





from typing import List, Optional, Union

import numpy as np

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


__lowerCamelCase	:					List[Any]			  =					logging.get_logger(__name__)



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :List[str]						   =       ['input_values', 'padding_mask']





      def __init__(			self	,							A_ = 1	,							A_ = 2_4000	,							A_ = 0.0	,							A_ = None	,							A_ = None	,							**A_	,							):
       '''simple docstring'''




       super().__init__(feature_size=A_	,							sampling_rate=A_	,							padding_value=A_	,							**A_       )
       UpperCamelCase     : Tuple	     =       chunk_length_s
       UpperCamelCase     : Dict	     =       overlap





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       if self.chunk_length_s is None:
        return None
       else:
        return int(self.chunk_length_s * self.sampling_rate       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       if self.chunk_length_s is None or self.overlap is None:
        return None
       else:
        return max(1	,							int((1.0 - self.overlap) * self.chunk_length       )       )





      def __call__(			self	,							A_	,							A_ = None	,							A_ = False	,							A_ = None	,							A_ = None	,							A_ = None	,							):
       '''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 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 padding and truncation:
        raise ValueError("Both padding and truncation were set. Make sure you only set one."       )
       elif padding is None:
        # by default let's pad the inputs
        UpperCamelCase     : str	     =       True

       UpperCamelCase     : str	     =       bool(
           isinstance(A_	,							(list, tuple)       ) and (isinstance(raw_audio[0]	,							(np.ndarray, tuple, list)       ))       )

       if is_batched:
        UpperCamelCase     : int	     =       [np.asarray(A_	,							dtype=np.floataa       ).T for audio in raw_audio]
       elif not is_batched and not isinstance(A_	,							np.ndarray       ):
        UpperCamelCase     : Tuple	     =       np.asarray(A_	,							dtype=np.floataa       )
       elif isinstance(A_	,							np.ndarray       ) and raw_audio.dtype is np.dtype(np.floataa       ):
        UpperCamelCase     : List[str]	     =       raw_audio.astype(np.floataa       )

       # always return batch
       if not is_batched:
        UpperCamelCase     : int	     =       [np.asarray(A_       ).T]

       # verify inputs are valid
       for idx, example in enumerate(A_       ):
        if example.ndim > 2:
         raise ValueError(F"""Expected input shape (channels, length) but got shape {example.shape}"""       )
        if self.feature_size == 1 and example.ndim != 1:
         raise ValueError(F"""Expected mono audio but example has {example.shape[-1]} channels"""       )
        if self.feature_size == 2 and example.shape[-1] != 2:
         raise ValueError(F"""Expected stereo audio but example has {example.shape[-1]} channels"""       )

       UpperCamelCase     : Tuple	     =       None
       UpperCamelCase     : List[str]	     =       BatchFeature({"input_values": raw_audio}       )
       if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
        if truncation:
         UpperCamelCase     : List[Any]	     =       min(array.shape[0] for array in raw_audio       )
         UpperCamelCase     : Union[str, Any]	     =       int(np.floor(max_length / self.chunk_stride       )       )
         UpperCamelCase     : Optional[Any]	     =       (nb_step - 1) * self.chunk_stride + self.chunk_length
        elif padding:
         UpperCamelCase     : List[str]	     =       max(array.shape[0] for array in raw_audio       )
         UpperCamelCase     : Dict	     =       int(np.ceil(max_length / self.chunk_stride       )       )
         UpperCamelCase     : Any	     =       (nb_step - 1) * self.chunk_stride + self.chunk_length
         UpperCamelCase     : str	     =       "max_length"
        else:
         UpperCamelCase     : Optional[int]	     =       input_values

        # normal padding on batch
       if padded_inputs is None:
        UpperCamelCase     : Union[str, Any]	     =       self.pad(
            A_	,							max_length=A_	,							truncation=A_	,							padding=A_	,							return_attention_mask=A_	,							)
        if padding:
         UpperCamelCase     : Any	     =       padded_inputs.pop("attention_mask"       )

       UpperCamelCase     : Union[str, Any]	     =       []
       for example in padded_inputs.pop("input_values"       ):
        if self.feature_size == 1:
         UpperCamelCase     : str	     =       example[..., None]
        input_values.append(example.T       )

       UpperCamelCase     : Union[str, Any]	     =       input_values
       if return_tensors is not None:
        UpperCamelCase     : int	     =       padded_inputs.convert_to_tensors(A_       )

       return padded_inputs







import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


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


__lowerCamelCase	:					Any			  =					r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""



class  A__       (	__snake_case      ):





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       raise NotImplementedError("StoppingCriteria needs to be subclassed"       )



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       max_length
       UpperCamelCase     : Dict	     =       max_position_embeddings





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       input_ids.shape[-1]
       UpperCamelCase     : str	     =       cur_len >= self.max_length
       if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
        logger.warning_once(
            "This is a friendly reminder - the current text generation call will exceed the model's predefined "
            F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
            "exceptions, performance degradation, or nothing at all."       )
       return is_done



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_       ):
       '''simple docstring'''




       warnings.warn(
           "The class `MaxNewTokensCriteria` is deprecated. "
           F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
           "with `max_length = start_length + max_new_tokens` instead."	,							A_	,							)
       UpperCamelCase     : Union[str, Any]	     =       start_length
       UpperCamelCase     : List[str]	     =       max_new_tokens
       UpperCamelCase     : Tuple	     =       start_length + max_new_tokens





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return input_ids.shape[-1] >= self.max_length



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       max_time
       UpperCamelCase     : Dict	     =       time.time() if initial_timestamp is None else initial_timestamp





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return time.time() - self.initial_timestamp > self.max_time



class  A__       (	__snake_case      ):





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return any(criteria(A_	,							A_       ) for criteria in self       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       for stopping_criterium in self:
        if isinstance(A_	,							A_       ):
         return stopping_criterium.max_length
        elif isinstance(A_	,							A_       ):
         return stopping_criterium.max_length
       return None



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			StoppingCriteriaList:
 UpperCamelCase     : Tuple	     =       stopping_criteria.max_length
 UpperCamelCase     : Union[str, Any]	     =       deepcopy(_lowerCAmelCase					)
 if stopping_max_length is not None and stopping_max_length != max_length:
  warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"							,       _lowerCAmelCase					)
 elif stopping_max_length is None:
  new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowerCAmelCase					)					)
 return new_stopping_criteria
