'''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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


SCREAMING_SNAKE_CASE_:     List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Union[str, Any]  ={"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE_:     Optional[int]  ={
    """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"""
        ),
    },
}
SCREAMING_SNAKE_CASE_:     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"""
        ),
    },
}
SCREAMING_SNAKE_CASE_:     List[str]  ={
    """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"""
        ),
    },
}

SCREAMING_SNAKE_CASE_:     str  ={
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
SCREAMING_SNAKE_CASE_:     str  ={
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
SCREAMING_SNAKE_CASE_:     List[Any]  ={
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


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




class        __A      ( UpperCAmelCase__	):
    a__       :						List[Any]          =  VOCAB_FILES_NAMES
    a__       :						List[str]          =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    a__       :						int          =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       :						Tuple          =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    a__       :						Any          =  DPRContextEncoderTokenizer




class        __A      ( UpperCAmelCase__	):
    a__       :						str          =  VOCAB_FILES_NAMES
    a__       :						Optional[int]          =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    a__       :						Tuple          =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       :						List[str]          =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    a__       :						Dict          =  DPRQuestionEncoderTokenizer




SCREAMING_SNAKE_CASE_:     Dict  =collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

SCREAMING_SNAKE_CASE_:     Optional[int]  =collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


SCREAMING_SNAKE_CASE_:     List[Any]  =R"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """




@add_start_docstrings(UpperCAmelCase__	)
class        __A      :



    def __call__(self    :    str   ,			__a    :    Any   ,			__a    :    str = None   ,			__a    :    Tuple = None   ,			__a    :    Optional[int] = False   ,			__a    :    Tuple = False   ,			__a    :    Any = None   ,			__a    :    Any = None   ,			__a    :    Tuple = None   ,			**__a    :    Optional[int]   ,			):
         if titles is None and texts is None:
              return super().__call__(
                  __lowercase   ,			padding=__lowercase   ,			truncation=__lowercase   ,			max_length=__lowercase   ,			return_tensors=__lowercase   ,			return_attention_mask=__lowercase   ,			**__lowercase   ,			)
         elif titles is None or texts is None:
              UpperCAmelCase_           =   titles if texts is None else texts
              return super().__call__(
                  __lowercase   ,			__lowercase   ,			padding=__lowercase   ,			truncation=__lowercase   ,			max_length=__lowercase   ,			return_tensors=__lowercase   ,			return_attention_mask=__lowercase   ,			**__lowercase   ,			)
         UpperCAmelCase_           =   titles if not isinstance(__lowercase   ,			__lowercase ) else [titles]
         UpperCAmelCase_           =   texts if not isinstance(__lowercase   ,			__lowercase ) else [texts]
         UpperCAmelCase_           =   len(__lowercase )
         UpperCAmelCase_           =   questions if not isinstance(__lowercase   ,			__lowercase ) else [questions] * n_passages
         assert len(__lowercase ) == len(
             __lowercase ), f"""There should be as many titles than texts but got {len(__lowercase )} titles and {len(__lowercase )} texts."""
         UpperCAmelCase_           =   super().__call__(__lowercase   ,			__lowercase   ,			padding=__lowercase   ,			truncation=__lowercase )['''input_ids''']
         UpperCAmelCase_           =   super().__call__(__lowercase   ,			add_special_tokens=__lowercase   ,			padding=__lowercase   ,			truncation=__lowercase )['''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(__lowercase   ,			__lowercase )
             ]
         }
         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(__lowercase   ,			padding=__lowercase   ,			max_length=__lowercase   ,			return_tensors=__lowercase )



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Dict   ,			__a    :    Optional[int]   ,			__a    :    List[str] = 16   ,			__a    :    List[Any] = 64   ,			__a    :    int = 4   ,			):
         UpperCAmelCase_           =   reader_input['''input_ids''']
         UpperCAmelCase_           =   reader_output[:3]
         UpperCAmelCase_           =   len(__lowercase )
         UpperCAmelCase_           =   sorted(range(__lowercase )   ,			reverse=__lowercase   ,			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(__lowercase )

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






    def 					_lowercase						(self    :    Dict   ,			__a    :    Union[str, Any]   ,			__a    :    Any   ,			__a    :    List[str]   ,			__a    :    Dict   ,			):
         UpperCAmelCase_           =   []
         for start_index, start_score in enumerate(__lowercase ):
              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(__lowercase   ,			key=lambda __a : x[1]   ,			reverse=__lowercase )
         UpperCAmelCase_           =   []
         for (start_index, end_index), score in scores:
              assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
              UpperCAmelCase_           =   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(__lowercase ) == top_spans:
                   break
         return chosen_span_intervals




@add_end_docstrings(UpperCAmelCase__	)
class        __A      ( UpperCAmelCase__		,	UpperCAmelCase__	):
    a__       :						Optional[int]          =  VOCAB_FILES_NAMES
    a__       :						Tuple          =  READER_PRETRAINED_VOCAB_FILES_MAP
    a__       :						Any          =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       :						Optional[int]          =  READER_PRETRAINED_INIT_CONFIGURATION
    a__       :						Union[str, Any]          =  ["""input_ids""", """attention_mask"""]
    a__       :						Optional[Any]          =  DPRReaderTokenizer








from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands



def 	lowerCAmelCase       (						)->       int:
				lowerCAmelCase_       :					int			 = ArgumentParser('''Transformers CLI tool'''       ,      usage='''transformers-cli <command> [<args>]'''       )
				lowerCAmelCase_       :					Dict			 = parser.add_subparsers(help='''transformers-cli command helpers'''       )

				# Register commands
				ConvertCommand.register_subcommand(lowerCAmelCase_       )
				DownloadCommand.register_subcommand(lowerCAmelCase_       )
				EnvironmentCommand.register_subcommand(lowerCAmelCase_       )
				RunCommand.register_subcommand(lowerCAmelCase_       )
				ServeCommand.register_subcommand(lowerCAmelCase_       )
				UserCommands.register_subcommand(lowerCAmelCase_       )
				AddNewModelCommand.register_subcommand(lowerCAmelCase_       )
				AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_       )
				LfsCommands.register_subcommand(lowerCAmelCase_       )
				PTtoTFCommand.register_subcommand(lowerCAmelCase_       )

				# Let's go
				lowerCAmelCase_       :					Union[str, Any]			 = parser.parse_args()

				if not hasattr(lowerCAmelCase_       ,      '''func'''       ):
								parser.print_help()
								exit(1       )

				# Run
				lowerCAmelCase_       :					List[Any]			 = args.func(lowerCAmelCase_       )
				service.run()


if __name__ == "__main__":
						main()