

"""simple docstring"""

import copy

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


_UpperCamelCase		    =						logging.get_logger(__name__)

_UpperCamelCase		    =						{}






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Tuple          =	'fsmt_decoder'






       def __init__(      self ,				snake_case=0 ,				snake_case=0      ):





        """simple docstring"""

        super().__init__()
        lowerCAmelCase__      :							str    =							vocab_size
        lowerCAmelCase__      :							Any    =							bos_token_id






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			str          =	'fsmt'
       __UpperCamelCase				:			Union[str, Any]          =	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






       def __init__(      self ,				snake_case=["en", "de"] ,				snake_case=42_024 ,				snake_case=42_024 ,				snake_case="relu" ,				snake_case=1_024 ,				snake_case=200 ,				snake_case=1_024 ,				snake_case=4_096 ,				snake_case=12 ,				snake_case=16 ,				snake_case=0.0 ,				snake_case=4_096 ,				snake_case=12 ,				snake_case=16 ,				snake_case=0.0 ,				snake_case=0.0 ,				snake_case=0.1 ,				snake_case=0.0 ,				snake_case=0.02 ,				snake_case=2 ,				snake_case=True ,				snake_case=True ,				snake_case=False ,				snake_case=5 ,				snake_case=1.0 ,				snake_case=False ,				snake_case=True ,				snake_case=1 ,				snake_case=0 ,				snake_case=2 ,				snake_case=2 ,				**snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							langs
        lowerCAmelCase__      :							Optional[int]    =							src_vocab_size
        lowerCAmelCase__      :							Optional[Any]    =							tgt_vocab_size
        lowerCAmelCase__      :							Any    =							d_model  # encoder_embed_dim and decoder_embed_dim

        lowerCAmelCase__      :							Optional[Any]    =							encoder_ffn_dim
        lowerCAmelCase__      :							Dict    =							encoder_layers
        lowerCAmelCase__      :							Dict    =							encoder_attention_heads
        lowerCAmelCase__      :							Dict    =							encoder_layerdrop
        lowerCAmelCase__      :							Optional[Any]    =							decoder_layerdrop
        lowerCAmelCase__      :							int    =							decoder_ffn_dim
        lowerCAmelCase__      :							Tuple    =							decoder_layers
        lowerCAmelCase__      :							str    =							decoder_attention_heads
        lowerCAmelCase__      :							int    =							max_position_embeddings
        lowerCAmelCase__      :							Union[str, Any]    =							init_std  # Normal(0, this parameter)
        lowerCAmelCase__      :							Optional[int]    =							activation_function

        lowerCAmelCase__      :							Any    =							DecoderConfig(vocab_size=snake_case ,				bos_token_id=snake_case      )
        if "decoder" in common_kwargs:
         del common_kwargs["decoder"]

        lowerCAmelCase__      :							str    =							scale_embedding  # scale factor will be sqrt(d_model) if True

        # 3 Types of Dropout
        lowerCAmelCase__      :							Any    =							attention_dropout
        lowerCAmelCase__      :							List[Any]    =							activation_dropout
        lowerCAmelCase__      :							Union[str, Any]    =							dropout

        lowerCAmelCase__      :							Optional[Any]    =							use_cache
        super().__init__(
            pad_token_id=snake_case ,				bos_token_id=snake_case ,				eos_token_id=snake_case ,				decoder_start_token_id=snake_case ,				is_encoder_decoder=snake_case ,				tie_word_embeddings=snake_case ,				forced_eos_token_id=snake_case ,				max_length=snake_case ,				num_beams=snake_case ,				length_penalty=snake_case ,				early_stopping=snake_case ,				**snake_case ,				)






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							copy.deepcopy(self.__dict__      )
        lowerCAmelCase__      :							Any    =							self.decoder.to_dict()
        lowerCAmelCase__      :							Optional[Any]    =							self.__class__.model_type
        return output









"""simple docstring"""

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		    =						logging.get_logger(__name__)






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Tuple          =	['input_values', 'attention_mask']






       def __init__(      self ,				snake_case=1 ,				snake_case=16_000 ,				snake_case=0.0 ,				snake_case=False ,				snake_case=True ,				**snake_case ,				):





        """simple docstring"""

        super().__init__(feature_size=snake_case ,				sampling_rate=snake_case ,				padding_value=snake_case ,				**snake_case      )
        lowerCAmelCase__      :							Dict    =							return_attention_mask
        lowerCAmelCase__      :							List[str]    =							do_normalize






       @staticmethod
       def  SCREAMING_SNAKE_CASE_  (      snake_case ,				snake_case ,				snake_case = 0.0      ):





        """simple docstring"""

        if attention_mask is not None:
         lowerCAmelCase__      :							List[str]    =							np.array(snake_case ,				np.intaa      )
         lowerCAmelCase__      :							str    =							[]

         for vector, length in zip(snake_case ,				attention_mask.sum(-1      )      ):
          lowerCAmelCase__      :							Any    =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7      )
          if length < normed_slice.shape[0]:
           lowerCAmelCase__      :							List[Any]    =							padding_value

          normed_input_values.append(snake_case      )
        else:
         lowerCAmelCase__      :							List[Any]    =							[(x - x.mean()) / np.sqrt(x.var() + 1e-7      ) for x in input_values]

        return normed_input_values






       def __call__(      self ,				snake_case ,				snake_case = False ,				snake_case = None ,				snake_case = False ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				**snake_case ,				):





        """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."      )

        lowerCAmelCase__      :							List[str]    =							isinstance(snake_case ,				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}"""      )
        lowerCAmelCase__      :							Tuple    =							is_batched_numpy or (
            isinstance(snake_case ,				(list, tuple)      ) and (isinstance(raw_speech[0] ,				(np.ndarray, tuple, list)      ))
        )

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

        # convert into correct format for padding
        lowerCAmelCase__      :							Tuple    =							BatchFeature({"input_values": raw_speech}      )

        lowerCAmelCase__      :							Any    =							self.pad(
            snake_case ,				padding=snake_case ,				max_length=snake_case ,				truncation=snake_case ,				pad_to_multiple_of=snake_case ,				return_attention_mask=snake_case ,				)

        # convert input values to correct format
        lowerCAmelCase__      :							List[Any]    =							padded_inputs["input_values"]
        if not isinstance(input_values[0] ,				np.ndarray      ):
         lowerCAmelCase__      :							Any    =							[np.asarray(snake_case ,				dtype=np.floataa      ) for array in input_values]
        elif (
            not isinstance(snake_case ,				np.ndarray      )
            and isinstance(input_values[0] ,				np.ndarray      )
            and input_values[0].dtype is np.dtype(np.floataa      )
        ):
         lowerCAmelCase__      :							str    =							[array.astype(np.floataa      ) for array in input_values]
        elif isinstance(snake_case ,				np.ndarray      ) and input_values.dtype is np.dtype(np.floataa      ):
         lowerCAmelCase__      :							List[Any]    =							input_values.astype(np.floataa      )

        # convert attention_mask to correct format
        lowerCAmelCase__      :							int    =							padded_inputs.get("attention_mask"      )
        if attention_mask is not None:
         lowerCAmelCase__      :							Tuple    =							[np.asarray(snake_case ,				dtype=np.intaa      ) for array in attention_mask]

        # zero-mean and unit-variance normalization
        if self.do_normalize:
         lowerCAmelCase__      :							List[Any]    =							(
             attention_mask
             if self._get_padding_strategies(snake_case ,				max_length=snake_case      ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         lowerCAmelCase__      :							Union[str, Any]    =							self.zero_mean_unit_var_norm(
             padded_inputs["input_values"] ,				attention_mask=snake_case ,				padding_value=self.padding_value      )

        if return_tensors is not None:
         lowerCAmelCase__      :							Optional[Any]    =							padded_inputs.convert_to_tensors(snake_case      )

        return padded_inputs






