import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


lowerCamelCase_    :		int							=	logging.get_logger(__name__)


class 						a__      (       a__		):
			A__    :       Dict        =      ['input_features']



			def __init__(						self				,						UpperCAmelCase=8_0				,						UpperCAmelCase=1_6_0_0_0				,						UpperCAmelCase=1_6_0				,						UpperCAmelCase=3_0				,						UpperCAmelCase=4_0_0				,						UpperCAmelCase=0.0				,						UpperCAmelCase=False				,						**UpperCAmelCase				,						)			->   Optional[int]:
								super().__init__(
								    feature_size=UpperCAmelCase				,						sampling_rate=UpperCAmelCase				,						padding_value=UpperCAmelCase				,						return_attention_mask=UpperCAmelCase				,						**UpperCAmelCase				,						)
								__a		 =       n_fft
								__a		 =       hop_length
								__a		 =       chunk_length
								__a		 =       chunk_length * sampling_rate
								__a		 =       self.n_samples // hop_length
								__a		 =       sampling_rate
								__a		 =       mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2				,						num_mel_filters=UpperCAmelCase				,						min_frequency=0.0				,						max_frequency=8_0_0_0.0				,						sampling_rate=UpperCAmelCase				,						norm='slaney'				,						mel_scale='slaney'				,						)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   int:
								__a		 =       spectrogram(
								    UpperCAmelCase				,						window_function(self.n_fft				,						'hann'   )				,						frame_length=self.n_fft				,						hop_length=self.hop_length				,						power=2.0				,						mel_filters=self.mel_filters				,						log_mel='log10'				,						)
								__a		 =       log_spec[:, :-1]
								__a		 =       np.maximum(UpperCAmelCase				,						log_spec.max() - 8.0   )
								__a		 =       (log_spec + 4.0) / 4.0
								return log_spec



			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def __SCREAMING_SNAKE_CASE							(						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = 0.0   )			->   Dict:
								if attention_mask is not None:
													__a		 =       np.array(UpperCAmelCase				,						np.intaa   )
													__a		 =       []

													for vector, length in zip(UpperCAmelCase				,						attention_mask.sum(-1   )   ):
																		__a		 =       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7   )
																		if length < normed_slice.shape[0]:
																							__a		 =       padding_value

																		normed_input_values.append(UpperCAmelCase   )
								else:
													__a		 =       [(x - x.mean()) / np.sqrt(x.var() + 1e-7   ) for x in input_values]

								return normed_input_values



			def __call__(						self				,						UpperCAmelCase				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = "max_length"				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   Union[str, Any]:

								if sampling_rate is not None:
													if sampling_rate != self.sampling_rate:
																		raise ValueError(
																		    f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																		    f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																		    f''' was sampled 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		 =       isinstance(UpperCAmelCase				,						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		 =       is_batched_numpy or (
								    isinstance(UpperCAmelCase				,						(list, tuple)   ) and (isinstance(raw_speech[0]				,						(np.ndarray, tuple, list)   ))
								)

								if is_batched:
													__a		 =       [np.asarray([speech]				,						dtype=np.floataa   ).T for speech in raw_speech]
								elif not is_batched and not isinstance(UpperCAmelCase				,						np.ndarray   ):
													__a		 =       np.asarray(UpperCAmelCase				,						dtype=np.floataa   )
								elif isinstance(UpperCAmelCase				,						np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
													__a		 =       raw_speech.astype(np.floataa   )

								# always return batch
								if not is_batched:
													__a		 =       [np.asarray([raw_speech]   ).T]

								__a		 =       BatchFeature({'input_features': raw_speech}   )

								# convert into correct format for padding

								__a		 =       self.pad(
								    UpperCAmelCase				,						padding=UpperCAmelCase				,						max_length=max_length if max_length else self.n_samples				,						truncation=UpperCAmelCase				,						pad_to_multiple_of=UpperCAmelCase				,						return_attention_mask=return_attention_mask or do_normalize				,						)

								# zero-mean and unit-variance normalization
								if do_normalize:
													__a		 =       self.zero_mean_unit_var_norm(
													    padded_inputs['input_features']				,						attention_mask=padded_inputs['attention_mask']				,						padding_value=self.padding_value				,						)
													__a		 =       np.stack(padded_inputs['input_features']				,						axis=0   )

								# make sure list is in array format
								__a		 =       padded_inputs.get('input_features'   ).transpose(2				,						0				,						1   )

								__a		 =       [self._np_extract_fbank_features(UpperCAmelCase   ) for waveform in input_features[0]]

								if isinstance(input_features[0]				,						UpperCAmelCase   ):
													__a		 =       [np.asarray(UpperCAmelCase				,						dtype=np.floataa   ) for feature in input_features]
								else:
													__a		 =       input_features

								if return_attention_mask:
													# rescale from sample (48000) to feature (3000)
													__a		 =       padded_inputs["attention_mask"][:, :: self.hop_length]

								if return_tensors is not None:
													__a		 =       padded_inputs.convert_to_tensors(UpperCAmelCase   )

								return padded_inputs







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       copy.deepcopy(self.__dict__   )
								__a		 =       self.__class__.__name__
								if "mel_filters" in output:
													del output["mel_filters"]
								return output





import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs


class 						a__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase=None				,						UpperCAmelCase=True				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   List[Any]:
								__a		 =       parent
								__a		 =       config_class
								__a		 =       has_text_modality
								__a		 =       kwargs
								__a		 =       common_properties



