



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
_lowercase:				Optional[int]       =	"stas/tiny-wmt19-en-ru"



class 			_lowercase							(     lowerCAmelCase,   unittest.TestCase			):





	"""simple docstring"""
	__A					=							FSMTTokenizer
	__A					=							False




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						super().setUp()

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

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

						a        =     os.path.join(self.tmpdirname	, VOCAB_FILES_NAMES["src_vocab_file"]	)
						a        =     os.path.join(self.tmpdirname	, VOCAB_FILES_NAMES["tgt_vocab_file"]	)
						a        =     os.path.join(self.tmpdirname	, "tokenizer_config.json"	)
						a        =     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	):
						"""simple docstring"""

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




	@cached_property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     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	):
						"""simple docstring"""

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

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

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




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.tokenizer_ru_en

						a        =     tokenizer.encode("sequence builders"	, add_special_tokens=lowerCamelCase_	)
						a        =     tokenizer.encode("multi-sequence build"	, add_special_tokens=lowerCamelCase_	)

						a        =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase_	)
						a        =     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	):
						"""simple docstring"""

						a        =     self.tokenizer_en_ru
						a        =     self.tokenizer_ru_en

						a        =     [
						    [
						        "Here's a little song I wrote. Don't worry, be happy.",
						        [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 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:
											a        =     tokenizer_enc.encode(lowerCamelCase_	, return_tensors=lowerCamelCase_	)
											self.assertListEqual(lowerCamelCase_	, lowerCamelCase_	)

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




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"	, do_lower_case=lowerCamelCase_	)
						a        =     tokenizer.tokenize("USA is United States of America"	)
						a        =     ["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	):
						"""simple docstring"""

						pass




	@unittest.skip("FSMTConfig.__init__  requires non-optional args"	)
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						pass





import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


_lowercase:				Optional[int]       =	logging.get_logger(__name__)



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	def __init__(self	, *lowerCamelCase_	, **lowerCamelCase_	):
						"""simple docstring"""

						warnings.warn(
						    "The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use DetrImageProcessor instead."	, lowerCamelCase_	, )
						super().__init__(*lowerCamelCase_	, **lowerCamelCase_	)
