
'''simple docstring'''




from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


lowercase			:				Tuple    		=			logging.get_logger(__name__)





class  A				(							__snake_case						):
     __magic_name__						=		['''audio_values''', '''audio_mask''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=1						,			SCREAMING_SNAKE_CASE=[16, 16]						,			SCREAMING_SNAKE_CASE=128						,			SCREAMING_SNAKE_CASE=44100						,			SCREAMING_SNAKE_CASE=86						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=0.0						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






         super().__init__(
             feature_size=SCREAMING_SNAKE_CASE						,			sampling_rate=SCREAMING_SNAKE_CASE						,			padding_value=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)

         A       :   str					=						spectrogram_length
         A       :   Optional[int]					=						num_channels
         A       :   str					=						patch_size
         A       :   Optional[Any]					=						feature_size // self.patch_size[1]
         A       :   str					=						n_fft
         A       :   Dict					=						sampling_rate // hop_length_to_sampling_rate
         A       :   Optional[int]					=						sampling_rate
         A       :   int					=						padding_value
         A       :   Union[str, Any]					=						mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2						,			num_mel_filters=SCREAMING_SNAKE_CASE						,			min_frequency=0.0						,			max_frequency=22_050.0						,			sampling_rate=SCREAMING_SNAKE_CASE						,			norm='''slaney'''						,			mel_scale='''slaney'''						,			).T





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				np.ndarray:






         """simple docstring"""






         A       :   Dict					=						spectrogram(
             SCREAMING_SNAKE_CASE						,			window_function(self.n_fft						,			'''hann'''	)						,			frame_length=self.n_fft						,			hop_length=self.hop_length						,			power=2.0						,			mel_filters=self.mel_filters.T						,			log_mel='''dB'''						,			db_range=80.0						,			)
         A       :   int					=						log_spec[:, :-1]
         A       :   Dict					=						log_spec - 20.0
         A       :   List[Any]					=						np.clip(log_spec / 40.0						,			-2.0						,			0.0	) + 1.0
         return log_spec





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = False						,			**SCREAMING_SNAKE_CASE						,			) ->				BatchFeature:






         """simple docstring"""






         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     '''This feature extractor is set to support sampling rate'''
                     F' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
                     F' with {self.sampling_rate} and not {sampling_rate}.'	)
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''	)

         A       :   List[str]					=						isinstance(SCREAMING_SNAKE_CASE						,			np.ndarray	) and len(raw_speech.shape	) > 1
         if is_batched_numpy and len(raw_speech.shape	) > 2:
             raise ValueError(F'Only mono-channel audio is supported for input to {self}'	)
         A       :   str					=						is_batched_numpy or (
             isinstance(SCREAMING_SNAKE_CASE						,			(list, tuple)	) and (isinstance(raw_speech[0]						,			(np.ndarray, tuple, list)	))
         )
         if is_batched:
             A       :   Tuple					=						[np.asarray([speech]						,			dtype=np.floataa	).T for speech in raw_speech]
         elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE						,			np.ndarray	):
             A       :   int					=						np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.floataa	)
         elif isinstance(SCREAMING_SNAKE_CASE						,			np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
             A       :   List[str]					=						raw_speech.astype(np.floataa	)
         # always return batch
         if not is_batched:
             A       :   List[Any]					=						[np.asarray([raw_speech]	).T]

         # Convert audio signals to log mel spectrograms, truncate by time axis
         A       :   Optional[Any]					=						[
             self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
         ]
         if isinstance(audio_features[0]						,			SCREAMING_SNAKE_CASE	):
             A       :   str					=						[np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.floataa	) for feature in audio_features]

         # Create audio attention mask
         A       :   Optional[int]					=						max(
             [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
         if return_attention_mask:
             A       :   List[Any]					=						[
                 (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             A       :   List[Any]					=						np.array(SCREAMING_SNAKE_CASE	).astype(np.floataa	)

         # convert into correct format for padding
         A       :   Tuple					=						max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
         A       :   List[str]					=						np.ones([len(SCREAMING_SNAKE_CASE	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
         A       :   int					=						padded_audio_features * self.padding_value
         for i in range(len(SCREAMING_SNAKE_CASE	)	):
             A       :   Optional[Any]					=						audio_features[i]
             A       :   str					=						feature

         # return as BatchFeature
         if return_attention_mask:
             A       :   int					=						{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
         else:
             A       :   List[str]					=						{'''audio_values''': padded_audio_features}

         A       :   Optional[int]					=						BatchFeature(data=SCREAMING_SNAKE_CASE						,			tensor_type=SCREAMING_SNAKE_CASE	)
         return encoded_inputs






'''simple docstring'''




import os
import sys
import unittest


lowercase			:				Dict    		=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowercase			:				Any    		=			os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
lowercase			:				Optional[int]    		=			os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')





class  A				(							unittest.TestCase						):





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         A       :   Tuple					=						get_test_to_tester_mapping(SCREAMING_SNAKE_CASE	)
         A       :   Any					=						get_test_to_tester_mapping(SCREAMING_SNAKE_CASE	)

         A       :   List[Any]					=						{'''BertModelTest''': '''BertModelTester'''}

         A       :   int					=						{
             '''BlipModelTest''': '''BlipModelTester''',
             '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
             '''BlipTextModelTest''': '''BlipTextModelTester''',
             '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
             '''BlipVQAModelTest''': '''BlipVQAModelTester''',
             '''BlipVisionModelTest''': '''BlipVisionModelTester''',
         }

         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)
         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Tuple					=						get_model_to_test_mapping(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						get_model_to_test_mapping(SCREAMING_SNAKE_CASE	)

         A       :   List[str]					=						{
             '''BertForMaskedLM''': ['''BertModelTest'''],
             '''BertForMultipleChoice''': ['''BertModelTest'''],
             '''BertForNextSentencePrediction''': ['''BertModelTest'''],
             '''BertForPreTraining''': ['''BertModelTest'''],
             '''BertForQuestionAnswering''': ['''BertModelTest'''],
             '''BertForSequenceClassification''': ['''BertModelTest'''],
             '''BertForTokenClassification''': ['''BertModelTest'''],
             '''BertLMHeadModel''': ['''BertModelTest'''],
             '''BertModel''': ['''BertModelTest'''],
         }

         A       :   Union[str, Any]					=						{
             '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
             '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
             '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
             '''BlipModel''': ['''BlipModelTest'''],
             '''BlipTextModel''': ['''BlipTextModelTest'''],
             '''BlipVisionModel''': ['''BlipVisionModelTest'''],
         }

         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)
         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   int					=						get_model_to_tester_mapping(SCREAMING_SNAKE_CASE	)
         A       :   Union[str, Any]					=						get_model_to_tester_mapping(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						{
             '''BertForMaskedLM''': ['''BertModelTester'''],
             '''BertForMultipleChoice''': ['''BertModelTester'''],
             '''BertForNextSentencePrediction''': ['''BertModelTester'''],
             '''BertForPreTraining''': ['''BertModelTester'''],
             '''BertForQuestionAnswering''': ['''BertModelTester'''],
             '''BertForSequenceClassification''': ['''BertModelTester'''],
             '''BertForTokenClassification''': ['''BertModelTester'''],
             '''BertLMHeadModel''': ['''BertModelTester'''],
             '''BertModel''': ['''BertModelTester'''],
         }

         A       :   str					=						{
             '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
             '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
             '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
             '''BlipModel''': ['''BlipModelTester'''],
             '''BlipTextModel''': ['''BlipTextModelTester'''],
             '''BlipVisionModel''': ['''BlipVisionModelTester'''],
         }

         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)
         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)




