





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     :			List[Any]     						=	"""stas/tiny-wmt19-en-ru"""

class 			__A(    __UpperCAmelCase    ,      unittest.TestCase			):
     __A							=							FSMTTokenizer
     __A							=							False

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            super().setUp()

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

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

            _UpperCamelCase														=    os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['''src_vocab_file''']   )
            _UpperCamelCase														=    os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['''tgt_vocab_file''']   )
            _UpperCamelCase														=    os.path.join(self.tmpdirname,							'''tokenizer_config.json'''   )
            _UpperCamelCase														=    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 						_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"""

            _UpperCamelCase														=    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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

            _UpperCamelCase														=    '''lower'''
            _UpperCamelCase														=    ['''low''', '''er</w>''']
            _UpperCamelCase														=    tokenizer.tokenize(A   )
            self.assertListEqual(A,							A   )

            _UpperCamelCase														=    tokens + ['''<unk>''']
            _UpperCamelCase														=    [14, 15, 20]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(A   ),							A   )

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.tokenizer_ru_en

            _UpperCamelCase														=    tokenizer.encode('''sequence builders''',							add_special_tokens=A   )
            _UpperCamelCase														=    tokenizer.encode('''multi-sequence build''',							add_special_tokens=A   )

            _UpperCamelCase														=    tokenizer.build_inputs_with_special_tokens(A   )
            _UpperCamelCase														=    tokenizer.build_inputs_with_special_tokens(A,							A   )

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

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.tokenizer_en_ru
            _UpperCamelCase														=    self.tokenizer_ru_en

            _UpperCamelCase														=    [
                [
                    '''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:
                   _UpperCamelCase														=    tokenizer_enc.encode(A,							return_tensors=A   )
                   self.assertListEqual(A,							A   )

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

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en''',							do_lower_case=A   )
            _UpperCamelCase														=    tokenizer.tokenize('''USA is United States of America'''   )
            _UpperCamelCase														=    ['''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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass



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





            """simple docstring"""

            pass













import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
       from PIL import Image

class 			__A(    unittest.TestCase    ,      __UpperCAmelCase			):

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    load_tool('''image-question-answering'''   )
            self.tool.setup()
            _UpperCamelCase														=    load_tool('''image-question-answering''',							remote=A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.tool(A,							'''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.remote_tool(A,							'''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.tool(image=A,							question='''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.remote_tool(image=A,							question='''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )






