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


snake_case__						      =			get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')



@require_torch
class 			UpperCamelCase					(			unittest.TestCase			):

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					tempfile.mkdtemp()

           _lowerCamelCase           =					SpeechTaTokenizer(A_    )
           tokenizer.save_pretrained(self.tmpdirname    )

           _lowerCamelCase           =					{
               '''feature_size''': 1,
               '''padding_value''': 0.0,
               '''sampling_rate''': 1_60_00,
               '''do_normalize''': False,
               '''num_mel_bins''': 80,
               '''hop_length''': 16,
               '''win_length''': 64,
               '''win_function''': '''hann_window''',
               '''fmin''': 80,
               '''fmax''': 76_00,
               '''mel_floor''': 1E-1_0,
               '''reduction_factor''': 2,
               '''return_attention_mask''': True,
           }

           _lowerCamelCase           =					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 		UpperCamelCase_    (       self					,					**A_    )			->					Tuple:






           """simple docstring"""
           return SpeechTaTokenizer.from_pretrained(self.tmpdirname					,					**A_    )



     def 		UpperCamelCase_    (       self					,					**A_    )			->					Optional[Any]:






           """simple docstring"""
           return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname					,					**A_    )



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






           """simple docstring"""
           shutil.rmtree(self.tmpdirname    )



     def 		UpperCamelCase_    (       self    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					self.get_tokenizer()
           _lowerCamelCase           =					self.get_feature_extractor()

           _lowerCamelCase           =					SpeechTaProcessor(tokenizer=A_					,					feature_extractor=A_    )

           processor.save_pretrained(self.tmpdirname    )
           _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					SpeechTaProcessor(tokenizer=self.get_tokenizer()					,					feature_extractor=self.get_feature_extractor()    )
           processor.save_pretrained(self.tmpdirname    )

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

           _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					Optional[Any]:






           """simple docstring"""
           _lowerCamelCase           =					self.get_feature_extractor()
           _lowerCamelCase           =					self.get_tokenizer()

           _lowerCamelCase           =					SpeechTaProcessor(tokenizer=A_					,					feature_extractor=A_    )

           _lowerCamelCase           =					floats_list((3, 10_00)    )

           _lowerCamelCase           =					feature_extractor(audio=A_					,					return_tensors='''np'''    )
           _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					self.get_feature_extractor()
           _lowerCamelCase           =					self.get_tokenizer()

           _lowerCamelCase           =					SpeechTaProcessor(tokenizer=A_					,					feature_extractor=A_    )

           _lowerCamelCase           =					floats_list((3, 10_00)    )

           _lowerCamelCase           =					feature_extractor(audio_target=A_					,					return_tensors='''np'''    )
           _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					self.get_feature_extractor()
           _lowerCamelCase           =					self.get_tokenizer()

           _lowerCamelCase           =					SpeechTaProcessor(tokenizer=A_					,					feature_extractor=A_    )

           _lowerCamelCase           =					'''This is a test string'''

           _lowerCamelCase           =					processor(text=A_    )
           _lowerCamelCase           =					tokenizer(A_    )

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



     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					self.get_feature_extractor()
           _lowerCamelCase           =					self.get_tokenizer()

           _lowerCamelCase           =					SpeechTaProcessor(tokenizer=A_					,					feature_extractor=A_    )

           _lowerCamelCase           =					'''This is a test string'''

           _lowerCamelCase           =					processor(text_target=A_    )
           _lowerCamelCase           =					tokenizer(A_    )

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



     def 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					self.get_feature_extractor()
           _lowerCamelCase           =					self.get_tokenizer()

           _lowerCamelCase           =					SpeechTaProcessor(tokenizer=A_					,					feature_extractor=A_    )

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

           _lowerCamelCase           =					processor.batch_decode(A_    )
           _lowerCamelCase           =					tokenizer.batch_decode(A_    )

           self.assertListEqual(A_					,					A_    )





     def 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					self.get_feature_extractor()
           _lowerCamelCase           =					self.get_tokenizer()

           _lowerCamelCase           =					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'''					,					)
from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    'cross_attention',
    '0.20.0',
    'Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.',
    standard_warn=False,
)


snake_case__						      =			AttentionProcessor



class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def __init__(       self					,					*A_					,					**A_    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"    )    )} instead.'
           deprecate('''cross_attention'''					,					'''0.20.0'''					,					A_					,					standard_warn=A_    )
           super().__init__(*A_					,					**A_    )