





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_snake_case           =					logging.get_logger(__name__)





class    lowerCAmelCase				(  A__   ):







  def __init__(     self    :List[str]       ,				_lowercase    :int       ,				_lowercase    :int       ,				_lowercase    :float       ,				**_lowercase    :int   ):






     '''simple docstring'''





     lowercase__					  =    feature_size
     lowercase__					  =    sampling_rate
     lowercase__					  =    padding_value

     lowercase__					  =    kwargs.pop("padding_side"       ,				"right"   )
     lowercase__					  =    kwargs.pop("return_attention_mask"       ,				__UpperCAmelCase   )

     super().__init__(**__UpperCAmelCase   )







  def     UpperCAmelCase	(     self    :int       ,				_lowercase    :Union[
          BatchFeature,
          List[BatchFeature],
          Dict[str, BatchFeature],
          Dict[str, List[BatchFeature]],
          List[Dict[str, BatchFeature]],
      ]       ,				_lowercase    :Union[bool, str, PaddingStrategy] = True       ,				_lowercase    :Optional[int] = None       ,				_lowercase    :bool = False       ,				_lowercase    :Optional[int] = None       ,				_lowercase    :Optional[bool] = None       ,				_lowercase    :Optional[Union[str, TensorType]] = None       ,				):






     '''simple docstring'''





     if isinstance(__UpperCAmelCase       ,				(list, tuple)   ) and isinstance(processed_features[0]       ,				(dict, BatchFeature)   ):
        lowercase__					  =    {
            key: [example[key] for example in processed_features] for key in processed_features[0].keys()
        }

     # The model's main input name, usually `input_values`, has be passed for padding
     if self.model_input_names[0] not in processed_features:
        raise ValueError(
            "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
            f''' to this method that includes {self.model_input_names[0]}, but you provided'''
            f''' {list(processed_features.keys()   )}'''   )

     lowercase__					  =    processed_features[self.model_input_names[0]]
     lowercase__					  =    (
         return_attention_mask if return_attention_mask is not None else self.return_attention_mask
     )

     if len(__UpperCAmelCase   ) == 0:
        if return_attention_mask:
           lowercase__					  =    []
        return processed_features

     # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
     # and rebuild them afterwards if no return_tensors is specified
     # Note that we lose the specific device the tensor may be on for PyTorch

     lowercase__					  =    required_input[0]
     if isinstance(__UpperCAmelCase       ,				(list, tuple)   ):
        # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
        lowercase__					  =    0
        while len(required_input[index]   ) == 0:
           index += 1
        if index < len(__UpperCAmelCase   ):
           lowercase__					  =    required_input[index][0]

     if return_tensors is None:
        if is_tf_tensor(__UpperCAmelCase   ):
           lowercase__					  =    "tf"
        elif is_torch_tensor(__UpperCAmelCase   ):
           lowercase__					  =    "pt"
        elif isinstance(__UpperCAmelCase       ,				(int, float, list, tuple, np.ndarray)   ):
           lowercase__					  =    "np"
        else:
           raise ValueError(
               f'''type of {first_element} unknown: {type(__UpperCAmelCase   )}. '''
               "Should be one of a python, numpy, pytorch or tensorflow object."   )

     for key, value in processed_features.items():
        if isinstance(value[0]       ,				(int, float)   ):
           lowercase__					  =    to_numpy(__UpperCAmelCase   )
        else:
           lowercase__					  =    [to_numpy(__UpperCAmelCase   ) for v in value]

        # Convert padding_strategy in PaddingStrategy
     lowercase__					  =    self._get_padding_strategies(padding=__UpperCAmelCase       ,				max_length=__UpperCAmelCase   )

     lowercase__					  =    processed_features[self.model_input_names[0]]

     lowercase__					  =    len(__UpperCAmelCase   )
     if not all(len(__UpperCAmelCase   ) == batch_size for v in processed_features.values()   ):
        raise ValueError("Some items in the output dictionary have a different batch size than others."   )

     lowercase__					  =    []
     for i in range(__UpperCAmelCase   ):
        lowercase__					  =    {k: v[i] for k, v in processed_features.items()}
        # truncation
        lowercase__					  =    self._truncate(
            __UpperCAmelCase       ,				max_length=__UpperCAmelCase       ,				pad_to_multiple_of=__UpperCAmelCase       ,				truncation=__UpperCAmelCase       ,				)
        truncated_inputs.append(__UpperCAmelCase   )

     if padding_strategy == PaddingStrategy.LONGEST:
        # make sure that `max_length` cannot be longer than the longest truncated length
        lowercase__					  =    max(len(input_slice[self.model_input_names[0]]   ) for input_slice in truncated_inputs   )
        lowercase__					  =    PaddingStrategy.MAX_LENGTH

     lowercase__					  =    {}
     for i in range(__UpperCAmelCase   ):
        # padding
        lowercase__					  =    self._pad(
            truncated_inputs[i]       ,				max_length=__UpperCAmelCase       ,				padding_strategy=__UpperCAmelCase       ,				pad_to_multiple_of=__UpperCAmelCase       ,				return_attention_mask=__UpperCAmelCase       ,				)

        for key, value in outputs.items():
           if key not in batch_outputs:
              lowercase__					  =    []
           if value.dtype is np.dtype(np.floataa   ):
              lowercase__					  =    value.astype(np.floataa   )
           batch_outputs[key].append(__UpperCAmelCase   )

     return BatchFeature(__UpperCAmelCase       ,				tensor_type=__UpperCAmelCase   )







  def     UpperCAmelCase	(     self    :Union[str, Any]       ,				_lowercase    :Union[Dict[str, np.ndarray], BatchFeature]       ,				_lowercase    :Optional[int] = None       ,				_lowercase    :PaddingStrategy = PaddingStrategy.DO_NOT_PAD       ,				_lowercase    :Optional[int] = None       ,				_lowercase    :Optional[bool] = None       ,				):






