

"""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', 'padding_mask']






       def __init__(   self			,					snake_case = 1			,					snake_case = 24_000			,					snake_case = 0.0			,					snake_case = None			,					snake_case = None			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(feature_size=snake_case			,					sampling_rate=snake_case			,					padding_value=snake_case			,					**snake_case   )
        lowerCAmelCase__   :				Optional[int] 			=				chunk_length_s
        lowerCAmelCase__   :				Union[str, Any] 			=				overlap






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       @property
       def 			SCREAMING_SNAKE_CASE_	(   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			,					snake_case			,					snake_case = None			,					snake_case = False			,					snake_case = None			,					snake_case = None			,					snake_case = 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
         lowerCAmelCase__   :				Union[str, Any] 			=				True

        lowerCAmelCase__   :				List[Any] 			=				bool(
            isinstance(snake_case			,					(list, tuple)   ) and (isinstance(raw_audio[0]			,					(np.ndarray, tuple, list)   ))   )

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

        # always return batch
        if not is_batched:
         lowerCAmelCase__   :				Dict 			=				[np.asarray(snake_case   ).T]

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

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

        # normal padding on batch
        if padded_inputs is None:
         lowerCAmelCase__   :				Dict 			=				self.pad(
             snake_case			,					max_length=snake_case			,					truncation=snake_case			,					padding=snake_case			,					return_attention_mask=snake_case			,					)
         if padding:
          lowerCAmelCase__   :				Optional[Any] 			=				padded_inputs.pop("attention_mask"   )

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

        lowerCAmelCase__   :				Union[str, Any] 			=				input_values
        if return_tensors is not None:
         lowerCAmelCase__   :				Dict 			=				padded_inputs.convert_to_tensors(snake_case   )

        return padded_inputs









"""simple docstring"""

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


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      str    =						'dpr'






       def __init__(   self			,					snake_case=30_522			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=512			,					snake_case=2			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=0			,					snake_case="absolute"			,					snake_case = 0			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(pad_token_id=snake_case			,					**snake_case   )

        lowerCAmelCase__   :				Dict 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				List[str] 			=				num_hidden_layers
        lowerCAmelCase__   :				List[Any] 			=				num_attention_heads
        lowerCAmelCase__   :				Any 			=				hidden_act
        lowerCAmelCase__   :				Any 			=				intermediate_size
        lowerCAmelCase__   :				str 			=				hidden_dropout_prob
        lowerCAmelCase__   :				List[str] 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				List[Any] 			=				max_position_embeddings
        lowerCAmelCase__   :				Optional[Any] 			=				type_vocab_size
        lowerCAmelCase__   :				Tuple 			=				initializer_range
        lowerCAmelCase__   :				Dict 			=				layer_norm_eps
        lowerCAmelCase__   :				Dict 			=				projection_dim
        lowerCAmelCase__   :				Any 			=				position_embedding_type






