'''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


__SCREAMING_SNAKE_CASE :  Optional[int]  =						get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")

@require_torch
class 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

      def   _A			( self		:       str ):
             _UpperCAmelCase  :						Union[str, Any]							   =					tempfile.mkdtemp()

             _UpperCAmelCase  :						str							   =					SpeechTaTokenizer(A )
             tokenizer.save_pretrained(self.tmpdirname )

             _UpperCAmelCase  :						Any							   =					{
                 "feature_size": 1,
                 "padding_value": 0.0,
                 "sampling_rate": 16000,
                 "do_normalize": False,
                 "num_mel_bins": 80,
                 "hop_length": 16,
                 "win_length": 64,
                 "win_function": "hann_window",
                 "fmin": 80,
                 "fmax": 7600,
                 "mel_floor": 1E-10,
                 "reduction_factor": 2,
                 "return_attention_mask": True,
             }

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




      def   _A			( self		:       Optional[int]		,      **A		:       Union[str, Any] ):
             return SpeechTaTokenizer.from_pretrained(self.tmpdirname		,      **A )




      def   _A			( self		:       str		,      **A		:       Any ):
             return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname		,      **A )




      def   _A			( self		:       List[Any] ):
             shutil.rmtree(self.tmpdirname )




      def   _A			( self		:       int ):
             _UpperCAmelCase  :						List[Any]							   =					self.get_tokenizer()
             _UpperCAmelCase  :						int							   =					self.get_feature_extractor()

             _UpperCAmelCase  :						Dict							   =					SpeechTaProcessor(tokenizer=A		,      feature_extractor=A )

             processor.save_pretrained(self.tmpdirname )
             _UpperCAmelCase  :						int							   =					SpeechTaProcessor.from_pretrained(self.tmpdirname )

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

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




      def   _A			( self		:       List[str] ):
             _UpperCAmelCase  :						Tuple							   =					SpeechTaProcessor(tokenizer=self.get_tokenizer()		,      feature_extractor=self.get_feature_extractor() )
             processor.save_pretrained(self.tmpdirname )

             _UpperCAmelCase  :						List[str]							   =					self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)" )
             _UpperCAmelCase  :						List[Any]							   =					self.get_feature_extractor(do_normalize=A		,      padding_value=1.0 )

             _UpperCAmelCase  :						Tuple							   =					SpeechTaProcessor.from_pretrained(
                 self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=A		,      padding_value=1.0 )

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

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




      def   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						Dict							   =					self.get_feature_extractor()
             _UpperCAmelCase  :						List[str]							   =					self.get_tokenizer()

             _UpperCAmelCase  :						int							   =					SpeechTaProcessor(tokenizer=A		,      feature_extractor=A )

             _UpperCAmelCase  :						List[str]							   =					floats_list((3, 1000) )

             _UpperCAmelCase  :						Optional[Any]							   =					feature_extractor(audio=A		,      return_tensors="np" )
             _UpperCAmelCase  :						Tuple							   =					processor(audio=A		,      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		:       int ):
             _UpperCAmelCase  :						List[Any]							   =					self.get_feature_extractor()
             _UpperCAmelCase  :						List[Any]							   =					self.get_tokenizer()

             _UpperCAmelCase  :						str							   =					SpeechTaProcessor(tokenizer=A		,      feature_extractor=A )

             _UpperCAmelCase  :						str							   =					floats_list((3, 1000) )

             _UpperCAmelCase  :						str							   =					feature_extractor(audio_target=A		,      return_tensors="np" )
             _UpperCAmelCase  :						List[str]							   =					processor(audio_target=A		,      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] ):
             _UpperCAmelCase  :						Any							   =					self.get_feature_extractor()
             _UpperCAmelCase  :						List[Any]							   =					self.get_tokenizer()

             _UpperCAmelCase  :						int							   =					SpeechTaProcessor(tokenizer=A		,      feature_extractor=A )

             _UpperCAmelCase  :						Optional[Any]							   =					"This is a test string"

             _UpperCAmelCase  :						Tuple							   =					processor(text=A )
             _UpperCAmelCase  :						Optional[Any]							   =					tokenizer(A )

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




      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						str							   =					self.get_feature_extractor()
             _UpperCAmelCase  :						List[str]							   =					self.get_tokenizer()

             _UpperCAmelCase  :						List[str]							   =					SpeechTaProcessor(tokenizer=A		,      feature_extractor=A )

             _UpperCAmelCase  :						str							   =					"This is a test string"

             _UpperCAmelCase  :						Optional[Any]							   =					processor(text_target=A )
             _UpperCAmelCase  :						List[Any]							   =					tokenizer(A )

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




      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						Any							   =					self.get_feature_extractor()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.get_tokenizer()

             _UpperCAmelCase  :						List[Any]							   =					SpeechTaProcessor(tokenizer=A		,      feature_extractor=A )

             _UpperCAmelCase  :						List[str]							   =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _UpperCAmelCase  :						Any							   =					processor.batch_decode(A )
             _UpperCAmelCase  :						Tuple							   =					tokenizer.batch_decode(A )

             self.assertListEqual(A		,      A )





      def   _A			( self		:       Any ):
             _UpperCAmelCase  :						Optional[Any]							   =					self.get_feature_extractor()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.get_tokenizer()

             _UpperCAmelCase  :						Optional[Any]							   =					SpeechTaProcessor(tokenizer=A		,      feature_extractor=A )

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


'''simple docstring'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__SCREAMING_SNAKE_CASE :  Optional[int]  =						logging.get_logger(__name__)

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						List[Any]      			=		"speech-encoder-decoder"
      __UpperCamelCase:						Optional[int]      			=		True




      def __init__( self		:       Optional[int]		,      **A		:       Tuple ):
             super().__init__(**A )
             if "encoder" not in kwargs or "decoder" not in kwargs:
                    raise ValueError(
                        F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
                        F""" `decoder` sub-configurations are passed, but only {kwargs}""" )

             _UpperCAmelCase  :						Dict							   =					kwargs.pop("encoder" )
             _UpperCAmelCase  :						Any							   =					encoder_config.pop("model_type" )
             _UpperCAmelCase  :						str							   =					kwargs.pop("decoder" )
             _UpperCAmelCase  :						int							   =					decoder_config.pop("model_type" )

             _UpperCAmelCase  :						List[Any]							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						str							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						Optional[int]							   =					True




      @classmethod
      def   _A			( cls		:       int		,      A		:       PretrainedConfig		,      A		:       PretrainedConfig		,      **A		:       Dict ):
             logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
             _UpperCAmelCase  :						Any							   =					True
             _UpperCAmelCase  :						Union[str, Any]							   =					True

             return cls(encoder=encoder_config.to_dict()		,      decoder=decoder_config.to_dict()		,      **A )





      def   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						List[str]							   =					copy.deepcopy(self.__dict__ )
             _UpperCAmelCase  :						Any							   =					self.encoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.decoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.__class__.model_type
             return output

