
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


_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": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
_A     =  {
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
_A     =  {
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


_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 			_lowerCAmelCase      (			__a ):
				_lowercase    	=VOCAB_FILES_NAMES
				_lowercase    	=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				_lowercase    	=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase    	=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				_lowercase    	=DPRContextEncoderTokenizer





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	=VOCAB_FILES_NAMES
				_lowercase    	=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				_lowercase    	=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase    	=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				_lowercase    	=DPRQuestionEncoderTokenizer


_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    [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 			_lowerCAmelCase      :




				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = False       ,       _UpperCamelCase = False       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       **_UpperCamelCase       ,       )       ->   BatchEncoding:
							if titles is None and texts is None:
										return super().__call__(
										    _UpperCamelCase       ,       padding=_UpperCamelCase       ,       truncation=_UpperCamelCase       ,       max_length=_UpperCamelCase       ,       return_tensors=_UpperCamelCase       ,       return_attention_mask=_UpperCamelCase       ,       **_UpperCamelCase       ,       )
							elif titles is None or texts is None:
										lowerCAmelCase_      			=			titles if texts is None else texts
										return super().__call__(
										    _UpperCamelCase       ,       _UpperCamelCase       ,       padding=_UpperCamelCase       ,       truncation=_UpperCamelCase       ,       max_length=_UpperCamelCase       ,       return_tensors=_UpperCamelCase       ,       return_attention_mask=_UpperCamelCase       ,       **_UpperCamelCase       ,       )
							lowerCAmelCase_      			=			titles if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) else [titles]
							lowerCAmelCase_      			=			texts if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) else [texts]
							lowerCAmelCase_      			=			len(_UpperCamelCase  )
							lowerCAmelCase_      			=			questions if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) else [questions] * n_passages
							assert len(_UpperCamelCase  ) == len(
							    _UpperCamelCase  ), f"""There should be as many titles than texts but got {len(_UpperCamelCase  )} titles and {len(_UpperCamelCase  )} texts."""
							lowerCAmelCase_      			=			super().__call__(_UpperCamelCase       ,       _UpperCamelCase       ,       padding=_UpperCamelCase       ,       truncation=_UpperCamelCase  )["input_ids"]
							lowerCAmelCase_      			=			super().__call__(_UpperCamelCase       ,       add_special_tokens=_UpperCamelCase       ,       padding=_UpperCamelCase       ,       truncation=_UpperCamelCase  )["input_ids"]
							lowerCAmelCase_      			=			{
							    "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(_UpperCamelCase       ,       _UpperCamelCase  )
							    ]
							}
							if return_attention_mask is not False:
										lowerCAmelCase_      			=			[]
										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_      			=			attention_mask
							return self.pad(_UpperCamelCase       ,       padding=_UpperCamelCase       ,       max_length=_UpperCamelCase       ,       return_tensors=_UpperCamelCase  )




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

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




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       )       ->   List[DPRSpanPrediction]:
							lowerCAmelCase_      			=			[]
							for start_index, start_score in enumerate(_UpperCamelCase  ):
										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_      			=			sorted(_UpperCamelCase       ,       key=lambda _UpperCamelCase  : x[1]       ,       reverse=_UpperCamelCase  )
							lowerCAmelCase_      			=			[]
							for (start_index, end_index), score in scores:
										assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
										lowerCAmelCase_      			=			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(_UpperCamelCase  ) == top_spans:
													break
							return chosen_span_intervals





@add_end_docstrings(__a )
class 			_lowerCAmelCase      (			__a      ,   __a ):
				_lowercase    	=VOCAB_FILES_NAMES
				_lowercase    	=READER_PRETRAINED_VOCAB_FILES_MAP
				_lowercase    	=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase    	=READER_PRETRAINED_INIT_CONFIGURATION
				_lowercase    	=['''input_ids''', '''attention_mask''']
				_lowercase    	=DPRReaderTokenizer





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A     =  logging.get_logger(__name__)

_A     =  {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''transfo-xl'''
				_lowercase    	=['''mems''']
				_lowercase    	={
				    '''n_token''': '''vocab_size''',
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(      self       ,       _UpperCamelCase=267_735       ,       _UpperCamelCase=[20_000, 40_000, 200_000]       ,       _UpperCamelCase=1_024       ,       _UpperCamelCase=1_024       ,       _UpperCamelCase=16       ,       _UpperCamelCase=64       ,       _UpperCamelCase=4_096       ,       _UpperCamelCase=4       ,       _UpperCamelCase=False       ,       _UpperCamelCase=18       ,       _UpperCamelCase=1_600       ,       _UpperCamelCase=1_000       ,       _UpperCamelCase=True       ,       _UpperCamelCase=True       ,       _UpperCamelCase=0       ,       _UpperCamelCase=-1       ,       _UpperCamelCase=True       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=0.0       ,       _UpperCamelCase=True       ,       _UpperCamelCase="normal"       ,       _UpperCamelCase=0.01       ,       _UpperCamelCase=0.01       ,       _UpperCamelCase=0.02       ,       _UpperCamelCase=1e-5       ,       _UpperCamelCase=0       ,       **_UpperCamelCase       ,       )       ->   Dict:
							lowerCAmelCase_      			=			vocab_size
							lowerCAmelCase_      			=			[]
							self.cutoffs.extend(_UpperCamelCase  )
							if proj_share_all_but_first:
										lowerCAmelCase_      			=			[False] + [True] * len(self.cutoffs  )
							else:
										lowerCAmelCase_      			=			[False] + [False] * len(self.cutoffs  )
							lowerCAmelCase_      			=			d_model
							lowerCAmelCase_      			=			d_embed
							lowerCAmelCase_      			=			d_head
							lowerCAmelCase_      			=			d_inner
							lowerCAmelCase_      			=			div_val
							lowerCAmelCase_      			=			pre_lnorm
							lowerCAmelCase_      			=			n_layer
							lowerCAmelCase_      			=			n_head
							lowerCAmelCase_      			=			mem_len
							lowerCAmelCase_      			=			same_length
							lowerCAmelCase_      			=			attn_type
							lowerCAmelCase_      			=			clamp_len
							lowerCAmelCase_      			=			sample_softmax
							lowerCAmelCase_      			=			adaptive
							lowerCAmelCase_      			=			dropout
							lowerCAmelCase_      			=			dropatt
							lowerCAmelCase_      			=			untie_r
							lowerCAmelCase_      			=			init
							lowerCAmelCase_      			=			init_range
							lowerCAmelCase_      			=			proj_init_std
							lowerCAmelCase_      			=			init_std
							lowerCAmelCase_      			=			layer_norm_epsilon
							super().__init__(eos_token_id=_UpperCamelCase       ,       **_UpperCamelCase  )




				@property
				def   __a		(      self  )       ->   List[Any]:
							# Message copied from Transformer-XL documentation
							logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""  )
							return -1




				@max_position_embeddings.setter
				def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
							# Message copied from Transformer-XL documentation
							raise NotImplementedError(
							    f"""The model {self.model_type} is one of the few models that has no sequence length limit."""  )



