




import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
UpperCamelCase__              =						'stas/tiny-wmt19-en-ru'


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = FSMTTokenizer
					_lowerCamelCase		  = False

					def 	lowercase_							(				self					):







						'''simple docstring'''




						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						lowerCamelCase_							:		Any							   =							[
						    '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>',
						]
						lowerCamelCase_							:		List[Any]							   =							dict(zip(__lowercase ,       range(len(__lowercase					)					)					)					)
						lowerCamelCase_							:		int							   =							['l o 123', 'lo w 1456', 'e r</w> 1789', '']

						lowerCamelCase_							:		Dict							   =							['en', 'ru']
						lowerCamelCase_							:		Union[str, Any]							   =							{
						    'langs': self.langs,
						    'src_vocab_size': 10,
						    'tgt_vocab_size': 20,
						}

						lowerCamelCase_							:		int							   =							os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['src_vocab_file']					)
						lowerCamelCase_							:		str							   =							os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['tgt_vocab_file']					)
						lowerCamelCase_							:		Union[str, Any]							   =							os.path.join(self.tmpdirname ,       'tokenizer_config.json'					)
						lowerCamelCase_							:		Optional[int]							   =							os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['merges_file']					)
						with open(self.src_vocab_file ,       'w'					) as fp:
							fp.write(json.dumps(__lowercase					)					)
						with open(self.tgt_vocab_file ,       'w'					) as fp:
							fp.write(json.dumps(__lowercase					)					)
						with open(self.merges_file ,       'w'					) as fp:
							fp.write('\n'.join(__lowercase					)					)
						with open(__lowercase ,       'w'					) as fp:
							fp.write(json.dumps(__lowercase					)					)

					@cached_property
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'					)

					@cached_property
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							FSMTTokenizer.from_pretrained(__lowercase					)
						self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang] ,       ['en', 'ru']					)
						self.assertEqual(tokenizer.src_vocab_size ,       21					)
						self.assertEqual(tokenizer.tgt_vocab_size ,       21					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							FSMTTokenizer(self.langs ,       self.src_vocab_file ,       self.tgt_vocab_file ,       self.merges_file					)

						lowerCamelCase_							:		int							   =							'lower'
						lowerCamelCase_							:		List[Any]							   =							['low', 'er</w>']
						lowerCamelCase_							:		int							   =							tokenizer.tokenize(__lowercase					)
						self.assertListEqual(__lowercase ,       __lowercase					)

						lowerCamelCase_							:		Optional[int]							   =							tokens + ['<unk>']
						lowerCamelCase_							:		str							   =							[14, 15, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase					) ,       __lowercase					)

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							self.tokenizer_ru_en

						lowerCamelCase_							:		Optional[int]							   =							tokenizer.encode('sequence builders' ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		int							   =							tokenizer.encode('multi-sequence build' ,       add_special_tokens=__lowercase					)

						lowerCamelCase_							:		Optional[Any]							   =							tokenizer.build_inputs_with_special_tokens(__lowercase					)
						lowerCamelCase_							:		int							   =							tokenizer.build_inputs_with_special_tokens(__lowercase ,       __lowercase					)

						assert encoded_sentence == text + [2]
						assert encoded_pair == text + [2] + text_a + [2]

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Any							   =							self.tokenizer_en_ru
						lowerCamelCase_							:		Optional[Any]							   =							self.tokenizer_ru_en

						lowerCamelCase_							:		str							   =							[
						    [
						        'Here\'s a little song I wrote. Don\'t worry, be happy.',
						        [2_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
						    ],
						    ['This is it. No more. I\'m done!', [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 427, 2]],
						]

						# if data needs to be recreated or added, run:
						# import torch
						# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
						# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

						for src_text, tgt_input_ids in targets:
							lowerCamelCase_							:		Optional[Any]							   =							tokenizer_enc.encode(__lowercase ,       return_tensors=__lowercase					)
							self.assertListEqual(__lowercase ,       __lowercase					)

							# and decode backward, using the reversed languages model
							lowerCamelCase_							:		Any							   =							tokenizer_dec.decode(__lowercase ,       skip_special_tokens=__lowercase					)
							self.assertEqual(__lowercase ,       __lowercase					)

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en' ,       do_lower_case=__lowercase					)
						lowerCamelCase_							:		Optional[Any]							   =							tokenizer.tokenize('USA is United States of America'					)
						lowerCamelCase_							:		Optional[int]							   =							['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
						self.assertListEqual(__lowercase ,       __lowercase					)

					@unittest.skip('FSMTConfig.__init__  requires non-optional args'					)
					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass





					@unittest.skip('FSMTConfig.__init__  requires non-optional args'					)
					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass






import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCamelCase__              =						1_0_0_0_0_0_0_0_0_0_0_0
UpperCamelCase__              =						1_0_0_0_0

UpperCamelCase__       , UpperCamelCase__              =						os.path.split(__file__)
UpperCamelCase__              =						os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






def 					snake_case					(							A						,   A			)							->							datasets.Dataset:




	'''simple docstring'''


	lowerCamelCase_							:		int							   =							pa.Table.from_pydict({'col': [0] * chunk_size}			)
	lowerCamelCase_							:		List[str]							   =							pa.concat_tables([table] * (num_examples // chunk_size)			)
	return datasets.Dataset(A						,   fingerprint='table_100B'			)


@dataclass
class 				__SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							np.random.default_rng(self.seed					)
						lowerCamelCase_							:		Any							   =							rng.integers(low=self.low ,       high=self.high ,       size=self.size					).tolist()

					def __iter__(				self					):







						'''simple docstring'''




						return iter(self._sampled_values					)





					def __len__(				self					):







						'''simple docstring'''




						return self.size






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							dataset[0]






@get_duration
def 					snake_case					(							A			)							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							dataset[-1]






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							dataset[range(len(A			) // 2						,   len(A			) // 2 + 1024			)]






@get_duration
def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							   =							dataset[RandIter(0						,   len(A			)						,   1024						,   seed=42			)]






def 					snake_case					(							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							{'num examples': SPEED_TEST_N_EXAMPLES}
	lowerCamelCase_							:		Optional[int]							   =							(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
	print('generating dataset'			)
	lowerCamelCase_							:		List[Any]							   =							generate_100B_dataset(num_examples=A						,   chunk_size=A			)
	print('Functions'			)
	for func in functions:
		print(func.__name__			)
		lowerCamelCase_							:		str							   =							func(A			)

	with open(A						,   'wb'			) as f:
		f.write(json.dumps(A			).encode('utf-8'			)			)


if __name__ == "__main__":  # useful to run the profiler
							benchmark_table_100B()
