
"""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 import BertTokenizer


_A     :			int  						=			logging.get_logger(__name__)

_A     :			Optional[Any]  						=			{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A     :			Union[str, Any]  						=			{
    '''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     :			Optional[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     :			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     :			Dict  						=			{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
_A     :			List[str]  						=			{
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
_A     :			Optional[Any]  						=			{
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


_A     :			Optional[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     :			Optional[int]  						=			{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}

class 						a__		( __a		):
   __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		):
   __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     :			Any  						=			collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

_A     :			int  						=			collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_A     :			List[str]  						=			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)

    Returns:
        `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 						a__		:
   def __call__(     self      ,       _a      ,       _a = None      ,       _a = None      ,       _a = False      ,       _a = False      ,       _a = None      ,       _a = None      ,       _a = None      ,       **_a      ,       ):
          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:
                 lowercase       :							int									=				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      ,       )
          lowercase       :							int									=				titles if not isinstance(_lowerCamelCase      ,       _lowerCamelCase							) else [titles]
          lowercase       :							str									=				texts if not isinstance(_lowerCamelCase      ,       _lowerCamelCase							) else [texts]
          lowercase       :							int									=				len(_lowerCamelCase							)
          lowercase       :							Union[str, Any]									=				questions if not isinstance(_lowerCamelCase      ,       _lowerCamelCase							) else [questions] * n_passages
          if len(_lowerCamelCase							) != len(_lowerCamelCase							):
                 raise ValueError(
                     f"""There should be as many titles than texts but got {len(_lowerCamelCase							)} titles and {len(_lowerCamelCase							)} texts."""							)
          lowercase       :							Any									=				super().__call__(_lowerCamelCase      ,       _lowerCamelCase      ,       padding=_lowerCamelCase      ,       truncation=_lowerCamelCase							)["input_ids"]
          lowercase       :							Any									=				super().__call__(_lowerCamelCase      ,       add_special_tokens=_lowerCamelCase      ,       padding=_lowerCamelCase      ,       truncation=_lowerCamelCase							)["input_ids"]
          lowercase       :							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(_lowerCamelCase      ,       _lowerCamelCase							)
              ]
          }
          if return_attention_mask is not False:
                 lowercase       :							int									=				[]
                 for input_ids in encoded_inputs["input_ids"]:
                        attention_mask.append([int(input_id != self.pad_token_id							) for input_id in input_ids]							)
                 lowercase       :							Union[str, Any]									=				attention_mask
          return self.pad(_lowerCamelCase      ,       padding=_lowerCamelCase      ,       max_length=_lowerCamelCase      ,       return_tensors=_lowerCamelCase							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a = 16      ,       _a = 64      ,       _a = 4      ,       ):
          lowercase       :							int									=				reader_input["input_ids"]
          lowercase						,       lowercase						,       lowercase       :							int									=				reader_output[:3]
          lowercase       :							Union[str, Any]									=				len(_lowerCamelCase							)
          lowercase       :							Dict									=				sorted(range(_lowerCamelCase							)      ,       reverse=_lowerCamelCase      ,       key=relevance_logits.__getitem__							)
          lowercase       :							Optional[Any]									=				[]
          for doc_id in sorted_docs:
                 lowercase       :							Any									=				list(input_ids[doc_id]							)
                 # assuming question & title information is at the beginning of the sequence
                 lowercase       :							List[str]									=				sequence_ids.index(self.sep_token_id      ,       2							) + 1  # second sep id
                 if sequence_ids[-1] == self.pad_token_id:
                        lowercase       :							str									=				sequence_ids.index(self.pad_token_id							)
                 else:
                        lowercase       :							Any									=				len(_lowerCamelCase							)

                 lowercase       :							List[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=_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      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a      ,       ):
          lowercase       :							str									=				[]
          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)							)
          lowercase       :							int									=				sorted(_lowerCamelCase      ,       key=lambda _a							: x[1]      ,       reverse=_lowerCamelCase							)
          lowercase       :							Optional[int]									=				[]
          for (start_index, end_index), score in scores:
                 if start_index > end_index:
                        raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""							)
                 lowercase       :							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(_lowerCamelCase							) == top_spans:
                        break
          return chosen_span_intervals


@add_end_docstrings(__a		)
class 						a__		( __a,     __a		):
   __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']



