

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: int  		=       AutoConfig.from_pretrained(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: List[str]  		=       FlaxAutoModelForSeqaSeqLM.from_config(config=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       checkpoints.load_tax_checkpoint(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Any  		=       "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

						if config.model_type == "t5":
												SCREAMING_SNAKE_CASE_: str  		=       "SelfAttention"
						if config.model_type == "longt5" and config.encoder_attention_type == "local":
												SCREAMING_SNAKE_CASE_: Any  		=       "LocalSelfAttention"
						elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												SCREAMING_SNAKE_CASE_: Any  		=       "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    ):
												SCREAMING_SNAKE_CASE_: int  		=       f"layers_{str(_UpperCAmelCase    )}"

												# Self-Attention
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
												SCREAMING_SNAKE_CASE_: int  		=       tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
												SCREAMING_SNAKE_CASE_: Dict  		=       tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
												SCREAMING_SNAKE_CASE_: str  		=       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":
																		SCREAMING_SNAKE_CASE_: int  		=       tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

												# Layer Normalization
												SCREAMING_SNAKE_CASE_: Any  		=       tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

												if split_mlp_wi:
																		SCREAMING_SNAKE_CASE_: int  		=       tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
												else:
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

												SCREAMING_SNAKE_CASE_: Dict  		=       tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

												# Layer Normalization
												SCREAMING_SNAKE_CASE_: Dict  		=       tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

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

												SCREAMING_SNAKE_CASE_: Any  		=       tax_attention_layer_norm

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

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

												SCREAMING_SNAKE_CASE_: str  		=       tax_mlp_wo
												SCREAMING_SNAKE_CASE_: int  		=       tax_mlp_layer_norm

												SCREAMING_SNAKE_CASE_: int  		=       flax_model_encoder_layer_block

						# Only for layer 0:
						SCREAMING_SNAKE_CASE_: Tuple  		=       tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
						SCREAMING_SNAKE_CASE_: List[str]  		=       tax_encoder_rel_embedding

						# Side/global relative position_bias + layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												SCREAMING_SNAKE_CASE_: List[Any]  		=       tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
												SCREAMING_SNAKE_CASE_: Any  		=       tax_encoder_global_rel_embedding

						# Assigning
						SCREAMING_SNAKE_CASE_: List[str]  		=       tax_model["target"]["encoder"]["encoder_norm"]["scale"]
						SCREAMING_SNAKE_CASE_: Any  		=       tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_layers    ):
												SCREAMING_SNAKE_CASE_: int  		=       f"layers_{str(_UpperCAmelCase    )}"

												# Self-Attention
												SCREAMING_SNAKE_CASE_: str  		=       tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
												SCREAMING_SNAKE_CASE_: List[str]  		=       tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
												SCREAMING_SNAKE_CASE_: List[str]  		=       tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

												# Layer Normalization
												SCREAMING_SNAKE_CASE_: int  		=       tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
												    "scale"
												]

												# Encoder-Decoder-Attention
												SCREAMING_SNAKE_CASE_: List[Any]  		=       tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
												SCREAMING_SNAKE_CASE_: Any  		=       tax_enc_dec_attention_module["key"]["kernel"]
												SCREAMING_SNAKE_CASE_: int  		=       tax_enc_dec_attention_module["out"]["kernel"]
												SCREAMING_SNAKE_CASE_: Tuple  		=       tax_enc_dec_attention_module["query"]["kernel"]
												SCREAMING_SNAKE_CASE_: List[str]  		=       tax_enc_dec_attention_module["value"]["kernel"]

												# Layer Normalization
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

												# MLP
												if split_mlp_wi:
																		SCREAMING_SNAKE_CASE_: Any  		=       tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
												else:
																		SCREAMING_SNAKE_CASE_: int  		=       tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

												SCREAMING_SNAKE_CASE_: int  		=       tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

												# Layer Normalization
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

												# Assigning
												SCREAMING_SNAKE_CASE_: List[str]  		=       flax_model.params["decoder"]["block"][str(_UpperCAmelCase    )]["layer"]
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       tax_attention_key
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tax_attention_out
												SCREAMING_SNAKE_CASE_: List[Any]  		=       tax_attention_query
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       tax_attention_value

												SCREAMING_SNAKE_CASE_: List[Any]  		=       tax_pre_attention_layer_norm

												SCREAMING_SNAKE_CASE_: Optional[int]  		=       tax_enc_dec_attention_key
												SCREAMING_SNAKE_CASE_: List[Any]  		=       tax_enc_dec_attention_out
												SCREAMING_SNAKE_CASE_: List[Any]  		=       tax_enc_dec_attention_query
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tax_enc_dec_attention_value

												SCREAMING_SNAKE_CASE_: int  		=       tax_cross_layer_norm

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

												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tax_mlp_wo

												SCREAMING_SNAKE_CASE_: Optional[int]  		=       txa_mlp_layer_norm

												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       flax_model_decoder_layer_block

