



"""simple docstring"""






import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case   					=				{
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
snake_case   					=				{
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
snake_case   					=				{
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case   					=				{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
snake_case   					=				{
    '''facebook/dpr-question_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-question_encoder-multiset-base''': 5_1_2,
}
snake_case   					=				{
    '''facebook/dpr-reader-single-nq-base''': 5_1_2,
    '''facebook/dpr-reader-multiset-base''': 5_1_2,
}


snake_case   					=				{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
snake_case   					=				{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
snake_case   					=				{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	Union[str, Any]           =  VOCAB_FILES_NAMES
   A__		:	Union[str, Any]           =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   A__		:	int           =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	List[Any]           =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  VOCAB_FILES_NAMES
   A__		:	Dict           =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   A__		:	Tuple           =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	List[str]           =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

snake_case   					=				collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

snake_case   					=				collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


snake_case   					=				r'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''



@add_start_docstrings(__SCREAMING_SNAKE_CASE  )
class     UpperCAmelCase    :



   def __call__(       self    :	Tuple							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Optional[str] = None							,       __lowerCamelCase    :	Optional[str] = None							,       __lowerCamelCase    :	Union[bool, str] = False							,       __lowerCamelCase    :	Union[bool, str] = False							,       __lowerCamelCase    :	Optional[int] = None							,       __lowerCamelCase    :	Optional[Union[str, TensorType]] = None							,       __lowerCamelCase    :	Optional[bool] = None							,       **__lowerCamelCase    :	int							,       ):



     """simple docstring"""
     if titles is None and texts is None:
       return super().__call__(
           __lowerCamelCase							,       padding=__lowerCamelCase							,       truncation=__lowerCamelCase							,       max_length=__lowerCamelCase							,       return_tensors=__lowerCamelCase							,       return_attention_mask=__lowerCamelCase							,       **__lowerCamelCase							,       )
     elif titles is None or texts is None:
       _snake_case   	=      titles if texts is None else texts
       return super().__call__(
           __lowerCamelCase							,       __lowerCamelCase							,       padding=__lowerCamelCase							,       truncation=__lowerCamelCase							,       max_length=__lowerCamelCase							,       return_tensors=__lowerCamelCase							,       return_attention_mask=__lowerCamelCase							,       **__lowerCamelCase							,       )
     _snake_case   	=      titles if not isinstance(__lowerCamelCase							,       __lowerCamelCase				) else [titles]
     _snake_case   	=      texts if not isinstance(__lowerCamelCase							,       __lowerCamelCase				) else [texts]
     _snake_case   	=      len(__lowerCamelCase				)
     _snake_case   	=      questions if not isinstance(__lowerCamelCase							,       __lowerCamelCase				) else [questions] * n_passages
     if len(__lowerCamelCase				) != len(__lowerCamelCase				):
       raise ValueError(
           f"""There should be as many titles than texts but got {len(__lowerCamelCase				)} titles and {len(__lowerCamelCase				)} texts."""				)
     _snake_case   	=      super().__call__(__lowerCamelCase							,       __lowerCamelCase							,       padding=__lowerCamelCase							,       truncation=__lowerCamelCase				)['''input_ids''']
     _snake_case   	=      super().__call__(__lowerCamelCase							,       add_special_tokens=__lowerCamelCase							,       padding=__lowerCamelCase							,       truncation=__lowerCamelCase				)['''input_ids''']
     _snake_case   	=      {
         '''input_ids''': [
             (encoded_question_and_title + encoded_text)[:max_length]
             if max_length is not None and truncation
             else encoded_question_and_title + encoded_text
             for encoded_question_and_title, encoded_text in zip(__lowerCamelCase							,       __lowerCamelCase				)
         ]
     }
     if return_attention_mask is not False:
       _snake_case   	=      []
       for input_ids in encoded_inputs["input_ids"]:
         attention_mask.append([int(input_id != self.pad_token_id				) for input_id in input_ids]				)
       _snake_case   	=      attention_mask
     return self.pad(__lowerCamelCase							,       padding=__lowerCamelCase							,       max_length=__lowerCamelCase							,       return_tensors=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	BatchEncoding							,       __lowerCamelCase    :	DPRReaderOutput							,       __lowerCamelCase    :	int = 1_6							,       __lowerCamelCase    :	int = 6_4							,       __lowerCamelCase    :	int = 4							,       ):



