



import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


__lowerCamelCase      =    logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
__lowerCamelCase      =    direct_transformers_import(Path(__file__).parent)


__lowerCamelCase      =    {
    """AutoTokenizer""": """PreTrainedTokenizerBase""",
    """AutoFeatureExtractor""": """FeatureExtractionMixin""",
    """AutoImageProcessor""": """ImageProcessingMixin""",
}



class 						_snake_case					(	a_ ):





 """simple docstring"""



 lowerCamelCase_								=	['feature_extractor', 'tokenizer']
 # Names need to be attr_class for attr in attributes
 lowerCamelCase_								=	None
 lowerCamelCase_								=	None
 lowerCamelCase_								=	None
 def __init__(  self		,			*a		,			**a    )		->							str:




   """simple docstring"""







   for key in kwargs:
     if key not in self.attributes:
       raise TypeError(f'''Unexpected keyword argument {key}.'''    )
   for arg, attribute_name in zip(a		,			self.attributes    ):
     if attribute_name in kwargs:
       raise TypeError(f'''Got multiple values for argument {attribute_name}.'''    )
     else:
       _A							=			arg

   if len(a    ) != len(self.attributes    ):
     raise ValueError(
         f'''This processor requires {len(self.attributes    )} arguments: {", ".join(self.attributes    )}. Got '''
         f'''{len(a    )} arguments instead.'''    )

   # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
   for attribute_name, arg in kwargs.items():
     _A							=			getattr(self		,			f'''{attribute_name}_class'''    )
     # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
     _A							=			AUTO_TO_BASE_CLASS_MAPPING.get(a		,			a    )
     if isinstance(a		,			a    ):
       _A							=			tuple(getattr(a		,			a    ) for n in class_name if n is not None    )
     else:
       _A							=			getattr(a		,			a    )

     if not isinstance(a		,			a    ):
       raise ValueError(
           f'''Received a {type(a    ).__name__} for argument {attribute_name}, but a {class_name} was expected.'''    )

     setattr(self		,			a		,			a    )
 def __repr__(  self    )		->							str:




   """simple docstring"""







   _A							=			[f'''- {name}: {repr(getattr(self		,			a    )    )}''' for name in self.attributes]
   _A							=			'\n'.join(a    )
   return f'''{self.__class__.__name__}:\n{attributes_repr}'''
 def lowercase_						(  self		,			a		,			a = False		,			**a    )		->							Any:




   """simple docstring"""







   os.makedirs(a		,			exist_ok=a    )

   if push_to_hub:
     _A							=			kwargs.pop('''commit_message'''		,			a    )
     _A							=			kwargs.pop('''repo_id'''		,			save_directory.split(os.path.sep    )[-1]    )
     _A							=			self._create_repo(a		,			**a    )
     _A							=			self._get_files_timestamps(a    )
   # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
   # loaded from the Hub.
   if self._auto_class is not None:
     _A							=			[getattr(self		,			a    ) for attribute_name in self.attributes]
     _A							=			[(a.init_kwargs if isinstance(a		,			a    ) else a) for a in attrs]
     custom_object_save(self		,			a		,			config=a    )

   for attribute_name in self.attributes:
     _A							=			getattr(self		,			a    )
     # Include the processor class in the attribute config so this processor can then be reloaded with the
     # `AutoProcessor` API.
     if hasattr(a		,			'''_set_processor_class'''    ):
       attribute._set_processor_class(self.__class__.__name__    )
     attribute.save_pretrained(a    )

   if self._auto_class is not None:
     # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
     for attribute_name in self.attributes:
       _A							=			getattr(self		,			a    )
       if isinstance(a		,			a    ):
         del attribute.init_kwargs["auto_map"]

   if push_to_hub:
     self._upload_modified_files(
         a		,			a		,			a		,			commit_message=a		,			token=kwargs.get('''use_auth_token'''    )		,			)
 @classmethod
 def lowercase_						(  cls		,			a		,			a = None		,			a = False		,			a = False		,			a = None		,			a = "main"		,			**a		,			)		->							Optional[Any]:




   """simple docstring"""







   _A							=			cache_dir
   _A							=			force_download
   _A							=			local_files_only
   _A							=			revision

   _A							=			kwargs.pop('''use_auth_token'''		,			a    )
   if use_auth_token is not None:
     warnings.warn(
         '''The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'''		,			a    )
     if token is not None:
       raise ValueError(
           '''`token` and `use_auth_token` are both specified. Please set only the argument `token`.'''    )
     _A							=			use_auth_token

   if token is not None:
     # change to `token` in a follow-up PR
     _A							=			token

   _A							=			cls._get_arguments_from_pretrained(a		,			**a    )
   return cls(*a    )
 @classmethod
 def lowercase_						(  cls		,			a="AutoProcessor"    )		->							Optional[int]:




