






"""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


A_		:		Any		     =			logging.get_logger(__name__)

A_		:		Dict		     =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_		:		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'
        ),
    },
}
A_		:		List[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_		:		List[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'
        ),
    },
}

A_		:		str		     =			{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
A_		:		Optional[Any]		     =			{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
A_		:		Any		     =			{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


A_		:		List[Any]		     =			{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A_		:		int		     =			{
    '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     _lowerCAmelCase(	a_ ):





       """simple docstring"""







       a    :		Any									=VOCAB_FILES_NAMES
       a    :		List[Any]									=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       a    :		Any									=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a    :		List[Any]									=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       a    :		Optional[Any]									=DPRContextEncoderTokenizer







class     _lowerCAmelCase(	a_ ):





       """simple docstring"""







       a    :		int									=VOCAB_FILES_NAMES
       a    :		List[Any]									=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       a    :		Union[str, Any]									=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a    :		Tuple									=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       a    :		List[str]									=DPRQuestionEncoderTokenizer







A_		:		List[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_		:		Tuple		     =			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:





       """simple docstring"""







       def __call__( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = False  ,      _lowerCamelCase = False  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):

         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_: List[Any]   				=    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_: Optional[Any]   				=    titles if not isinstance(lowercase_  ,      lowercase_							) else [titles]
         UpperCamelCase_: List[Any]   				=    texts if not isinstance(lowercase_  ,      lowercase_							) else [texts]
         UpperCamelCase_: Optional[Any]   				=    len(lowercase_							)
         UpperCamelCase_: str   				=    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_: Any   				=    super().__call__(lowercase_  ,      lowercase_  ,      padding=lowercase_  ,      truncation=lowercase_							)['input_ids']
         UpperCamelCase_: int   				=    super().__call__(lowercase_  ,      add_special_tokens=lowercase_  ,      padding=lowercase_  ,      truncation=lowercase_							)['input_ids']
         UpperCamelCase_: int   				=    {
             '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_: Optional[Any]   				=    []
           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_: Tuple   				=    attention_mask
         return self.pad(lowercase_  ,      padding=lowercase_  ,      max_length=lowercase_  ,      return_tensors=lowercase_							)





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

           UpperCamelCase_: Any   				=    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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[int]   				=    []
         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_: List[Any]   				=    sorted(lowercase_  ,      key=lambda _lowerCamelCase							: x[1]  ,      reverse=lowercase_							)
         UpperCamelCase_: Union[str, Any]   				=    []
         for (start_index, end_index), score in scores:
           assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
           UpperCamelCase_: Dict   				=    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(a_ )
class     _lowerCAmelCase(	a_							,     a_ ):





       """simple docstring"""







       a    :		Any									=VOCAB_FILES_NAMES
       a    :		Dict									=READER_PRETRAINED_VOCAB_FILES_MAP
       a    :		Any									=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a    :		List[Any]									=READER_PRETRAINED_INIT_CONFIGURATION
       a    :		Tuple									=['''input_ids''', '''attention_mask''']
       a    :		Any									=DPRReaderTokenizer







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


A_		:		Any		     =			'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'

def   snake_case ()	->    Union[str, Any]:
  UpperCamelCase_: Tuple   				=    _ask_options(
      'In which compute environment are you running?'     ,   ['This machine', 'AWS (Amazon SageMaker)']     ,   _convert_compute_environment     ,   )
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    UpperCamelCase_: List[str]   				=    get_sagemaker_input()
  else:
    UpperCamelCase_: List[str]   				=    get_cluster_input()
  return config

def   snake_case (UpperCAmelCase__=None  )	->    Union[str, Any]:
  if subparsers is not None:
    UpperCamelCase_: List[Any]   				=    subparsers.add_parser('config'     ,   description=UpperCAmelCase__  )
  else:
    UpperCamelCase_: List[Any]   				=    argparse.ArgumentParser('Accelerate config command'     ,   description=UpperCAmelCase__  )

  parser.add_argument(
      '--config_file'     ,   default=UpperCAmelCase__     ,   help=(
          'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
          'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
          'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
          'with \'huggingface\'.'
      )     ,   )

  if subparsers is not None:
    parser.set_defaults(func=UpperCAmelCase__  )
  return parser

def   snake_case (UpperCAmelCase__  )	->    List[Any]:
  UpperCamelCase_: Union[str, Any]   				=    get_user_input()
  if args.config_file is not None:
    UpperCamelCase_: Tuple   				=    args.config_file
  else:
    if not os.path.isdir(UpperCAmelCase__  ):
      os.makedirs(UpperCAmelCase__  )
    UpperCamelCase_: Dict   				=    default_yaml_config_file

  if config_file.endswith('.json'  ):
    config.to_json_file(UpperCAmelCase__  )
  else:
    config.to_yaml_file(UpperCAmelCase__  )
  print(F'''accelerate configuration saved at {config_file}'''  )

def   snake_case ()	->    str:
  UpperCamelCase_: Tuple   				=    config_command_parser()
  UpperCamelCase_: int   				=    parser.parse_args()
  config_command(UpperCAmelCase__  )


if __name__ == "__main__":
   main()