
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


lowercase        =  logging.get_logger(__name__)

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

lowercase        =  {
    '''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'''
        ),
    },
}
lowercase        =  {
    '''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'''
        ),
    },
}
lowercase        =  {
    '''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'''
        ),
    },
}

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


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





class       __A(				UpperCAmelCase       ):
     SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE			    =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE			    =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE			    =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class       __A(				UpperCAmelCase       ):
     SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE			    =    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE			    =    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE			    =    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
lowercase        =  collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

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


lowercase        =  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(UpperCAmelCase       )
class       __A:

     def __call__(	self					:     Optional[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Optional[str] = None   ,					__UpperCamelCase					:     Optional[str] = None   ,					__UpperCamelCase					:     Union[bool, str] = False   ,					__UpperCamelCase					:     Union[bool, str] = False   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					**__UpperCamelCase					:     Any   ,					):
            if titles is None and texts is None:
                   return super().__call__(
                       __UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
            elif titles is None or texts is None:
                   lowerCamelCase_							  =					titles if texts is None else texts
                   return super().__call__(
                       __UpperCamelCase   ,					__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
            lowerCamelCase_							  =					titles if not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else [titles]
            lowerCamelCase_							  =					texts if not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else [texts]
            lowerCamelCase_							  =					len(__UpperCamelCase   )
            lowerCamelCase_							  =					questions if not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else [questions] * n_passages
            if len(__UpperCamelCase   ) != len(__UpperCamelCase   ):
                   raise ValueError(
                       F'''There should be as many titles than texts but got {len(__UpperCamelCase   )} titles and {len(__UpperCamelCase   )} texts.'''   )
            lowerCamelCase_							  =					super().__call__(__UpperCamelCase   ,					__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   )["""input_ids"""]
            lowerCamelCase_							  =					super().__call__(__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   )["""input_ids"""]
            lowerCamelCase_							  =					{
                """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(__UpperCamelCase   ,					__UpperCamelCase   )
                ]
            }
            if return_attention_mask is not False:
                   lowerCamelCase_							  =					[]
                   for input_ids in encoded_inputs["input_ids"]:
                          attention_mask.append([int(input_id != self.pad_token_id   ) for input_id in input_ids]   )
                   lowerCamelCase_							  =					attention_mask
            return self.pad(__UpperCamelCase   ,					padding=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   )

     def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     BatchEncoding   ,					__UpperCamelCase					:     DPRReaderOutput   ,					__UpperCamelCase					:     int = 1_6   ,					__UpperCamelCase					:     int = 6_4   ,					__UpperCamelCase					:     int = 4   ,					):
            lowerCamelCase_							  =					reader_input["""input_ids"""]
            lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					reader_output[:3]
            lowerCamelCase_							  =					len(__UpperCamelCase   )
            lowerCamelCase_							  =					sorted(range(__UpperCamelCase   )   ,					reverse=__UpperCamelCase   ,					key=relevance_logits.__getitem__   )
            lowerCamelCase_							  =					[]
            for doc_id in sorted_docs:
                   lowerCamelCase_							  =					list(input_ids[doc_id]   )
                   # assuming question & title information is at the beginning of the sequence
                   lowerCamelCase_							  =					sequence_ids.index(self.sep_token_id   ,					2   ) + 1  # second sep id
                   if sequence_ids[-1] == self.pad_token_id:
                          lowerCamelCase_							  =					sequence_ids.index(self.pad_token_id   )
                   else:
                          lowerCamelCase_							  =					len(__UpperCamelCase   )

                   lowerCamelCase_							  =					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=__UpperCamelCase   ,					top_spans=__UpperCamelCase   ,					)
                   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=__UpperCamelCase   ,					start_index=__UpperCamelCase   ,					end_index=__UpperCamelCase   ,					text=self.decode(sequence_ids[start_index : end_index + 1]   )   ,					)   )
                   if len(__UpperCamelCase   ) >= num_spans:
                          break
            return nbest_spans_predictions[:num_spans]



     def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ,					):
            lowerCamelCase_							  =					[]
            for start_index, start_score in enumerate(__UpperCamelCase   ):
                   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)   )
            lowerCamelCase_							  =					sorted(__UpperCamelCase   ,					key=lambda __UpperCamelCase   : x[1]   ,					reverse=__UpperCamelCase   )
            lowerCamelCase_							  =					[]
            for (start_index, end_index), score in scores:
                   if start_index > end_index:
                          raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]'''   )
                   lowerCamelCase_							  =					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(__UpperCamelCase   ) == top_spans:
                          break
            return chosen_span_intervals





@add_end_docstrings(UpperCAmelCase       )
class       __A(				UpperCAmelCase     ,		UpperCAmelCase       ):
     SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE			    =    READER_PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE			    =    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE			    =    READER_PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE			    =    ['''input_ids''', '''attention_mask''']







from typing import TYPE_CHECKING

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


lowercase        =  {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''RagModel''',
		    '''RagPreTrainedModel''',
		    '''RagSequenceForGeneration''',
		    '''RagTokenForGeneration''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''TFRagModel''',
		    '''TFRagPreTrainedModel''',
		    '''TFRagSequenceForGeneration''',
		    '''TFRagTokenForGeneration''',
		]


if TYPE_CHECKING:
		from .configuration_rag import RagConfig
		from .retrieval_rag import RagRetriever
		from .tokenization_rag import RagTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_rag import (
				    TFRagModel,
				    TFRagPreTrainedModel,
				    TFRagSequenceForGeneration,
				    TFRagTokenForGeneration,
				)

else:
		import sys

		lowercase        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





