
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							=       logging.get_logger(__name__)






class   UpperCAmelCase		(						A_ ):
   A__					:		List[Any]						     =     ["input_features"]



   def __init__(self		:					List[str]    ,						snake_case__		:					int=80    ,						snake_case__		:					List[Any]=1_60_00    ,						snake_case__		:					List[Any]=1_60    ,						snake_case__		:					Tuple=30    ,						snake_case__		:					Dict=4_00    ,						snake_case__		:					str=0.0    ,						snake_case__		:					Optional[int]=False    ,						**snake_case__		:					Dict    ,						)		->	List[str]:


          '''simple docstring'''


          super().__init__(
              feature_size=snake_case__    ,						sampling_rate=snake_case__    ,						padding_value=snake_case__    ,						return_attention_mask=snake_case__    ,						**snake_case__    ,						)
          snake_case       :   Dict							=      n_fft
          snake_case       :   List[Any]							=      hop_length
          snake_case       :   List[Any]							=      chunk_length
          snake_case       :   List[str]							=      chunk_length * sampling_rate
          snake_case       :   int							=      self.n_samples // hop_length
          snake_case       :   List[Any]							=      sampling_rate
          snake_case       :   List[str]							=      mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2    ,						num_mel_filters=snake_case__    ,						min_frequency=0.0    ,						max_frequency=8000.0    ,						sampling_rate=snake_case__    ,						norm="slaney"    ,						mel_scale="slaney"    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					np.array )		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   Any							=      spectrogram(
              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    ,						log_mel="log10"    ,						)
          snake_case       :   str							=      log_spec[:, :-1]
          snake_case       :   Any							=      np.maximum(snake_case__    ,						log_spec.max() - 8.0 )
          snake_case       :   List[str]							=      (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   (snake_case__		:					List[np.ndarray]    ,						snake_case__		:					List[np.ndarray]    ,						snake_case__		:					float = 0.0 )		->	List[np.ndarray]:


          '''simple docstring'''


          if attention_mask is not None:
                 snake_case       :   Any							=      np.array(snake_case__    ,						np.intaa )
                 snake_case       :   Dict							=      []

                 for vector, length in zip(snake_case__    ,						attention_mask.sum(-1 ) ):
                        snake_case       :   Dict							=      (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
                        if length < normed_slice.shape[0]:
                               snake_case       :   Union[str, Any]							=      padding_value

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

          return normed_input_values



   def __call__(self		:					str    ,						snake_case__		:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[Union[str, TensorType]] = None    ,						snake_case__		:					Optional[bool] = None    ,						snake_case__		:					Optional[str] = "max_length"    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[bool] = None    ,						**snake_case__		:					List[Any]    ,						)		->	BatchFeature:


          '''simple docstring'''


          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." )

          snake_case       :   Optional[int]							=      isinstance(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}""" )
          snake_case       :   List[Any]							=      is_batched_numpy or (
              isinstance(snake_case__    ,						(list, tuple) ) and (isinstance(raw_speech[0]    ,						(np.ndarray, tuple, list) ))
          )

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

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

          snake_case       :   Optional[int]							=      BatchFeature({"input_features": raw_speech} )

          # convert into correct format for padding

          snake_case       :   Optional[Any]							=      self.pad(
              snake_case__    ,						padding=snake_case__    ,						max_length=max_length if max_length else self.n_samples    ,						truncation=snake_case__    ,						pad_to_multiple_of=snake_case__    ,						return_attention_mask=return_attention_mask or do_normalize    ,						)

          # zero-mean and unit-variance normalization
          if do_normalize:
                 snake_case       :   Optional[Any]							=      self.zero_mean_unit_var_norm(
                     padded_inputs["input_features"]    ,						attention_mask=padded_inputs["attention_mask"]    ,						padding_value=self.padding_value    ,						)
                 snake_case       :   Union[str, Any]							=      np.stack(padded_inputs["input_features"]    ,						axis=0 )

          # make sure list is in array format
          snake_case       :   List[Any]							=      padded_inputs.get("input_features" ).transpose(2    ,						0    ,						1 )

          snake_case       :   str							=      [self._np_extract_fbank_features(snake_case__ ) for waveform in input_features[0]]

          if isinstance(input_features[0]    ,						snake_case__ ):
                 snake_case       :   Dict							=      [np.asarray(snake_case__    ,						dtype=np.floataa ) for feature in input_features]
          else:
                 snake_case       :   Tuple							=      input_features

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

          if return_tensors is not None:
                 snake_case       :   int							=      padded_inputs.convert_to_tensors(snake_case__ )

          return padded_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Dict[str, Any]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      copy.deepcopy(self.__dict__ )
          snake_case       :   Dict							=      self.__class__.__name__
          if "mel_filters" in output:
                 del output["mel_filters"]
          return output









'''simple docstring'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__lowercase	:						Dict       =	logging.get_logger(__name__)

__lowercase	:						Optional[Any]       =	{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "umt5"
	A_				=    ["past_key_values"]




	def __init__(   self						,				__a=25_0112						,				__a=512						,				__a=64						,				__a=1024						,				__a=8						,				__a=None						,				__a=6						,				__a=32						,				__a=128						,				__a=0.1						,				__a=1E-6						,				__a=1.0						,				__a="gated-gelu"						,				__a=True						,				__a=True						,				__a="T5Tokenizer"						,				__a=True						,				__a=0						,				__a=1						,				__a=0						,				**__a						,				):

						'''simple docstring'''





						super().__init__(
						    is_encoder_decoder=__a						,				tokenizer_class=__a						,				tie_word_embeddings=__a						,				pad_token_id=__a						,				eos_token_id=__a						,				decoder_start_token_id=__a						,				**__a						,				)
						__a			:       Any					    =  vocab_size
						__a			:       Any					    =  d_model
						__a			:       str					    =  d_kv
						__a			:       Dict					    =  d_ff
						__a			:       Union[str, Any]					    =  num_layers
						__a			:       int					    =  (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__a			:       Optional[int]					    =  num_heads
						__a			:       Tuple					    =  relative_attention_num_buckets
						__a			:       Optional[Any]					    =  relative_attention_max_distance
						__a			:       Optional[int]					    =  dropout_rate
						__a			:       List[Any]					    =  layer_norm_epsilon
						__a			:       int					    =  initializer_factor
						__a			:       Union[str, Any]					    =  feed_forward_proj
						__a			:       Any					    =  use_cache

						__a			:       List[Any]					    =  self.feed_forward_proj.split('-'							)
						__a			:       Dict					    =  act_info[-1]
						__a			:       Dict					    =  act_info[0] == 'gated'

						if len(__a							) > 1 and act_info[0] != "gated" or len(__a							) > 2:
											raise ValueError(
											    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
											    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
											    '\'gated-gelu\' or \'relu\''							)

						if feed_forward_proj == "gated-gelu":
											__a			:       Optional[int]					    =  'gelu_new'




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.d_model




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_heads





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_layers





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  {
						    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
						    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
						}
						if self.use_past:
											__a			:       Dict					    =  'past_encoder_sequence + sequence'
											__a			:       Tuple					    =  {0: 'batch'}
											__a			:       Tuple					    =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
											__a			:       List[Any]					    =  {0: 'batch', 1: 'decoder_sequence'}
											__a			:       int					    =  {0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
											self.fill_with_past_key_values_(__a						,				direction='inputs'							)

						return common_inputs




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 13





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 5E-4



