






"""simple docstring"""



from typing import TYPE_CHECKING

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


A__	:    List[str]			 = {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    List[Any]			 = ['LxmertTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Optional[Any]			 = [
						    'LxmertEncoder',
						    'LxmertForPreTraining',
						    'LxmertForQuestionAnswering',
						    'LxmertModel',
						    'LxmertPreTrainedModel',
						    'LxmertVisualFeatureEncoder',
						    'LxmertXLayer',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    List[Any]			 = [
						    'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFLxmertForPreTraining',
						    'TFLxmertMainLayer',
						    'TFLxmertModel',
						    'TFLxmertPreTrainedModel',
						    'TFLxmertVisualFeatureEncoder',
						]


if TYPE_CHECKING:
						from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
						from .tokenization_lxmert import LxmertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_lxmert_fast import LxmertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lxmert import (
												    LxmertEncoder,
												    LxmertForPreTraining,
												    LxmertForQuestionAnswering,
												    LxmertModel,
												    LxmertPreTrainedModel,
												    LxmertVisualFeatureEncoder,
												    LxmertXLayer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_lxmert import (
												    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLxmertForPreTraining,
												    TFLxmertMainLayer,
												    TFLxmertModel,
												    TFLxmertPreTrainedModel,
												    TFLxmertVisualFeatureEncoder,
												)

else:
						import sys

						A__	:    str			 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











"""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 import BertTokenizer


A__	:    Tuple			 = logging.get_logger(__name__)

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

A__	:    List[Any]			 = {
    '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__	:    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'
        ),
    },
}
A__	:    int			 = {
    '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__	:    Dict			 = {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
A__	:    Dict			 = {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
A__	:    Any			 = {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


A__	:    Union[str, Any]			 = {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A__	:    Tuple			 = {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
A__	:    Any			 = {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:List[Any]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Dict             =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Any             =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Tuple             =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:str             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Tuple             =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Dict             =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Dict             =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


A__	:    Union[str, Any]			 = collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

A__	:    Union[str, Any]			 = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


A__	:    List[Any]			 = 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(snake_case__       )
class        lowercase__      :


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


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

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


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  List[int]       ,	snake_case__					:  List[int]       ,	snake_case__					:  int       ,	snake_case__					:  int       ,	):
										lowerCamelCase_	:							Any									=[]
										for start_index, start_score in enumerate(snake_case__ ):
																	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_	:							List[str]									=sorted(snake_case__       ,	key=lambda snake_case__ : x[1]       ,	reverse=snake_case__ )
										lowerCamelCase_	:							List[Any]									=[]
										for (start_index, end_index), score in scores:
																	if start_index > end_index:
																								raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""" )
																	lowerCamelCase_	:							Tuple									=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(snake_case__ ) == top_spans:
																								break
										return chosen_span_intervals







@add_end_docstrings(snake_case__       )
class        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:List[str]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Union[str, Any]             =			READER_PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:int             =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:int             =			READER_PRETRAINED_INIT_CONFIGURATION
			_UpperCAmelCase		:Optional[Any]             =			["input_ids", "attention_mask"]



