




"""simple docstring"""





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       int				      =       TaConfig.from_pretrained(__A		)
						snake_case__							:       List[Any]				      =       FlaxTaForConditionalGeneration(config=__A		)
						snake_case__							:       Optional[Any]				      =       checkpoints.load_tax_checkpoint(__A		)

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

						# Encoder
						for layer_index in range(config.num_layers		):
												snake_case__							:       Union[str, Any]				      =       f'''layers_{str(__A		)}'''

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

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

												if split_mlp_wi:
																		snake_case__							:       Dict				      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		snake_case__							:       Tuple				      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		snake_case__							:       Optional[int]				      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												snake_case__							:       List[str]				      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												snake_case__							:       Optional[int]				      =       tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												snake_case__							:       Union[str, Any]				      =       tax_attention_key
												snake_case__							:       str				      =       tax_attention_out
												snake_case__							:       Any				      =       tax_attention_query
												snake_case__							:       Tuple				      =       tax_attention_value

												snake_case__							:       Optional[Any]				      =       tax_attention_layer_norm

												if split_mlp_wi:
																		snake_case__							:       Union[str, Any]				      =       tax_mlp_wi_a
																		snake_case__							:       Tuple				      =       tax_mlp_wi_a
												else:
																		snake_case__							:       List[Any]				      =       tax_mlp_wi

												snake_case__							:       int				      =       tax_mlp_wo
												snake_case__							:       int				      =       tax_mlp_layer_norm

						# Only for layer 0:
						snake_case__							:       str				      =       tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
						snake_case__							:       Union[str, Any]				      =       tax_encoder_rel_embedding

						# Assigning
						snake_case__							:       Optional[Any]				      =       tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
						snake_case__							:       List[Any]				      =       tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_layers		):
												snake_case__							:       Optional[int]				      =       f'''layers_{str(__A		)}'''

												# Self-Attention
												snake_case__							:       int				      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
												snake_case__							:       str				      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
												snake_case__							:       int				      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
												snake_case__							:       Optional[int]				      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

												# Layer Normalization
												snake_case__							:       Union[str, Any]				      =       tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
												    '''scale'''
												]

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

												# Layer Normalization
												snake_case__							:       int				      =       tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

												# MLP
												if split_mlp_wi:
																		snake_case__							:       Tuple				      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		snake_case__							:       Tuple				      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		snake_case__							:       Any				      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												snake_case__							:       Any				      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

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

												# Assigning
												snake_case__							:       Tuple				      =       tax_attention_key
												snake_case__							:       Any				      =       tax_attention_out
												snake_case__							:       Optional[Any]				      =       tax_attention_query
												snake_case__							:       List[str]				      =       tax_attention_value

												snake_case__							:       Any				      =       tax_pre_attention_layer_norm

												snake_case__							:       Tuple				      =       tax_enc_dec_attention_key
												snake_case__							:       Optional[int]				      =       tax_enc_dec_attention_out
												snake_case__							:       List[Any]				      =       tax_enc_dec_attention_query
												snake_case__							:       str				      =       tax_enc_dec_attention_value

												snake_case__							:       int				      =       tax_cross_layer_norm

												if split_mlp_wi:
																		snake_case__							:       Dict				      =       tax_mlp_wi_a
																		snake_case__							:       int				      =       tax_mlp_wi_a
												else:
																		snake_case__							:       Dict				      =       tax_mlp_wi

												snake_case__							:       Optional[Any]				      =       tax_mlp_wo

												snake_case__							:       Any				      =       txa_mlp_layer_norm

						# Decoder Normalization
						snake_case__							:       int				      =       tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
						snake_case__							:       List[Any]				      =       txa_decoder_norm

						# Only for layer 0:
						snake_case__							:       List[str]				      =       tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
						snake_case__							:       Optional[int]				      =       tax_decoder_rel_embedding

						# Token Embeddings
						snake_case__							:       Dict				      =       tax_model['''target''']['''token_embedder''']['''embedding''']
						snake_case__							:       Dict				      =       txa_token_embeddings

