


import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class       A    (   A_					,				unittest.TestCase ):
							UpperCamelCase_   :							List[Any]       =DebertaTokenizer
							UpperCamelCase_   :							List[Any]       =True
							UpperCamelCase_   :							Optional[Any]       =DebertaTokenizerFast


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

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__lowercase=			[
									    'l',
									    'o',
									    'w',
									    'e',
									    'r',
									    's',
									    't',
									    'i',
									    'd',
									    'n',
									    '\u0120',
									    '\u0120l',
									    '\u0120n',
									    '\u0120lo',
									    '\u0120low',
									    'er',
									    '\u0120lowest',
									    '\u0120newer',
									    '\u0120wider',
									    '[UNK]',
									]
									__lowercase=			dict(zip(lowerCAmelCase      ,				range(len(lowerCAmelCase					)					)					)					)
									__lowercase=			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
									__lowercase=			{'unk_token': '[UNK]'}

									__lowercase=			os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES['vocab_file']					)
									__lowercase=			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					):
									__lowercase=			'lower newer'
									__lowercase=			'lower newer'
									return input_text, output_text


							def 			_A       (self					):
									__lowercase=			self.get_tokenizer()
									__lowercase=			'lower newer'
									__lowercase=			['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
									__lowercase=			tokenizer.tokenize(lowerCAmelCase					)
									self.assertListEqual(lowerCAmelCase      ,				lowerCAmelCase					)

									__lowercase=			tokens + [tokenizer.unk_token]
									__lowercase=			[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase					)      ,				lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			self.get_tokenizer()
									__lowercase=			tokenizer('Hello'      ,				'World'					)
									__lowercase=			[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
									self.assertListEqual(tokd['token_type_ids']      ,				lowerCAmelCase					)


							@slow
							def 			_A       (self					):
									__lowercase=			self.tokenizer_class.from_pretrained('microsoft/deberta-base'					)

									__lowercase=			tokenizer.encode('sequence builders'      ,				add_special_tokens=lowerCAmelCase					)
									__lowercase=			tokenizer.encode('multi-sequence build'      ,				add_special_tokens=lowerCAmelCase					)

									__lowercase=			tokenizer.encode(
									    'sequence builders'      ,				add_special_tokens=lowerCAmelCase      ,				add_prefix_space=lowerCAmelCase					)
									__lowercase=			tokenizer.encode(
									    'sequence builders'      ,				'multi-sequence build'      ,				add_special_tokens=lowerCAmelCase      ,				add_prefix_space=lowerCAmelCase					)

									__lowercase=			tokenizer.build_inputs_with_special_tokens(lowerCAmelCase					)
									__lowercase=			tokenizer.build_inputs_with_special_tokens(lowerCAmelCase      ,				lowerCAmelCase					)

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode







							@slow
							def 			_A       (self					):
									__lowercase=			[self.tokenizer_class]
									if self.test_rust_tokenizer:
											tokenizer_classes.append(self.rust_tokenizer_class					)

									for tokenizer_class in tokenizer_classes:
											__lowercase=			tokenizer_class.from_pretrained('microsoft/deberta-base'					)

											__lowercase=			[
											    'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
											    'ALBERT incorporates two parameter reduction techniques',
											    'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
											    ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
											    ' vocabulary embedding.',
											]

											__lowercase=			tokenizer(lowerCAmelCase      ,				padding=lowerCAmelCase					)
											__lowercase=			[tokenizer.decode(lowerCAmelCase      ,				skip_special_tokens=lowerCAmelCase					) for seq in encoding['input_ids']]

											# fmt: off
											__lowercase=			{
											    'input_ids': [
											        [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [1, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 4, 2]
											    ],
											    'token_type_ids': [
											        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
											    ],
											    'attention_mask': [
											        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
											    ]
											}
											# fmt: on

											__lowercase=			[
											    'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
											    'ALBERT incorporates two parameter reduction techniques',
											    'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
											    ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
											    ' vocabulary embedding.',
											]

											self.assertDictEqual(encoding.data      ,				lowerCAmelCase					)

											for expected, decoded in zip(lowerCAmelCase      ,				lowerCAmelCase					):
													self.assertEqual(lowerCAmelCase      ,				lowerCAmelCase					)










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        _lowerCamelCase(	lowercase__=None   , lowercase__=None	)						->	Dict:

		'''simple docstring'''







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





@dataclass
class       A    :
							UpperCamelCase_   :							str       =field(
							    metadata={'''help''': '''The csv file to plot.'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={'''help''': '''Disable logarithmic scale when plotting'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={
							        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
							    }					,				)
							UpperCamelCase_   :							Optional[str]       =field(
							    default=A_					,				metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}					,				)
							UpperCamelCase_   :							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        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







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







def        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







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





class       A    :


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

									with open(self.args.csv_file      ,				newline=''					) as csv_file:
											__lowercase=			csv.DictReader(lowerCAmelCase					)
											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 			_A       (self					):
									__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=lowerCAmelCase      ,				)
													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(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.'

									__lowercase=			title_str[:-4]
									__lowercase=			'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        _lowerCamelCase(	)						->	str:

		'''simple docstring'''







		__lowercase=			HfArgumentParser(lowercase__	)
		__lowercase=			parser.parse_args_into_dataclasses()[0]
		__lowercase=			Plot(args=lowercase__	)
		plot.plot()


if __name__ == "__main__":
	main()






