




"""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 (				_lowercase=None			,_lowercase=None    ):


    """simple docstring"""







    return field(default_factory=lambda: default			,metadata=_lowercase    )

@dataclass
class 				snake_case_			:







   """simple docstring"""




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





def      __snake_case (				_lowercase    ):


    """simple docstring"""







    try:
        int(_lowercase    )
        return True
    except ValueError:
        return False





def      __snake_case (				_lowercase    ):


    """simple docstring"""







    try:
        float(_lowercase    )
        return True
    except ValueError:
        return False

class 				snake_case_			:







   """simple docstring"""




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

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







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

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

           UpperCamelCase              =	(
               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              =	np.asarray(
                       [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,					dtype=lowerCamelCase_       ,					)
               else:
                   UpperCamelCase              =	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))              =	(
                   ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
               )

               UpperCamelCase              =	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.'

       UpperCamelCase              =	title_str[:-4]
       UpperCamelCase              =	'''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 (				):


    """simple docstring"""







    UpperCamelCase              =	HfArgumentParser(_lowercase    )
    UpperCamelCase              =	parser.parse_args_into_dataclasses()[0]
    UpperCamelCase              =	Plot(args=_lowercase    )
    plot.plot()


if __name__ == "__main__":
  main()



"""simple docstring"""






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin






@require_jieba
class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							CpmAntTokenizer
		A    =							False
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					super().setUp()

					_UpperCamelCase					:str					     =	[
					    '''<d>''',
					    '''</d>''',
					    '''<s>''',
					    '''</s>''',
					    '''</_>''',
					    '''<unk>''',
					    '''<pad>''',
					    '''</n>''',
					    '''我''',
					    '''是''',
					    '''C''',
					    '''P''',
					    '''M''',
					    '''A''',
					    '''n''',
					    '''t''',
					]
					_UpperCamelCase					:List[str]					     =	os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''vocab_file''']   )
					with open(self.vocab_file					,				'''w'''					,				encoding='''utf-8'''   ) as vocab_writer:
								vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]   )   )





		@tooslow
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b'''   )
					_UpperCamelCase					:List[str]					     =	'''今天天气真好！'''
					_UpperCamelCase					:Tuple					     =	['''今天''', '''天气''', '''真''', '''好''', '''！''']
					_UpperCamelCase					:Tuple					     =	tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	'''今天天气真好！'''
					_UpperCamelCase					:Optional[int]					     =	[tokenizer.bos_token] + tokens

					_UpperCamelCase					:List[str]					     =	[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	tokenizer.decode(SCREAMING_SNAKE_CASE__   )
					self.assertEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

