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


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







class __snake_case				(						__snake_case		):
   lowerCAmelCase_														=							["input_features", "attention_mask"]




   def __init__(    self			:				Tuple       ,	_lowercase			:				List[Any]=80       ,	_lowercase			:				str=1_60_00       ,	_lowercase			:				str=80       ,	_lowercase			:				Union[str, Any]=0.0       ,	_lowercase			:				Any=True       ,	_lowercase			:				str=True       ,	_lowercase			:				Optional[Any]=True       ,	**_lowercase			:				str       ,	):



     """simple docstring"""






     super().__init__(feature_size=_lowercase       ,	sampling_rate=_lowercase       ,	padding_value=_lowercase       ,	**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      num_mel_bins
     SCREAMING_SNAKE_CASE__										=      do_ceptral_normalize
     SCREAMING_SNAKE_CASE__										=      normalize_means
     SCREAMING_SNAKE_CASE__										=      normalize_vars
     SCREAMING_SNAKE_CASE__										=      True




   def 	__a						(    self			:				int       ,	_lowercase			:				Optional[Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
     SCREAMING_SNAKE_CASE__										=      torch.from_numpy(_lowercase      ).unsqueeze(0      )
     SCREAMING_SNAKE_CASE__										=      ta_kaldi.fbank(_lowercase       ,	num_mel_bins=self.num_mel_bins       ,	sample_frequency=self.sampling_rate      )
     return features.numpy()




   @staticmethod
   def 	__a						(    _lowercase			:				Any       ,	_lowercase			:				List[str]       ,	_lowercase			:				List[str] = True       ,	_lowercase			:				Tuple = True       ,	_lowercase			:				Optional[Any] = 0.0       ,	):



     """simple docstring"""






     if normalize_means:
       SCREAMING_SNAKE_CASE__										=      x[:input_length].mean(axis=0      )
       SCREAMING_SNAKE_CASE__										=      np.subtract(_lowercase       ,	_lowercase      )
     if normalize_vars:
       SCREAMING_SNAKE_CASE__										=      x[:input_length].std(axis=0      )
       SCREAMING_SNAKE_CASE__										=      np.divide(_lowercase       ,	_lowercase      )

     if input_length < x.shape[0]:
       SCREAMING_SNAKE_CASE__										=      padding_value

     # make sure array is in float32
     SCREAMING_SNAKE_CASE__										=      x.astype(np.floataa      )

     return x




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Any       ,	_lowercase			:				List[Any] = None      ):



     """simple docstring"""






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




   def __call__(    self			:				Optional[Any]       ,	_lowercase			:				List[Any]       ,	_lowercase			:				List[Any] = False       ,	_lowercase			:				str = None       ,	_lowercase			:				Union[str, Any] = False       ,	_lowercase			:				Any = None       ,	_lowercase			:				Tuple = None       ,	_lowercase			:				Optional[Any] = None       ,	_lowercase			:				Union[str, Any] = None       ,	**_lowercase			:				Optional[int]       ,	):



     """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."""      )

     SCREAMING_SNAKE_CASE__										=      isinstance(_lowercase       ,	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}"""      )
     SCREAMING_SNAKE_CASE__										=      is_batched_numpy or (
         isinstance(_lowercase       ,	(list, tuple)      ) and (isinstance(raw_speech[0]       ,	(np.ndarray, tuple, list)      ))
     )

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

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

     # extract fbank features
     SCREAMING_SNAKE_CASE__										=      [self._extract_fbank_features(_lowercase      ) for waveform in raw_speech]

     # convert into correct format for padding
     SCREAMING_SNAKE_CASE__										=      BatchFeature({"""input_features""": features}      )

     SCREAMING_SNAKE_CASE__										=      self.pad(
         _lowercase       ,	padding=_lowercase       ,	max_length=_lowercase       ,	truncation=_lowercase       ,	pad_to_multiple_of=_lowercase       ,	return_attention_mask=_lowercase       ,	**_lowercase       ,	)

     # make sure list is in array format
     SCREAMING_SNAKE_CASE__										=      padded_inputs.get("""input_features"""      )
     if isinstance(input_features[0]       ,	_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      [np.asarray(_lowercase       ,	dtype=np.floataa      ) for feature in input_features]

     SCREAMING_SNAKE_CASE__										=      padded_inputs.get("""attention_mask"""      )
     if attention_mask is not None:
       SCREAMING_SNAKE_CASE__										=      [np.asarray(_lowercase       ,	dtype=np.intaa      ) for array in attention_mask]

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

     if return_tensors is not None:
       SCREAMING_SNAKE_CASE__										=      padded_inputs.convert_to_tensors(_lowercase      )

     return padded_inputs


'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase			:				List[str]    		=			logging.get_logger(__name__)

lowercase			:				str    		=			{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class  A				(							__snake_case						):
     __magic_name__						=		'''gpt_neo'''
     __magic_name__						=		['''past_key_values''']
     __magic_name__						=		{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





     def __init__(							self						,			SCREAMING_SNAKE_CASE=50257						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=24						,			SCREAMING_SNAKE_CASE=[[["global", "local"], 12]]						,			SCREAMING_SNAKE_CASE=16						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=256						,			SCREAMING_SNAKE_CASE="gelu_new"						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=1e-5						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=50256						,			SCREAMING_SNAKE_CASE=50256						,			**SCREAMING_SNAKE_CASE						,			) ->				int:






