




# Copyright 2022 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
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available







def lowerCamelCase__  (     _lowercase=None ):

     '''simple docstring'''


     if subparsers is not None:
          UpperCAmelCase_     :				Optional[Any]  =     subparsers.add_parser('''env''' )
     else:
          UpperCAmelCase_     :				List[str]  =     argparse.ArgumentParser('''Accelerate env command''' )

     parser.add_argument(
         '''--config_file'''					,		default=_lowercase					,		help='''The config file to use for the default values in the launching script.''' )

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







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     torch.__version__
     UpperCAmelCase_     :				Optional[Any]  =     torch.cuda.is_available()
     UpperCAmelCase_     :				int  =     is_xpu_available()
     UpperCAmelCase_     :				Union[str, Any]  =     is_npu_available()

     UpperCAmelCase_     :				Optional[Any]  =     '''Not found'''
     # Get the default from the config file.
     if args.config_file is not None or os.path.isfile(_lowercase ):
          UpperCAmelCase_     :				List[Any]  =     load_config_from_file(args.config_file ).to_dict()

     UpperCAmelCase_     :				Tuple  =     {
         '''`Accelerate` version''': version,
         '''Platform''': platform.platform(),
         '''Python version''': platform.python_version(),
         '''Numpy version''': np.__version__,
         '''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
         '''PyTorch XPU available''': str(_lowercase ),
         '''PyTorch NPU available''': str(_lowercase ),
         '''System RAM''': f'''{psutil.virtual_memory().total / 1024 ** 3:.2f} GB''',
     }
     if pt_cuda_available:
          UpperCAmelCase_     :				int  =     torch.cuda.get_device_name()

     print('''\nCopy-and-paste the text below in your GitHub issue\n''' )
     print('''\n'''.join([f'''- {prop}: {val}''' for prop, val in info.items()] ) )

     print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:''' )
     UpperCAmelCase_     :				Optional[int]  =     (
         '''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
         if isinstance(_lowercase					,		_lowercase )
         else f'''\t{accelerate_config}'''
     )
     print(_lowercase )

     UpperCAmelCase_     :				int  =     accelerate_config

     return info







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[str]  =     env_command_parser()
     UpperCAmelCase_     :				Tuple  =     parser.parse_args()
     env_command(_lowercase )
     return 0


if __name__ == "__main__":
     raise SystemExit(main())





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 import BertTokenizer


__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     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


__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'\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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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     __a:






    """simple docstring"""





    def __call__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						BatchEncoding:
         if titles is None and texts is None:
              return super().__call__(
                  _SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE  ,return_attention_mask=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)
         elif titles is None or texts is None:
              UpperCAmelCase_     :				List[str]  =     titles if texts is None else texts
              return super().__call__(
                  _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE  ,return_attention_mask=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				List[Any]  =     titles if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) else [titles]
         UpperCAmelCase_     :				List[str]  =     texts if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) else [texts]
         UpperCAmelCase_     :				Any  =     len(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[Any]  =     questions if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) else [questions] * n_passages
         if len(_SCREAMING_SNAKE_CASE    ) != len(_SCREAMING_SNAKE_CASE    ):
              raise ValueError(
                  f'''There should be as many titles than texts but got {len(_SCREAMING_SNAKE_CASE    )} titles and {len(_SCREAMING_SNAKE_CASE    )} texts.'''    )
         UpperCAmelCase_     :				Tuple  =     super().__call__(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE    )['''input_ids''']
         UpperCAmelCase_     :				int  =     super().__call__(_SCREAMING_SNAKE_CASE  ,add_special_tokens=_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE    )['''input_ids''']
         UpperCAmelCase_     :				Optional[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(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
             ]
         }
         if return_attention_mask is not False:
              UpperCAmelCase_     :				List[str]  =     []
              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_     :				Dict  =     attention_mask
         return self.pad(_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,max_length=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE    )





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

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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,)					->						List[DPRSpanPrediction]:
         UpperCAmelCase_     :				Tuple  =     []
         for start_index, start_score in enumerate(_SCREAMING_SNAKE_CASE    ):
              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_     :				int  =     sorted(_SCREAMING_SNAKE_CASE  ,key=lambda _SCREAMING_SNAKE_CASE    : x[1]  ,reverse=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Union[str, Any]  =     []
         for (start_index, end_index), score in scores:
              if start_index > end_index:
                   raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''    )
              UpperCAmelCase_     :				str  =     end_index - start_index + 1
              if length > max_answer_length:
                   raise ValueError(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(_SCREAMING_SNAKE_CASE    ) == top_spans:
                   break
         return chosen_span_intervals


@add_end_docstrings(_a  )
class     __a(	_a							,		_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = READER_PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = READER_PRETRAINED_INIT_CONFIGURATION
    lowerCAmelCase         = ['''input_ids''', '''attention_mask''']