






from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 							_lowerCAmelCase(			UpperCAmelCase_				,      UpperCAmelCase_				,      UpperCAmelCase_   ):





       """simple docstring"""







       a							:						str										=[r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']





       @register_to_config
       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = 5_0_2_5_7    ,     _lowerCamelCase = 1_0_2_4    ,     _lowerCamelCase = 7_6_8    ,     _lowerCamelCase = 1_2    ,     _lowerCamelCase = 1_2    ,     _lowerCamelCase = None    ,     _lowerCamelCase = "gelu_new"    ,     _lowerCamelCase = 0.1    ,     _lowerCamelCase = 0.1    ,     _lowerCamelCase = 0.1    ,     _lowerCamelCase = 1e-5    ,     _lowerCamelCase = 0.0_2    ,     _lowerCamelCase = True    ,     _lowerCamelCase = True    ,     _lowerCamelCase = False    ,     _lowerCamelCase = False    ,     ):
         super().__init__()

         UpperCamelCase_:   int        =							prefix_length

         if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
           raise ValueError(
               f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
               f''' `n_embd`: {n_embd} are not equal.'''				)

         UpperCamelCase_:   Optional[int]        =							prefix_inner_dim
         UpperCamelCase_:   str        =							prefix_hidden_dim

         UpperCamelCase_:   Optional[int]        =							(
             nn.Linear(self.prefix_inner_dim    ,     self.prefix_hidden_dim				)
             if self.prefix_hidden_dim is not None
             else nn.Identity()
         )
         UpperCamelCase_:   int        =							(
             nn.Linear(self.prefix_hidden_dim    ,     _lowerCamelCase				) if self.prefix_hidden_dim is not None else nn.Identity()
         )

         UpperCamelCase_:   List[Any]        =							GPTaConfig(
             vocab_size=_lowerCamelCase    ,     n_positions=_lowerCamelCase    ,     n_embd=_lowerCamelCase    ,     n_layer=_lowerCamelCase    ,     n_head=_lowerCamelCase    ,     n_inner=_lowerCamelCase    ,     activation_function=_lowerCamelCase    ,     resid_pdrop=_lowerCamelCase    ,     embd_pdrop=_lowerCamelCase    ,     attn_pdrop=_lowerCamelCase    ,     layer_norm_epsilon=_lowerCamelCase    ,     initializer_range=_lowerCamelCase    ,     scale_attn_weights=_lowerCamelCase    ,     use_cache=_lowerCamelCase    ,     scale_attn_by_inverse_layer_idx=_lowerCamelCase    ,     reorder_and_upcast_attn=_lowerCamelCase    ,     )
         UpperCamelCase_:   Optional[Any]        =							GPTaLMHeadModel(_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     ):
         UpperCamelCase_:   List[str]        =							self.transformer.transformer.wte(_lowerCamelCase				)
         UpperCamelCase_:   Any        =							self.encode_prefix(_lowerCamelCase				)
         UpperCamelCase_:   Union[str, Any]        =							self.decode_prefix(_lowerCamelCase				)
         UpperCamelCase_:   str        =							torch.cat((prefix_embeds, embedding_text)    ,     dim=1				)

         if labels is not None:
           UpperCamelCase_:   Tuple        =							self.get_dummy_token(input_ids.shape[0]    ,     input_ids.device				)
           UpperCamelCase_:   List[Any]        =							torch.cat((dummy_token, input_ids)    ,     dim=1				)
         UpperCamelCase_:   Optional[Any]        =							self.transformer(inputs_embeds=_lowerCamelCase    ,     labels=_lowerCamelCase    ,     attention_mask=_lowerCamelCase				)
         if self.prefix_hidden_dim is not None:
           return out, hidden
         else:
           return out





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         return torch.zeros(_lowerCamelCase    ,     self.prefix_length    ,     dtype=torch.intaa    ,     device=_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase				):
         return self.encode_prefix(_lowerCamelCase				)





