





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[Any]      =       TaConfig.from_pretrained(_lowercase  )
						_UpperCamelCase     :   Dict      =       FlaxTaForConditionalGeneration(config=_lowercase  )
						_UpperCamelCase     :   List[str]      =       checkpoints.load_tax_checkpoint(_lowercase  )

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

						# Encoder
						for layer_index in range(config.num_layers  ):
												_UpperCamelCase     :   Dict      =       F"""layers_{str(_lowercase  )}"""

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

												# Layer Normalization
												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

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

												# Assigning
												_UpperCamelCase     :   Dict      =       tax_attention_key
												_UpperCamelCase     :   Dict      =       tax_attention_out
												_UpperCamelCase     :   List[str]      =       tax_attention_query
												_UpperCamelCase     :   Dict      =       tax_attention_value

												_UpperCamelCase     :   Dict      =       tax_attention_layer_norm

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

												_UpperCamelCase     :   str      =       tax_mlp_wo
												_UpperCamelCase     :   int      =       tax_mlp_layer_norm

						# Only for layer 0:
						_UpperCamelCase     :   Any      =       tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
						_UpperCamelCase     :   Optional[Any]      =       tax_encoder_rel_embedding

						# Assigning
						_UpperCamelCase     :   int      =       tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
						_UpperCamelCase     :   Tuple      =       tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_layers  ):
												_UpperCamelCase     :   int      =       F"""layers_{str(_lowercase  )}"""

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

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

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

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

												# MLP
												if split_mlp_wi:
																		_UpperCamelCase     :   str      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		_UpperCamelCase     :   Union[str, Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												_UpperCamelCase     :   Dict      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												_UpperCamelCase     :   Dict      =       tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												_UpperCamelCase     :   Any      =       tax_attention_key
												_UpperCamelCase     :   str      =       tax_attention_out
												_UpperCamelCase     :   int      =       tax_attention_query
												_UpperCamelCase     :   Tuple      =       tax_attention_value

												_UpperCamelCase     :   Optional[int]      =       tax_pre_attention_layer_norm

												_UpperCamelCase     :   List[Any]      =       tax_enc_dec_attention_key
												_UpperCamelCase     :   Optional[Any]      =       tax_enc_dec_attention_out
												_UpperCamelCase     :   Tuple      =       tax_enc_dec_attention_query
												_UpperCamelCase     :   Tuple      =       tax_enc_dec_attention_value

												_UpperCamelCase     :   str      =       tax_cross_layer_norm

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

												_UpperCamelCase     :   str      =       tax_mlp_wo

												_UpperCamelCase     :   Dict      =       txa_mlp_layer_norm

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

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

						# Token Embeddings
						_UpperCamelCase     :   List[str]      =       tax_model['''target''']['''token_embedder''']['''embedding''']
						_UpperCamelCase     :   Tuple      =       txa_token_embeddings

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

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


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











'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
					from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

					from .test_feature_extraction_speechta import floats_list


lowerCamelCase_					:			List[str]     						=				get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')
@require_torch
class  _SCREAMING_SNAKE_CASE				(		unittest.TestCase						):


			'''simple docstring'''





			def 					A   ( self				:    Union[str, Any]      )				->     Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__       =			tempfile.mkdtemp()

									UpperCamelCase__       =			SpeechTaTokenizer(lowercase      )
									tokenizer.save_pretrained(self.tmpdirname      )

									UpperCamelCase__       =			{
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 1_6_0_0_0,
									    """do_normalize""": False,
									    """num_mel_bins""": 8_0,
									    """hop_length""": 1_6,
									    """win_length""": 6_4,
									    """win_function""": """hann_window""",
									    """fmin""": 8_0,
									    """fmax""": 7_6_0_0,
									    """mel_floor""": 1e-10,
									    """reduction_factor""": 2,
									    """return_attention_mask""": True,
									}

									UpperCamelCase__       =			os.path.join(self.tmpdirname			,   lowercase      )
									with open(self.feature_extraction_file			,   """w"""			,   encoding="""utf-8"""      ) as fp:
															fp.write(json.dumps(lowercase      ) + """\n"""      )






			def 					A   ( self				:    Union[str, Any]			,   **lowercase				:    List[Any]      )				->     Any:

