





"""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		:				Optional[int]  			=    logging.get_logger(__name__)

a		:				int  			=    {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a		:				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"
        ),
    },
}
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[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"
        ),
    },
}

a		:				Optional[Any]  			=    {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
a		:				Dict  			=    {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
a		:				Optional[int]  			=    {
    "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		:				Any  			=    {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
a		:				str  			=    {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}


class  __UpperCamelCase       (		a__		):
       lowerCamelCase     :      Optional[Any]         =VOCAB_FILES_NAMES
       lowerCamelCase     :      List[Any]         =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase     :      Tuple         =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase     :      Optional[Any]         =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       lowerCamelCase     :      List[str]         =DPRContextEncoderTokenizer


class  __UpperCamelCase       (		a__		):
       lowerCamelCase     :      Optional[int]         =VOCAB_FILES_NAMES
       lowerCamelCase     :      Union[str, Any]         =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase     :      Dict         =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase     :      Union[str, Any]         =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       lowerCamelCase     :      List[Any]         =DPRQuestionEncoderTokenizer
a		:				List[Any]  			=    collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

a		:				List[str]  			=    collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


a		:				List[str]  			=    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  __UpperCamelCase       :






       def __call__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				Dict:
        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:
         a   :	Union[str, Any]			=       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_   ,      )
        a   :	Union[str, Any]			=       titles if not isinstance(UpperCamelCase_   ,      UpperCamelCase_   ) else [titles]
        a   :	List[Any]			=       texts if not isinstance(UpperCamelCase_   ,      UpperCamelCase_   ) else [texts]
        a   :	Union[str, Any]			=       len(UpperCamelCase_   )
        a   :	Dict			=       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."""
        a   :	Dict			=       super().__call__(UpperCamelCase_   ,      UpperCamelCase_   ,      padding=UpperCamelCase_   ,      truncation=UpperCamelCase_   )["input_ids"]
        a   :	str			=       super().__call__(UpperCamelCase_   ,      add_special_tokens=UpperCamelCase_   ,      padding=UpperCamelCase_   ,      truncation=UpperCamelCase_   )["input_ids"]
        a   :	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(UpperCamelCase_   ,      UpperCamelCase_   )
            ]
        }
        if return_attention_mask is not False:
         a   :	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]   )
         a   :	str			=       attention_mask
        return self.pad(UpperCamelCase_   ,      padding=UpperCamelCase_   ,      max_length=UpperCamelCase_   ,      return_tensors=UpperCamelCase_   )






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

         a   :	Optional[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=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   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      )			->				Optional[Any]:
        a   :	List[Any]			=       []
        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)   )
        a   :	List[Any]			=       sorted(UpperCamelCase_   ,      key=lambda lowerCAmelCase__   : x[1]   ,      reverse=UpperCamelCase_   )
        a   :	Optional[int]			=       []
        for (start_index, end_index), score in scores:
         assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
         a   :	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(UpperCamelCase_   ) == top_spans:
          break
        return chosen_span_intervals


@add_end_docstrings(a__		)
class  __UpperCamelCase       (		a__	, a__		):
       lowerCamelCase     :      int         =VOCAB_FILES_NAMES
       lowerCamelCase     :      int         =READER_PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase     :      Dict         =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase     :      Dict         =READER_PRETRAINED_INIT_CONFIGURATION
       lowerCamelCase     :      int         =["input_ids", "attention_mask"]
       lowerCamelCase     :      str         =DPRReaderTokenizer









"""simple docstring"""




# 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.test_utils import execute_subprocess_async






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str=None      )				->Optional[Any]:






	'''simple docstring'''






	if subparsers is not None:
		a   :	Dict			=       subparsers.add_parser("test"      )
	else:
		a   :	Tuple			=       argparse.ArgumentParser("Accelerate test command"      )

	parser.add_argument(
	    "--config_file"					, default=_lowercase					, 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=_lowercase      )
	return parser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->str:






	'''simple docstring'''






	a   :	List[Any]			=       os.path.sep.join(__file__.split(os.path.sep      )[:-2] + ["test_utils", "scripts", "test_script.py"]      )

	if args.config_file is None:
		a   :	int			=       script_name
	else:
		a   :	int			=       F"""--config_file={args.config_file} {script_name}"""

	a   :	Optional[int]			=       ["accelerate-launch"] + test_args.split()
	a   :	Optional[int]			=       execute_subprocess_async(_lowercase					, env=os.environ.copy()      )
	if result.returncode == 0:
		print("Test is a success! You are ready for your distributed training!"      )






def    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






	'''simple docstring'''






	a   :	Any			=       test_command_parser()
	a   :	Union[str, Any]			=       parser.parse_args()
	test_command(_lowercase      )


if __name__ == "__main__":
		main()


