





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						Optional[Any]:
		__lowercase					=    TaConfig.from_pretrained(snake_case       )
		__lowercase					=    FlaxTaForConditionalGeneration(config=snake_case       )
		__lowercase					=    checkpoints.load_tax_checkpoint(snake_case       )

		__lowercase					=    'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

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

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

				# Layer Normalization
				__lowercase					=    tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

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

				__lowercase					=    tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

				# Layer Normalization
				__lowercase					=    tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

				# Assigning
				__lowercase					=    tax_attention_key
				__lowercase					=    tax_attention_out
				__lowercase					=    tax_attention_query
				__lowercase					=    tax_attention_value

				__lowercase					=    tax_attention_layer_norm

				if split_mlp_wi:
						__lowercase					=    tax_mlp_wi_a
						__lowercase					=    tax_mlp_wi_a
				else:
						__lowercase					=    tax_mlp_wi

				__lowercase					=    tax_mlp_wo
				__lowercase					=    tax_mlp_layer_norm

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

		# Assigning
		__lowercase					=    tax_model['target']['encoder']['encoder_norm']['scale']
		__lowercase					=    tax_encoder_norm

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

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

				# Layer Normalization
				__lowercase					=    tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
				    'scale'
				]

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

				# Layer Normalization
				__lowercase					=    tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

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

				__lowercase					=    tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

				# Layer Normalization
				__lowercase					=    tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

				# Assigning
				__lowercase					=    tax_attention_key
				__lowercase					=    tax_attention_out
				__lowercase					=    tax_attention_query
				__lowercase					=    tax_attention_value

				__lowercase					=    tax_pre_attention_layer_norm

				__lowercase					=    tax_enc_dec_attention_key
				__lowercase					=    tax_enc_dec_attention_out
				__lowercase					=    tax_enc_dec_attention_query
				__lowercase					=    tax_enc_dec_attention_value

				__lowercase					=    tax_cross_layer_norm

				if split_mlp_wi:
						__lowercase					=    tax_mlp_wi_a
						__lowercase					=    tax_mlp_wi_a
				else:
						__lowercase					=    tax_mlp_wi

				__lowercase					=    tax_mlp_wo

				__lowercase					=    txa_mlp_layer_norm

		# Decoder Normalization
		__lowercase					=    tax_model['target']['decoder']['decoder_norm']['scale']
		__lowercase					=    txa_decoder_norm

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

		# Token Embeddings
		__lowercase					=    tax_model['target']['token_embedder']['embedding']
		__lowercase					=    txa_token_embeddings

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

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


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











import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_torch
@require_torchaudio
@require_sentencepiece
class 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''




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

			'''simple docstring'''




			__lowercase					=    tempfile.mkdtemp()

			__lowercase					=    ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
			__lowercase					=    dict(zip(__lowerCamelCase				,			range(len(__lowerCamelCase      )      )      )      )
			__lowercase					=    Path(self.tmpdirname      )
			save_json(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['vocab_file']      )
			if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
					copyfile(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['spm_file']      )

			__lowercase					=    SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
			tokenizer.save_pretrained(self.tmpdirname      )

			__lowercase					=    {
			    'feature_size': 24,
			    'num_mel_bins': 24,
			    'padding_value': 0.0,
			    'sampling_rate': 16_000,
			    'return_attention_mask': False,
			    'do_normalize': True,
			}
			save_json(__lowerCamelCase				,			save_dir / FEATURE_EXTRACTOR_NAME      )




	def       UpperCAmelCase (	self       :    Dict				,			**__lowerCamelCase       :    Dict      )					->   Union[str, Any]:

			'''simple docstring'''




			return SpeechaTextTokenizer.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    str				,			**__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			shutil.rmtree(self.tmpdirname      )




	def       UpperCAmelCase (	self       :    str      )					->   Any:

			'''simple docstring'''




			__lowercase					=    self.get_tokenizer()
			__lowercase					=    self.get_feature_extractor()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			processor.save_pretrained(self.tmpdirname      )
			__lowercase					=    SpeechaTextProcessor.from_pretrained(self.tmpdirname      )

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

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




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

			'''simple docstring'''




			__lowercase					=    SpeechaTextProcessor(
			    tokenizer=self.get_tokenizer()				,			feature_extractor=self.get_feature_extractor()      )
			processor.save_pretrained(self.tmpdirname      )

			__lowercase					=    self.get_tokenizer(bos_token='(BOS)'				,			eos_token='(EOS)'      )
			__lowercase					=    self.get_feature_extractor(do_normalize=__lowerCamelCase				,			padding_value=1.0      )

			__lowercase					=    SpeechaTextProcessor.from_pretrained(
			    self.tmpdirname				,			bos_token='(BOS)'				,			eos_token='(EOS)'				,			do_normalize=__lowerCamelCase				,			padding_value=1.0      )

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

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




	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    floats_list((3, 1_000)      )

			__lowercase					=    feature_extractor(__lowerCamelCase				,			return_tensors='np'      )
			__lowercase					=    processor(__lowerCamelCase				,			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       UpperCAmelCase (	self       :    Union[str, Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    'This is a test string'

			__lowercase					=    processor(text=__lowerCamelCase      )

			__lowercase					=    tokenizer(__lowerCamelCase      )

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




	def       UpperCAmelCase (	self       :    str      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

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

			__lowercase					=    processor.batch_decode(__lowerCamelCase      )
			__lowercase					=    tokenizer.batch_decode(__lowerCamelCase      )

			self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    Tuple      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

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