         """simple docstring"""






         A       :   Union[str, Any]					=						vocab_size
         A       :   Optional[Any]					=						max_position_embeddings
         A       :   Dict					=						hidden_size
         A       :   Optional[Any]					=						num_layers
         A       :   Tuple					=						num_heads
         A       :   int					=						intermediate_size
         A       :   Optional[Any]					=						window_size
         A       :   List[Any]					=						activation_function
         A       :   Union[str, Any]					=						resid_dropout
         A       :   Any					=						embed_dropout
         A       :   List[Any]					=						attention_dropout
         A       :   str					=						classifier_dropout
         A       :   List[Any]					=						layer_norm_epsilon
         A       :   str					=						initializer_range
         A       :   List[str]					=						use_cache

         A       :   Optional[int]					=						bos_token_id
         A       :   List[Any]					=						eos_token_id

         A       :   int					=						attention_types
         A       :   int					=						self.expand_attention_types_params(SCREAMING_SNAKE_CASE	)

         if len(self.attention_layers	) != self.num_layers:
             raise ValueError(
                 '''Configuration for convolutional module is incorrect. '''
                 '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
                 F'but is `len(config.attention_layers) = {len(self.attention_layers	)}`, '
                 F'`config.num_layers = {self.num_layers}`. '
                 '''`config.attention_layers` is prepared using `config.attention_types`. '''
                 '''Please verify the value of `config.attention_types` argument.'''	)

         super().__init__(bos_token_id=SCREAMING_SNAKE_CASE						,			eos_token_id=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   List[str]					=						[]
         for item in attention_types:
             for _ in range(item[1]	):
                 attentions.extend(item[0]	)
         return attentions







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

    A       :   Tuple					=						input.size()
    A       :   Union[str, Any]					=						len(snake_case__						)
    A       :   List[str]					=						shape[dimension]

    A       :   Union[str, Any]					=						torch.arange(0		,				snake_case__		,				snake_case__						)
    A       :   List[str]					=						torch.div(sizedim - size		,				snake_case__		,				rounding_mode='''floor'''						) + 1
    A       :   Optional[int]					=						torch.arange(snake_case__						) + low_indices[:min_length][:, None]

    A       :   str					=						[slice(snake_case__						)] * rank
    A       :   List[Any]					=						indices
    A       :   Union[str, Any]					=						input[s]

    A       :   List[str]					=						list(range(0		,				rank + 1						)						)
    perm.append(perm.pop(dimension + 1						)						)

    return sliced.permute(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

    A       :   List[str]					=						torch.arange(1		,				snake_case__						)
    A       :   Optional[int]					=						torch.remainder(snake_case__		,				snake_case__						)
    A       :   Optional[int]					=						remainders == 0
    A       :   Optional[Any]					=						candidates[divisor_indices]
    A       :   Optional[int]					=						torch.max(snake_case__						)
    return largest_divisor, torch.div(snake_case__		,				snake_case__		,				rounding_mode='''floor'''						)







class  A				(							__snake_case						):





     @property
     def 				__lowerCAmelCase							(							self	) ->				Mapping[str, Mapping[int, str]]:






         """simple docstring"""






         A       :   Tuple					=						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
         if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE						,			direction='''inputs'''	)
             A       :   Optional[Any]					=						{0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
             A       :   Dict					=						{0: '''batch''', 1: '''sequence'''}

         return common_inputs





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return self._config.num_heads





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = -1						,			SCREAMING_SNAKE_CASE = -1						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			) ->				Mapping[str, Any]:






         """simple docstring"""






         A       :   List[str]					=						super(SCREAMING_SNAKE_CASE						,			self	).generate_dummy_inputs(
             SCREAMING_SNAKE_CASE						,			batch_size=SCREAMING_SNAKE_CASE						,			seq_length=SCREAMING_SNAKE_CASE						,			is_pair=SCREAMING_SNAKE_CASE						,			framework=SCREAMING_SNAKE_CASE	)

         # We need to order the input in the way they appears in the forward()
         A       :   Any					=						OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

         # Need to add the past_keys
         if self.use_past:
             if not is_torch_available():
                 raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
             else:
                 import torch

                 A,  A       :   Dict					=						common_inputs['''input_ids'''].shape
                 # Not using the same length for past_key_values
                 A       :   str					=						seqlen + 2
                 A       :   List[Any]					=						(
                     batch,
                     self.num_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // self.num_attention_heads,
                 )
                 A       :   Any					=						[
                     (torch.zeros(SCREAMING_SNAKE_CASE	), torch.zeros(SCREAMING_SNAKE_CASE	)) for _ in range(self.num_layers	)
                 ]

         A       :   str					=						common_inputs['''attention_mask''']
         if self.use_past:
             A       :   Optional[int]					=						ordered_inputs['''attention_mask'''].dtype
             A       :   List[str]					=						torch.cat(
                 [ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			dtype=SCREAMING_SNAKE_CASE	)]						,			dim=1	)

         return ordered_inputs





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return 13




