



"""simple docstring"""






import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Optional[int]  )->      Dict:
								lowerCamelCase__	:	List[str]				=tempfile.mkdtemp()
								lowerCamelCase__	:	List[str]				=5

								# Realm tok
								lowerCamelCase__	:	Optional[int]				=[
								    '''[UNK]''',
								    '''[CLS]''',
								    '''[SEP]''',
								    '''[PAD]''',
								    '''[MASK]''',
								    '''test''',
								    '''question''',
								    '''this''',
								    '''is''',
								    '''the''',
								    '''first''',
								    '''second''',
								    '''third''',
								    '''fourth''',
								    '''fifth''',
								    '''record''',
								    '''want''',
								    '''##want''',
								    '''##ed''',
								    '''wa''',
								    '''un''',
								    '''runn''',
								    '''##ing''',
								    ''',''',
								    '''low''',
								    '''lowest''',
								]
								lowerCamelCase__	:	Optional[int]				=os.path.join(self.tmpdirname,						'''realm_tokenizer'''  )
								os.makedirs(lowerCamelCase,						exist_ok=lowerCamelCase  )
								lowerCamelCase__	:	Dict				=os.path.join(lowerCamelCase,						VOCAB_FILES_NAMES['''vocab_file''']  )
								with open(self.vocab_file,						'''w''',						encoding='''utf-8'''  ) as vocab_writer:
											vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )

								lowerCamelCase__	:	Dict				=os.path.join(self.tmpdirname,						'''realm_block_records'''  )
								os.makedirs(lowerCamelCase,						exist_ok=lowerCamelCase  )



					def 	snake_case ( self							:						str  )->      RealmTokenizer:
								return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname,						'''realm_tokenizer'''  )  )



					def 	snake_case ( self							:						str  )->      List[Any]:
								shutil.rmtree(self.tmpdirname  )



					def 	snake_case ( self							:						Optional[int]  )->      Union[str, Any]:
								lowerCamelCase__	:	List[str]				=RealmConfig(num_block_records=self.num_block_records  )
								return config



					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Union[str, Any]				=Dataset.from_dict(
								    {
								        '''id''': ['''0''', '''1'''],
								        '''question''': ['''foo''', '''bar'''],
								        '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
								    }  )
								return dataset



					def 	snake_case ( self							:						Dict  )->      Tuple:
								lowerCamelCase__	:	Any				=np.array(
								    [
								        b'''This is the first record''',
								        b'''This is the second record''',
								        b'''This is the third record''',
								        b'''This is the fourth record''',
								        b'''This is the fifth record''',
								        b'''This is a longer longer longer record''',
								    ],						dtype=lowerCamelCase,						)
								return block_records



					def 	snake_case ( self							:						Optional[Any]  )->      Any:
								lowerCamelCase__	:	List[str]				=RealmRetriever(
								    block_records=self.get_dummy_block_records(),						tokenizer=self.get_tokenizer(),						)
								return retriever



					def 	snake_case ( self							:						List[str]  )->      Optional[Any]:
								lowerCamelCase__	:	Dict				=self.get_config()
								lowerCamelCase__	:	List[Any]				=self.get_dummy_retriever()
								lowerCamelCase__	:	Any				=retriever.tokenizer

								lowerCamelCase__	:	Union[str, Any]				=np.array([0, 3],						dtype='''long'''  )
								lowerCamelCase__	:	Optional[int]				=tokenizer(['''Test question''']  ).input_ids
								lowerCamelCase__	:	List[str]				=tokenizer(
								    ['''the fourth'''],						add_special_tokens=lowerCamelCase,						return_token_type_ids=lowerCamelCase,						return_attention_mask=lowerCamelCase,						).input_ids
								lowerCamelCase__	:	Dict				=config.reader_seq_len

								lowerCamelCase__	:	Tuple				=retriever(
								    lowerCamelCase,						lowerCamelCase,						answer_ids=lowerCamelCase,						max_length=lowerCamelCase,						return_tensors='''np'''  )

								self.assertEqual(len(lowerCamelCase  ),						2  )
								self.assertEqual(len(lowerCamelCase  ),						2  )
								self.assertEqual(len(lowerCamelCase  ),						2  )
								self.assertEqual(concat_inputs.input_ids.shape,						(2, 10)  )
								self.assertEqual(concat_inputs.attention_mask.shape,						(2, 10)  )
								self.assertEqual(concat_inputs.token_type_ids.shape,						(2, 10)  )
								self.assertEqual(concat_inputs.special_tokens_mask.shape,						(2, 10)  )
								self.assertEqual(
								    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]  ),						['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]'''],						)
								self.assertEqual(
								    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]  ),						['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]'''],						)



					def 	snake_case ( self							:						Optional[Any]  )->      List[Any]:
								lowerCamelCase__	:	Any				=self.get_config()
								lowerCamelCase__	:	Dict				=self.get_dummy_retriever()
								lowerCamelCase__	:	Any				=retriever.tokenizer

