

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)







'''simple docstring'''




import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler





class     __magic_name__						:







			def __init__(	self				:  str				,			lowercase_				:  Dict    ):
										if isinstance(lowercase_				,			lowercase_    ):
																	# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																	# modified it, it will not be accepted here again, since `auto` values would have been overridden
																	lowercase_					:			List[Any]										=       deepcopy(lowercase_    )
										elif os.path.exists(lowercase_    ):
																	with io.open(lowercase_				,			"""r"""				,			encoding="""utf-8"""    ) as f:
																								lowercase_					:			Union[str, Any]										=       json.load(lowercase_    )
										else:
																	try:
																								lowercase_					:			int										=       baseaa.urlsafe_baadecode(lowercase_    ).decode("""utf-8"""    )
																								lowercase_					:			str										=       json.loads(lowercase_    )
																	except (UnicodeDecodeError, AttributeError, ValueError):
																								raise ValueError(
																								    f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''    )

										lowercase_					:			Any										=       config

										self.set_stage_and_offload()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										# zero stage - this is done as early as possible, before model is created, to allow
										# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
										# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
										lowercase_					:			Tuple										=       self.get_value("""zero_optimization.stage"""				,			-1    )

										# offload
										lowercase_					:			str										=       False
										if self.is_zeroa() or self.is_zeroa():
																	lowercase_					:			Dict										=       set(["""cpu""", """nvme"""]    )
																	lowercase_					:			List[Any]										=       set(
																	    [
																	        self.get_value("""zero_optimization.offload_optimizer.device"""    ),
																	        self.get_value("""zero_optimization.offload_param.device"""    ),
																	    ]    )
																	if len(offload_devices & offload_devices_valid    ) > 0:
																								lowercase_					:			Tuple										=       True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Any    ):
										lowercase_					:			Optional[Any]										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Tuple										=       ds_key_long.split("""."""    )
										lowercase_					:			Union[str, Any]										=       nodes.pop()
										for node in nodes:
																	lowercase_					:			List[str]										=       config.get(lowercase_    )
																	if config is None:
																								return None, ds_key

										return config, ds_key







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  List[str]				,			lowercase_				:  List[str]=None    ):
										lowercase_						,						lowercase_					:			List[Any]										=       self.find_config_node(lowercase_    )
										if config is None:
																	return default
										return config.get(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[int]				,			lowercase_				:  int=False    ):
										lowercase_					:			int										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Dict										=       ds_key_long.split("""."""    )
										for node in nodes:
																	lowercase_					:			List[Any]										=       config
																	lowercase_					:			Dict										=       config.get(lowercase_    )
																	if config is None:
																								if must_exist:
																															raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''    )
																								else:
																															return

        # if found remove it
										if parent_config is not None:
																	parent_config.pop(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  Tuple    ):
										lowercase_					:			str										=       self.get_value(lowercase_    )
										return False if value is None else bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Union[str, Any]										=       self.get_value(lowercase_    )
										return False if value is None else not bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										return self._stage == 2







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										return self._stage == 3







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return self._offload





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Any										=       engine







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  int				,			**lowercase_				:  str    ):
										# runs backpropagation and handles mixed precision
										self.engine.backward(lowercase_				,			**lowercase_    )

										# Deepspeed's `engine.step` performs the following operations:
										# - gradient accumulation check
										# - gradient clipping
										# - optimizer step
										# - zero grad
										# - checking overflow
										# - lr_scheduler step (only if engine.lr_scheduler is not None)
										self.engine.step()
										# and this plugin overrides the above calls with no-ops when Accelerate runs under
										# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
										# training loop that works transparently under many training regimes.





class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			device_placement=lowercase_				,			scaler=lowercase_    )
										lowercase_					:			Any										=       hasattr(self.optimizer				,			"""overflow"""    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Tuple=None    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										if self.__has_overflow__:
																	return self.optimizer.overflow
										return False





class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Dict				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]=0.0_01				,			lowercase_				:  List[str]=0				,			**lowercase_				:  List[Any]    ):
										lowercase_					:			str										=       params
										lowercase_					:			List[Any]										=       lr
										lowercase_					:			int										=       weight_decay
										lowercase_					:			Union[str, Any]										=       kwargs





class     __magic_name__						:







			def __init__(	self				:  Tuple				,			lowercase_				:  Optional[Any]				,			lowercase_				:  List[str]=None				,			lowercase_				:  int=0				,			**lowercase_				:  int    ):
										lowercase_					:			Union[str, Any]										=       optimizer
										lowercase_					:			List[str]										=       total_num_steps
										lowercase_					:			Dict										=       warmup_num_steps
										lowercase_					:			Dict										=       kwargs





