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
_lowerCamelCase    :      List[Any]	 =						'''stas/tiny-wmt19-en-ru'''


class   lowercase			(   a ,				unittest.TestCase   ):
							lowercase__  :				List[str]									=							FSMTTokenizer
							lowercase__  :				Tuple									=							False




							def 		__snake_case(  self      :    List[Any]					)    -> List[Any]:



												'''simple docstring'''

												super().setUp()

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

												SCREAMING_SNAKE_CASE				=				["en", "ru"]
												SCREAMING_SNAKE_CASE				=				{
												    "langs": self.langs,
												    "src_vocab_size": 10,
												    "tgt_vocab_size": 20,
												}

												SCREAMING_SNAKE_CASE				=				os.path.join(self.tmpdirname		,							VOCAB_FILES_NAMES["src_vocab_file"]					)
												SCREAMING_SNAKE_CASE				=				os.path.join(self.tmpdirname		,							VOCAB_FILES_NAMES["tgt_vocab_file"]					)
												SCREAMING_SNAKE_CASE				=				os.path.join(self.tmpdirname		,							"tokenizer_config.json"					)
												SCREAMING_SNAKE_CASE				=				os.path.join(self.tmpdirname		,							VOCAB_FILES_NAMES["merges_file"]					)
												with open(self.src_vocab_file		,							"w"					) as fp:
																	fp.write(json.dumps(_UpperCamelCase					)					)
												with open(self.tgt_vocab_file		,							"w"					) as fp:
																	fp.write(json.dumps(_UpperCamelCase					)					)
												with open(self.merges_file		,							"w"					) as fp:
																	fp.write("\n".join(_UpperCamelCase					)					)
												with open(_UpperCamelCase		,							"w"					) as fp:
																	fp.write(json.dumps(_UpperCamelCase					)					)




							@cached_property
							def 		__snake_case(  self      :    Optional[int]					)    -> Tuple:



												'''simple docstring'''

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




							@cached_property
							def 		__snake_case(  self      :    List[str]					)    -> Union[str, Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self      :    Optional[int]					)    -> int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				FSMTTokenizer.from_pretrained(_UpperCamelCase					)
												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 		__snake_case(  self      :    List[Any]					)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				FSMTTokenizer(self.langs		,							self.src_vocab_file		,							self.tgt_vocab_file		,							self.merges_file					)

												SCREAMING_SNAKE_CASE				=				"lower"
												SCREAMING_SNAKE_CASE				=				["low", "er</w>"]
												SCREAMING_SNAKE_CASE				=				tokenizer.tokenize(_UpperCamelCase					)
												self.assertListEqual(_UpperCamelCase		,							_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				tokens + ["<unk>"]
												SCREAMING_SNAKE_CASE				=				[14, 15, 20]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase					)		,							_UpperCamelCase					)




							@slow
							def 		__snake_case(  self      :    Optional[int]					)    -> Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.tokenizer_ru_en

												SCREAMING_SNAKE_CASE				=				tokenizer.encode("sequence builders"		,							add_special_tokens=_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				tokenizer.encode("multi-sequence build"		,							add_special_tokens=_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				tokenizer.build_inputs_with_special_tokens(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				tokenizer.build_inputs_with_special_tokens(_UpperCamelCase		,							_UpperCamelCase					)

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




							@slow
							def 		__snake_case(  self      :    Any					)    -> Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.tokenizer_en_ru
												SCREAMING_SNAKE_CASE				=				self.tokenizer_ru_en

												SCREAMING_SNAKE_CASE				=				[
												    [
												        "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:
																	SCREAMING_SNAKE_CASE				=				tokenizer_enc.encode(_UpperCamelCase		,							return_tensors=_UpperCamelCase					)
																	self.assertListEqual(_UpperCamelCase		,							_UpperCamelCase					)

																	# and decode backward, using the reversed languages model
																	SCREAMING_SNAKE_CASE				=				tokenizer_dec.decode(_UpperCamelCase		,							skip_special_tokens=_UpperCamelCase					)
																	self.assertEqual(_UpperCamelCase		,							_UpperCamelCase					)




							@slow
							def 		__snake_case(  self      :    List[Any]					)    -> Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"		,							do_lower_case=_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				tokenizer.tokenize("USA is United States of America"					)
												SCREAMING_SNAKE_CASE				=				["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
												self.assertListEqual(_UpperCamelCase		,							_UpperCamelCase					)




							@unittest.skip("FSMTConfig.__init__  requires non-optional args"					)
							def 		__snake_case(  self      :    Union[str, Any]					)    -> str:



												'''simple docstring'''

												pass





							@unittest.skip("FSMTConfig.__init__  requires non-optional args"					)
							def 		__snake_case(  self      :    Union[str, Any]					)    -> Tuple:



												'''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


_lowerCamelCase    :      int	 =						10_00_00_00_00_00
_lowerCamelCase    :      Union[str, Any]	 =						1_00_00

_lowerCamelCase ,  _lowerCamelCase    :      List[str]	 =						os.path.split(__file__)
_lowerCamelCase    :      Any	 =						os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							int     ):
					SCREAMING_SNAKE_CASE				=				pa.Table.from_pydict({"col": [0] * chunk_size}     )
					SCREAMING_SNAKE_CASE				=				pa.concat_tables([table] * (num_examples // chunk_size)     )
					return datasets.Dataset(UpperCAmelCase__					,       fingerprint="table_100B"     )


@dataclass
class   lowercase			:
							lowercase__  :				int
							lowercase__  :				int
							lowercase__  :				int
							lowercase__  :				int




							def 		__snake_case(  self      :    Tuple					)    -> Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				np.random.default_rng(self.seed					)
												SCREAMING_SNAKE_CASE				=				rng.integers(low=self.low		,							high=self.high		,							size=self.size					).tolist()




							def __iter__(  self      :    Union[str, Any]					)    -> Dict:



												'''simple docstring'''

												return iter(self._sampled_values					)





							def __len__(  self      :    Any					)    -> int:



												'''simple docstring'''

												return self.size





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[0]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[-1]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[range(len(UpperCAmelCase__     ) // 2					,       len(UpperCAmelCase__     ) // 2 + 1_0_2_4     )]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[RandIter(0					,       len(UpperCAmelCase__     )					,       1_0_2_4					,       seed=4_2     )]





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				{"num examples": SPEED_TEST_N_EXAMPLES}
					SCREAMING_SNAKE_CASE				=				(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print("generating dataset"     )
					SCREAMING_SNAKE_CASE				=				generate_100B_dataset(num_examples=UpperCAmelCase__					,       chunk_size=UpperCAmelCase__     )
					print("Functions"     )
					for func in functions:
										print(func.__name__     )
										SCREAMING_SNAKE_CASE				=				func(UpperCAmelCase__     )

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


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




