






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       List[Any]							       = MvpTokenizer
				_UpperCamelCase					:       Optional[Any]							       = MvpTokenizerFast
				_UpperCamelCase					:       List[Any]							       = True
				_UpperCamelCase					:       Dict							       = filter_roberta_detectors


				def 						__a					(					self      ):
											super().setUp()
											_lowercase							:   Optional[int] 							=   [
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											_lowercase							:   str 							=   dict(zip(_lowerCAmelCase ,						range(len(_lowerCAmelCase      )      )      )      )
											_lowercase							:   Optional[int] 							=   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											_lowercase							:   Optional[Any] 							=   {'unk_token': '<unk>'}

											_lowercase							:   str 							=   os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['vocab_file']      )
											_lowercase							:   str 							=   os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['merges_file']      )
											with open(self.vocab_file ,						'w' ,						encoding='utf-8'      ) as fp:
																		fp.write(json.dumps(_lowerCAmelCase      ) + '\n'      )
											with open(self.merges_file ,						'w' ,						encoding='utf-8'      ) as fp:
																		fp.write('\n'.join(_lowerCAmelCase      )      )


				def 						__a					(					self ,						**_lowerCAmelCase      ):
											kwargs.update(self.special_tokens_map      )
											return self.tokenizer_class.from_pretrained(self.tmpdirname ,						**_lowerCAmelCase      )


				def 						__a					(					self ,						**_lowerCAmelCase      ):
											kwargs.update(self.special_tokens_map      )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,						**_lowerCAmelCase      )


				def 						__a					(					self ,						_lowerCAmelCase      ):
											return "lower newer", "lower newer"


				@cached_property
				def 						__a					(					self      ):
											return MvpTokenizer.from_pretrained('RUCAIBox/mvp'      )


				@cached_property
				def 						__a					(					self      ):
											return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'      )


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   int 							=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											_lowercase							:   Tuple 							=   [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   List[Any] 							=   tokenizer(_lowerCAmelCase ,						max_length=len(_lowerCAmelCase      ) ,						padding=_lowerCAmelCase ,						return_tensors='pt'      )
																		self.assertIsInstance(_lowerCAmelCase ,						_lowerCAmelCase      )

																		self.assertEqual((2, 9) ,						batch.input_ids.shape      )
																		self.assertEqual((2, 9) ,						batch.attention_mask.shape      )
																		_lowercase							:   str 							=   batch.input_ids.tolist()[0]
																		self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )
																		# Test that special tokens are reset


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   str 							=   tokenizer(_lowerCAmelCase ,						padding=_lowerCAmelCase ,						return_tensors='pt'      )
																		# check if input_ids are returned and no labels
																		self.assertIn('input_ids' ,						_lowerCAmelCase      )
																		self.assertIn('attention_mask' ,						_lowerCAmelCase      )
																		self.assertNotIn('labels' ,						_lowerCAmelCase      )
																		self.assertNotIn('decoder_attention_mask' ,						_lowerCAmelCase      )


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   Optional[int] 							=   [
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   List[str] 							=   tokenizer(text_target=_lowerCAmelCase ,						max_length=3_2 ,						padding='max_length' ,						return_tensors='pt'      )
																		self.assertEqual(3_2 ,						targets['input_ids'].shape[1]      )


				@require_torch
				def 						__a					(					self      ):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   Optional[int] 							=   tokenizer(
																		    ['I am a small frog' * 1_0_2_4, 'I am a small frog'] ,						padding=_lowerCAmelCase ,						truncation=_lowerCAmelCase ,						return_tensors='pt'      )
																		self.assertIsInstance(_lowerCAmelCase ,						_lowerCAmelCase      )
																		self.assertEqual(batch.input_ids.shape ,						(2, 1_0_2_4)      )


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   ['A long paragraph for summarization.']
											_lowercase							:   Any 							=   [
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		_lowercase							:   Optional[Any] 							=   tokenizer(_lowerCAmelCase ,						text_target=_lowerCAmelCase ,						return_tensors='pt'      )
																		_lowercase							:   Any 							=   inputs['input_ids']
																		_lowercase							:   Union[str, Any] 							=   inputs['labels']
																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )


				def 						__a					(					self      ):
											pass


				def 						__a					(					self      ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																									_lowercase							:   Optional[int] 							=   self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase ,						**_lowerCAmelCase      )
																									_lowercase							:   Optional[Any] 							=   self.tokenizer_class.from_pretrained(_lowerCAmelCase ,						**_lowerCAmelCase      )
																									_lowercase							:   Optional[Any] 							=   'A, <mask> AllenNLP sentence.'
																									_lowercase							:   Union[str, Any] 							=   tokenizer_r.encode_plus(_lowerCAmelCase ,						add_special_tokens=_lowerCAmelCase ,						return_token_type_ids=_lowerCAmelCase      )
																									_lowercase							:   Optional[Any] 							=   tokenizer_p.encode_plus(_lowerCAmelCase ,						add_special_tokens=_lowerCAmelCase ,						return_token_type_ids=_lowerCAmelCase      )

																									# token_type_ids should put 0 everywhere
																									self.assertEqual(sum(tokens_r['token_type_ids']      ) ,						sum(tokens_p['token_type_ids']      )      )

																									# attention_mask should put 1 everywhere, so sum over length should be 1
																									self.assertEqual(
																									    sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      ) ,						sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      ) ,						)

																									_lowercase							:   Dict 							=   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
																									_lowercase							:   Dict 							=   tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )

																									# Rust correctly handles the space before the mask while python doesnt
																									self.assertSequenceEqual(tokens_p['input_ids'] ,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )
																									self.assertSequenceEqual(tokens_r['input_ids'] ,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )

																									self.assertSequenceEqual(
																									    _lowerCAmelCase ,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
																									self.assertSequenceEqual(
																									    _lowerCAmelCase ,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )





'''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   A_      (       SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None						)			->str:
  return field(default_factory=lambda: default ,	metadata=SCREAMING_SNAKE_CASE_						)





@dataclass
class       _a    :


  """simple docstring"""

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






def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  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       _a    :


  """simple docstring"""

  def __init__(		self      :       str   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       args
    lowercase_     =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

    with open(self.args.csv_file   ,				newline=""""""      ) as csv_file:
      lowercase_     =       csv.DictReader(lowercase_      )
      for row in reader:
        lowercase_     =       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
          lowercase_     =       int(row["""result"""]      )
        elif can_convert_to_float(row["""result"""]      ):
          # value is not None
          lowercase_     =       float(row["""result"""]      )





  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       plt.subplots()
    lowercase_     =       """Time usage""" if self.args.is_time else """Memory usage"""
    lowercase_     =       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()      ):
      lowercase_     =       sorted(set(self.result_dict[model_name]["""bsz"""]      )      )
      lowercase_     =       sorted(set(self.result_dict[model_name]["""seq_len"""]      )      )
      lowercase_     =       self.result_dict[model_name]["""result"""]

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

      lowercase_     =       (
          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:
          lowercase_     =       np.asarray(
              [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,				dtype=lowercase_   ,				)
        else:
          lowercase_     =       np.asarray(
              [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]   ,				dtype=np.floataa   ,				)

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

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

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

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

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

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






def   A_      (       )			->Tuple:
  lowercase_     =       HfArgumentParser(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       parser.parse_args_into_dataclasses()[0]
  lowercase_     =       Plot(args=SCREAMING_SNAKE_CASE_						)
  plot.plot()


if __name__ == "__main__":
       main()