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       self.config_class(**self.inputs_dict   )
								__a		 =       (
								    ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
								    if self.common_properties is None
								    else self.common_properties
								)

								# Add common fields for text models
								if self.has_text_modality:
													common_properties.extend(['vocab_size']   )

								# Test that config has the common properties as getters
								for prop in common_properties:
													self.parent.assertTrue(hasattr(UpperCAmelCase				,						UpperCAmelCase   )				,						msg=f'''`{prop}` does not exist'''   )

								# Test that config has the common properties as setter
								for idx, name in enumerate(UpperCAmelCase   ):
													try:
																		setattr(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )
																		self.parent.assertEqual(
																		    getattr(UpperCAmelCase				,						UpperCAmelCase   )				,						UpperCAmelCase				,						msg=f'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase				,						UpperCAmelCase   )}'''   )
													except NotImplementedError:
																		# Some models might not be able to implement setters for common_properties
																		# In that case, a NotImplementedError is raised
																		pass

        # Test if config class can be called with Config(prop_name=..)
								for idx, name in enumerate(UpperCAmelCase   ):
													try:
																		__a		 =       self.config_class(**{name: idx}   )
																		self.parent.assertEqual(
																		    getattr(UpperCAmelCase				,						UpperCAmelCase   )				,						UpperCAmelCase				,						msg=f'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase				,						UpperCAmelCase   )}'''   )
													except NotImplementedError:
																		# Some models might not be able to implement setters for common_properties
																		# In that case, a NotImplementedError is raised
																		pass



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.config_class(**self.inputs_dict   )
								__a		 =       json.loads(config.to_json_string()   )
								for key, value in self.inputs_dict.items():
													self.parent.assertEqual(obj[key]				,						UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.config_class(**self.inputs_dict   )

								with tempfile.TemporaryDirectory() as tmpdirname:
													__a		 =       os.path.join(UpperCAmelCase				,						'config.json'   )
													config_first.to_json_file(UpperCAmelCase   )
													__a		 =       self.config_class.from_json_file(UpperCAmelCase   )

								self.parent.assertEqual(config_second.to_dict()				,						config_first.to_dict()   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.config_class(**self.inputs_dict   )

								with tempfile.TemporaryDirectory() as tmpdirname:
													config_first.save_pretrained(UpperCAmelCase   )
													__a		 =       self.config_class.from_pretrained(UpperCAmelCase   )

								self.parent.assertEqual(config_second.to_dict()				,						config_first.to_dict()   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       self.config_class(**self.inputs_dict   )

								__a		 =       'test'
								with tempfile.TemporaryDirectory() as tmpdirname:
													__a		 =       os.path.join(UpperCAmelCase				,						UpperCAmelCase   )
													config_first.save_pretrained(UpperCAmelCase   )
													__a		 =       self.config_class.from_pretrained(UpperCAmelCase				,						subfolder=UpperCAmelCase   )

								self.parent.assertEqual(config_second.to_dict()				,						config_first.to_dict()   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       self.config_class(**self.inputs_dict				,						num_labels=5   )
								self.parent.assertEqual(len(config.idalabel   )				,						5   )
								self.parent.assertEqual(len(config.labelaid   )				,						5   )

								__a		 =       3
								self.parent.assertEqual(len(config.idalabel   )				,						3   )
								self.parent.assertEqual(len(config.labelaid   )				,						3   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								if self.config_class.is_composition:
													return
								__a		 =       self.config_class()
								self.parent.assertIsNotNone(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       copy.deepcopy(UpperCAmelCase   )
								__a		 =       self.config_class(**UpperCAmelCase   )
								__a		 =       []
								for key, value in config_common_kwargs.items():
													if key == "torch_dtype":
																		if not is_torch_available():
																							continue
																		else:
																							import torch

																							if config.torch_dtype != torch.floataa:
																												wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa)   )
													elif getattr(UpperCAmelCase				,						UpperCAmelCase   ) != value:
																		wrong_values.append((key, getattr(UpperCAmelCase				,						UpperCAmelCase   ), value)   )

								if len(UpperCAmelCase   ) > 0:
													__a		 =       '\n'.join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]   )
													raise ValueError(f'''The following keys were not properly set in the config:\n{errors}'''   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								self.create_and_test_config_common_properties()
								self.create_and_test_config_to_json_string()
								self.create_and_test_config_to_json_file()
								self.create_and_test_config_from_and_save_pretrained()
								self.create_and_test_config_from_and_save_pretrained_subfolder()
								self.create_and_test_config_with_num_labels()
								self.check_config_can_be_init_without_params()
								self.check_config_arguments_init()




