import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[Any]			):
						UpperCamelCase_		:							Dict  =		test_file.split(os.path.sep			)
						if components[0:2] != ["tests", "models"]:
												raise ValueError(
												    """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
												    f'''{test_file} instead.'''			)
						UpperCamelCase_		:							str  =		components[-1]
						if not test_fn.endswith("""py"""			):
												raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''			)
						if not test_fn.startswith("""test_modeling_"""			):
												raise ValueError(
												    f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''			)

						UpperCamelCase_		:							int  =		components[:-1] + [test_fn.replace(""".py"""		, """"""			)]
						UpperCamelCase_		:							Union[str, Any]  =		""".""".join(_SCREAMING_SNAKE_CASE			)

						return test_module_path






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[Any]			):
						UpperCamelCase_		:							List[str]  =		get_module_path(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							str  =		importlib.import_module(_SCREAMING_SNAKE_CASE			)

						return test_module






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[Any]			):
						UpperCamelCase_		:							Optional[int]  =		[]
						UpperCamelCase_		:							Dict  =		get_test_module(_SCREAMING_SNAKE_CASE			)
						for attr in dir(_SCREAMING_SNAKE_CASE			):
												if attr.endswith("""ModelTester"""			):
																		tester_classes.append(getattr(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)			)

    # sort with class names
						return sorted(_SCREAMING_SNAKE_CASE		, key=lambda _SCREAMING_SNAKE_CASE			: x.__name__			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[Any]			):
						UpperCamelCase_		:							Tuple  =		[]
						UpperCamelCase_		:							List[Any]  =		get_test_module(_SCREAMING_SNAKE_CASE			)
						for attr in dir(_SCREAMING_SNAKE_CASE			):
												UpperCamelCase_		:							Union[str, Any]  =		getattr(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
												# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
												# `all_model_classes` is not empty (which also excludes other special classes).
												UpperCamelCase_		:							Union[str, Any]  =		getattr(_SCREAMING_SNAKE_CASE		, """all_model_classes"""		, []			)
												if len(_SCREAMING_SNAKE_CASE			) > 0:
																		test_classes.append(_SCREAMING_SNAKE_CASE			)

    # sort with class names
						return sorted(_SCREAMING_SNAKE_CASE		, key=lambda _SCREAMING_SNAKE_CASE			: x.__name__			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple			):
						UpperCamelCase_		:							Tuple  =		get_test_classes(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							List[str]  =		set()
						for test_class in test_classes:
												model_classes.update(test_class.all_model_classes			)

						# sort with class names
						return sorted(_SCREAMING_SNAKE_CASE		, key=lambda _SCREAMING_SNAKE_CASE			: x.__name__			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							str  =		test_class()
						if hasattr(_SCREAMING_SNAKE_CASE		, """setUp"""			):
												test.setUp()

						UpperCamelCase_		:							Optional[int]  =		None
						if hasattr(_SCREAMING_SNAKE_CASE		, """model_tester"""			):
												# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
												if test.model_tester is not None:
																		UpperCamelCase_		:							Tuple  =		test.model_tester.__class__

						return model_tester






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]		, _SCREAMING_SNAKE_CASE     :				Any			):
						UpperCamelCase_		:							Optional[int]  =		get_test_classes(_SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							Tuple  =		[]
						for test_class in test_classes:
												if model_class in test_class.all_model_classes:
																		target_test_classes.append(_SCREAMING_SNAKE_CASE			)

    # sort with class names
						return sorted(_SCREAMING_SNAKE_CASE		, key=lambda _SCREAMING_SNAKE_CASE			: x.__name__			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				int			):
						UpperCamelCase_		:							Dict  =		get_test_classes_for_model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							List[Any]  =		[]
						for test_class in test_classes:
												UpperCamelCase_		:							str  =		get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE			)
												if tester_class is not None:
																		tester_classes.append(_SCREAMING_SNAKE_CASE			)

    # sort with class names
						return sorted(_SCREAMING_SNAKE_CASE		, key=lambda _SCREAMING_SNAKE_CASE			: x.__name__			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str			):
						UpperCamelCase_		:							Dict  =		get_test_classes(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Tuple  =		{test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE			) for test_class in test_classes}
						return test_tester_mapping






