
				_UpperCamelCase							:			List[Any]     =   tax_enc_dec_attention_module['''key''']['''kernel''']
				_UpperCamelCase							:			Optional[int]     =   tax_enc_dec_attention_module['''out''']['''kernel''']
				_UpperCamelCase							:			List[Any]     =   tax_enc_dec_attention_module['''query''']['''kernel''']
				_UpperCamelCase							:			int     =   tax_enc_dec_attention_module['''value''']['''kernel''']

				# Layer Normalization
				_UpperCamelCase							:			Any     =   tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

				# MLP
				if split_mlp_wi:
						_UpperCamelCase							:			List[str]     =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
						_UpperCamelCase							:			Optional[Any]     =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
				else:
						_UpperCamelCase							:			Dict     =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

				_UpperCamelCase							:			Dict     =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

				# Layer Normalization
				_UpperCamelCase							:			Any     =   tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

				# Assigning
				_UpperCamelCase							:			str     =   flax_model.params['''decoder''']['''block'''][str(a_						)]['''layer''']
				_UpperCamelCase							:			Optional[Any]     =   tax_attention_key
				_UpperCamelCase							:			Optional[Any]     =   tax_attention_out
				_UpperCamelCase							:			str     =   tax_attention_query
				_UpperCamelCase							:			Union[str, Any]     =   tax_attention_value

				_UpperCamelCase							:			str     =   tax_pre_attention_layer_norm

				_UpperCamelCase							:			List[str]     =   tax_enc_dec_attention_key
				_UpperCamelCase							:			Union[str, Any]     =   tax_enc_dec_attention_out
				_UpperCamelCase							:			Any     =   tax_enc_dec_attention_query
				_UpperCamelCase							:			Any     =   tax_enc_dec_attention_value

				_UpperCamelCase							:			List[Any]     =   tax_cross_layer_norm

				if split_mlp_wi:
						_UpperCamelCase							:			Optional[int]     =   tax_mlp_wi_a
						_UpperCamelCase							:			Any     =   tax_mlp_wi_a
				else:
						_UpperCamelCase							:			Optional[int]     =   tax_mlp_wi

				_UpperCamelCase							:			Dict     =   tax_mlp_wo

				_UpperCamelCase							:			Optional[int]     =   txa_mlp_layer_norm

				_UpperCamelCase							:			Tuple     =   flax_model_decoder_layer_block

		# Decoder Normalization
		_UpperCamelCase							:			str     =   tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
		_UpperCamelCase							:			int     =   txa_decoder_norm

		# Only for layer 0:
		_UpperCamelCase							:			Optional[Any]     =   tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
		_UpperCamelCase							:			Dict     =   tax_decoder_rel_embedding

		# Token Embeddings
		_UpperCamelCase							:			List[Any]     =   tax_model['''target''']['''token_embedder''']['''embedding''']
		_UpperCamelCase							:			List[str]     =   txa_token_embeddings

		# LM Head (only in v1.1 and LongT5 checkpoints)
		if "logits_dense" in tax_model["target"]["decoder"]:
				_UpperCamelCase							:			Optional[Any]     =   tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

		flax_model.save_pretrained(a_						)
		print('''T5X Model was sucessfully converted!'''						)


if __name__ == "__main__":
	_UpperCAmelCase :   Optional[Any]			    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
	)
	parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
	parser.add_argument(
	    """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
	)
	_UpperCAmelCase :   Any			    =	parser.parse_args()
	convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)













from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

lowercase    				=   TypeVar("T")
lowercase    				=   TypeVar("U")




class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       List[Any]:
								snake_case_           =				key
								snake_case_           =				val
								snake_case_           =				None
								snake_case_           =				None






	def __repr__(			self   )			->       str:
								return (
								    F'''Node: key: {self.key}, val: {self.val}, '''
								    F'''has next: {bool(self.next   )}, has prev: {bool(self.prev   )}'''
								)






class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	def __init__(			self   )			->       None:
								snake_case_           =				DoubleLinkedListNode(a			,     a   )
								snake_case_           =				DoubleLinkedListNode(a			,     a   )
								snake_case_     ,						snake_case_           =				self.rear, self.head




	def __repr__(			self   )			->       str:
								snake_case_           =				['DoubleLinkedList']
								snake_case_           =				self.head
								while node.next is not None:
															rep.append(str(a   )   )
															snake_case_           =				node.next
								rep.append(str(self.rear   )   )
								return ",\n    ".join(a   )