									'''simple docstring'''







									return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,   **lowercase      )






			def 					A   ( self				:    str			,   **lowercase				:    Tuple      )				->     Optional[Any]:

									'''simple docstring'''







									return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,   **lowercase      )






			def 					A   ( self				:    Union[str, Any]      )				->     str:

									'''simple docstring'''







									shutil.rmtree(self.tmpdirname      )






			def 					A   ( self				:    List[Any]      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_tokenizer()
									UpperCamelCase__       =			self.get_feature_extractor()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									processor.save_pretrained(self.tmpdirname      )
									UpperCamelCase__       =			SpeechTaProcessor.from_pretrained(self.tmpdirname      )

									self.assertEqual(processor.tokenizer.get_vocab()			,   tokenizer.get_vocab()      )
									self.assertIsInstance(processor.tokenizer			,   lowercase      )

									self.assertEqual(processor.feature_extractor.to_json_string()			,   feature_extractor.to_json_string()      )
									self.assertIsInstance(processor.feature_extractor			,   lowercase      )






			def 					A   ( self				:    Tuple      )				->     Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=self.get_tokenizer()			,   feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

									UpperCamelCase__       =			self.get_tokenizer(bos_token="""(BOS)"""			,   eos_token="""(EOS)"""      )
									UpperCamelCase__       =			self.get_feature_extractor(do_normalize=lowercase			,   padding_value=1.0      )

									UpperCamelCase__       =			SpeechTaProcessor.from_pretrained(
									    self.tmpdirname			,   bos_token="""(BOS)"""			,   eos_token="""(EOS)"""			,   do_normalize=lowercase			,   padding_value=1.0      )

									self.assertEqual(processor.tokenizer.get_vocab()			,   tokenizer_add_kwargs.get_vocab()      )
									self.assertIsInstance(processor.tokenizer			,   lowercase      )

									self.assertEqual(processor.feature_extractor.to_json_string()			,   feature_extractor_add_kwargs.to_json_string()      )
									self.assertIsInstance(processor.feature_extractor			,   lowercase      )






			def 					A   ( self				:    List[Any]      )				->     Dict:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			floats_list((3, 1_0_0_0)      )

									UpperCamelCase__       =			feature_extractor(audio=lowercase			,   return_tensors="""np"""      )
									UpperCamelCase__       =			processor(audio=lowercase			,   return_tensors="""np"""      )

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()			,   input_processor[key].sum()			,   delta=1e-2      )






			def 					A   ( self				:    List[str]      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			floats_list((3, 1_0_0_0)      )

									UpperCamelCase__       =			feature_extractor(audio_target=lowercase			,   return_tensors="""np"""      )
									UpperCamelCase__       =			processor(audio_target=lowercase			,   return_tensors="""np"""      )

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()			,   input_processor[key].sum()			,   delta=1e-2      )






			def 					A   ( self				:    Optional[Any]      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			"""This is a test string"""

									UpperCamelCase__       =			processor(text=lowercase      )
									UpperCamelCase__       =			tokenizer(lowercase      )

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]			,   encoded_processor[key]      )






			def 					A   ( self				:    List[Any]      )				->     int:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			"""This is a test string"""

									UpperCamelCase__       =			processor(text_target=lowercase      )
									UpperCamelCase__       =			tokenizer(lowercase      )

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]			,   encoded_processor[key]      )






			def 					A   ( self				:    Any      )				->     Tuple:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									UpperCamelCase__       =			processor.batch_decode(lowercase      )
									UpperCamelCase__       =			tokenizer.batch_decode(lowercase      )

									self.assertListEqual(lowercase			,   lowercase      )







			def 					A   ( self				:    List[Any]      )				->     List[str]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									self.assertListEqual(
									    processor.model_input_names			,   feature_extractor.model_input_names			,   msg="""`processor` and `feature_extractor` model input names do not match"""			,   )
