



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


_lowercase						  =		logging.get_logger(__name__)

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

_lowercase						  =		{
    "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"
        ),
    },
}
_lowercase						  =		{
    "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"
        ),
    },
}
_lowercase						  =		{
    "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"
        ),
    },
}

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


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



class     _UpperCAmelCase			(       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
     UpperCamelCase__		    =				DPRContextEncoderTokenizer







class     _UpperCAmelCase			(       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
     UpperCamelCase__		    =				DPRQuestionEncoderTokenizer







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

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


_lowercase						  =		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(A__     )
class     _UpperCAmelCase			:




     def __call__(				self ,						a__ ,						a__ = None ,						a__ = None ,						a__ = False ,						a__ = False ,						a__ = None ,						a__ = None ,						a__ = None ,						**a__ ,						):
         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:
             A__		 =					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__ ,						)
         A__		 =					titles if not isinstance(a__ ,						a__) else [titles]
         A__		 =					texts if not isinstance(a__ ,						a__) else [texts]
         A__		 =					len(a__)
         A__		 =					questions if not isinstance(a__ ,						a__) else [questions] * n_passages
         assert len(a__) == len(
             a__), F"There should be as many titles than texts but got {len(a__)} titles and {len(a__)} texts."
         A__		 =					super().__call__(a__ ,						a__ ,						padding=a__ ,						truncation=a__)['''input_ids''']
         A__		 =					super().__call__(a__ ,						add_special_tokens=a__ ,						padding=a__ ,						truncation=a__)['''input_ids''']
         A__		 =					{
             '''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:
             A__		 =					[]
             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__		 =					attention_mask
         return self.pad(a__ ,						padding=a__ ,						max_length=a__ ,						return_tensors=a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = 1_6 ,						a__ = 6_4 ,						a__ = 4 ,						):
         A__		 =					reader_input['''input_ids''']
         A__ ,	A__ ,	A__		 =					reader_output[:3]
         A__		 =					len(a__)
         A__		 =					sorted(range(a__) ,						reverse=a__ ,						key=relevance_logits.__getitem__)
         A__		 =					[]
         for doc_id in sorted_docs:
             A__		 =					list(input_ids[doc_id])
             # assuming question & title information is at the beginning of the sequence
             A__		 =					sequence_ids.index(self.sep_token_id ,						2) + 1  # second sep id
             if sequence_ids[-1] == self.pad_token_id:
                 A__		 =					sequence_ids.index(self.pad_token_id)
             else:
                 A__		 =					len(a__)

             A__		 =					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 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					[]
         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))
         A__		 =					sorted(a__ ,						key=lambda a__: x[1] ,						reverse=a__)
         A__		 =					[]
         for (start_index, end_index), score in scores:
             assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
             A__		 =					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(a__) == top_spans:
                 break
         return chosen_span_intervals







@add_end_docstrings(A__     )
class     _UpperCAmelCase			(       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''']
     UpperCamelCase__		    =				DPRReaderTokenizer





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_lowercase						  =		{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''albert'''




     def __init__(				self ,						a__=3_0_0_0_0 ,						a__=1_2_8 ,						a__=4_0_9_6 ,						a__=1_2 ,						a__=1 ,						a__=6_4 ,						a__=1_6_3_8_4 ,						a__=1 ,						a__="gelu_new" ,						a__=0 ,						a__=0 ,						a__=5_1_2 ,						a__=2 ,						a__=0.0_2 ,						a__=1e-12 ,						a__=0.1 ,						a__="absolute" ,						a__=0 ,						a__=2 ,						a__=3 ,						**a__ ,						):
         super().__init__(pad_token_id=a__ ,						bos_token_id=a__ ,						eos_token_id=a__ ,						**a__)

         A__		 =					vocab_size
         A__		 =					embedding_size
         A__		 =					hidden_size
         A__		 =					num_hidden_layers
         A__		 =					num_hidden_groups
         A__		 =					num_attention_heads
         A__		 =					inner_group_num
         A__		 =					hidden_act
         A__		 =					intermediate_size
         A__		 =					hidden_dropout_prob
         A__		 =					attention_probs_dropout_prob
         A__		 =					max_position_embeddings
         A__		 =					type_vocab_size
         A__		 =					initializer_range
         A__		 =					layer_norm_eps
         A__		 =					classifier_dropout_prob
         A__		 =					position_embedding_type







class     _UpperCAmelCase			(       A__     ):




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