
"""simple docstring"""





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  torch.FloatTensor



class 						SCREAMING_SNAKE_CASE_ (						__a     ,				__a ):




		"""simple docstring"""





		@register_to_config
		def __init__(     self							,  lowerCAmelCase__ = 1_6							,  lowerCAmelCase__ = 8_8							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 1							,  lowerCAmelCase__ = 0.0							,  lowerCAmelCase__ = 3_2							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = "geglu"							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = True							,  ):
				super().__init__()
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = attention_head_dim
				__SCREAMING_SNAKE_CASE            = num_attention_heads * attention_head_dim

				__SCREAMING_SNAKE_CASE            = in_channels

				__SCREAMING_SNAKE_CASE            = torch.nn.GroupNorm(num_groups=lowerCAmelCase__							,  num_channels=lowerCAmelCase__							,  eps=1E-6							,  affine=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__)

				# 3. Define transformers blocks
				__SCREAMING_SNAKE_CASE            = nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  dropout=lowerCAmelCase__							,  cross_attention_dim=lowerCAmelCase__							,  activation_fn=lowerCAmelCase__							,  attention_bias=lowerCAmelCase__							,  double_self_attention=lowerCAmelCase__							,  norm_elementwise_affine=lowerCAmelCase__							,  )
				        for d in range(lowerCAmelCase__)
				    ])

				__SCREAMING_SNAKE_CASE            = nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=1							,  lowerCAmelCase__=None							,  lowerCAmelCase__ = True							,  ):
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = hidden_states.shape
				__SCREAMING_SNAKE_CASE            = batch_frames // num_frames

				__SCREAMING_SNAKE_CASE            = hidden_states

				__SCREAMING_SNAKE_CASE            = hidden_states[None, :].reshape(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = hidden_states.permute(0							,  2							,  1							,  3							,  4)

				__SCREAMING_SNAKE_CASE            = self.norm(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = hidden_states.permute(0							,  3							,  4							,  2							,  1).reshape(batch_size * height * width							,  lowerCAmelCase__							,  lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = self.proj_in(lowerCAmelCase__)

				# 2. Blocks
				for block in self.transformer_blocks:
						__SCREAMING_SNAKE_CASE            = block(
						    lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  timestep=lowerCAmelCase__							,  cross_attention_kwargs=lowerCAmelCase__							,  class_labels=lowerCAmelCase__							,  )

				# 3. Output
				__SCREAMING_SNAKE_CASE            = self.proj_out(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = (
				    hidden_states[None, None, :]
				    .reshape(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)
				    .permute(0							,  3							,  4							,  1							,  2)
				    .contiguous()
				)
				__SCREAMING_SNAKE_CASE            = hidden_states.reshape(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = hidden_states + residual

				if not return_dict:
						return (output,)

				return TransformerTemporalModelOutput(sample=lowerCAmelCase__)






'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


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





class  A				(							__snake_case						):
     __magic_name__						=		['''input_features''', '''attention_mask''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE=80						,			SCREAMING_SNAKE_CASE=16000						,			SCREAMING_SNAKE_CASE=80						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			**SCREAMING_SNAKE_CASE						,			) ->				int:






         """simple docstring"""






         super().__init__(feature_size=SCREAMING_SNAKE_CASE						,			sampling_rate=SCREAMING_SNAKE_CASE						,			padding_value=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						num_mel_bins
         A       :   Tuple					=						do_ceptral_normalize
         A       :   Dict					=						normalize_means
         A       :   List[Any]					=						normalize_vars
         A       :   List[str]					=						True





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






         """simple docstring"""






         A       :   List[Any]					=						waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
         A       :   Any					=						torch.from_numpy(SCREAMING_SNAKE_CASE	).unsqueeze(0	)
         A       :   Any					=						ta_kaldi.fbank(SCREAMING_SNAKE_CASE						,			num_mel_bins=self.num_mel_bins						,			sample_frequency=self.sampling_rate	)
         return features.numpy()





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = 0.0						,			) ->				np.ndarray:






         """simple docstring"""






         if normalize_means:
             A       :   Dict					=						x[:input_length].mean(axis=0	)
             A       :   Optional[Any]					=						np.subtract(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         if normalize_vars:
             A       :   str					=						x[:input_length].std(axis=0	)
             A       :   int					=						np.divide(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         if input_length < x.shape[0]:
             A       :   List[str]					=						padding_value

         # make sure array is in float32
         A       :   Tuple					=						x.astype(np.floataa	)

         return x





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[np.ndarray]:






         """simple docstring"""






         A       :   List[Any]					=						attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
         return [
             self.utterance_cmvn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			self.normalize_means						,			self.normalize_vars						,			self.padding_value	)
             for x, n in zip(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         ]





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				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} was trained using a sampling rate of'
                     F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                     F' {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[Any]					=						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       :   Tuple					=						is_batched_numpy or (
             isinstance(SCREAMING_SNAKE_CASE						,			(list, tuple)	) and (isinstance(raw_speech[0]						,			(np.ndarray, tuple, list)	))
         )

         if is_batched:
             A       :   Union[str, Any]					=						[np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.floataa	) for speech in raw_speech]
         elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE						,			np.ndarray	):
             A       :   Union[str, Any]					=						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       :   Optional[int]					=						raw_speech.astype(np.floataa	)

         # always return batch
         if not is_batched:
             A       :   Any					=						[raw_speech]

         # extract fbank features
         A       :   List[str]					=						[self._extract_fbank_features(SCREAMING_SNAKE_CASE	) for waveform in raw_speech]

         # convert into correct format for padding
         A       :   str					=						BatchFeature({'''input_features''': features}	)

         A       :   Union[str, Any]					=						self.pad(
             SCREAMING_SNAKE_CASE						,			padding=SCREAMING_SNAKE_CASE						,			max_length=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE						,			pad_to_multiple_of=SCREAMING_SNAKE_CASE						,			return_attention_mask=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)

         # make sure list is in array format
         A       :   List[str]					=						padded_inputs.get('''input_features'''	)
         if isinstance(input_features[0]						,			SCREAMING_SNAKE_CASE	):
             A       :   str					=						[np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.floataa	) for feature in input_features]

         A       :   Union[str, Any]					=						padded_inputs.get('''attention_mask'''	)
         if attention_mask is not None:
             A       :   Union[str, Any]					=						[np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.intaa	) for array in attention_mask]

         # Utterance-level cepstral mean and variance normalization
         if self.do_ceptral_normalize:
             A       :   Dict					=						(
                 np.array(SCREAMING_SNAKE_CASE						,			dtype=np.intaa	)
                 if self._get_padding_strategies(SCREAMING_SNAKE_CASE						,			max_length=SCREAMING_SNAKE_CASE	) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             A       :   List[Any]					=						self.normalize(
                 padded_inputs['''input_features''']						,			attention_mask=SCREAMING_SNAKE_CASE	)

         if return_tensors is not None:
             A       :   int					=						padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE	)

         return padded_inputs