     """simple docstring"""
     _snake_case   	=      reader_input['''input_ids''']
     _snake_case	,     _snake_case	,     _snake_case   	=      reader_output[:3]
     _snake_case   	=      len(__lowerCamelCase				)
     _snake_case   	=      sorted(range(__lowerCamelCase				)							,       reverse=__lowerCamelCase							,       key=relevance_logits.__getitem__				)
     _snake_case   	=      []
     for doc_id in sorted_docs:
       _snake_case   	=      list(input_ids[doc_id]				)
       # assuming question & title information is at the beginning of the sequence
       _snake_case   	=      sequence_ids.index(self.sep_token_id							,       2				) + 1  # second sep id
       if sequence_ids[-1] == self.pad_token_id:
         _snake_case   	=      sequence_ids.index(self.pad_token_id				)
       else:
         _snake_case   	=      len(__lowerCamelCase				)

       _snake_case   	=      self._get_best_spans(
           start_logits=start_logits[doc_id][passage_offset:sequence_len]							,       end_logits=end_logits[doc_id][passage_offset:sequence_len]							,       max_answer_length=__lowerCamelCase							,       top_spans=__lowerCamelCase							,       )
       for start_index, end_index in best_spans:
         start_index += passage_offset
         end_index += passage_offset
         nbest_spans_predictions.append(
             DPRSpanPrediction(
                 span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]							,       relevance_score=relevance_logits[doc_id]							,       doc_id=__lowerCamelCase							,       start_index=__lowerCamelCase							,       end_index=__lowerCamelCase							,       text=self.decode(sequence_ids[start_index : end_index + 1]				)							,       )				)
       if len(__lowerCamelCase				) >= num_spans:
         break
     return nbest_spans_predictions[:num_spans]





   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int							,       ):



     """simple docstring"""
     _snake_case   	=      []
     for start_index, start_score in enumerate(__lowerCamelCase				):
       for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]				):
         scores.append(((start_index, start_index + answer_length), start_score + end_score)				)
     _snake_case   	=      sorted(__lowerCamelCase							,       key=lambda __lowerCamelCase				: x[1]							,       reverse=__lowerCamelCase				)
     _snake_case   	=      []
     for (start_index, end_index), score in scores:
       if start_index > end_index:
         raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""				)
       _snake_case   	=      end_index - start_index + 1
       if length > max_answer_length:
         raise ValueError(f"""Span is too long: {length} > {max_answer_length}"""				)
       if any(
           start_index <= prev_start_index <= prev_end_index <= end_index
           or prev_start_index <= start_index <= end_index <= prev_end_index
           for (prev_start_index, prev_end_index) in chosen_span_intervals				):
         continue
       chosen_span_intervals.append((start_index, end_index)				)

       if len(__lowerCamelCase				) == top_spans:
         break
     return chosen_span_intervals



@add_end_docstrings(__SCREAMING_SNAKE_CASE  )
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE  ):
   A__		:	Optional[int]           =  VOCAB_FILES_NAMES
   A__		:	Optional[int]           =  READER_PRETRAINED_VOCAB_FILES_MAP
   A__		:	Tuple           =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	List[str]           =  READER_PRETRAINED_INIT_CONFIGURATION
   A__		:	Optional[int]           =  ['''input_ids''', '''attention_mask''']













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__      			=						{
    '''configuration_poolformer''': [
        '''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''PoolFormerConfig''',
        '''PoolFormerOnnxConfig''',
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						['''PoolFormerFeatureExtractor''']
			UpperCAmelCase__      			=						['''PoolFormerImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						[
			    '''POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''PoolFormerForImageClassification''',
			    '''PoolFormerModel''',
			    '''PoolFormerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_poolformer import (
			    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    PoolFormerConfig,
			    PoolFormerOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_poolformer import PoolFormerFeatureExtractor
						from .image_processing_poolformer import PoolFormerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_poolformer import (
						    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PoolFormerForImageClassification,
						    PoolFormerModel,
						    PoolFormerPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase__      			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)





