


"""simple docstring"""
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
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							"""stas/tiny-wmt19-en-ru"""



class      _UpperCAmelCase					(				__snake_case,							unittest.TestCase							):






		'''simple docstring'''

		lowerCamelCase__           =FSMTTokenizer
		lowerCamelCase__           =False



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			super().setUp()

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

			__snake_case      :		Optional[int]         =							['''en''', '''ru''']
			__snake_case      :		int         =							{
			    '''langs''': self.langs,
			    '''src_vocab_size''': 10,
			    '''tgt_vocab_size''': 20,
			}

			__snake_case      :		List[str]         =							os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES['''src_vocab_file''']   )
			__snake_case      :		Optional[Any]         =							os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES['''tgt_vocab_file''']   )
			__snake_case      :		List[Any]         =							os.path.join(self.tmpdirname							,			'''tokenizer_config.json'''   )
			__snake_case      :		Optional[int]         =							os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES['''merges_file''']   )
			with open(self.src_vocab_file							,			'''w'''   ) as fp:
				fp.write(json.dumps(a_   )   )
			with open(self.tgt_vocab_file							,			'''w'''   ) as fp:
				fp.write(json.dumps(a_   )   )
			with open(self.merges_file							,			'''w'''   ) as fp:
				fp.write('''\n'''.join(a_   )   )
			with open(a_							,			'''w'''   ) as fp:
				fp.write(json.dumps(a_   )   )



		@cached_property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

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



		@cached_property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

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



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							FSMTTokenizer.from_pretrained(a_   )
			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 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							FSMTTokenizer(self.langs							,			self.src_vocab_file							,			self.tgt_vocab_file							,			self.merges_file   )

			__snake_case      :		List[str]         =							'''lower'''
			__snake_case      :		Optional[int]         =							['''low''', '''er</w>''']
			__snake_case      :		Dict         =							tokenizer.tokenize(a_   )
			self.assertListEqual(a_							,			a_   )

			__snake_case      :		Union[str, Any]         =							tokens + ['''<unk>''']
			__snake_case      :		int         =							[14, 15, 20]
			self.assertListEqual(tokenizer.convert_tokens_to_ids(a_   )							,			a_   )



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		str         =							self.tokenizer_ru_en

			__snake_case      :		str         =							tokenizer.encode('''sequence builders'''							,			add_special_tokens=a_   )
			__snake_case      :		List[str]         =							tokenizer.encode('''multi-sequence build'''							,			add_special_tokens=a_   )

			__snake_case      :		str         =							tokenizer.build_inputs_with_special_tokens(a_   )
			__snake_case      :		str         =							tokenizer.build_inputs_with_special_tokens(a_							,			a_   )

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



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							self.tokenizer_en_ru
			__snake_case      :		List[Any]         =							self.tokenizer_ru_en

			__snake_case      :		Tuple         =							[
			    [
			        '''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:
				__snake_case      :		Optional[int]         =							tokenizer_enc.encode(a_							,			return_tensors=a_   )
				self.assertListEqual(a_							,			a_   )

				# and decode backward, using the reversed languages model
				__snake_case      :		int         =							tokenizer_dec.decode(a_							,			skip_special_tokens=a_   )
				self.assertEqual(a_							,			a_   )



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''							,			do_lower_case=a_   )
			__snake_case      :		str         =							tokenizer.tokenize('''USA is United States of America'''   )
			__snake_case      :		List[Any]         =							['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
			self.assertListEqual(a_							,			a_   )



		@unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			pass



		@unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			pass









import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


__UpperCAmelCase			:			Optional[Any]			      =					2 << 20  # 2mb
__UpperCAmelCase			:			List[Any]			      =					get_logger(__name__)



class  UpperCAmelCase_						(				_a):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		self._options.get_all_options().get('''runner'''    )
              return runner in [None, "DirectRunner", "PortableRunner"]




def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=False							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		FileSystems
       if fs.exists(SCREAMING_SNAKE_CASE_							):
              if force_upload:
                     logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""							)
              else:
                     logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""							)
                     return
       with fs.create(SCREAMING_SNAKE_CASE_							) as remote_file:
              with open(SCREAMING_SNAKE_CASE_				,      '''rb'''							) as local_file:
                     UpperCamelCase     :						List[str] 						=		local_file.read(SCREAMING_SNAKE_CASE_							)
                     while chunk:
                            remote_file.write(SCREAMING_SNAKE_CASE_							)
                            UpperCamelCase     :						Any 						=		local_file.read(SCREAMING_SNAKE_CASE_							)


def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[str]=False							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		FileSystems
       if os.path.exists(SCREAMING_SNAKE_CASE_							):
              if force_download:
                     logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""							)
              else:
                     logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""							)
                     return
       with fs.open(SCREAMING_SNAKE_CASE_							) as remote_file:
              with open(SCREAMING_SNAKE_CASE_				,      '''wb'''							) as local_file:
                     UpperCamelCase     :						List[str] 						=		remote_file.read(SCREAMING_SNAKE_CASE_							)
                     while chunk:
                            local_file.write(SCREAMING_SNAKE_CASE_							)
                            UpperCamelCase     :						str 						=		remote_file.read(SCREAMING_SNAKE_CASE_							)

