

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 _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =VOCAB_FILES_NAMES
			lowerCamelCase__							     =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__							     =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__							     =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =VOCAB_FILES_NAMES
			lowerCamelCase__							     =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__							     =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__							     =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'\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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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(__A )
class _UpperCamelCase				:
			'''simple docstring'''







			def __call__(      self				:			Union[str, Any]   ,   a				:			str   ,   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   ,   )				->							BatchEncoding:

				"""simple docstring"""

				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:
					SCREAMING_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   ,   )
				SCREAMING_SNAKE_CASE :   Dict					  =							titles if not isinstance(a   ,   a						) else [titles]
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							texts if not isinstance(a   ,   a						) else [texts]
				SCREAMING_SNAKE_CASE :   str					  =							len(a						)
				SCREAMING_SNAKE_CASE :   Optional[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."						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							super().__call__(a   ,   a   ,   padding=a   ,   truncation=a						)["input_ids"]
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							super().__call__(a   ,   add_special_tokens=a   ,   padding=a   ,   truncation=a						)["input_ids"]
				SCREAMING_SNAKE_CASE :   int					  =							{
				    "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:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							[]
					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 :   Dict					  =							attention_mask
				return self.pad(a   ,   padding=a   ,   max_length=a   ,   return_tensors=a						)
			def 				__UpperCamelCase			(      self				:			List[str]   ,   a				:			BatchEncoding   ,   a				:			DPRReaderOutput   ,   a				:			int = 16   ,   a				:			int = 64   ,   a				:			int = 4   ,   )				->							List[DPRSpanPrediction]:

				"""simple docstring"""

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

					SCREAMING_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=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 				__UpperCamelCase			(      self				:			str   ,   a				:			List[int]   ,   a				:			List[int]   ,   a				:			int   ,   a				:			int   ,   )				->							List[DPRSpanPrediction]:

				"""simple docstring"""

				SCREAMING_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)						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							sorted(a   ,   key=lambda a						: x[1]   ,   reverse=a						)
				SCREAMING_SNAKE_CASE :   str					  =							[]
				for (start_index, end_index), score in scores:
					if start_index > end_index:
						raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]"						)
					SCREAMING_SNAKE_CASE :   int					  =							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(__A )
class _UpperCamelCase				(				__A				,						__A ):
			'''simple docstring'''







			lowerCamelCase__							     =VOCAB_FILES_NAMES
			lowerCamelCase__							     =READER_PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__							     =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__							     =READER_PRETRAINED_INIT_CONFIGURATION
			lowerCamelCase__							     =['input_ids', 'attention_mask']


from typing import TYPE_CHECKING

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


a_      =     {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     ['PLBartTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'PLBartForCausalLM',
			    'PLBartForConditionalGeneration',
			    'PLBartForSequenceClassification',
			    'PLBartModel',
			    'PLBartPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_plbart import PLBartTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_plbart import (
						    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PLBartForCausalLM,
						    PLBartForConditionalGeneration,
						    PLBartForSequenceClassification,
						    PLBartModel,
						    PLBartPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure)