






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   _A       (    SCREAMING_SNAKE_CASE__  :     Optional[Any]=None					,  SCREAMING_SNAKE_CASE__  :     Tuple=None	):
    return field(default_factory=lambda: default					,  metadata=SCREAMING_SNAKE_CASE__	)







@dataclass
class       UpperCAmelCase_						:



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







def   _A       (    SCREAMING_SNAKE_CASE__  :     int	):
    try:
        int(SCREAMING_SNAKE_CASE__	)
        return True
    except ValueError:
        return False


def   _A       (    SCREAMING_SNAKE_CASE__  :     int	):
    try:
        float(SCREAMING_SNAKE_CASE__	)
        return True
    except ValueError:
        return False







class       UpperCAmelCase_						:



      """simple docstring"""
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_			)    ->				Dict:
          UpperCamelCase	:Union[str, Any]     							=       args
          UpperCamelCase	:int     							=       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

          with open(self.args.csv_file   ,    newline=''''''			) as csv_file:
              UpperCamelCase	:Tuple     							=       csv.DictReader(SCREAMING_SNAKE_CASE_			)
              for row in reader:
                  UpperCamelCase	:Dict     							=       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
                      UpperCamelCase	:str     							=       int(row['''result''']			)
                  elif can_convert_to_float(row['''result''']			):
                      # value is not None
                      UpperCamelCase	:Tuple     							=       float(row['''result''']			)




      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          UpperCamelCase							,       UpperCamelCase	:Dict     							=       plt.subplots()
          UpperCamelCase	:str     							=       '''Time usage''' if self.args.is_time else '''Memory usage'''
          UpperCamelCase	:int     							=       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()			):
              UpperCamelCase	:List[str]     							=       sorted(set(self.result_dict[model_name]['''bsz''']			)			)
              UpperCamelCase	:int     							=       sorted(set(self.result_dict[model_name]['''seq_len''']			)			)
              UpperCamelCase	:str     							=       self.result_dict[model_name]['''result''']

              ((UpperCamelCase)							,       (UpperCamelCase))	:Dict     							=       (
                  (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
              )

              UpperCamelCase	:Union[str, Any]     							=       (
                  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:
                      UpperCamelCase	:Optional[Any]     							=       np.asarray(
                          [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,    dtype=SCREAMING_SNAKE_CASE_   ,    )
                  else:
                      UpperCamelCase	:Optional[int]     							=       np.asarray(
                          [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]   ,    dtype=np.floataa   ,    )

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

                  UpperCamelCase	:Union[str, Any]     							=       np.asarray(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)[: len(SCREAMING_SNAKE_CASE_			)]
                  plt.scatter(
                      SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''			)
                  plt.plot(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    '''--'''			)

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

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

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

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







def   _A       (    ):
    UpperCamelCase	:int     							=       HfArgumentParser(SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:List[str]     							=       parser.parse_args_into_dataclasses()[0]
    UpperCamelCase	:List[str]     							=       Plot(args=SCREAMING_SNAKE_CASE__	)
    plot.plot()


if __name__ == "__main__":
 main()













from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[int]									=DPRContextEncoderTokenizer
      UpperCamelCase_    : Any									=DPRContextEncoderTokenizerFast
      UpperCamelCase_    : Optional[int]									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRQuestionEncoderTokenizer
      UpperCamelCase_    : Union[str, Any]									=DPRQuestionEncoderTokenizerFast
      UpperCamelCase_    : Any									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRReaderTokenizer
      UpperCamelCase_    : List[Any]									=DPRReaderTokenizerFast
      UpperCamelCase_    : Union[str, Any]									=True
      @slow
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:Optional[int]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[str]     							=       tokenizer.encode('''text sequence ''' * 4   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[int]     							=       [[101] + text_a + [102] + text_a + [102] + text_a]
          UpperCamelCase	:Tuple     							=       BatchEncoding({'''input_ids''': input_ids}			)

          UpperCamelCase	:Dict     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:List[Any]     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:Tuple     							=       [0]
          UpperCamelCase	:Union[str, Any]     							=       DPRReaderOutput(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          UpperCamelCase							,       UpperCamelCase	:Union[str, Any]     							=       8, 9
          UpperCamelCase	:Any     							=       10
          UpperCamelCase	:Optional[Any]     							=       10
          UpperCamelCase	:Optional[int]     							=       tokenizer.decode_best_spans(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].start_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].end_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].doc_id   ,    0			)




      @slow
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:List[str]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Any     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:str     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Union[str, Any]     							=       tokenizer.encode('''text sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[Any]     							=       [101] + text_a + [102] + text_a + [102] + text_a
          UpperCamelCase	:Any     							=       tokenizer(questions=['''question sequence''']   ,    titles=['''title sequence''']   ,    texts=['''text sequence''']			)
          self.assertIn('''input_ids'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertIn('''attention_mask'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertListEqual(encoded_input['''input_ids'''][0]   ,    SCREAMING_SNAKE_CASE_			)