								lowerCamelCase__	:	Optional[Any]				=np.array([0, 3, 5],						dtype='''long'''  )
								lowerCamelCase__	:	int				=tokenizer(['''Test question''']  ).input_ids
								lowerCamelCase__	:	Optional[Any]				=tokenizer(
								    ['''the fourth''', '''longer longer'''],						add_special_tokens=lowerCamelCase,						return_token_type_ids=lowerCamelCase,						return_attention_mask=lowerCamelCase,						).input_ids
								lowerCamelCase__	:	Union[str, Any]				=config.reader_seq_len

								lowerCamelCase__	:	List[Any]				=retriever(
								    lowerCamelCase,						lowerCamelCase,						answer_ids=lowerCamelCase,						max_length=lowerCamelCase,						return_tensors='''np'''  )

								self.assertEqual([False, True, True],						lowerCamelCase  )
								self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]],						lowerCamelCase  )
								self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]],						lowerCamelCase  )




					def 	snake_case ( self							:						int  )->      Tuple:
								lowerCamelCase__	:	List[str]				=self.get_dummy_retriever()
								retriever.save_pretrained(os.path.join(self.tmpdirname,						'''realm_block_records'''  )  )

								# Test local path
								lowerCamelCase__	:	Any				=retriever.from_pretrained(os.path.join(self.tmpdirname,						'''realm_block_records'''  )  )
								self.assertEqual(retriever.block_records[0],						b'''This is the first record'''  )

								# Test mocked remote path
								with patch('''transformers.models.realm.retrieval_realm.hf_hub_download'''  ) as mock_hf_hub_download:
											lowerCamelCase__	:	Optional[int]				=os.path.join(
											    os.path.join(self.tmpdirname,						'''realm_block_records'''  ),						_REALM_BLOCK_RECORDS_FILENAME  )
											lowerCamelCase__	:	Dict				=RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa'''  )

								self.assertEqual(retriever.block_records[0],						b'''This is the first record'''  )










"""simple docstring"""






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				=AutoConfig.from_pretrained(__lowerCamelCase			)
			lowerCamelCase__	:	Any				=FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCamelCase			)
			lowerCamelCase__	:	Union[str, Any]				=checkpoints.load_tax_checkpoint(__lowerCamelCase			)

			lowerCamelCase__	:	Union[str, Any]				='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

			if config.model_type == "t5":
						lowerCamelCase__	:	List[str]				='''SelfAttention'''
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						lowerCamelCase__	:	List[Any]				='''LocalSelfAttention'''
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCamelCase__	:	Optional[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			):
						lowerCamelCase__	:	List[Any]				=f'''layers_{str(__lowerCamelCase			)}'''

						# Self-Attention
						lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
						lowerCamelCase__	:	Optional[int]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
						lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
						lowerCamelCase__	:	List[Any]				=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":
									lowerCamelCase__	:	str				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

						# Layer Normalization
						lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

						if split_mlp_wi:
									lowerCamelCase__	:	Optional[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									lowerCamelCase__	:	Dict				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	Tuple				=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

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

						lowerCamelCase__	:	List[str]				=tax_attention_layer_norm

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

						if split_mlp_wi:
									lowerCamelCase__	:	Optional[int]				=tax_mlp_wi_a
									lowerCamelCase__	:	Optional[int]				=tax_mlp_wi_a
						else:
									lowerCamelCase__	:	Union[str, Any]				=tax_mlp_wi

						lowerCamelCase__	:	str				=tax_mlp_wo
						lowerCamelCase__	:	Optional[Any]				=tax_mlp_layer_norm

						lowerCamelCase__	:	Optional[int]				=flax_model_encoder_layer_block

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

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

			# Assigning
			lowerCamelCase__	:	int				=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
			lowerCamelCase__	:	List[Any]				=tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers			):
						lowerCamelCase__	:	Dict				=f'''layers_{str(__lowerCamelCase			)}'''

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

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

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

						# Layer Normalization
						lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

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

						# Layer Normalization
						lowerCamelCase__	:	str				=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

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

						lowerCamelCase__	:	List[str]				=tax_pre_attention_layer_norm

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

						lowerCamelCase__	:	Dict				=tax_cross_layer_norm

						if split_mlp_wi:
									lowerCamelCase__	:	Tuple				=tax_mlp_wi_a
									lowerCamelCase__	:	int				=tax_mlp_wi_a
						else:
									lowerCamelCase__	:	List[Any]				=tax_mlp_wi

						lowerCamelCase__	:	Dict				=tax_mlp_wo

						lowerCamelCase__	:	Tuple				=txa_mlp_layer_norm

						lowerCamelCase__	:	Optional[Any]				=flax_model_decoder_layer_block

			# Decoder Normalization
			lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
			lowerCamelCase__	:	int				=txa_decoder_norm

			# Only for layer 0:
			lowerCamelCase__	:	Tuple				=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
			lowerCamelCase__	:	Tuple				=tax_decoder_rel_embedding

			# Token Embeddings
			lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''token_embedder''']['''embedding''']
			lowerCamelCase__	:	Dict				=txa_token_embeddings

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

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


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