       @torch.no_grad()
       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   Dict        =							torch.split(_lowerCamelCase    ,     1    ,     dim=0				)
         UpperCamelCase_:   Optional[Any]        =							[]
         UpperCamelCase_:   Optional[Any]        =							[]
         for feature in features:
           UpperCamelCase_:   Any        =							self.decode_prefix(feature.to(_lowerCamelCase				)				)  # back to the clip feature
           # Only support beam search for now
           UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							self.generate_beam(
               input_embeds=_lowerCamelCase    ,     device=_lowerCamelCase    ,     eos_token_id=_lowerCamelCase				)
           generated_tokens.append(output_tokens[0]				)
           generated_seq_lengths.append(seq_lengths[0]				)
         UpperCamelCase_:   Optional[Any]        =							torch.stack(_lowerCamelCase				)
         UpperCamelCase_:   str        =							torch.stack(_lowerCamelCase				)
         return generated_tokens, generated_seq_lengths





       @torch.no_grad()
       def      _a       (   self    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase = 5    ,     _lowerCamelCase = 6_7    ,     _lowerCamelCase = 1.0    ,     _lowerCamelCase = None    ,     ):
         UpperCamelCase_:   Tuple        =							eos_token_id
         UpperCamelCase_:   Union[str, Any]        =							None
         UpperCamelCase_:   Union[str, Any]        =							None
         UpperCamelCase_:   Tuple        =							torch.ones(_lowerCamelCase    ,     device=_lowerCamelCase    ,     dtype=torch.int				)
         UpperCamelCase_:   Optional[int]        =							torch.zeros(_lowerCamelCase    ,     device=_lowerCamelCase    ,     dtype=torch.bool				)

         if input_embeds is not None:
           UpperCamelCase_:   List[Any]        =							input_embeds
         else:
           UpperCamelCase_:   int        =							self.transformer.transformer.wte(_lowerCamelCase				)

         for i in range(_lowerCamelCase				):
           UpperCamelCase_:   int        =							self.transformer(inputs_embeds=_lowerCamelCase				)
           UpperCamelCase_:   int        =							outputs.logits
           UpperCamelCase_:   str        =							logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
           UpperCamelCase_:   str        =							logits.softmax(-1				).log()

           if scores is None:
             UpperCamelCase_ ,UpperCamelCase_:   str        =							logits.topk(_lowerCamelCase    ,     -1				)
             UpperCamelCase_:   Optional[Any]        =							generated.expand(_lowerCamelCase    ,     *generated.shape[1:]				)
             UpperCamelCase_ ,UpperCamelCase_:   Union[str, Any]        =							next_tokens.permute(1    ,     0				), scores.squeeze(0				)
             if tokens is None:
               UpperCamelCase_:   Optional[int]        =							next_tokens
             else:
               UpperCamelCase_:   Union[str, Any]        =							tokens.expand(_lowerCamelCase    ,     *tokens.shape[1:]				)
               UpperCamelCase_:   Any        =							torch.cat((tokens, next_tokens)    ,     dim=1				)
           else:
             UpperCamelCase_:   Union[str, Any]        =							-float(np.inf				)
             UpperCamelCase_:   Optional[Any]        =							0
             UpperCamelCase_:   List[str]        =							scores[:, None] + logits
             seq_lengths[~is_stopped] += 1
             UpperCamelCase_:   int        =							scores_sum / seq_lengths[:, None]
             UpperCamelCase_ ,UpperCamelCase_:   Dict        =							scores_sum_average.view(-1				).topk(_lowerCamelCase    ,     -1				)
             UpperCamelCase_:   List[Any]        =							next_tokens // scores_sum.shape[1]
             UpperCamelCase_:   int        =							seq_lengths[next_tokens_source]
             UpperCamelCase_:   Union[str, Any]        =							next_tokens % scores_sum.shape[1]
             UpperCamelCase_:   Tuple        =							next_tokens.unsqueeze(1				)
             UpperCamelCase_:   List[str]        =							tokens[next_tokens_source]
             UpperCamelCase_:   Dict        =							torch.cat((tokens, next_tokens)    ,     dim=1				)
             UpperCamelCase_:   Tuple        =							generated[next_tokens_source]
             UpperCamelCase_:   List[str]        =							scores_sum_average * seq_lengths
             UpperCamelCase_:   List[str]        =							is_stopped[next_tokens_source]

           UpperCamelCase_:   Tuple        =							self.transformer.transformer.wte(next_tokens.squeeze()				).view(generated.shape[0]    ,     1    ,     -1				)
           UpperCamelCase_:   Optional[int]        =							torch.cat((generated, next_token_embed)    ,     dim=1				)
           UpperCamelCase_:   Dict        =							is_stopped + next_tokens.eq(_lowerCamelCase				).squeeze()
           if is_stopped.all():
             break