						# Decoder Normalization
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       tax_model["target"]["decoder"]["decoder_norm"]["scale"]
						SCREAMING_SNAKE_CASE_: Any  		=       txa_decoder_norm

						# Only for layer 0:
						SCREAMING_SNAKE_CASE_: Tuple  		=       tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
						SCREAMING_SNAKE_CASE_: Any  		=       tax_decoder_rel_embedding

						# Token Embeddings
						SCREAMING_SNAKE_CASE_: Dict  		=       tax_model["target"]["token_embedder"]["embedding"]
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       txa_token_embeddings

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

						flax_model.save_pretrained(_UpperCAmelCase    )
						print("T5X Model was sucessfully converted!"    )


if __name__ == "__main__":
						lowerCAmelCase							:  List[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."""
						)
						lowerCAmelCase							:  List[str]   	=						parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     List[Any]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       name
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       val



			def __str__( self  :     Dict):
									return F"{self.__class__.__name__}({self.name}, {self.val})"




			def __lt__( self  :     List[str]   ,						lowerCAmelCase__  :     Any):
									return self.val < other.val







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Tuple   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: str  		=       {}
									SCREAMING_SNAKE_CASE_: int  		=       {}
									SCREAMING_SNAKE_CASE_: Any  		=       self.build_heap(lowerCAmelCase__)



			def __getitem__( self  :     List[Any]   ,						lowerCAmelCase__  :     Dict):
									return self.get_value(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Dict):
									return (idx - 1) // 2



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[Any]):
									return idx * 2 + 1



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple):
									return idx * 2 + 2



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Optional[int]):
									return self.heap_dict[key]



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       len(lowerCAmelCase__) - 1
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_parent_idx(lowerCAmelCase__)

									for idx, i in enumerate(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       idx
															SCREAMING_SNAKE_CASE_: str  		=       i.val

									for i in range(lowerCAmelCase__   ,						-1   ,						-1):
															self.sift_down(lowerCAmelCase__   ,						lowerCAmelCase__)
									return array



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]):
									while True:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_left_child_idx(lowerCAmelCase__)  # noqa: E741
															SCREAMING_SNAKE_CASE_: Dict  		=       self.get_right_child_idx(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       idx
															if l < len(lowerCAmelCase__) and array[l] < array[idx]:
																					SCREAMING_SNAKE_CASE_: List[str]  		=       l
															if r < len(lowerCAmelCase__) and array[r] < array[smallest]:
																					SCREAMING_SNAKE_CASE_: str  		=       r

															if smallest != idx:
																					SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       array[smallest], array[idx]
																					(
																					    (
																					    SCREAMING_SNAKE_CASE_
																					)		,    (
																					    SCREAMING_SNAKE_CASE_
																					)		,    
																					): Optional[Any]  		=       (
																					    self.idx_of_element[array[smallest]],
																					    self.idx_of_element[array[idx]],
																					)
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       smallest
															else:
																					break



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: Any  		=       self.get_parent_idx(lowerCAmelCase__)
									while p >= 0 and self.heap[p] > self.heap[idx]:
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.heap[idx], self.heap[p]
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       (
															    self.idx_of_element[self.heap[idx]],
															    self.idx_of_element[self.heap[p]],
															)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       p
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_parent_idx(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return self.heap[0]



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.heap[-1], self.heap[0]
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       (
									    self.idx_of_element[self.heap[-1]],
									    self.idx_of_element[self.heap[0]],
									)

									SCREAMING_SNAKE_CASE_: int  		=       self.heap.pop()
									del self.idx_of_element[x]
									self.sift_down(0   ,						self.heap)
									return x



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Tuple):
									self.heap.append(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       len(self.heap) - 1
									SCREAMING_SNAKE_CASE_: List[str]  		=       node.val
									self.sift_up(len(self.heap) - 1)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return len(self.heap) == 0




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[int]):
									assert (
									    self.heap[self.idx_of_element[node]].val > new_value
									), "newValue must be less that current value"
									SCREAMING_SNAKE_CASE_: Any  		=       new_value
									SCREAMING_SNAKE_CASE_: Tuple  		=       new_value
									self.sift_up(self.idx_of_element[node])







lowerCAmelCase							:  int   	=						Node("""R""", -1)
lowerCAmelCase							:  str   	=						Node("""B""", 6)
lowerCAmelCase							:  str   	=						Node("""A""", 3)
lowerCAmelCase							:  List[str]   	=						Node("""X""", 1)
lowerCAmelCase							:  Union[str, Any]   	=						Node("""E""", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowerCAmelCase							:  Optional[Any]   	=						MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
						print(i)

print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
						print(i)

if __name__ == "__main__":
						import doctest

						doctest.testmod()






