






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


__lowerCamelCase      :    str   		=   logging.get_logger(__name__)

__lowerCamelCase      :    List[Any]   		=   {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase      :    Dict   		=   {
    """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      :    Optional[int]   		=   {
    """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      :    List[Any]   		=   {
    """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      :    int   		=   {
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
__lowerCamelCase      :    Tuple   		=   {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
__lowerCamelCase      :    Optional[int]   		=   {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


__lowerCamelCase      :    Any   		=   {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
__lowerCamelCase      :    List[Any]   		=   {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
__lowerCamelCase      :    Any   		=   {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	VOCAB_FILES_NAMES
					a_			=	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					a_			=	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_			=	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	VOCAB_FILES_NAMES
					a_			=	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					a_			=	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_			=	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase      :    int   		=   collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

__lowerCamelCase      :    List[Any]   		=   collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


__lowerCamelCase      :    int   		=   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 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






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



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						BatchEncoding							,	__A  :						DPRReaderOutput							,	__A  :						int = 1_6							,	__A  :						int = 6_4							,	__A  :						int = 4							,	):
						snake_case__				:   Optional[int]     =						reader_input["input_ids"]
						snake_case__,		snake_case__,		snake_case__				:   Union[str, Any]     =						reader_output[:3]
						snake_case__				:   Tuple     =						len(__A )
						snake_case__				:   Union[str, Any]     =						sorted(range(__A )							,	reverse=__A							,	key=relevance_logits.__getitem__ )
						snake_case__				:   List[DPRReaderOutput]     =						[]
						for doc_id in sorted_docs:
							snake_case__				:   List[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__				:   Tuple     =						sequence_ids.index(self.pad_token_id )
							else:
								snake_case__				:   int     =						len(__A )

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






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



@add_end_docstrings(UpperCamelCase_		)
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     UpperCamelCase_		):





					"""simple docstring"""






					a_			=	VOCAB_FILES_NAMES
					a_			=	READER_PRETRAINED_VOCAB_FILES_MAP
					a_			=	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_			=	READER_PRETRAINED_INIT_CONFIGURATION
					a_			=	["input_ids", "attention_mask"]











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase      :    List[str]   		=   {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   ["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   ["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   [
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   [
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    int   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