         UpperCamelCase_:   List[Any]        =							scores / seq_lengths
         UpperCamelCase_:   List[Any]        =							scores.argsort(descending=_lowerCamelCase				)
         # tokens tensors are already padded to max_seq_length
         UpperCamelCase_:   str        =							[tokens[i] for i in order]
         UpperCamelCase_:   str        =							torch.stack(_lowerCamelCase    ,     dim=0				)
         UpperCamelCase_:   Union[str, Any]        =							torch.tensor([seq_lengths[i] for i in order]    ,     dtype=seq_lengths.dtype				)
         return output_texts, seq_lengths






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

import numpy as np

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


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







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Tuple  		=					["""input_values""", """attention_mask"""]





   def __init__(			self       ,      snake_case = 1       ,      snake_case = 16_000       ,      snake_case = 0.0       ,      snake_case = False       ,      snake_case = 80       ,      snake_case = 16       ,      snake_case = 64       ,      snake_case = "hann_window"       ,      snake_case = 1.0       ,      snake_case = 80       ,      snake_case = 7_600       ,      snake_case = 1E-10       ,      snake_case = 2       ,      snake_case = True       ,      **snake_case       ,      )					->	Dict:
      """simple docstring"""





      super().__init__(feature_size=snake_case       ,      sampling_rate=snake_case       ,      padding_value=snake_case       ,      **snake_case   )
      a__						:    Any   		=	do_normalize
      a__						:    List[str]   		=	return_attention_mask

      a__						:    List[Any]   		=	num_mel_bins
      a__						:    List[str]   		=	hop_length
      a__						:    int   		=	win_length
      a__						:    List[Any]   		=	win_function
      a__						:    List[str]   		=	frame_signal_scale
      a__						:    List[Any]   		=	fmin
      a__						:    Optional[Any]   		=	fmax
      a__						:    Union[str, Any]   		=	mel_floor
      a__						:    Union[str, Any]   		=	reduction_factor

      a__						:    List[str]   		=	win_length * sampling_rate // 1_000
      a__						:    List[Any]   		=	hop_length * sampling_rate // 1_000
      a__						:    List[Any]   		=	optimal_fft_length(self.sample_size   )
      a__						:    Dict   		=	(self.n_fft // 2) + 1

      a__						:    str   		=	window_function(window_length=self.sample_size       ,      name=self.win_function       ,      periodic=snake_case   )

      a__						:    Tuple   		=	mel_filter_bank(
          num_frequency_bins=self.n_freqs       ,      num_mel_filters=self.num_mel_bins       ,      min_frequency=self.fmin       ,      max_frequency=self.fmax       ,      sampling_rate=self.sampling_rate       ,      norm="slaney"       ,      mel_scale="slaney"       ,      )

      if frame_signal_scale != 1.0:
         warnings.warn(
             "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"       ,      snake_case       ,      )
      if reduction_factor != 2.0:
         warnings.warn(
             "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"       ,      snake_case       ,      )





   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def 						_snake_case			(			snake_case       ,      snake_case       ,      snake_case = 0.0   )					->	List[np.ndarray]:
      """simple docstring"""





      if attention_mask is not None:
         a__						:    Tuple   		=	np.array(snake_case       ,      np.intaa   )
         a__						:    List[str]   		=	[]

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

            normed_input_values.append(snake_case   )
      else:
         a__						:    Any   		=	[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

      return normed_input_values





   def 						_snake_case			(			self       ,      snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      a__						:    str   		=	spectrogram(
          snake_case       ,      window=self.window       ,      frame_length=self.sample_size       ,      hop_length=self.sample_stride       ,      fft_length=self.n_fft       ,      mel_filters=self.mel_filters       ,      mel_floor=self.mel_floor       ,      log_mel="log10"       ,      )
      return log_mel_spec.T





   def __call__(			self       ,      snake_case = None       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      **snake_case       ,      )					->	BatchFeature:
      """simple docstring"""





      if audio is None and audio_target is None:
         raise ValueError("You must provide either `audio` or `audio_target` values."   )

