




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


__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        _SCREAMING_SNAKE_CASE						(     a__   ):







 '''simple docstring'''
 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        _SCREAMING_SNAKE_CASE						(     a__   ):







 '''simple docstring'''
 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'''
    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(a__   )
class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __call__(self    :       List[str]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       List[Any] = None	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       Any = False	,			UpperCAmelCase_    :       Any = False	,			UpperCAmelCase_    :       Any = None	,			UpperCAmelCase_    :       Tuple = None	,			UpperCAmelCase_    :       Any = None	,			**UpperCAmelCase_    :       List[str]	,			)	->BatchEncoding:



   '''simple docstring'''



   if titles is None and texts is None:
     return super().__call__(
         _lowerCamelCase	,			padding=_lowerCamelCase	,			truncation=_lowerCamelCase	,			max_length=_lowerCamelCase	,			return_tensors=_lowerCamelCase	,			return_attention_mask=_lowerCamelCase	,			**_lowerCamelCase	,			)
   elif titles is None or texts is None:
     lowerCamelCase__:				str        =titles if texts is None else texts
     return super().__call__(
         _lowerCamelCase	,			_lowerCamelCase	,			padding=_lowerCamelCase	,			truncation=_lowerCamelCase	,			max_length=_lowerCamelCase	,			return_tensors=_lowerCamelCase	,			return_attention_mask=_lowerCamelCase	,			**_lowerCamelCase	,			)
   lowerCamelCase__:				Tuple        =titles if not isinstance(_lowerCamelCase	,			_lowerCamelCase) else [titles]
   lowerCamelCase__:				List[str]        =texts if not isinstance(_lowerCamelCase	,			_lowerCamelCase) else [texts]
   lowerCamelCase__:				List[str]        =len(_lowerCamelCase)
   lowerCamelCase__:				Dict        =questions if not isinstance(_lowerCamelCase	,			_lowerCamelCase) else [questions] * n_passages
   assert len(_lowerCamelCase) == len(
       _lowerCamelCase), F"""There should be as many titles than texts but got {len(_lowerCamelCase)} titles and {len(_lowerCamelCase)} texts."""
   lowerCamelCase__:				List[str]        =super().__call__(_lowerCamelCase	,			_lowerCamelCase	,			padding=_lowerCamelCase	,			truncation=_lowerCamelCase)['''input_ids''']
   lowerCamelCase__:				List[str]        =super().__call__(_lowerCamelCase	,			add_special_tokens=_lowerCamelCase	,			padding=_lowerCamelCase	,			truncation=_lowerCamelCase)['''input_ids''']
   lowerCamelCase__:				List[str]        ={
       '''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(_lowerCamelCase	,			_lowerCamelCase)
       ]
   }
   if return_attention_mask is not False:
     lowerCamelCase__:				List[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])
     lowerCamelCase__:				List[Any]        =attention_mask
   return self.pad(_lowerCamelCase	,			padding=_lowerCamelCase	,			max_length=_lowerCamelCase	,			return_tensors=_lowerCamelCase)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       Optional[Any] = 16	,			UpperCAmelCase_    :       Optional[int] = 64	,			UpperCAmelCase_    :       Any = 4	,			)	->List[DPRSpanPrediction]:



   '''simple docstring'''



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

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







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       Union[str, Any]	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       List[str]	,			)	->List[DPRSpanPrediction]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =[]
   for start_index, start_score in enumerate(_lowerCamelCase):
     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__:				Optional[int]        =sorted(_lowerCamelCase	,			key=lambda UpperCAmelCase_: x[1]	,			reverse=_lowerCamelCase)
   lowerCamelCase__:				Optional[Any]        =[]
   for (start_index, end_index), score in scores:
     assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
     lowerCamelCase__:				Tuple        =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(_lowerCamelCase) == top_spans:
       break
   return chosen_span_intervals



@add_end_docstrings(a__   )
class        _SCREAMING_SNAKE_CASE						(     a__   ,							a__   ):







 '''simple docstring'''
 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








import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment



@require_huggingface_suite
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =inspect.getfile(accelerate.test_utils)
   lowerCamelCase__:				str        =os.path.sep.join(
       mod_file.split(os.path.sep)[:-1] + ["scripts", "external_deps", "test_metrics.py"])

   from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

   lowerCamelCase__:				str        =test_metrics

 @require_cpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Any:



   '''simple docstring'''



   debug_launcher(self.test_metrics.main	,			num_processes=1)

 @require_cpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->int:



   '''simple docstring'''



   debug_launcher(self.test_metrics.main)

 @require_single_gpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->int:



   '''simple docstring'''



   self.test_metrics.main()







 @require_multi_gpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Dict:



   '''simple docstring'''



   print(F"""Found {torch.cuda.device_count()} devices.""")
   lowerCamelCase__:				Optional[Any]        =["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
   with patch_environment(omp_num_threads=1):
     execute_subprocess_async(UpperCAmelCase_	,			env=os.environ.copy())

