



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


A__								=						logging.get_logger(__name__)

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

A__								=						{
    '''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'''
        ),
    },
}
A__								=						{
    '''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'''
        ),
    },
}
A__								=						{
    '''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'''
        ),
    },
}

A__								=						{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
A__								=						{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
A__								=						{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


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


class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    int											=					VOCAB_FILES_NAMES
		__lowerCAmelCase	:    List[Any]											=					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		__lowerCAmelCase	:    int											=					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowerCAmelCase	:    Optional[int]											=					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    Any											=					VOCAB_FILES_NAMES
		__lowerCAmelCase	:    List[Any]											=					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		__lowerCAmelCase	:    Tuple											=					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowerCAmelCase	:    Optional[int]											=					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

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

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


A__								=						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(__lowerCamelCase				)
class       a							:



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



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

										snake_case__	:      Union[str, Any]     		=	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=__lowercase   ,top_spans=__lowercase   ,)
										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=__lowercase   ,start_index=__lowercase   ,end_index=__lowercase   ,text=self.decode(sequence_ids[start_index : end_index + 1]  )   ,)  )
										if len(__lowercase  ) >= num_spans:
														break
						return nbest_spans_predictions[:num_spans]






		def 	__lowerCamelCase			(     self     :Dict   ,__lowercase     :List[int]   ,__lowercase     :List[int]   ,__lowercase     :int   ,__lowercase     :int   ,):
						snake_case__	:      List[str]     		=	[]
						for start_index, start_score in enumerate(__lowercase  ):
										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__	:      Dict     		=	sorted(__lowercase   ,key=lambda __lowercase  : x[1]   ,reverse=__lowercase  )
						snake_case__	:      List[Any]     		=	[]
						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__	:      Tuple     		=	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(__lowercase  ) == top_spans:
														break
						return chosen_span_intervals


@add_end_docstrings(__lowerCamelCase				)
class       a							(			__lowerCamelCase  ,							__lowerCamelCase				):
		__lowerCAmelCase	:    Union[str, Any]											=					VOCAB_FILES_NAMES
		__lowerCAmelCase	:    Union[str, Any]											=					READER_PRETRAINED_VOCAB_FILES_MAP
		__lowerCAmelCase	:    Optional[Any]											=					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowerCAmelCase	:    Optional[Any]											=					READER_PRETRAINED_INIT_CONFIGURATION
		__lowerCAmelCase	:    Tuple											=					["""input_ids""", """attention_mask"""]






from typing import TYPE_CHECKING

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


A__								=						{
    '''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
    '''tokenization_ctrl''': ['''CTRLTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A__								=						[
				    '''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''CTRLForSequenceClassification''',
				    '''CTRLLMHeadModel''',
				    '''CTRLModel''',
				    '''CTRLPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A__								=						[
				    '''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFCTRLForSequenceClassification''',
				    '''TFCTRLLMHeadModel''',
				    '''TFCTRLModel''',
				    '''TFCTRLPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
				from .tokenization_ctrl import CTRLTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ctrl import (
								    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CTRLForSequenceClassification,
								    CTRLLMHeadModel,
								    CTRLModel,
								    CTRLPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ctrl import (
								    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFCTRLForSequenceClassification,
								    TFCTRLLMHeadModel,
								    TFCTRLModel,
								    TFCTRLPreTrainedModel,
								)

else:
				import sys

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

