
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								=					'stas/tiny-wmt19-en-ru'
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE		,						unittest.TestCase				):
						__A:						List[str]				   =      FSMTTokenizer
						__A:						Union[str, Any]				   =      False

						def 						a__ (					self		:			Any			):
													super().setUp()

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

													_UpperCAmelCase  :			Union[str, Any]	       =		["en", "ru"]
													_UpperCAmelCase  :			List[Any]	       =		{
													    "langs": self.langs,
													    "src_vocab_size": 10,
													    "tgt_vocab_size": 20,
													}

													_UpperCAmelCase  :			str	       =		os.path.join(self.tmpdirname	,						VOCAB_FILES_NAMES["src_vocab_file"]			)
													_UpperCAmelCase  :			Any	       =		os.path.join(self.tmpdirname	,						VOCAB_FILES_NAMES["tgt_vocab_file"]			)
													_UpperCAmelCase  :			List[str]	       =		os.path.join(self.tmpdirname	,						"tokenizer_config.json"			)
													_UpperCAmelCase  :			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 						a__ (					self		:			str			):
													return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"			)

						@cached_property
						def 						a__ (					self		:			Optional[Any]			):
													return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru"			)

						def 						a__ (					self		:			List[str]			):
													_UpperCAmelCase  :			Dict	       =		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 						a__ (					self		:			Any			):
													_UpperCAmelCase  :			List[Any]	       =		FSMTTokenizer(self.langs	,						self.src_vocab_file	,						self.tgt_vocab_file	,						self.merges_file			)

													_UpperCAmelCase  :			Tuple	       =		"lower"
													_UpperCAmelCase  :			Optional[int]	       =		["low", "er</w>"]
													_UpperCAmelCase  :			int	       =		tokenizer.tokenize(_lowerCamelCase			)
													self.assertListEqual(_lowerCamelCase	,						_lowerCamelCase			)

													_UpperCAmelCase  :			int	       =		tokens + ["<unk>"]
													_UpperCAmelCase  :			List[str]	       =		[14, 15, 20]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase			)	,						_lowerCamelCase			)

						@slow
						def 						a__ (					self		:			int			):
													_UpperCAmelCase  :			Optional[int]	       =		self.tokenizer_ru_en

													_UpperCAmelCase  :			int	       =		tokenizer.encode("sequence builders"	,						add_special_tokens=_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		tokenizer.encode("multi-sequence build"	,						add_special_tokens=_lowerCamelCase			)

													_UpperCAmelCase  :			int	       =		tokenizer.build_inputs_with_special_tokens(_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		tokenizer.build_inputs_with_special_tokens(_lowerCamelCase	,						_lowerCamelCase			)

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

						@slow
						def 						a__ (					self		:			List[str]			):
													_UpperCAmelCase  :			Union[str, Any]	       =		self.tokenizer_en_ru
													_UpperCAmelCase  :			Dict	       =		self.tokenizer_ru_en

													_UpperCAmelCase  :			List[Any]	       =		[
													    [
													        "Here's a little song I wrote. Don't worry, be happy.",
													        [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
													    ],
													    ["This is it. No more. I'm done!", [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 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:
																				_UpperCAmelCase  :			List[str]	       =		tokenizer_enc.encode(_lowerCamelCase	,						return_tensors=_lowerCamelCase			)
																				self.assertListEqual(_lowerCamelCase	,						_lowerCamelCase			)

																				# and decode backward, using the reversed languages model
																				_UpperCAmelCase  :			Union[str, Any]	       =		tokenizer_dec.decode(_lowerCamelCase	,						skip_special_tokens=_lowerCamelCase			)
																				self.assertEqual(_lowerCamelCase	,						_lowerCamelCase			)

						@slow
						def 						a__ (					self		:			List[str]			):
													_UpperCAmelCase  :			Optional[Any]	       =		FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"	,						do_lower_case=_lowerCamelCase			)
													_UpperCAmelCase  :			Union[str, Any]	       =		tokenizer.tokenize("USA is United States of America"			)
													_UpperCAmelCase  :			str	       =		["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 						a__ (					self		:			str			):
													pass






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






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		torch.tensor(
													    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,						device=_lowerCamelCase	,						dtype=torch.long	,						)  # J'aime le camembert !
													with torch.no_grad():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						device=_lowerCamelCase	,						dtype=torch.float	,						)
													# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
													# camembert.eval()
													# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

													self.assertTrue(torch.allclose(output[:, :3, :3]	,						_lowerCamelCase	,						atol=1E-4			)			)