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

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


if __name__ == "__main__":
							_lowercase						   =     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."""
							)
							_lowercase						   =     parser.parse_args()
							convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer







class __snake_case     (     tf.keras.layers.Layer						):







   def __init__( self						:		Optional[int]   ,		_snake_case						:		List   ,		_snake_case						:		bool   ,		_snake_case						:		int = None   ,		_snake_case						:		int = None   ,		_snake_case						:		int = None   ,		_snake_case						:		str = "longest"   ,		_snake_case						:		bool = True   ,		_snake_case						:		int = 512   ,		_snake_case						:		int = None   ,		_snake_case						:		bool = True   ,		_snake_case						:		bool = True   ,		_snake_case						:		bool = True   ,		**_snake_case						:		Any   ,		):

        """simple docstring"""


        super().__init__()
        if use_fast_bert_tokenizer:
             UpperCAmelCase_		=				FastBertTokenizer(
                 _snake_case   ,		token_out_type=tf.intaa   ,		lower_case_nfd_strip_accents=_snake_case   ,		**_snake_case)
        else:
             UpperCAmelCase_		=				tf.lookup.StaticVocabularyTable(
                 tf.lookup.KeyValueTensorInitializer(
                     keys=_snake_case   ,		key_dtype=tf.string   ,		values=tf.range(tf.size(_snake_case   ,		out_type=tf.intaa)   ,		dtype=tf.intaa)   ,		value_dtype=tf.intaa   ,		)   ,		num_oov_buckets=1   ,		)
             UpperCAmelCase_		=				BertTokenizerLayer(
                 _snake_case   ,		token_out_type=tf.intaa   ,		lower_case=_snake_case   ,		**_snake_case)

        UpperCAmelCase_		=				vocab_list
        UpperCAmelCase_		=				do_lower_case
        UpperCAmelCase_		=				cls_token_id or vocab_list.index('''[CLS]''')
        UpperCAmelCase_		=				sep_token_id or vocab_list.index('''[SEP]''')
        UpperCAmelCase_		=				pad_token_id or vocab_list.index('''[PAD]''')
        UpperCAmelCase_		=				ShrinkLongestTrimmer(max_length - 3   ,		axis=1)  # Allow room for special tokens
        UpperCAmelCase_		=				max_length
        UpperCAmelCase_		=				padding
        UpperCAmelCase_		=				truncation
        UpperCAmelCase_		=				pad_to_multiple_of
        UpperCAmelCase_		=				return_token_type_ids
        UpperCAmelCase_		=				return_attention_mask







   @classmethod
   def lowerCamelCase		( cls						:		Optional[Any]   ,		_snake_case						:		"PreTrainedTokenizerBase"   ,		**_snake_case						:		str):  # noqa: F821

        """simple docstring"""


        UpperCAmelCase_		=				kwargs.pop('''do_lower_case'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        UpperCAmelCase_		=				kwargs.pop('''cls_token_id'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        UpperCAmelCase_		=				kwargs.pop('''sep_token_id'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        UpperCAmelCase_		=				kwargs.pop('''pad_token_id'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        UpperCAmelCase_		=				tokenizer.get_vocab()
        UpperCAmelCase_		=				sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]   ,		key=lambda _snake_case: x[1])
        UpperCAmelCase_		=				[entry[0] for entry in vocab]
        return cls(
            vocab_list=_snake_case   ,		do_lower_case=_snake_case   ,		cls_token_id=_snake_case   ,		sep_token_id=_snake_case   ,		pad_token_id=_snake_case   ,		**_snake_case   ,		)







   @classmethod
   def lowerCamelCase		( cls						:		List[str]   ,		_snake_case						:		Union[str, os.PathLike]   ,		*_snake_case						:		Union[str, Any]   ,		**_snake_case						:		Union[str, Any]):

        """simple docstring"""


        try:
             UpperCAmelCase_		=				BertTokenizer.from_pretrained(_snake_case   ,		*_snake_case   ,		**_snake_case)
        except:  # noqa: E722
             from .tokenization_bert_fast import BertTokenizerFast

             UpperCAmelCase_		=				BertTokenizerFast.from_pretrained(_snake_case   ,		*_snake_case   ,		**_snake_case)
        return cls.from_tokenizer(_snake_case   ,		**_snake_case)







   def lowerCamelCase		( self						:		int   ,		_snake_case						:		str):

