






"""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


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase         =							{
    "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"
        ),
    },
}
_lowerCAmelCase         =							{
    "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"
        ),
    },
}
_lowerCAmelCase         =							{
    "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"
        ),
    },
}

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


_lowerCAmelCase         =							{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_lowerCAmelCase         =							{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_lowerCAmelCase         =							{
    "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						):





	"""simple docstring"""




	_A       :				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       :				Optional[int]    =				DPRContextEncoderTokenizer






class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[Any]    =				VOCAB_FILES_NAMES
	_A       :				List[Any]    =				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	_A       :				List[str]    =				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A       :				str    =				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	_A       :				Tuple    =				DPRQuestionEncoderTokenizer






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

_lowerCAmelCase         =							collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


_lowerCAmelCase         =							r"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "



@add_start_docstrings(_SCREAMING_SNAKE_CASE						)
class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __call__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		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:
			A_		:						Any	    =   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_     ,     )
		A_		:						int	    =   titles if not isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							) else [titles]
		A_		:						Union[str, Any]	    =   texts if not isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							) else [texts]
		A_		:						Any	    =   len(lowerCAmelCase_							)
		A_		:						Dict	    =   questions if not isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							) else [questions] * n_passages
		assert len(lowerCAmelCase_							) == len(
		    lowerCAmelCase_							), f"""There should be as many titles than texts but got {len(lowerCAmelCase_							)} titles and {len(lowerCAmelCase_							)} texts."""
		A_		:						List[Any]	    =   super().__call__(lowerCAmelCase_     ,     lowerCAmelCase_     ,     padding=lowerCAmelCase_     ,     truncation=lowerCAmelCase_							)["""input_ids"""]
		A_		:						List[str]	    =   super().__call__(lowerCAmelCase_     ,     add_special_tokens=lowerCAmelCase_     ,     padding=lowerCAmelCase_     ,     truncation=lowerCAmelCase_							)["""input_ids"""]
		A_		:						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(lowerCAmelCase_     ,     lowerCAmelCase_							)
		    ]
		}
		if return_attention_mask is not False:
			A_		:						Dict	    =   []
			for input_ids in encoded_inputs["input_ids"]:
				attention_mask.append([int(input_id != self.pad_token_id							) for input_id in input_ids]							)
			A_		:						int	    =   attention_mask
		return self.pad(lowerCAmelCase_     ,     padding=lowerCAmelCase_     ,     max_length=lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = 16     ,     lowerCAmelCase_ = 64     ,     lowerCAmelCase_ = 4     ,     ):
		A_		:						str	    =   reader_input["""input_ids"""]
		A_    ,     A_    ,     A_		:						Union[str, Any]	    =   reader_output[:3]
		A_		:						List[str]	    =   len(lowerCAmelCase_							)
		A_		:						int	    =   sorted(range(lowerCAmelCase_							)     ,     reverse=lowerCAmelCase_     ,     key=relevance_logits.__getitem__							)
		A_		:						List[DPRReaderOutput]	    =   []
		for doc_id in sorted_docs:
			A_		:						str	    =   list(input_ids[doc_id]							)
			# assuming question & title information is at the beginning of the sequence
			A_		:						Dict	    =   sequence_ids.index(self.sep_token_id     ,     2							) + 1  # second sep id
			if sequence_ids[-1] == self.pad_token_id:
				A_		:						Tuple	    =   sequence_ids.index(self.pad_token_id							)
			else:
				A_		:						Union[str, Any]	    =   len(lowerCAmelCase_							)

			A_		:						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=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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     ):
		A_		:						str	    =   []
		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)							)
		A_		:						Optional[Any]	    =   sorted(lowerCAmelCase_     ,     key=lambda lowerCAmelCase_							: x[1]     ,     reverse=lowerCAmelCase_							)
		A_		:						Dict	    =   []
		for (start_index, end_index), score in scores:
			assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
			A_		:						List[Any]	    =   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(lowerCAmelCase_							) == top_spans:
				break
		return chosen_span_intervals






@add_end_docstrings(_SCREAMING_SNAKE_CASE						)
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				VOCAB_FILES_NAMES
	_A       :				Tuple    =				READER_PRETRAINED_VOCAB_FILES_MAP
	_A       :				List[Any]    =				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A       :				Optional[int]    =				READER_PRETRAINED_INIT_CONFIGURATION
	_A       :				Optional[int]    =				["""input_ids""", """attention_mask"""]
	_A       :				Dict    =				DPRReaderTokenizer









"""simple docstring"""
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				List[Any]    =				"""ViTImageProcessor"""
	_A       :				Optional[Any]    =				("""CLIPTokenizer""", """CLIPTokenizerFast""")





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   None
		if "feature_extractor" in kwargs:
			warnings.warn(
			    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
			    """ instead."""     ,     lowerCAmelCase_     ,     )
			A_		:						Union[str, Any]	    =   kwargs.pop("""feature_extractor"""							)

		A_		:						Optional[int]	    =   image_processor if image_processor is not None else feature_extractor
		if image_processor is None:
			raise ValueError("""You need to specify an `image_processor`."""							)
		if tokenizer is None:
			raise ValueError("""You need to specify a `tokenizer`."""							)

		super().__init__(lowerCAmelCase_     ,     lowerCAmelCase_							)





	def __call__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		if text is None and visual_prompt is None and images is None:
			raise ValueError("""You have to specify either text, visual prompt or images."""							)

		if text is not None and visual_prompt is not None:
			raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""							)

		if text is not None:
			A_		:						Tuple	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if visual_prompt is not None:
			A_		:						Union[str, Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						Any	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if visual_prompt is not None and images is not None:
			A_		:						List[str]	    =   {
			    """pixel_values""": image_features.pixel_values,
			    """conditional_pixel_values""": prompt_features.pixel_values,
			}
			return encoding
		elif text is not None and images is not None:
			A_		:						str	    =   image_features.pixel_values
			return encoding
		elif text is not None:
			return encoding
		elif visual_prompt is not None:
			A_		:						Any	    =   {
			    """conditional_pixel_values""": prompt_features.pixel_values,
			}
			return encoding
		else:
			return BatchEncoding(data=dict(**lowerCAmelCase_							)     ,     tensor_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		warnings.warn(
		    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,     lowerCAmelCase_     ,     )
		return self.image_processor_class





	@property
	def    lowerCamelCase(self							):
		warnings.warn(
		    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,     lowerCAmelCase_     ,     )
		return self.image_processor

