


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str     ,					UpperCamelCase		:      int     ,					UpperCamelCase		:      Optional[int]	)     ->	Any:



	"""simple docstring"""






	a_     			=   TaConfig.from_pretrained(UpperCamelCase	)
	a_     			=   FlaxTaForConditionalGeneration(config=UpperCamelCase	)
	a_     			=   checkpoints.load_tax_checkpoint(UpperCamelCase	)

	a_     			=   """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

	# Encoder
	for layer_index in range(config.num_layers	):
		a_     			=   F"""layers_{str(UpperCamelCase	)}"""

		# Self-Attention
		a_     			=   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
		a_     			=   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
		a_     			=   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
		a_     			=   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

		# Layer Normalization
		a_     			=   tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

		if split_mlp_wi:
			a_     			=   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
			a_     			=   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
		else:
			a_     			=   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

		a_     			=   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

		# Layer Normalization
		a_     			=   tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

		# Assigning
		a_     			=   tax_attention_key
		a_     			=   tax_attention_out
		a_     			=   tax_attention_query
		a_     			=   tax_attention_value

		a_     			=   tax_attention_layer_norm

		if split_mlp_wi:
			a_     			=   tax_mlp_wi_a
			a_     			=   tax_mlp_wi_a
		else:
			a_     			=   tax_mlp_wi

		a_     			=   tax_mlp_wo
		a_     			=   tax_mlp_layer_norm

	# Only for layer 0:
	a_     			=   tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
	a_     			=   tax_encoder_rel_embedding

	# Assigning
	a_     			=   tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
	a_     			=   tax_encoder_norm

	# Decoder
	for layer_index in range(config.num_decoder_layers	):
		a_     			=   F"""layers_{str(UpperCamelCase	)}"""

		# Self-Attention
		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

		# Layer Normalization
		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
		    """scale"""
		]

		# Encoder-Decoder-Attention
		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
		    """kernel"""
		]
		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
		    """kernel"""
		]
		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
		    """kernel"""
		]
		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
		    """kernel"""
		]

		# Layer Normalization
		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

		# MLP
		if split_mlp_wi:
			a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
			a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
		else:
			a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

		# Layer Normalization
		a_     			=   tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

		# Assigning
		a_     			=   tax_attention_key
		a_     			=   tax_attention_out
		a_     			=   tax_attention_query
		a_     			=   tax_attention_value

		a_     			=   tax_pre_attention_layer_norm

		a_     			=   tax_enc_dec_attention_key
		a_     			=   tax_enc_dec_attention_out
		a_     			=   tax_enc_dec_attention_query
		a_     			=   tax_enc_dec_attention_value

		a_     			=   tax_cross_layer_norm

		if split_mlp_wi:
			a_     			=   tax_mlp_wi_a
			a_     			=   tax_mlp_wi_a
		else:
			a_     			=   tax_mlp_wi

		a_     			=   tax_mlp_wo

		a_     			=   txa_mlp_layer_norm

	# Decoder Normalization
	a_     			=   tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
	a_     			=   txa_decoder_norm

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

	# Token Embeddings
	a_     			=   tax_model["""target"""]["""token_embedder"""]["""embedding"""]
	a_     			=   txa_token_embeddings

	# LM Head (only in v1.1 checkpoints)
	if "logits_dense" in tax_model["target"]["decoder"]:
		a_     			=   tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

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


if __name__ == "__main__":
			_A							     =       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
			)
			parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
			parser.add_argument(
			    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
			)
			_A							     =       parser.parse_args()
			convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_A							     =       set()
_A							     =       get_logger(__name__)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[str] = None	)     ->	Optional[int]:



	"""simple docstring"""






	def decorator(UpperCamelCase		:      Callable	):
		global _emitted_deprecation_warnings

		if inspect.isclass(UpperCamelCase	):
			a_     			=   deprecated_class_or_function.__init__
			a_     			=   deprecated_class_or_function.__name__
		else:
			a_     			=   deprecated_class_or_function
			a_     			=   deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			a_     			=   name if name != """__init__""" else deprecated_function.__qualname__.split("""."""	)[-2]

		a_     			=   (
		    F"""{name} is deprecated and will be removed in the next major version of datasets.""" + F""" {help_message}"""
		    if help_message
		    else """"""
		)

		@wraps(UpperCamelCase	)
		def wrapper(*UpperCamelCase		:      List[str]     ,					**UpperCamelCase		:      Union[str, Any]	):
			a_     			=   hash(UpperCamelCase	)
			if func_hash not in _emitted_deprecation_warnings:
				warnings.warn(UpperCamelCase     ,					category=UpperCamelCase     ,					stacklevel=2	)
				_emitted_deprecation_warnings.add(UpperCamelCase	)
			return deprecated_function(*UpperCamelCase     ,					**UpperCamelCase	)

		a_     			=   """deprecated"""

		if inspect.isclass(UpperCamelCase	):
			a_     			=   wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator

class 			lowerCamelCase_					(    enum.EnumMeta    ):





							def __getattribute__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   super().__getattribute__(_SCREAMING_SNAKE_CASE    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       enum.Enum    ) and obj._on_access:
									obj._on_access()
								return obj





							def __getitem__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   super().__getitem__(_SCREAMING_SNAKE_CASE    )
								if member._on_access:
									member._on_access()
								return member





							def __call__(							cls			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None			,       *, _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=1    ):
								a_     			=   super().__call__(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       module=_SCREAMING_SNAKE_CASE			,       qualname=_SCREAMING_SNAKE_CASE			,       type=_SCREAMING_SNAKE_CASE			,       start=_SCREAMING_SNAKE_CASE    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       enum.Enum    ) and obj._on_access:
									obj._on_access()
								return obj







class 			lowerCamelCase_					(    enum.Enum   ,		metaclass=_SCREAMING_SNAKE_CASE    ):





							def __new__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   object.__new__(cls    )
								a_     			=   value
								a_     			=   member.deprecate
								return member





							@property
							def   __magic_name__      (							self    ):
								return ""





							def   __magic_name__      (							self    ):
								a_     			=   f""" {self.help_message}""" if self.help_message else """"""
								warnings.warn(
								    f"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
								    + help_message			,       _SCREAMING_SNAKE_CASE			,       stacklevel=3			,       )