

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


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







class 				a__				(      UpperCAmelCase				):







  """simple docstring"""




  UpperCAmelCase__					:       Tuple					=["input_values", "padding_mask"]


  def __init__(	self			:       Optional[Any] , UpperCAmelCase__			:       Dict = 1 , UpperCAmelCase__			:       Union[str, Any] = 2_4_0_0_0 , UpperCAmelCase__			:       Union[str, Any] = 0.0 , UpperCAmelCase__			:       Any = None , UpperCAmelCase__			:       Optional[Any] = None , **UpperCAmelCase__			:       Tuple , )    ->Optional[int]:
        """simple docstring"""







        super().__init__(feature_size=__A , sampling_rate=__A , padding_value=__A , **__A						)
        SCREAMING_SNAKE_CASE       :					List[Any]        =      chunk_length_s
        SCREAMING_SNAKE_CASE       :					str        =      overlap


  @property
  def        _lowercase					(	self			:       Optional[int]						)    ->Dict:
        """simple docstring"""







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


  @property
  def        _lowercase					(	self			:       List[str]						)    ->Optional[int]:
        """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			:       Dict , UpperCAmelCase__			:       Optional[int] , UpperCAmelCase__			:       Dict = None , UpperCAmelCase__			:       Optional[int] = False , UpperCAmelCase__			:       Tuple = None , UpperCAmelCase__			:       Dict = None , UpperCAmelCase__			:       Union[str, Any] = None , )    ->Dict:
        """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
              SCREAMING_SNAKE_CASE       :					int        =      True

        SCREAMING_SNAKE_CASE       :					int        =      bool(
            isinstance(__A , (list, tuple)						) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list)						))						)

        if is_batched:
              SCREAMING_SNAKE_CASE       :					List[Any]        =      [np.asarray(__A , dtype=np.floataa						).T for audio in raw_audio]
        elif not is_batched and not isinstance(__A , np.ndarray						):
              SCREAMING_SNAKE_CASE       :					Optional[Any]        =      np.asarray(__A , dtype=np.floataa						)
        elif isinstance(__A , np.ndarray						) and raw_audio.dtype is np.dtype(np.floataa						):
              SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      raw_audio.astype(np.floataa						)

        # always return batch
        if not is_batched:
              SCREAMING_SNAKE_CASE       :					List[str]        =      [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"						)

        SCREAMING_SNAKE_CASE       :					List[Any]        =      None
        SCREAMING_SNAKE_CASE       :					Tuple        =      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:
                    SCREAMING_SNAKE_CASE       :					Optional[Any]        =      min(array.shape[0] for array in raw_audio						)
                    SCREAMING_SNAKE_CASE       :					str        =      int(np.floor(max_length / self.chunk_stride						)						)
                    SCREAMING_SNAKE_CASE       :					str        =      (nb_step - 1) * self.chunk_stride + self.chunk_length
              elif padding:
                    SCREAMING_SNAKE_CASE       :					str        =      max(array.shape[0] for array in raw_audio						)
                    SCREAMING_SNAKE_CASE       :					Dict        =      int(np.ceil(max_length / self.chunk_stride						)						)
                    SCREAMING_SNAKE_CASE       :					Optional[Any]        =      (nb_step - 1) * self.chunk_stride + self.chunk_length
                    SCREAMING_SNAKE_CASE       :					Dict        =      "max_length"
              else:
                    SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      input_values

               # normal padding on batch
        if padded_inputs is None:
              SCREAMING_SNAKE_CASE       :					str        =      self.pad(
                  __A , max_length=__A , truncation=__A , padding=__A , return_attention_mask=__A , )
              if padding:
                    SCREAMING_SNAKE_CASE       :					Dict        =      padded_inputs.pop("""attention_mask"""						)

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

        SCREAMING_SNAKE_CASE       :					int        =      input_values
        if return_tensors is not None:
              SCREAMING_SNAKE_CASE       :					List[Any]        =      padded_inputs.convert_to_tensors(__A						)

        return padded_inputs








from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		["image_processor", "tokenizer"]
   __A :      Dict     		=		"BridgeTowerImageProcessor"
   __A :      Optional[int]     		=		("RobertaTokenizer", "RobertaTokenizerFast")






   def __init__(		self						,				__A						,				__A ):






     """simple docstring"""





     super().__init__(__A						,				__A )






   def __call__(		self						,				__A						,				__A = None						,				__A = True						,				__A = False						,				__A = None						,				__A = None						,				__A = 0						,				__A = None						,				__A = None						,				__A = None						,				__A = False						,				__A = False						,				__A = False						,				__A = False						,				__A = True						,				__A = None						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					str    =				self.tokenizer(
         text=__A						,				add_special_tokens=__A						,				padding=__A						,				truncation=__A						,				max_length=__A						,				stride=__A						,				pad_to_multiple_of=__A						,				return_token_type_ids=__A						,				return_attention_mask=__A						,				return_overflowing_tokens=__A						,				return_special_tokens_mask=__A						,				return_offsets_mapping=__A						,				return_length=__A						,				verbose=__A						,				return_tensors=__A						,				**__A						,				)
     # add pixel_values + pixel_mask
     lowerCamelCase :					int    =				self.image_processor(
         __A						,				return_tensors=__A						,				do_normalize=__A						,				do_center_crop=__A						,				**__A )
     encoding.update(__A )

     return encoding






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





     return self.tokenizer.batch_decode(*__A						,				**__A )






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





     return self.tokenizer.decode(*__A						,				**__A )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.tokenizer.model_input_names
     lowerCamelCase :					int    =				self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



