





"""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      (			a_						:Optional[int]=None   ,       a_						:str=None)		->		Optional[Any]:
				return field(default_factory=lambda: default   ,       metadata=a_)




@dataclass
class 				__lowercase			:







					'''simple docstring'''




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






def     __A      (			a_						:Dict)		->		Optional[int]:
				try:
								int(a_)
								return True
				except ValueError:
								return False

def     __A      (			a_						:int)		->		List[str]:
				try:
								float(a_)
								return True
				except ValueError:
								return False




class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase     ):
									__a :						Union[str, Any]			       =  args
									__a :						Optional[int]			       =  defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

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







					def  _lowerCamelCase		(						self     ):
									__a       ,						__a :						Tuple			       =  plt.subplots()
									__a :						int			       =  '''Time usage''' if self.args.is_time else '''Memory usage'''
									__a :						Optional[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()     ):
													__a :						Dict			       =  sorted(set(self.result_dict[model_name]['''bsz''']     )     )
													__a :						Tuple			       =  sorted(set(self.result_dict[model_name]['''seq_len''']     )     )
													__a :						Tuple			       =  self.result_dict[model_name]['''result''']

													((__a)       ,						(__a)) :						Optional[Any]			       =  (
													    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
													)

													__a :						Optional[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:
																					__a :						List[Any]			       =  np.asarray(
																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,			dtype=_UpperCAmelCase	,			)
																	else:
																					__a :						Optional[Any]			       =  np.asarray(
																					    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]	,			dtype=np.floataa	,			)

																	((__a)       ,						(__a)) :						List[str]			       =  (
																	    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																	)

																	__a :						Optional[Any]			       =  np.asarray(_UpperCAmelCase	,			_UpperCAmelCase     )[: len(_UpperCAmelCase     )]
																	plt.scatter(
																	    _UpperCAmelCase	,			_UpperCAmelCase	,			label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""     )
																	plt.plot(_UpperCAmelCase	,			_UpperCAmelCase	,			'''--'''     )

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

									__a :						Optional[int]			       =  title_str[:-4]
									__a :						Optional[Any]			       =  '''Time in s''' if self.args.is_time else '''Memory in MB'''

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

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






def     __A      (			)		->		List[Any]:
				__a :						List[str]			       =  HfArgumentParser(a_)
				__a :						str			       =  parser.parse_args_into_dataclasses()[0]
				__a :						Tuple			       =  Plot(args=a_)
				plot.plot()


if __name__ == "__main__":
		main()






"""simple docstring"""






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  OpenAIGPTTokenizer
					__lowerCAmelCase  =  OpenAIGPTTokenizerFast
					__lowerCAmelCase  =  True
					__lowerCAmelCase  =  False







					def  _lowerCamelCase		(						self     ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__a :						List[str]			       =  [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''w</w>''',
									    '''r</w>''',
									    '''t</w>''',
									    '''lo''',
									    '''low''',
									    '''er</w>''',
									    '''low</w>''',
									    '''lowest</w>''',
									    '''newer</w>''',
									    '''wider</w>''',
									    '''<unk>''',
									]
									__a :						str			       =  dict(zip(_UpperCAmelCase	,			range(len(_UpperCAmelCase     )     )     )     )
									__a :						List[Any]			       =  ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

									__a :						Union[str, Any]			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''vocab_file''']     )
									__a :						Dict			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''merges_file''']     )
									with open(self.vocab_file	,			'''w'''     ) as fp:
													fp.write(json.dumps(_UpperCAmelCase     )     )
									with open(self.merges_file	,			'''w'''     ) as fp:
													fp.write('''\n'''.join(_UpperCAmelCase     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									return "lower newer", "lower newer"







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  OpenAIGPTTokenizer(self.vocab_file	,			self.merges_file     )

									__a :						Tuple			       =  '''lower'''
									__a :						Union[str, Any]			       =  ['''low''', '''er</w>''']
									__a :						str			       =  tokenizer.tokenize(_UpperCAmelCase     )
									self.assertListEqual(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						List[Any]			       =  tokens + ['''<unk>''']
									__a :						Tuple			       =  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase=15     ):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
																	__a :						Any			       =  self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,			**_UpperCAmelCase     )

																	# Simple input
																	__a :						str			       =  '''This is a simple input'''
																	__a :						Dict			       =  ['''This is a simple input 1''', '''This is a simple input 2''']
																	__a :						List[Any]			       =  ('''This is a simple input''', '''This is a pair''')
																	__a :						Optional[Any]			       =  [
																	    ('''This is a simple input 1''', '''This is a simple input 2'''),
																	    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																	]

																	# Simple input tests
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)







					def  _lowerCamelCase		(						self     ):
									pass






@require_ftfy
@require_spacy
@require_tokenizers
class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					pass