
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy




class        _SCREAMING_SNAKE_CASE	(   SCREAMING_SNAKE_CASE_			):
      _A    :		List[str]     						=  """EncodecFeatureExtractor"""
      _A    :		List[Any]     						=  ("""T5Tokenizer""", """T5TokenizerFast""")
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase			):
          super().__init__(lowerCamelCase		,						lowerCamelCase			)
          snake_case__    =					self.feature_extractor
          snake_case__    =					False
      def A_      (      self		,						lowerCamelCase=None		,						lowerCamelCase=None		,						lowerCamelCase=True			):
          return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase		,						language=lowerCamelCase		,						no_timestamps=lowerCamelCase			)
      def __call__(      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          # For backward compatibility
          if self._in_target_context_manager:
              return self.current_processor(*lowerCamelCase		,						**lowerCamelCase			)

          snake_case__    =					kwargs.pop("audio"		,						lowerCamelCase			)
          snake_case__    =					kwargs.pop("sampling_rate"		,						lowerCamelCase			)
          snake_case__    =					kwargs.pop("text"		,						lowerCamelCase			)
          if len(lowerCamelCase			) > 0:
              snake_case__    =					args[0]
              snake_case__    =					args[1:]

          if audio is None and text is None:
              raise ValueError("You need to specify either an `audio` or `text` input to process."			)

          if text is not None:
              snake_case__    =					self.tokenizer(lowerCamelCase		,						**lowerCamelCase			)

          if audio is not None:
              snake_case__    =					self.feature_extractor(lowerCamelCase		,						*lowerCamelCase		,						sampling_rate=lowerCamelCase		,						**lowerCamelCase			)

          if audio is None:
              return inputs

          elif text is None:
              return audio_inputs

          else:
              snake_case__    =					audio_inputs['input_values']
              if "padding_mask" in audio_inputs:
                  snake_case__    =					audio_inputs['padding_mask']
              return inputs
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          snake_case__    =					kwargs.pop("audio"		,						lowerCamelCase			)
          snake_case__    =					kwargs.pop("padding_mask"		,						lowerCamelCase			)

          if len(lowerCamelCase			) > 0:
              snake_case__    =					args[0]
              snake_case__    =					args[1:]

          if audio_values is not None:
              return self._decode_audio(lowerCamelCase		,						padding_mask=lowerCamelCase			)
          else:
              return self.tokenizer.batch_decode(*lowerCamelCase		,						**lowerCamelCase			)
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          return self.tokenizer.decode(*lowerCamelCase		,						**lowerCamelCase			)



      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase = None			):
          snake_case__    =					to_numpy(lowerCamelCase			)
          snake_case__    =					audio_values.shape

          if padding_mask is None:
              return list(lowerCamelCase			)

          snake_case__    =					to_numpy(lowerCamelCase			)

          # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
          # token (so that the generated audio values are **not** treated as padded tokens)
          snake_case__    =					seq_len - padding_mask.shape[-1]
          snake_case__    =					1 - self.feature_extractor.padding_value
          snake_case__    =					np.pad(lowerCamelCase		,						((0, 0), (0, difference))		,						"constant"		,						constant_values=lowerCamelCase			)

          snake_case__    =					audio_values.tolist()
          for i in range(lowerCamelCase			):
              snake_case__    =					np.asarray(audio_values[i]			)[
                  padding_mask[i][None, :] != self.feature_extractor.padding_value
              ]
              snake_case__    =					sliced_audio.reshape(lowerCamelCase		,						-1			)

          return audio_values








"""simple docstring"""







from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_							=    logging.get_logger(__name__)

a_							=    {
    """microsoft/xprophetnet-large-wiki100-cased""": (
        """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
    ),
}







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			a_       :			List[Any]												=			"""xlm-prophetnet"""
			a_       :			Any												=			["""past_key_values"""]
			a_       :			Optional[int]												=			{
			    """num_attention_heads""": """num_encoder_attention_heads""",
			}
			def __init__(	self				,   A = 0.1				,   A = "gelu"				,   A = 3_0522				,   A = 1024				,   A = 4096				,   A = 12				,   A = 16				,   A = 4096				,   A = 12				,   A = 16				,   A = 0.1				,   A = 0.1				,   A = 512				,   A = 0.0_2				,   A = True				,   A = True				,   A = 0				,   A = 2				,   A = 32				,   A = 128				,   A = False				,   A = 0.0				,   A = True				,   A = 0				,   A = 1				,   A = 2				,   **A				,   ):
						_lowerCamelCase					:					Dict	       =    vocab_size
						_lowerCamelCase					:					List[Any]	       =    hidden_size
						_lowerCamelCase					:					List[Any]	       =    encoder_ffn_dim
						_lowerCamelCase					:					Any	       =    num_encoder_layers
						_lowerCamelCase					:					Any	       =    num_encoder_attention_heads
						_lowerCamelCase					:					List[str]	       =    decoder_ffn_dim
						_lowerCamelCase					:					Optional[int]	       =    num_decoder_layers
						_lowerCamelCase					:					List[str]	       =    num_decoder_attention_heads
						_lowerCamelCase					:					Optional[Any]	       =    max_position_embeddings
						_lowerCamelCase					:					Optional[Any]	       =    init_std  # Normal(0, this parameter)
						_lowerCamelCase					:					Optional[int]	       =    activation_function

						# parameters for xlmprophetnet
						_lowerCamelCase					:					Any	       =    ngram
						_lowerCamelCase					:					Dict	       =    num_buckets
						_lowerCamelCase					:					Dict	       =    relative_max_distance
						_lowerCamelCase					:					Optional[Any]	       =    disable_ngram_loss
						_lowerCamelCase					:					Union[str, Any]	       =    eps

						# 3 Types of Dropout
						_lowerCamelCase					:					int	       =    attention_dropout
						_lowerCamelCase					:					str	       =    activation_dropout
						_lowerCamelCase					:					Optional[int]	       =    dropout

						_lowerCamelCase					:					List[Any]	       =    use_cache

						super().__init__(
						    pad_token_id=A				,   bos_token_id=A				,   eos_token_id=A				,   is_encoder_decoder=A				,   add_cross_attention=A				,   decoder_start_token_id=A				,   **A				,   )
			@property
			def    _lowerCAmelCase		(	self							):
						return self.num_encoder_layers + self.num_decoder_layers

			@num_hidden_layers.setter
			def    _lowerCAmelCase		(	self				,   A							):
						raise NotImplementedError(
						    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
						    ' `num_decoder_layers`.'							)





