
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


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
					SCREAMING_SNAKE_CASE           =     DPRContextEncoderTokenizer





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
					SCREAMING_SNAKE_CASE           =     DPRQuestionEncoderTokenizer
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)

    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(UpperCAmelCase							)
class    __A:

					def __call__(						self       :  Optional[int] ,       __UpperCamelCase       :  Optional[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       :  List[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
												assert len(__UpperCamelCase ) == len(
												    __UpperCamelCase ), 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       :  List[Any] ,       __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       :  List[Any] ,       __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:
																			assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
																			lowerCamelCase_					=       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(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''']
					SCREAMING_SNAKE_CASE           =     DPRReaderTokenizer







from typing import TYPE_CHECKING

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


lowercase   =	{
    '''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:
		lowercase   =	['''BlipImageProcessor''']


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

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	[
		    '''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

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





