'''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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


UpperCamelCase_      	=	logging.get_logger(__name__)

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

UpperCamelCase_      	=	{
    '''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'''
        ),
    },
}
UpperCamelCase_      	=	{
    '''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'''
        ),
    },
}
UpperCamelCase_      	=	{
    '''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'''
        ),
    },
}

UpperCamelCase_      	=	{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
UpperCamelCase_      	=	{
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
UpperCamelCase_      	=	{
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


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




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= VOCAB_FILES_NAMES
						A_			:							Dict     		= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						A_			:							List[str]     		= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_			:							Optional[Any]     		= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
						A_			:							List[str]     		= DPRContextEncoderTokenizer




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							str     		= VOCAB_FILES_NAMES
						A_			:							Any     		= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						A_			:							str     		= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_			:							Optional[Any]     		= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
						A_			:							List[Any]     		= DPRQuestionEncoderTokenizer
UpperCamelCase_      	=	collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

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


UpperCamelCase_      	=	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)

    Return:
        `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 _SCREAMING_SNAKE_CASE:




						def __call__(    self :       List[str]							,							UpperCamelCase_ :       str							,							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_ :       Optional[Any]							,							)     ->				BatchEncoding:
													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:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = 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_							,							)
													SCREAMING_SNAKE_CASE__     :Tuple						    = titles if not isinstance(UpperCamelCase_							,							UpperCamelCase_      ) else [titles]
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = texts if not isinstance(UpperCamelCase_							,							UpperCamelCase_      ) else [texts]
													SCREAMING_SNAKE_CASE__     :Any						    = len(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = questions if not isinstance(UpperCamelCase_							,							UpperCamelCase_      ) else [questions] * n_passages
													assert len(UpperCamelCase_      ) == len(
													    UpperCamelCase_      ), f'''There should be as many titles than texts but got {len(UpperCamelCase_      )} titles and {len(UpperCamelCase_      )} texts.'''
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = super().__call__(UpperCamelCase_							,							UpperCamelCase_							,							padding=UpperCamelCase_							,							truncation=UpperCamelCase_      )['input_ids']
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = super().__call__(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_							,							padding=UpperCamelCase_							,							truncation=UpperCamelCase_      )['input_ids']
													SCREAMING_SNAKE_CASE__     :List[str]						    = {
													    '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:
																				SCREAMING_SNAKE_CASE__     :str						    = []
																				for input_ids in encoded_inputs["input_ids"]:
																											attention_mask.append([int(input_id != self.pad_token_id      ) for input_id in input_ids]      )
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = attention_mask
													return self.pad(UpperCamelCase_							,							padding=UpperCamelCase_							,							max_length=UpperCamelCase_							,							return_tensors=UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       BatchEncoding							,							UpperCamelCase_ :       DPRReaderOutput							,							UpperCamelCase_ :       int = 16							,							UpperCamelCase_ :       int = 64							,							UpperCamelCase_ :       int = 4							,							)     ->				List[DPRSpanPrediction]:
													SCREAMING_SNAKE_CASE__     :str						    = reader_input['input_ids']
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[str]						    = reader_output[:3]
													SCREAMING_SNAKE_CASE__     :Any						    = len(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = sorted(range(UpperCamelCase_      )							,							reverse=UpperCamelCase_							,							key=relevance_logits.__getitem__      )
													SCREAMING_SNAKE_CASE__     :List[DPRReaderOutput]						    = []
													for doc_id in sorted_docs:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = list(input_ids[doc_id]      )
																				# assuming question & title information is at the beginning of the sequence
																				SCREAMING_SNAKE_CASE__     :Any						    = sequence_ids.index(self.sep_token_id							,							2      ) + 1  # second sep id
																				if sequence_ids[-1] == self.pad_token_id:
																											SCREAMING_SNAKE_CASE__     :str						    = sequence_ids.index(self.pad_token_id      )
																				else:
																											SCREAMING_SNAKE_CASE__     :Optional[int]						    = len(UpperCamelCase_      )

																				SCREAMING_SNAKE_CASE__     :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=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      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       List[int]							,							UpperCamelCase_ :       List[int]							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       int							,							)     ->				List[DPRSpanPrediction]:
													SCREAMING_SNAKE_CASE__     :Dict						    = []
													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)      )
													SCREAMING_SNAKE_CASE__     :str						    = sorted(UpperCamelCase_							,							key=lambda UpperCamelCase_      : x[1]							,							reverse=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = []
													for (start_index, end_index), score in scores:
																				assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
																				SCREAMING_SNAKE_CASE__     :str						    = end_index - start_index + 1
																				assert length <= max_answer_length, 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(_SCREAMING_SNAKE_CASE					)
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE					):
						A_			:							str     		= VOCAB_FILES_NAMES
						A_			:							Dict     		= READER_PRETRAINED_VOCAB_FILES_MAP
						A_			:							Union[str, Any]     		= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_			:							List[str]     		= READER_PRETRAINED_INIT_CONFIGURATION
						A_			:							Tuple     		= ['input_ids', 'attention_mask']
						A_			:							Optional[Any]     		= DPRReaderTokenizer







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_      	=	{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	['''BlipImageProcessor''']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BlipModel''',
		    '''BlipPreTrainedModel''',
		    '''BlipForConditionalGeneration''',
		    '''BlipForQuestionAnswering''',
		    '''BlipVisionModel''',
		    '''BlipTextModel''',
		    '''BlipForImageTextRetrieval''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFBlipModel''',
		    '''TFBlipPreTrainedModel''',
		    '''TFBlipForConditionalGeneration''',
		    '''TFBlipForQuestionAnswering''',
		    '''TFBlipVisionModel''',
		    '''TFBlipTextModel''',
		    '''TFBlipForImageTextRetrieval''',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

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






