




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		:    str	      =		'stas/tiny-wmt19-en-ru'




class    snake_case__							(_UpperCamelCase   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= FSMTTokenizer
				SCREAMING_SNAKE_CASE_	:							Optional[int]										= False





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					int:
							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    :					Union[str, Any]							) ->					Optional[Any]:
							return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"							)





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





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					List[Any]:
							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    :					Any							) ->					int:
							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    :					Optional[int]							) ->					Any:
							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    :					Union[str, Any]							) ->					Optional[Any]:
							a   =      self.tokenizer_en_ru
							a   =      self.tokenizer_ru_en

							a   =      [
							    [
							        "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:
										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    :					List[Any]							) ->					Dict:
							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    :					str							) ->					Any:
							pass





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












def       __magic_name__    (			A		: float       ):






			'''simple docstring'''







			return -num if num < 0 else num




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_min() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs_val(A       ) < abs_val(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs(A       ) > abs(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max_sort() arg is an empty sequence"       )
			return sorted(A,  key=A       )[-1]




def       __magic_name__    (			):






			'''simple docstring'''







			assert abs_val(0       ) == 0
			assert abs_val(34       ) == 34
			assert abs_val(-100000000000       ) == 100000000000

			a   =      [-3, -1, 2, -11]
			assert abs_max(A       ) == -11
			assert abs_max_sort(A       ) == -11
			assert abs_min(A       ) == -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		test_abs_val()
		print(abs_val(-34))  # --> 34






