




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


lowerCamelCase    :		str			   =       logging.get_logger(__name__)

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

lowerCamelCase    :		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"
        ),
    },
}
lowerCamelCase    :		Optional[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"
        ),
    },
}
lowerCamelCase    :		Any			   =       {
    "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"
        ),
    },
}

lowerCamelCase    :		Tuple			   =       {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
lowerCamelCase    :		Optional[int]			   =       {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
lowerCamelCase    :		List[Any]			   =       {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 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   A(						UpperCamelCase       ):



 '''simple docstring'''


 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

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

lowerCamelCase    :		str			   =       collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


lowerCamelCase    :		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    [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(UpperCamelCase       )
class   A:



 '''simple docstring'''


 def __call__(    self :  List[str]						,							A_ :  List[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_ :  str						,							)       ->     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:
             lowerCamelCase_							=   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_						,							)
       lowerCamelCase_							=   titles if not isinstance(A_						,							A_  ) else [titles]
       lowerCamelCase_							=   texts if not isinstance(A_						,							A_  ) else [texts]
       lowerCamelCase_							=   len(A_  )
       lowerCamelCase_							=   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."""
       lowerCamelCase_							=   super().__call__(A_						,							A_						,							padding=A_						,							truncation=A_  )['input_ids']
       lowerCamelCase_							=   super().__call__(A_						,							add_special_tokens=A_						,							padding=A_						,							truncation=A_  )['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(A_						,							A_  )
           ]
       }
       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(A_						,							padding=A_						,							max_length=A_						,							return_tensors=A_  )

 def     a__			(    self :  Optional[int]						,							A_ :  BatchEncoding						,							A_ :  DPRReaderOutput						,							A_ :  int = 16						,							A_ :  int = 64						,							A_ :  int = 4						,							)       ->     List[DPRSpanPrediction]:

       """simple docstring"""






       lowerCamelCase_							=   reader_input['input_ids']
       lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   reader_output[:3]
       lowerCamelCase_							=   len(A_  )
       lowerCamelCase_							=   sorted(range(A_  )						,							reverse=A_						,							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(A_  )

             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=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     a__			(    self :  List[str]						,							A_ :  List[int]						,							A_ :  List[int]						,							A_ :  int						,							A_ :  int						,							)       ->     List[DPRSpanPrediction]:

       """simple docstring"""






       lowerCamelCase_							=   []
       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)  )
       lowerCamelCase_							=   sorted(A_						,							key=lambda A_  : x[1]						,							reverse=A_  )
       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(A_  ) == top_spans:
                   break
       return chosen_span_intervals







@add_end_docstrings(UpperCamelCase       )
class   A(						UpperCamelCase							,				UpperCamelCase       ):



 '''simple docstring'''


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


lowerCamelCase    :		Optional[Any]			   =       logging.get_logger(__name__)

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







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''transfo-xl'''
 UpperCamelCase						       =						['''mems''']
 UpperCamelCase						       =						{
     '''n_token''': '''vocab_size''',
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }

 def __init__(    self :  Any						,							A_ :  Optional[Any]=267735						,							A_ :  Optional[Any]=[20000, 40000, 200000]						,							A_ :  Union[str, Any]=1024						,							A_ :  Optional[Any]=1024						,							A_ :  Optional[int]=16						,							A_ :  Any=64						,							A_ :  List[Any]=4096						,							A_ :  str=4						,							A_ :  int=False						,							A_ :  List[Any]=18						,							A_ :  Optional[int]=1600						,							A_ :  Union[str, Any]=1000						,							A_ :  Optional[Any]=True						,							A_ :  Optional[int]=True						,							A_ :  List[str]=0						,							A_ :  int=-1						,							A_ :  List[Any]=True						,							A_ :  List[Any]=0.1						,							A_ :  str=0.0						,							A_ :  Dict=True						,							A_ :  Dict="normal"						,							A_ :  Dict=0.01						,							A_ :  Optional[Any]=0.01						,							A_ :  Any=0.02						,							A_ :  int=1E-5						,							A_ :  List[str]=0						,							**A_ :  Optional[Any]						,							)       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   []
       self.cutoffs.extend(A_  )
       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=A_						,							**A_  )

 @property
 def     a__			(    self :  Tuple  )       ->     Any:

       """simple docstring"""






       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 :  Dict						,							A_ :  Optional[int]  )       ->     List[Any]:

       """simple docstring"""






       raise NotImplementedError(
           f"""The model {self.model_type} is one of the few models that has no sequence length limit."""  )