   """simple docstring"""







   if not isinstance(a		,			a    ):
     _A							=			auto_class.__name__

   import transformers.models.auto as auto_module

   if not hasattr(a		,			a    ):
     raise ValueError(f'''{auto_class} is not a valid auto class.'''    )

   _A							=			auto_class
 @classmethod
 def lowercase_						(  cls		,			a		,			**a    )		->							Optional[Any]:




   """simple docstring"""







   _A							=			[]
   for attribute_name in cls.attributes:
     _A							=			getattr(cls		,			f'''{attribute_name}_class'''    )
     if isinstance(a		,			a    ):
       _A							=			tuple(getattr(a		,			a    ) if n is not None else None for n in class_name    )
       _A							=			kwargs.get('''use_fast'''		,			a    )
       if use_fast and classes[1] is not None:
         _A							=			classes[1]
       else:
         _A							=			classes[0]
     else:
       _A							=			getattr(a		,			a    )

     args.append(attribute_class.from_pretrained(a		,			**a    )    )
   return args



 @property
 def lowercase_						(  self    )		->							Optional[Any]:




   """simple docstring"""







   _A							=			getattr(self		,			self.attributes[0]    )
   return getattr(a		,			'''model_input_names'''		,			a    )







__lowerCamelCase      =    copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
    __lowerCamelCase      =    ProcessorMixin.push_to_hub.__doc__.format(
        object="""processor""", object_class="""AutoProcessor""", object_files="""processor files"""
    )
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
UpperCAmelCase							= '''stas/tiny-wmt19-en-ru'''




class       __lowerCAmelCase     (				a_,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		str 			=       FSMTTokenizer
 _lowerCamelCase  :		Tuple 			=       False

 def     __snake_case    (	self				:						Dict     )       ->							Union[str, Any]:
        super().setUp()

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

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

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

 @cached_property
 def     __snake_case    (	self				:						Optional[int]     )       ->							Any:
        return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'     )

 def     __snake_case    (	self				:						Optional[Any]     )       ->							Any:
        _UpperCamelCase   :				Optional[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     __snake_case    (	self				:						List[str]     )       ->							List[Any]:
        _UpperCamelCase   :				Union[str, Any]       =     FSMTTokenizer(self.langs				,	self.src_vocab_file				,	self.tgt_vocab_file				,	self.merges_file     )

        _UpperCamelCase   :				Optional[int]       =     'lower'
        _UpperCamelCase   :				str       =     ['low', 'er</w>']
        _UpperCamelCase   :				Optional[Any]       =     tokenizer.tokenize(lowerCamelCase     )
        self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

        _UpperCamelCase   :				Tuple       =     tokens + ['<unk>']
        _UpperCamelCase   :				int       =     [1_4, 1_5, 2_0]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase     )				,	lowerCamelCase     )

 @slow
 def     __snake_case    (	self				:						List[Any]     )       ->							Union[str, Any]:
        _UpperCamelCase   :				Dict       =     self.tokenizer_ru_en

        _UpperCamelCase   :				List[str]       =     tokenizer.encode('sequence builders'				,	add_special_tokens=lowerCamelCase     )
        _UpperCamelCase   :				Any       =     tokenizer.encode('multi-sequence build'				,	add_special_tokens=lowerCamelCase     )

        _UpperCamelCase   :				List[Any]       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase     )
        _UpperCamelCase   :				Tuple       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase				,	lowerCamelCase     )

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

 @slow
 def     __snake_case    (	self				:						List[str]     )       ->							Optional[int]:
        _UpperCamelCase   :				Optional[Any]       =     self.tokenizer_en_ru
        _UpperCamelCase   :				Union[str, Any]       =     self.tokenizer_ru_en

        _UpperCamelCase   :				List[str]       =     [
            [
                '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   :				Union[str, Any]       =     tokenizer_enc.encode(lowerCamelCase				,	return_tensors=lowerCamelCase     )
               self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

               # and decode backward, using the reversed languages model
               _UpperCamelCase   :				Optional[int]       =     tokenizer_dec.decode(lowerCamelCase				,	skip_special_tokens=lowerCamelCase     )
               self.assertEqual(lowerCamelCase				,	lowerCamelCase     )

 @slow
 def     __snake_case    (	self				:						Any     )       ->							Tuple:
        _UpperCamelCase   :				Optional[int]       =     FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'				,	do_lower_case=lowerCamelCase     )
        _UpperCamelCase   :				str       =     tokenizer.tokenize('USA is United States of America'     )
        _UpperCamelCase   :				Optional[int]       =     ['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     __snake_case    (	self				:						Dict     )       ->							List[str]:
        pass

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