        """simple docstring"""


        if self.do_lower_case:
             UpperCAmelCase_		=				case_fold_utfa(_snake_case)
        UpperCAmelCase_		=				self.tf_tokenizer.tokenize(_snake_case)
        return tokens.merge_dims(1   ,		-1)







   def lowerCamelCase		( self						:		int   ,		_snake_case						:		str   ,		_snake_case						:		str=None   ,		_snake_case						:		Union[str, Any]=None   ,		_snake_case						:		str=None   ,		_snake_case						:		int=None   ,		_snake_case						:		Optional[int]=None   ,		_snake_case						:		Optional[Any]=None   ,		_snake_case						:		List[str]=None   ,		):

        """simple docstring"""


        if padding is None:
             UpperCAmelCase_		=				self.padding
        if padding not in ("longest", "max_length"):
             raise ValueError('''Padding must be either \'longest\' or \'max_length\'!''')
        if max_length is not None and text_pair is not None:
             # Because we have to instantiate a Trimmer to do it properly
             raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!''')
        if max_length is None:
             UpperCAmelCase_		=				self.max_length
        if truncation is None:
             UpperCAmelCase_		=				self.truncation
        if pad_to_multiple_of is None:
             UpperCAmelCase_		=				self.pad_to_multiple_of
        if return_token_type_ids is None:
             UpperCAmelCase_		=				self.return_token_type_ids
        if return_attention_mask is None:
             UpperCAmelCase_		=				self.return_attention_mask
        if not isinstance(_snake_case   ,		tf.Tensor):
             UpperCAmelCase_		=				tf.convert_to_tensor(_snake_case)
        if text_pair is not None and not isinstance(_snake_case   ,		tf.Tensor):
             UpperCAmelCase_		=				tf.convert_to_tensor(_snake_case)
        if text_pair is not None:
             if text.shape.rank > 1:
                  raise ValueError('''text argument should not be multidimensional when a text pair is supplied!''')
             if text_pair.shape.rank > 1:
                  raise ValueError('''text_pair should not be multidimensional!''')
        if text.shape.rank == 2:
             UpperCAmelCase_	,			UpperCAmelCase_		=				text[:, 0], text[:, 1]
        UpperCAmelCase_		=				self.unpaired_tokenize(_snake_case)
        if text_pair is None:  # Unpaired text
             if truncation:
                  UpperCAmelCase_		=				text[:, : max_length - 2]  # Allow room for special tokens
             UpperCAmelCase_	,			UpperCAmelCase_		=				combine_segments(
                 (text,)   ,		start_of_sequence_id=self.cls_token_id   ,		end_of_segment_id=self.sep_token_id)
        else:  # Paired text
             UpperCAmelCase_		=				self.unpaired_tokenize(_snake_case)
             if truncation:
                  UpperCAmelCase_	,			UpperCAmelCase_		=				self.paired_trimmer.trim([text, text_pair])
             UpperCAmelCase_	,			UpperCAmelCase_		=				combine_segments(
                 (text, text_pair)   ,		start_of_sequence_id=self.cls_token_id   ,		end_of_segment_id=self.sep_token_id)
        if padding == "longest":
             UpperCAmelCase_		=				input_ids.bounding_shape(axis=1)
             if pad_to_multiple_of is not None:
                  # No ceiling division in tensorflow, so we negate floordiv instead
                  UpperCAmelCase_		=				pad_to_multiple_of * (-tf.math.floordiv(-pad_length   ,		_snake_case))
        else:
             UpperCAmelCase_		=				max_length

        UpperCAmelCase_	,			UpperCAmelCase_		=				pad_model_inputs(_snake_case   ,		max_seq_length=_snake_case   ,		pad_value=self.pad_token_id)
        UpperCAmelCase_		=				{'''input_ids''': input_ids}
        if return_attention_mask:
             UpperCAmelCase_		=				attention_mask
        if return_token_type_ids:
             UpperCAmelCase_	,			UpperCAmelCase_		=				pad_model_inputs(
                 _snake_case   ,		max_seq_length=_snake_case   ,		pad_value=self.pad_token_id)
             UpperCAmelCase_		=				token_type_ids
        return output







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        return {
            "vocab_list": self.vocab_list,
            "do_lower_case": self.do_lower_case,
            "cls_token_id": self.cls_token_id,
            "sep_token_id": self.sep_token_id,
            "pad_token_id": self.pad_token_id,
        }