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								snake_case_           =				self.rear.prev

								# All nodes other than self.head are guaranteed to have non-None previous
								assert previous is not None

								snake_case_           =				node
								snake_case_           =				previous
								snake_case_           =				node
								snake_case_           =				self.rear






	def 				_UpperCamelCase     (			self			,     a   )			->       DoubleLinkedListNode[T, U] | None:

								if node.prev is None or node.next is None:
															return None

								snake_case_           =				node.next
								snake_case_           =				node.prev
								snake_case_           =				None
								snake_case_           =				None
								return node






class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	lowerCAmelCase						=					{}




	def __init__(			self			,     a   )			->       str:
								snake_case_           =				DoubleLinkedList()
								snake_case_           =				capacity
								snake_case_           =				0
								snake_case_           =				0
								snake_case_           =				0
								snake_case_           =				{}




	def __repr__(			self   )			->       str:
								return (
								    F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
								    F'''capacity={self.capacity}, current size={self.num_keys})'''
								)




	def __contains__(			self			,     a   )			->       bool:
								return key in self.cache




	def 				_UpperCamelCase     (			self			,     a   )			->       U | None:
								# Note: pythonic interface would throw KeyError rather than return None

								if key in self.cache:
															self.hits += 1
															snake_case_           =				self.cache[key]
															snake_case_           =				self.list.remove(self.cache[key]   )
															assert node == value_node

															# node is guaranteed not None because it is in self.cache
															assert node is not None
															self.list.add(a   )
															return node.val
								self.miss += 1
								return None




	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       None:

								if key not in self.cache:
															if self.num_keys >= self.capacity:
																						# delete first node (oldest) when over capacity
																						snake_case_           =				self.list.head.next

																						# guaranteed to have a non-None first node when num_keys > 0
																						# explain to type checker via assertions
																						assert first_node is not None
																						assert first_node.key is not None
																						assert (
																						    self.list.remove(a   ) is not None
																						)  # node guaranteed to be in list assert node.key is not None

																						del self.cache[first_node.key]
																						self.num_keys -= 1
															snake_case_           =				DoubleLinkedListNode(a			,     a   )
															self.list.add(self.cache[key]   )
															self.num_keys += 1

								else:
															# bump node to the end of the list, update value
															snake_case_           =				self.list.remove(self.cache[key]   )
															assert node is not None  # node guaranteed to be in list
															snake_case_           =				value
															self.list.add(a   )






	@classmethod
	def 				_UpperCamelCase     (			cls			,     a = 1_28   )			->       Callable[[Callable[[T], U]], Callable[..., U]]:

								def cache_decorator_inner(a   ) -> Callable[..., U]:
															def cache_decorator_wrapper(*a   ) -> U:
																						if func not in cls.decorator_function_to_instance_map:
																													snake_case_           =				LRUCache(a   )

																						snake_case_           =				cls.decorator_function_to_instance_map[func].get(args[0]   )
																						if result is None:
																													snake_case_           =				func(*a   )
																													cls.decorator_function_to_instance_map[func].put(args[0]			,     a   )
																						return result

															def cache_info() -> LRUCache[T, U]:
																						return cls.decorator_function_to_instance_map[func]

															setattr(a			,     'cache_info'			,     a   )  # noqa: B010

															return cache_decorator_wrapper

								return cache_decorator_inner


if __name__ == "__main__":
	import doctest

	doctest.testmod()





