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 :     Tuple          =  """stas/tiny-wmt19-en-ru"""






class 		a__						( _UpperCAmelCase   ,		unittest.TestCase ):
					A     	=							FSMTTokenizer
					A     	=							False

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							super().setUp()

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

							SCREAMING_SNAKE_CASE_  :		Dict    				=   ["en", "ru"]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   {
							    "langs": self.langs,
							    "src_vocab_size": 10,
							    "tgt_vocab_size": 20,
							}

							SCREAMING_SNAKE_CASE_  :		Any    				=   os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["src_vocab_file"]   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["tgt_vocab_file"]   )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   os.path.join(self.tmpdirname,"tokenizer_config.json"   )
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["merges_file"]   )
							with open(self.src_vocab_file,"w"   ) as fp:
									fp.write(json.dumps(lowerCAmelCase__   )   )
							with open(self.tgt_vocab_file,"w"   ) as fp:
									fp.write(json.dumps(lowerCAmelCase__   )   )
							with open(self.merges_file,"w"   ) as fp:
									fp.write("\n".join(lowerCAmelCase__   )   )
							with open(lowerCAmelCase__,"w"   ) as fp:
									fp.write(json.dumps(lowerCAmelCase__   )   )

					@cached_property
					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






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

					@cached_property
					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   FSMTTokenizer.from_pretrained(lowerCAmelCase__   )
							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     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






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

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   "lower"
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ["low", "er</w>"]
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   tokenizer.tokenize(lowerCAmelCase__   )
							self.assertListEqual(lowerCAmelCase__,lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_  :		str    				=   tokens + ["<unk>"]
							SCREAMING_SNAKE_CASE_  :		Any    				=   [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__   ),lowerCAmelCase__   )

					@slow
					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   self.tokenizer_ru_en

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   tokenizer.encode("sequence builders",add_special_tokens=lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   tokenizer.encode("multi-sequence build",add_special_tokens=lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_  :		Dict    				=   tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__,lowerCAmelCase__   )

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

					@slow
					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   self.tokenizer_en_ru
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.tokenizer_ru_en

							SCREAMING_SNAKE_CASE_  :		Any    				=   [
							    [
							        "Here\'s a little song I wrote. Don\'t worry, be happy.",
							        [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 1_3160, 5, 26, 6445, 7, 2],
							    ],
							    ["This is it. No more. I\'m done!", [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 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_  :		List[str]    				=   tokenizer_enc.encode(lowerCAmelCase__,return_tensors=lowerCAmelCase__   )
									self.assertListEqual(lowerCAmelCase__,lowerCAmelCase__   )

									# and decode backward, using the reversed languages model
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   tokenizer_dec.decode(lowerCAmelCase__,skip_special_tokens=lowerCAmelCase__   )
									self.assertEqual(lowerCAmelCase__,lowerCAmelCase__   )

					@slow
					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






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

					@unittest.skip("FSMTConfig.__init__  requires non-optional args"   )
					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							pass






					@unittest.skip("FSMTConfig.__init__  requires non-optional args"   )
					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""simple docstring"""






							pass

from ..utils import DummyObject, requires_backends






class 		a__						( metaclass=A__ ):
					A     	=							['tensorflow_text']

					def __init__(   self				:	List[str],*_A				:	Optional[Any],**_A				:	int   ):







							"""simple docstring"""






							requires_backends(self,["tensorflow_text"]   )
