'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCamelCase       				=	{
    '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
    '''tokenization_splinter''': ['''SplinterTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       				=	['''SplinterTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       				=	[
        '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''SplinterForQuestionAnswering''',
        '''SplinterForPreTraining''',
        '''SplinterLayer''',
        '''SplinterModel''',
        '''SplinterPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
    from .tokenization_splinter import SplinterTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_splinter_fast import SplinterTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_splinter import (
            SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
            SplinterForPreTraining,
            SplinterForQuestionAnswering,
            SplinterLayer,
            SplinterModel,
            SplinterPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase       				=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''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
__lowerCamelCase       				=	'''stas/tiny-wmt19-en-ru'''




class 							UpperCAmelCase ( _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				FSMTTokenizer
 UpperCAmelCase				=				False


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    super().setUp()

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

    UpperCAmelCase__  :Dict            =			['''en''', '''ru''']
    UpperCAmelCase__  :Tuple            =			{
        '''langs''': self.langs,
        '''src_vocab_size''': 1_0,
        '''tgt_vocab_size''': 2_0,
    }

    UpperCAmelCase__  :Any            =			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__  :Dict            =			os.path.join(self.tmpdirname ,					'''tokenizer_config.json'''   )
    UpperCAmelCase__  :Union[str, 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 					__SCREAMING_SNAKE_CASE				(			self       :  List[Any]   ):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''   )


 @cached_property
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple   ):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    UpperCAmelCase__  :int            =			FSMTTokenizer.from_pretrained(__lowerCamelCase   )
    self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang] ,					['''en''', '''ru''']   )
    self.assertEqual(tokenizer.src_vocab_size ,					2_1   )
    self.assertEqual(tokenizer.tgt_vocab_size ,					2_1   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int]   ):
    UpperCAmelCase__  :Tuple            =			FSMTTokenizer(self.langs ,					self.src_vocab_file ,					self.tgt_vocab_file ,					self.merges_file   )

    UpperCAmelCase__  :Any            =			'''lower'''
    UpperCAmelCase__  :List[Any]            =			['''low''', '''er</w>''']
    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.tokenize(__lowerCamelCase   )
    self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )

    UpperCAmelCase__  :Dict            =			tokens + ['''<unk>''']
    UpperCAmelCase__  :Union[str, Any]            =			[1_4, 1_5, 2_0]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase   ) ,					__lowerCamelCase   )


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any   ):
    UpperCAmelCase__  :str            =			self.tokenizer_ru_en

    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.encode('''sequence builders''' ,					add_special_tokens=__lowerCamelCase   )
    UpperCAmelCase__  :Dict            =			tokenizer.encode('''multi-sequence build''' ,					add_special_tokens=__lowerCamelCase   )

    UpperCAmelCase__  :Any            =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   )
    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ,					__lowerCamelCase   )

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


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :List[str]            =			self.tokenizer_en_ru
    UpperCAmelCase__  :Union[str, Any]            =			self.tokenizer_ru_en

    UpperCAmelCase__  :Any            =			[
        [
            '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
            [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
        ],
        ['''This is it. No more. I\'m done!''', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 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[Any]            =			tokenizer_enc.encode(__lowerCamelCase ,					return_tensors=__lowerCamelCase   )
       self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )

       # and decode backward, using the reversed languages model
       UpperCAmelCase__  :str            =			tokenizer_dec.decode(__lowerCamelCase ,					skip_special_tokens=__lowerCamelCase   )
       self.assertEqual(__lowerCamelCase ,					__lowerCamelCase   )


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :Optional[Any]            =			FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en''' ,					do_lower_case=__lowerCamelCase   )
    UpperCAmelCase__  :str            =			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 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    pass





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



