
'''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
__lowercase							:				Dict					=       '''stas/tiny-wmt19-en-ru'''

class 		__lowercase			(					_lowercase ,       unittest.TestCase      ):
   lowerCamelCase     :						str		 =     FSMTTokenizer
   lowerCamelCase     :						List[str]		 =     False

   def     UpperCAmelCase__	(self ):
        super().setUp()

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

        lowerCamelCase_   : int       =						['''en''', '''ru''']
        lowerCamelCase_   : List[str]       =						{
            '''langs''': self.langs,
            '''src_vocab_size''': 1_0,
            '''tgt_vocab_size''': 2_0,
        }

        lowerCamelCase_   : Dict       =						os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES['''src_vocab_file'''] )
        lowerCamelCase_   : Optional[Any]       =						os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES['''tgt_vocab_file'''] )
        lowerCamelCase_   : Tuple       =						os.path.join(self.tmpdirname     ,				'''tokenizer_config.json''' )
        lowerCamelCase_   : 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(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 ):
        return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en''' )

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

   def     UpperCAmelCase__	(self ):
        lowerCamelCase_   : Any       =						FSMTTokenizer.from_pretrained(A )
        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     UpperCAmelCase__	(self ):
        lowerCamelCase_   : int       =						FSMTTokenizer(self.langs     ,				self.src_vocab_file     ,				self.tgt_vocab_file     ,				self.merges_file )

        lowerCamelCase_   : Optional[Any]       =						'''lower'''
        lowerCamelCase_   : Dict       =						['''low''', '''er</w>''']
        lowerCamelCase_   : List[Any]       =						tokenizer.tokenize(A )
        self.assertListEqual(A     ,				A )

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

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

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

        lowerCamelCase_   : Union[str, Any]       =						tokenizer.build_inputs_with_special_tokens(A )
        lowerCamelCase_   : Any       =						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 ):
        lowerCamelCase_   : str       =						self.tokenizer_en_ru
        lowerCamelCase_   : int       =						self.tokenizer_ru_en

        lowerCamelCase_   : int       =						[
            [
                '''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:
             lowerCamelCase_   : str       =						tokenizer_enc.encode(A     ,				return_tensors=A )
             self.assertListEqual(A     ,				A )

             # and decode backward, using the reversed languages model
             lowerCamelCase_   : Union[str, Any]       =						tokenizer_dec.decode(A     ,				skip_special_tokens=A )
             self.assertEqual(A     ,				A )

   @slow
   def     UpperCAmelCase__	(self ):
        lowerCamelCase_   : Tuple       =						FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''     ,				do_lower_case=A )
        lowerCamelCase_   : Optional[Any]       =						tokenizer.tokenize('''USA is United States of America''' )
        lowerCamelCase_   : 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     UpperCAmelCase__	(self ):
        pass






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




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowercase							:				List[str]					=       {
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SamModel''',
					    '''SamPreTrainedModel''',
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				int					=       [
					    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFSamModel''',
					    '''TFSamPreTrainedModel''',
					]
try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''SamImageProcessor''']


if TYPE_CHECKING:
					from .configuration_sam import (
					    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SamConfig,
					    SamMaskDecoderConfig,
					    SamPromptEncoderConfig,
					    SamVisionConfig,
					)
					from .processing_sam import SamProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_sam import SamImageProcessor

else:
					import sys

					__lowercase							:				List[str]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