      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 audio 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."   )

      if audio is not None:
         a__						:    Dict   		=	self._process_audio(
             snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      **snake_case       ,      )
      else:
         a__						:    Optional[int]   		=	None

      if audio_target is not None:
         a__						:    List[Any]   		=	self._process_audio(
             snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      **snake_case       ,      )

         if inputs is None:
            return inputs_target
         else:
            a__						:    Tuple   		=	inputs_target["input_values"]
            a__						:    Tuple   		=	inputs_target.get("attention_mask"   )
            if decoder_attention_mask is not None:
               a__						:    Tuple   		=	decoder_attention_mask

      return inputs





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = False       ,      snake_case = False       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      **snake_case       ,      )					->	BatchFeature:
      """simple docstring"""





      a__						:    Optional[int]   		=	isinstance(snake_case       ,      np.ndarray   ) and len(speech.shape   ) > 1
      if is_batched_numpy and len(speech.shape   ) > 2:
         raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""   )
      a__						:    List[Any]   		=	is_batched_numpy or (
          isinstance(snake_case       ,      (list, tuple)   ) and (isinstance(speech[0]       ,      (np.ndarray, tuple, list)   ))
      )

      if is_batched:
         a__						:    int   		=	[np.asarray(snake_case       ,      dtype=np.floataa   ) for speech in speech]
      elif not is_batched and not isinstance(snake_case       ,      np.ndarray   ):
         a__						:    Any   		=	np.asarray(snake_case       ,      dtype=np.floataa   )
      elif isinstance(snake_case       ,      np.ndarray   ) and speech.dtype is np.dtype(np.floataa   ):
         a__						:    List[Any]   		=	speech.astype(np.floataa   )

      # always return batch
      if not is_batched:
         a__						:    Union[str, Any]   		=	[speech]

      # needed to make pad() work on spectrogram inputs
      a__						:    Optional[Any]   		=	self.feature_size

      # convert into correct format for padding
      if is_target:
         a__						:    List[str]   		=	[self._extract_mel_features(snake_case   ) for waveform in speech]
         a__						:    Optional[Any]   		=	BatchFeature({"input_values": features}   )
         a__						:    str   		=	self.num_mel_bins
      else:
         a__						:    int   		=	BatchFeature({"input_values": speech}   )

      a__						:    int   		=	self.pad(
          snake_case       ,      padding=snake_case       ,      max_length=snake_case       ,      truncation=snake_case       ,      pad_to_multiple_of=snake_case       ,      return_attention_mask=snake_case       ,      **snake_case       ,      )

      a__						:    Any   		=	feature_size_hack

      # convert input values to correct format
      a__						:    Tuple   		=	padded_inputs["input_values"]
      if not isinstance(input_values[0]       ,      np.ndarray   ):
         a__						:    int   		=	[np.asarray(snake_case       ,      dtype=np.floataa   ) for array in input_values]
      elif (
          not isinstance(snake_case       ,      np.ndarray   )
          and isinstance(input_values[0]       ,      np.ndarray   )
          and input_values[0].dtype is np.dtype(np.floataa   )
      ):
         a__						:    Union[str, Any]   		=	[array.astype(np.floataa   ) for array in input_values]
      elif isinstance(snake_case       ,      np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
         a__						:    Optional[int]   		=	input_values.astype(np.floataa   )

      # convert attention_mask to correct format
      a__						:    Optional[int]   		=	padded_inputs.get("attention_mask"   )
      if attention_mask is not None:
         a__						:    Tuple   		=	[np.asarray(snake_case       ,      dtype=np.intaa   ) for array in attention_mask]

      # zero-mean and unit-variance normalization
      if not is_target and self.do_normalize:
         a__						:    Any   		=	(
             attention_mask
             if self._get_padding_strategies(snake_case       ,      max_length=snake_case   ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         a__						:    Optional[Any]   		=	self.zero_mean_unit_var_norm(
             padded_inputs["input_values"]       ,      attention_mask=snake_case       ,      padding_value=self.padding_value   )

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

      return padded_inputs





   def 						_snake_case			(			self   )					->	Dict[str, Any]:
      """simple docstring"""





      a__						:    int   		=	super().to_dict()

      # Don't serialize these as they are derived from the other properties.
      a__						:    str   		=	["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
      for name in names:
         if name in output:
            del output[name]

      return output

