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


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""input_values""", """padding_mask"""]

  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   int = 1  ,				__SCREAMING_SNAKE_CASE			:   int = 2_40_00  ,				__SCREAMING_SNAKE_CASE			:   float = 0.0  ,				__SCREAMING_SNAKE_CASE			:   float = None  ,				__SCREAMING_SNAKE_CASE			:   float = None  ,				**__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				):
   super().__init__(feature_size=__SCREAMING_SNAKE_CASE  ,				sampling_rate=__SCREAMING_SNAKE_CASE  ,				padding_value=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
   __a							=	chunk_length_s
   __a							=	overlap

  @property
  def 		_UpperCAmelCase (		self			:   Dict							):
   if self.chunk_length_s is None:
    return None
   else:
    return int(self.chunk_length_s * self.sampling_rate							)

  @property
  def 		_UpperCAmelCase (		self			:   Any							):
   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			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[bool, str, PaddingStrategy]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = False  ,				__SCREAMING_SNAKE_CASE			:   Optional[int] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, TensorType]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[int] = None  ,				):
   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
    __a							=	True

   __a							=	bool(
       isinstance(__SCREAMING_SNAKE_CASE  ,				(list, tuple)							) and (isinstance(raw_audio[0]  ,				(np.ndarray, tuple, list)							))							)

   if is_batched:
    __a							=	[np.asarray(__SCREAMING_SNAKE_CASE  ,				dtype=np.floataa							).T for audio in raw_audio]
   elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE  ,				np.ndarray							):
    __a							=	np.asarray(__SCREAMING_SNAKE_CASE  ,				dtype=np.floataa							)
   elif isinstance(__SCREAMING_SNAKE_CASE  ,				np.ndarray							) and raw_audio.dtype is np.dtype(np.floataa							):
    __a							=	raw_audio.astype(np.floataa							)

   # always return batch
   if not is_batched:
    __a							=	[np.asarray(__SCREAMING_SNAKE_CASE							).T]

   # verify inputs are valid
   for idx, example in enumerate(__SCREAMING_SNAKE_CASE							):
    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"""							)

   __a							=	None
   __a							=	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:
     __a							=	min(array.shape[0] for array in raw_audio							)
     __a							=	int(np.floor(max_length / self.chunk_stride							)							)
     __a							=	(nb_step - 1) * self.chunk_stride + self.chunk_length
    elif padding:
     __a							=	max(array.shape[0] for array in raw_audio							)
     __a							=	int(np.ceil(max_length / self.chunk_stride							)							)
     __a							=	(nb_step - 1) * self.chunk_stride + self.chunk_length
     __a							=	"max_length"
    else:
     __a							=	input_values

        # normal padding on batch
   if padded_inputs is None:
    __a							=	self.pad(
        __SCREAMING_SNAKE_CASE  ,				max_length=__SCREAMING_SNAKE_CASE  ,				truncation=__SCREAMING_SNAKE_CASE  ,				padding=__SCREAMING_SNAKE_CASE  ,				return_attention_mask=__SCREAMING_SNAKE_CASE  ,				)
    if padding:
     __a							=	padded_inputs.pop("attention_mask"							)

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

   __a							=	input_values
   if return_tensors is not None:
    __a							=	padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE							)

   return padded_inputs



from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Any				  =		"""T5Config"""
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig


