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


UpperCAmelCase							= logging.get_logger(__name__)

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


UpperCAmelCase							= {
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Optional[int] 			=       ['feature_extractor', 'tokenizer']
 # Names need to be attr_class for attr in attributes
 _lowerCamelCase  :		List[Any] 			=       None
 _lowerCamelCase  :		Optional[Any] 			=       None
 _lowerCamelCase  :		Tuple 			=       None

 def __init__(	self				:						Tuple				,	*lowerCamelCase				:						Optional[Any]				,	**lowerCamelCase				:						List[Any]     )       ->							str:
        # Sanitize args and kwargs
        for key in kwargs:
               if key not in self.attributes:
                      raise TypeError(f"Unexpected keyword argument {key}."     )
        for arg, attribute_name in zip(lowerCamelCase				,	self.attributes     ):
               if attribute_name in kwargs:
                      raise TypeError(f"Got multiple values for argument {attribute_name}."     )
               else:
                      _UpperCamelCase   :				Any       =     arg

        if len(lowerCamelCase     ) != len(self.attributes     ):
               raise ValueError(
                   f"This processor requires {len(self.attributes     )} arguments: {', '.join(self.attributes     )}. Got "
                   f"{len(lowerCamelCase     )} 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():
               _UpperCamelCase   :				Union[str, Any]       =     getattr(self				,	f"{attribute_name}_class"     )
               # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
               _UpperCamelCase   :				str       =     AUTO_TO_BASE_CLASS_MAPPING.get(lowerCamelCase				,	lowerCamelCase     )
               if isinstance(lowerCamelCase				,	lowerCamelCase     ):
                      _UpperCamelCase   :				int       =     tuple(getattr(lowerCamelCase				,	lowerCamelCase     ) for n in class_name if n is not None     )
               else:
                      _UpperCamelCase   :				Optional[int]       =     getattr(lowerCamelCase				,	lowerCamelCase     )

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

               setattr(self				,	lowerCamelCase				,	lowerCamelCase     )

 def __repr__(	self				:						Dict     )       ->							str:
        _UpperCamelCase   :				List[str]       =     [f"- {name}: {repr(getattr(self				,	lowerCamelCase     )     )}" for name in self.attributes]
        _UpperCamelCase   :				int       =     '\n'.join(lowerCamelCase     )
        return f"{self.__class__.__name__}:\n{attributes_repr}"

 def     __snake_case    (	self				:						Tuple				,	lowerCamelCase				:						int				,	lowerCamelCase				:						bool = False				,	**lowerCamelCase				:						Optional[Any]     )       ->							Any:
        os.makedirs(lowerCamelCase				,	exist_ok=lowerCamelCase     )

        if push_to_hub:
               _UpperCamelCase   :				Optional[int]       =     kwargs.pop('commit_message'				,	lowerCamelCase     )
               _UpperCamelCase   :				List[Any]       =     kwargs.pop('repo_id'				,	save_directory.split(os.path.sep     )[-1]     )
               _UpperCamelCase   :				Optional[int]       =     self._create_repo(lowerCamelCase				,	**lowerCamelCase     )
               _UpperCamelCase   :				List[str]       =     self._get_files_timestamps(lowerCamelCase     )
        # 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:
               _UpperCamelCase   :				Optional[int]       =     [getattr(self				,	lowerCamelCase     ) for attribute_name in self.attributes]
               _UpperCamelCase   :				int       =     [(a.init_kwargs if isinstance(lowerCamelCase				,	lowerCamelCase     ) else a) for a in attrs]
               custom_object_save(self				,	lowerCamelCase				,	config=lowerCamelCase     )

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

        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:
                      _UpperCamelCase   :				Optional[Any]       =     getattr(self				,	lowerCamelCase     )
                      if isinstance(lowerCamelCase				,	lowerCamelCase     ):
                             del attribute.init_kwargs["auto_map"]

        if push_to_hub:
               self._upload_modified_files(
                   lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	commit_message=lowerCamelCase				,	token=kwargs.get('use_auth_token'     )				,	)

 @classmethod
 def     __snake_case    (	cls				:						Tuple				,	lowerCamelCase				:						Union[str, os.PathLike]				,	lowerCamelCase				:						Optional[Union[str, os.PathLike]] = None				,	lowerCamelCase				:						bool = False				,	lowerCamelCase				:						bool = False				,	lowerCamelCase				:						Optional[Union[str, bool]] = None				,	lowerCamelCase				:						str = "main"				,	**lowerCamelCase				:						Tuple				,	)       ->							Optional[Any]:
        _UpperCamelCase   :				Any       =     cache_dir
        _UpperCamelCase   :				Optional[int]       =     force_download
        _UpperCamelCase   :				int       =     local_files_only
        _UpperCamelCase   :				Dict       =     revision

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

        if token is not None:
               # change to `token` in a follow-up PR
               _UpperCamelCase   :				List[Any]       =     token

        _UpperCamelCase   :				List[Any]       =     cls._get_arguments_from_pretrained(lowerCamelCase				,	**lowerCamelCase     )
        return cls(*lowerCamelCase     )

 @classmethod
 def     __snake_case    (	cls				:						Dict				,	lowerCamelCase				:						Optional[Any]="AutoProcessor"     )       ->							Optional[int]:
        if not isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				Tuple       =     auto_class.__name__

        import transformers.models.auto as auto_module

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

        _UpperCamelCase   :				Union[str, Any]       =     auto_class

 @classmethod
 def     __snake_case    (	cls				:						int				,	lowerCamelCase				:						Any				,	**lowerCamelCase				:						List[Any]     )       ->							Optional[Any]:
        _UpperCamelCase   :				Optional[int]       =     []
        for attribute_name in cls.attributes:
               _UpperCamelCase   :				Dict       =     getattr(cls				,	f"{attribute_name}_class"     )
               if isinstance(lowerCamelCase				,	lowerCamelCase     ):
                      _UpperCamelCase   :				Any       =     tuple(getattr(lowerCamelCase				,	lowerCamelCase     ) if n is not None else None for n in class_name     )
                      _UpperCamelCase   :				List[Any]       =     kwargs.get('use_fast'				,	lowerCamelCase     )
                      if use_fast and classes[1] is not None:
                             _UpperCamelCase   :				Optional[int]       =     classes[1]
                      else:
                             _UpperCamelCase   :				Any       =     classes[0]
               else:
                      _UpperCamelCase   :				Any       =     getattr(lowerCamelCase				,	lowerCamelCase     )

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

 @property
 def     __snake_case    (	self				:						List[Any]     )       ->							Optional[Any]:
        _UpperCamelCase   :				List[str]       =     getattr(self				,	self.attributes[0]     )
        return getattr(lowerCamelCase				,	'model_input_names'				,	lowerCamelCase     )







UpperCAmelCase							= copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
 UpperCAmelCase							= 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