'''simple docstring'''

import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser



def snake_case_      (					__snake_case  :							Tuple=None	,   __snake_case  :							int=None)			->					int:
   return field(default_factory=lambda: default	,   metadata=__snake_case)


@dataclass
class     __UpperCAmelCase       :
  __A :				str											= field(
      metadata={'help': 'The csv file to plot.'}						,       )
  __A :				bool											= field(
      default=__a						,       metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}						,       )
  __A :				bool											= field(
      default=__a						,       metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}						,       )
  __A :				bool											= field(
      default=__a						,       metadata={'help': 'Disable logarithmic scale when plotting'}						,       )
  __A :				bool											= field(
      default=__a						,       metadata={
          'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
      }						,       )
  __A :				Optional[str]											= field(
      default=__a						,       metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}						,       )
  __A :				Optional[List[str]]											= list_field(
      default=__a						,       metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}		)






def snake_case_      (					__snake_case  :							Optional[Any])			->					Dict:
   try:
      int(__snake_case)
      return True
   except ValueError:
      return False



def snake_case_      (					__snake_case  :							Dict)			->					int:
   try:
      float(__snake_case)
      return True
   except ValueError:
      return False


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      args
     lowerCAmelCase_			=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

     with open(self.args.csv_file    ,     newline='''''' ) as csv_file:
        lowerCAmelCase_			=      csv.DictReader(_lowerCamelCase )
        for row in reader:
           lowerCAmelCase_			=      row['''model''']
           self.result_dict[model_name]["bsz"].append(int(row['''batch_size'''] ) )
           self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length'''] ) )
           if can_convert_to_int(row['''result'''] ):
              # value is not None
              lowerCAmelCase_			=      int(row['''result'''] )
           elif can_convert_to_float(row['''result'''] ):
              # value is not None
              lowerCAmelCase_			=      float(row['''result'''] )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      plt.subplots()
     lowerCAmelCase_			=      '''Time usage''' if self.args.is_time else '''Memory usage'''
     lowerCAmelCase_			=      title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

     if not self.args.no_log_scale:
        # set logarithm scales
        ax.set_xscale('''log''' )
        ax.set_yscale('''log''' )

     for axis in [ax.xaxis, ax.yaxis]:
        axis.set_major_formatter(ScalarFormatter() )

     for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
        lowerCAmelCase_			=      sorted(set(self.result_dict[model_name]['''bsz'''] ) )
        lowerCAmelCase_			=      sorted(set(self.result_dict[model_name]['''seq_len'''] ) )
        lowerCAmelCase_			=      self.result_dict[model_name]['''result''']

        ((lowerCAmelCase_)	,(lowerCAmelCase_))			=      (
            (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
        )

        lowerCAmelCase_			=      (
            model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
        )

        for inner_loop_value in inner_loop_array:
           if self.args.plot_along_batch:
              lowerCAmelCase_			=      np.asarray(
                  [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,     dtype=_lowerCamelCase    ,     )
           else:
              lowerCAmelCase_			=      np.asarray(
                  [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]    ,     dtype=np.floataa    ,     )

           ((lowerCAmelCase_)	,(lowerCAmelCase_))			=      (
               ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
           )

           lowerCAmelCase_			=      np.asarray(_lowerCamelCase    ,     _lowerCamelCase )[: len(_lowerCamelCase )]
           plt.scatter(
               _lowerCamelCase    ,     _lowerCamelCase    ,     label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}''' )
           plt.plot(_lowerCamelCase    ,     _lowerCamelCase    ,     '''--''' )

        title_str += F''' {label_model_name} vs.'''

     lowerCAmelCase_			=      title_str[:-4]
     lowerCAmelCase_			=      '''Time in s''' if self.args.is_time else '''Memory in MB'''

     # plot
     plt.title(_lowerCamelCase )
     plt.xlabel(_lowerCamelCase )
     plt.ylabel(_lowerCamelCase )
     plt.legend()

     if self.args.figure_png_file is not None:
        plt.savefig(self.args.figure_png_file )
     else:
        plt.show()






def snake_case_      (					)			->					Tuple:
   lowerCAmelCase_			=      HfArgumentParser(__snake_case)
   lowerCAmelCase_			=      parser.parse_args_into_dataclasses()[0]
   lowerCAmelCase_			=      Plot(args=__snake_case)
   plot.plot()


if __name__ == "__main__":
 main()