     '''simple docstring'''





     lowercase__					  =    processed_features[self.model_input_names[0]]

     if padding_strategy == PaddingStrategy.LONGEST:
        lowercase__					  =    len(__UpperCAmelCase   )

     if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
        lowercase__					  =    ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

     lowercase__					  =    padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__UpperCAmelCase   ) < max_length

     if return_attention_mask and "attention_mask" not in processed_features:
        lowercase__					  =    np.ones(len(__UpperCAmelCase   )       ,				dtype=np.intaa   )

     if needs_to_be_padded:
        lowercase__					  =    max_length - len(__UpperCAmelCase   )
        if self.padding_side == "right":
           if return_attention_mask:
              lowercase__					  =    np.pad(
                  processed_features["attention_mask"]       ,				(0, difference)   )
           lowercase__					  =    ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
           lowercase__					  =    np.pad(
               __UpperCAmelCase       ,				__UpperCAmelCase       ,				"constant"       ,				constant_values=self.padding_value   )
        elif self.padding_side == "left":
           if return_attention_mask:
              lowercase__					  =    np.pad(
                  processed_features["attention_mask"]       ,				(difference, 0)   )
           lowercase__					  =    ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
           lowercase__					  =    np.pad(
               __UpperCAmelCase       ,				__UpperCAmelCase       ,				"constant"       ,				constant_values=self.padding_value   )
        else:
           raise ValueError("Invalid padding strategy:" + str(self.padding_side   )   )

     return processed_features







  def     UpperCAmelCase	(     self    :Optional[int]       ,				_lowercase    :Union[Dict[str, np.ndarray], BatchFeature]       ,				_lowercase    :Optional[int] = None       ,				_lowercase    :Optional[int] = None       ,				_lowercase    :Optional[bool] = None       ,				):






     '''simple docstring'''





     if not truncation:
        return processed_features
     elif truncation and max_length is None:
        raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."   )

     lowercase__					  =    processed_features[self.model_input_names[0]]

     # find `max_length` that fits `pad_to_multiple_of`
     if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
        lowercase__					  =    ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

     lowercase__					  =    len(__UpperCAmelCase   ) > max_length

     if needs_to_be_truncated:
        lowercase__					  =    processed_features[self.model_input_names[0]][:max_length]
        if "attention_mask" in processed_features:
           lowercase__					  =    processed_features["attention_mask"][:max_length]

     return processed_features







  def     UpperCAmelCase	(     self    :Dict       ,				_lowercase    :Optional[Any]=False       ,				_lowercase    :Any=None   ):






     '''simple docstring'''





     if padding is not False:
        if padding is True:
           lowercase__					  =    PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
        elif not isinstance(__UpperCAmelCase       ,				__UpperCAmelCase   ):
           lowercase__					  =    PaddingStrategy(__UpperCAmelCase   )
        elif isinstance(__UpperCAmelCase       ,				__UpperCAmelCase   ):
           lowercase__					  =    padding
     else:
        lowercase__					  =    PaddingStrategy.DO_NOT_PAD

     # Set max length if needed
     if max_length is None:
        if padding_strategy == PaddingStrategy.MAX_LENGTH:
           raise ValueError(
               f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''   )

        # Test if we have a padding value
     if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
        raise ValueError(
            "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
            " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."   )

     return padding_strategy









"""simple docstring"""




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


A_				:      List[Any]           =			logging.get_logger(__name__)

A_				:      Optional[int]           =			{
    "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  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[int]       		=				'dpr'

			def __init__(       self					:    Optional[int]     ,					__UpperCAmelCase					:    List[str]=3_0_5_2_2     ,					__UpperCAmelCase					:    int=7_6_8     ,					__UpperCAmelCase					:    Optional[Any]=1_2     ,					__UpperCAmelCase					:    Dict=1_2     ,					__UpperCAmelCase					:    Any=3_0_7_2     ,					__UpperCAmelCase					:    int="gelu"     ,					__UpperCAmelCase					:    int=0.1     ,					__UpperCAmelCase					:    Optional[Any]=0.1     ,					__UpperCAmelCase					:    Any=5_1_2     ,					__UpperCAmelCase					:    Union[str, Any]=2     ,					__UpperCAmelCase					:    Any=0.02     ,					__UpperCAmelCase					:    Dict=1e-12     ,					__UpperCAmelCase					:    Optional[Any]=0     ,					__UpperCAmelCase					:    Union[str, Any]="absolute"     ,					__UpperCAmelCase					:    int = 0     ,					**__UpperCAmelCase					:    Dict     ,					) ->     Tuple:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   type_vocab_size
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   projection_dim
						SCREAMING_SNAKE_CASE__										=   position_embedding_type


