
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_	:    Union[str, Any]  					=       logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
UpperCamelCase_	:    Optional[Any]  					=       direct_transformers_import(Path(__file__).parent)


UpperCamelCase_	:    Union[str, Any]  					=       {
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}




class      __lowercase	(	lowercase__					):
 _A   			=      ["""feature_extractor""", """tokenizer"""]
 # Names need to be attr_class for attr in attributes
 _A   			=      None
 _A   			=      None
 _A   			=      None






 def __init__(self							:   Dict			,  *snake_case							:   int			,  **snake_case							:   Union[str, Any]						)   -> int:
       # 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(_A			,  self.attributes						):
             if attribute_name in kwargs:
                   raise TypeError(F"""Got multiple values for argument {attribute_name}."""						)
             else:
                   _lowercase					:  Any    			=      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():
             _lowercase					:  List[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.
             _lowercase					:  int    			=      AUTO_TO_BASE_CLASS_MAPPING.get(_A			,  _A						)
             if isinstance(_A			,  _A						):
                   _lowercase					:  List[Any]    			=      tuple(getattr(_A			,  _A						) for n in class_name if n is not None						)
             else:
                   _lowercase					:  Optional[int]    			=      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							:   Union[str, Any]						)   -> Any:
       _lowercase					:  int    			=      [F"""- {name}: {repr(getattr(self			,  _A						)						)}""" for name in self.attributes]
       _lowercase					:  Union[str, Any]    			=      '\n'.join(_A						)
       return F"""{self.__class__.__name__}:\n{attributes_repr}"""






 def 			_a(self							:   List[Any]			,  snake_case							:   Any			,  snake_case							:   bool = False			,  **snake_case							:   List[str]						)   -> int:
       os.makedirs(_A			,  exist_ok=_A						)

       if push_to_hub:
             _lowercase					:  Dict    			=      kwargs.pop("commit_message"			,  _A						)
             _lowercase					:  Optional[Any]    			=      kwargs.pop("repo_id"			,  save_directory.split(os.path.sep						)[-1]						)
             _lowercase					:  List[str]    			=      self._create_repo(_A			,  **_A						)
             _lowercase					:  Optional[Any]    			=      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:
             _lowercase					:  Optional[Any]    			=      [getattr(self			,  _A						) for attribute_name in self.attributes]
             _lowercase					:  Dict    			=      [(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:
             _lowercase					:  Union[str, Any]    			=      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:
                   _lowercase					:  Optional[int]    			=      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 			_a(cls							:   str			,  snake_case							:   Union[str, os.PathLike]			,  snake_case							:   Optional[Union[str, os.PathLike]] = None			,  snake_case							:   bool = False			,  snake_case							:   bool = False			,  snake_case							:   Optional[Union[str, bool]] = None			,  snake_case							:   str = "main"			,  **snake_case							:   Any			,  )   -> Optional[int]:
       _lowercase					:  str    			=      cache_dir
       _lowercase					:  Any    			=      force_download
       _lowercase					:  List[str]    			=      local_files_only
       _lowercase					:  Optional[int]    			=      revision

       _lowercase					:  Optional[Any]    			=      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`."						)
             _lowercase					:  str    			=      use_auth_token

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

       _lowercase					:  Dict    			=      cls._get_arguments_from_pretrained(_A			,  **_A						)
       return cls(*_A						)






 @classmethod
 def 			_a(cls							:   str			,  snake_case							:   Optional[Any]="AutoProcessor"						)   -> Tuple:
       if not isinstance(_A			,  _A						):
             _lowercase					:  Dict    			=      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."""						)

       _lowercase					:  Tuple    			=      auto_class






 @classmethod
 def 			_a(cls							:   Dict			,  snake_case							:   int			,  **snake_case							:   List[Any]						)   -> str:
       _lowercase					:  List[str]    			=      []
       for attribute_name in cls.attributes:
             _lowercase					:  List[str]    			=      getattr(cls			,  F"""{attribute_name}_class"""						)
             if isinstance(_A			,  _A						):
                   _lowercase					:  int    			=      tuple(getattr(_A			,  _A						) if n is not None else None for n in class_name						)
                   _lowercase					:  Tuple    			=      kwargs.get("use_fast"			,  _A						)
                   if use_fast and classes[1] is not None:
                         _lowercase					:  Tuple    			=      classes[1]
                   else:
                         _lowercase					:  Optional[Any]    			=      classes[0]
             else:
                   _lowercase					:  List[str]    			=      getattr(_A			,  _A						)

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






 @property
 def 			_a(self							:   Tuple						)   -> Dict:
       _lowercase					:  Optional[Any]    			=      getattr(self			,  self.attributes[0]						)
       return getattr(_A			,  "model_input_names"			,  _A						)


UpperCamelCase_	:    str  					=       copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
       UpperCamelCase_	:    str  					=       ProcessorMixin.push_to_hub.__doc__.format(
           object="""processor""", object_class="""AutoProcessor""", object_files="""processor files"""
       )






import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class      __lowercase	(	__snake_case      ,   unittest.TestCase					):
	_A   			=      BertweetTokenizer
	_A   			=      False






	def 			_a(self							:   Tuple						)   -> Any:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							_lowercase					:  Tuple    			=      ["I", "m", "V@@", "R@@", "r", "e@@"]
							_lowercase					:  Tuple    			=      dict(zip(snake_case			,  range(len(snake_case						)						)						)						)
							_lowercase					:  Tuple    			=      ["#version: 0.2", "a m</w>"]
							_lowercase					:  Optional[Any]    			=      {"unk_token": "<unk>"}

							_lowercase					:  int    			=      os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]						)
							_lowercase					:  Optional[int]    			=      os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["merges_file"]						)
							with open(self.vocab_file			,  "w"			,  encoding="utf-8"						) as fp:
													for token in vocab_tokens:
																			fp.write(F"""{token} {vocab_tokens[token]}\n"""						)
							with open(self.merges_file			,  "w"			,  encoding="utf-8"						) as fp:
													fp.write("\n".join(snake_case						)						)






	def 			_a(self							:   List[Any]			,  **snake_case							:   Tuple						)   -> Union[str, Any]:
							kwargs.update(self.special_tokens_map						)
							return BertweetTokenizer.from_pretrained(self.tmpdirname			,  **snake_case						)






	def 			_a(self							:   Any			,  snake_case							:   Any						)   -> List[Any]:
							_lowercase					:  Tuple    			=      "I am VinAI Research"
							_lowercase					:  Optional[int]    			=      "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
							return input_text, output_text






	def 			_a(self							:   str						)   -> Optional[int]:
							_lowercase					:  str    			=      BertweetTokenizer(self.vocab_file			,  self.merges_file			,  **self.special_tokens_map						)
							_lowercase					:  Optional[Any]    			=      "I am VinAI Research"
							_lowercase					:  str    			=      "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
							_lowercase					:  Tuple    			=      tokenizer.tokenize(snake_case						)
							self.assertListEqual(snake_case			,  snake_case						)

							_lowercase					:  str    			=      tokens + [tokenizer.unk_token]

							_lowercase					:  str    			=      [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case						)			,  snake_case						)