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							Optional[int]  =		get_model_classes(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Optional[Any]  =		{
						    model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			) for model_class in model_classes
						}
						return model_test_mapping






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple			):
						UpperCamelCase_		:							Optional[int]  =		get_model_classes(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Dict  =		{
						    model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			) for model_class in model_classes
						}
						return model_to_tester_mapping







def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				int			):
						if isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			):
												return o
						elif isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			):
												return o.__name__
						elif isinstance(_SCREAMING_SNAKE_CASE		, (list, tuple)			):
												return [to_json(_SCREAMING_SNAKE_CASE			) for x in o]
						elif isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			):
												return {to_json(_SCREAMING_SNAKE_CASE			): to_json(_SCREAMING_SNAKE_CASE			) for k, v in o.items()}
						else:
												return o







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	"▁"

SCREAMING_SNAKE_CASE		:     Dict         =	{"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE		:     List[Any]         =	{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

SCREAMING_SNAKE_CASE		:     Any         =	{
    "google/reformer-crime-and-punishment": 524288,
}

class        UpperCamelCase						(				__a				):
						a__		:Optional[Any]      =			VOCAB_FILES_NAMES
						a__		:List[Any]      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:List[Any]      =			['''input_ids''', '''attention_mask''']





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase="</s>"			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase=[]			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				None:
												UpperCamelCase_		:							List[str]  =		{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=__UpperCamelCase			,     unk_token=__UpperCamelCase			,     additional_special_tokens=__UpperCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							int  =		vocab_file
												UpperCamelCase_		:							str  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Dict:
												return self.sp_model.get_piece_size()





						def        A_							(self						)				->				Dict[str, int]:
												UpperCamelCase_		:							str  =		{self.convert_ids_to_tokens(__UpperCamelCase						): i for i in range(self.vocab_size						)}
												vocab.update(self.added_tokens_encoder						)
												return vocab





						def __getstate__(self						)				->				Any:
												UpperCamelCase_		:							Dict  =		self.__dict__.copy()
												UpperCamelCase_		:							List[str]  =		None
												return state





						def __setstate__(self			,     __UpperCamelCase						)				->				List[Any]:
												UpperCamelCase_		:							List[Any]  =		d

												# for backward compatibility
												if not hasattr(self			,     """sp_model_kwargs"""						):
																		UpperCamelCase_		:							Any  =		{}

												UpperCamelCase_		:							Dict  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(self.vocab_file						)





						def        A_							(self			,     __UpperCamelCase						)				->				List[str]:
												return self.sp_model.encode(__UpperCamelCase			,     out_type=__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Union[str, Any]:
												return self.sp_model.piece_to_id(__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Dict:
												if index < self.sp_model.get_piece_size():
																		UpperCamelCase_		:							Union[str, Any]  =		self.sp_model.IdToPiece(__UpperCamelCase						)
												return token





						def        A_							(self			,     __UpperCamelCase						)				->				int:
												UpperCamelCase_		:							Dict  =		[]
												UpperCamelCase_		:							Union[str, Any]  =		""""""
												for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																								out_string += self.sp_model.decode(__UpperCamelCase						) + token
																								UpperCamelCase_		:							Optional[Any]  =		[]
																		else:
																								current_sub_tokens.append(__UpperCamelCase						)
												out_string += self.sp_model.decode(__UpperCamelCase						)
												return out_string.strip()





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												if not os.path.isdir(__UpperCamelCase						):
																		logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
																		return
												UpperCamelCase_		:							Tuple  =		os.path.join(
												    __UpperCamelCase			,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

												if os.path.abspath(self.vocab_file						) != os.path.abspath(__UpperCamelCase						) and os.path.isfile(self.vocab_file						):
																		copyfile(self.vocab_file			,     __UpperCamelCase						)
												elif not os.path.isfile(self.vocab_file						):
																		with open(__UpperCamelCase			,     """wb"""						) as fi:
																								UpperCamelCase_		:							int  =		self.sp_model.serialized_model_proto()
																								fi.write(__UpperCamelCase						)

												return (out_vocab_file,)






