





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				:    Any       =   logging.get_logger(__name__)

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

__A				:    List[str]       =   {
    "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				:    Union[str, Any]       =   {
    "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				:    Tuple       =   {
    "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				:    Tuple       =   {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
__A				:    str       =   {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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

class 	A_       (a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION

class 	A_       (a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__A				:    Union[str, Any]       =   collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

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


__A				:    int       =   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 	A_       :
	def __call__(    self							,					_A							,					_A = None							,					_A = None							,					_A = False							,					_A = False							,					_A = None							,					_A = None							,					_A = None							,					**_A							,					):






				'''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:
							UpperCAmelCase   		=				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							,					)
				UpperCAmelCase   		=				titles if not isinstance(_A							,					_A		) else [titles]
				UpperCAmelCase   		=				texts if not isinstance(_A							,					_A		) else [texts]
				UpperCAmelCase   		=				len(_A		)
				UpperCAmelCase   		=				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."""		)
				UpperCAmelCase   		=				super().__call__(_A							,					_A							,					padding=_A							,					truncation=_A		)['''input_ids''']
				UpperCAmelCase   		=				super().__call__(_A							,					add_special_tokens=_A							,					padding=_A							,					truncation=_A		)['''input_ids''']
				UpperCAmelCase   		=				{
				    '''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:
							UpperCAmelCase   		=				[]
							for input_ids in encoded_inputs["input_ids"]:
										attention_mask.append([int(input_id != self.pad_token_id		) for input_id in input_ids]		)
							UpperCAmelCase   		=				attention_mask
				return self.pad(_A							,					padding=_A							,					max_length=_A							,					return_tensors=_A		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A = 1_6							,					_A = 6_4							,					_A = 4							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				reader_input['''input_ids''']
				UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase   		=				reader_output[:3]
				UpperCAmelCase   		=				len(_A		)
				UpperCAmelCase   		=				sorted(range(_A		)							,					reverse=_A							,					key=relevance_logits.__getitem__		)
				UpperCAmelCase   		=				[]
				for doc_id in sorted_docs:
							UpperCAmelCase   		=				list(input_ids[doc_id]		)
							# assuming question & title information is at the beginning of the sequence
							UpperCAmelCase   		=				sequence_ids.index(self.sep_token_id							,					2		) + 1  # second sep id
							if sequence_ids[-1] == self.pad_token_id:
										UpperCAmelCase   		=				sequence_ids.index(self.pad_token_id		)
							else:
										UpperCAmelCase   		=				len(_A		)

							UpperCAmelCase   		=				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							,					_A							,					_A							,					_A							,					_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				[]
				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)		)
				UpperCAmelCase   		=				sorted(_A							,					key=lambda _A		: x[1]							,					reverse=_A		)
				UpperCAmelCase   		=				[]
				for (start_index, end_index), score in scores:
							if start_index > end_index:
										raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""		)
							UpperCAmelCase   		=				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 	A_       (a_     ,							a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			READER_PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			READER_PRETRAINED_INIT_CONFIGURATION
	UpperCAmelCase__					=			['''input_ids''', '''attention_mask''']










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A				:    int       =   logging.get_logger(__name__)

__A				:    Tuple       =   {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 	A_       (a_			):
	UpperCAmelCase__					=			'''big_bird'''
	def __init__(    self							,					_A=5_0_3_5_8							,					_A=7_6_8							,					_A=1_2							,					_A=1_2							,					_A=3_0_7_2							,					_A="gelu_new"							,					_A=0.1							,					_A=0.1							,					_A=4_0_9_6							,					_A=2							,					_A=0.02							,					_A=1E-12							,					_A=True							,					_A=0							,					_A=1							,					_A=2							,					_A=6_6							,					_A="block_sparse"							,					_A=True							,					_A=False							,					_A=6_4							,					_A=3							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    pad_token_id=_A							,					bos_token_id=_A							,					eos_token_id=_A							,					sep_token_id=_A							,					**_A							,					)

				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				max_position_embeddings
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_act
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				type_vocab_size
				UpperCAmelCase   		=				layer_norm_eps
				UpperCAmelCase   		=				use_cache

				UpperCAmelCase   		=				rescale_embeddings
				UpperCAmelCase   		=				attention_type
				UpperCAmelCase   		=				use_bias
				UpperCAmelCase   		=				block_size
				UpperCAmelCase   		=				num_random_blocks
				UpperCAmelCase   		=				classifier_dropout

class 	A_       (a_			):
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				if self.task == "multiple-choice":
							UpperCAmelCase   		=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
							UpperCAmelCase   		=				{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				    ]		)



