






import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowercase     :							Tuple	     =			logging.get_logger(__name__)

lowercase     :							Dict	     =			{
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowercase     :							Dict	     =			{
    '''source_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
    },
    '''target_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
    },
    '''vocab''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
    },
    '''tokenizer_config_file''': {
        '''Helsinki-NLP/opus-mt-en-de''': (
            '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
        )
    },
}

lowercase     :							Optional[int]	     =			{'''Helsinki-NLP/opus-mt-en-de''': 5_12}
lowercase     :							str	     =			{}




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      str						 =    VOCAB_FILES_NAMES
						A   :      Optional[int]						 =    PRETRAINED_VOCAB_FILES_MAP
						A   :      int						 =    PRETRAINED_INIT_CONFIGURATION
						A   :      Dict						 =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A   :      Tuple						 =    ['input_ids', 'attention_mask']
						A   :      str						 =    re.compile('>>.+<<'    )  # type: re.Pattern






						def __init__(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE=None						, _SCREAMING_SNAKE_CASE="<unk>"						, _SCREAMING_SNAKE_CASE="</s>"						, _SCREAMING_SNAKE_CASE="<pad>"						, _SCREAMING_SNAKE_CASE=512						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE=False						, **_SCREAMING_SNAKE_CASE						, )       -> None:
								snake_case_ :							Tuple         =				{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
								    source_lang=_SCREAMING_SNAKE_CASE						, target_lang=_SCREAMING_SNAKE_CASE						, unk_token=_SCREAMING_SNAKE_CASE						, eos_token=_SCREAMING_SNAKE_CASE						, pad_token=_SCREAMING_SNAKE_CASE						, model_max_length=_SCREAMING_SNAKE_CASE						, sp_model_kwargs=self.sp_model_kwargs						, target_vocab_file=_SCREAMING_SNAKE_CASE						, separate_vocabs=_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE						, )
								assert Path(_SCREAMING_SNAKE_CASE ).exists(), f'''cannot find spm source {source_spm}'''

								snake_case_ :							str         =				separate_vocabs
								snake_case_ :							List[str]         =				load_json(_SCREAMING_SNAKE_CASE )
								if self.unk_token not in self.encoder:
										raise KeyError("<unk> token must be in vocab" )
								assert self.pad_token in self.encoder

								if separate_vocabs:
										snake_case_ :							Optional[int]         =				load_json(_SCREAMING_SNAKE_CASE )
										snake_case_ :							Union[str, Any]         =				{v: k for k, v in self.target_encoder.items()}
										snake_case_ :							str         =				[]
								else:
										snake_case_ :							Optional[int]         =				{v: k for k, v in self.encoder.items()}
										snake_case_ :							list         =				[k for k in self.encoder if k.startswith(">>" ) and k.endswith("<<" )]

								snake_case_ :							Optional[int]         =				source_lang
								snake_case_ :							List[Any]         =				target_lang
								snake_case_ :							Dict         =				[source_spm, target_spm]

								# load SentencePiece model for pre-processing
								snake_case_ :							Tuple         =				load_spm(_SCREAMING_SNAKE_CASE						, self.sp_model_kwargs )
								snake_case_ :							int         =				load_spm(_SCREAMING_SNAKE_CASE						, self.sp_model_kwargs )
								snake_case_ :							Dict         =				self.spm_source
								snake_case_ :							Any         =				self.encoder

								# Multilingual target side: default to using first supported language code.

								self._setup_normalizer()






						def 					_lowerCAmelCase				(		self )       -> List[Any]:
								try:
										from sacremoses import MosesPunctNormalizer

										snake_case_ :							Any         =				MosesPunctNormalizer(self.source_lang ).normalize
								except (ImportError, FileNotFoundError):
										warnings.warn("Recommended: pip install sacremoses." )
										snake_case_ :							Union[str, Any]         =				lambda _SCREAMING_SNAKE_CASE : x






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> str:
								return self.punc_normalizer(_SCREAMING_SNAKE_CASE ) if x else ""






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> Optional[Any]:
								return self.current_encoder.get(_SCREAMING_SNAKE_CASE						, self.current_encoder[self.unk_token] )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> Optional[Any]:
								snake_case_ :							List[str]         =				self.language_code_re.match(_SCREAMING_SNAKE_CASE )
								snake_case_ :							list         =				[match.group(0 )] if match else []
								return code, self.language_code_re.sub(""						, _SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> List[str]:
								snake_case_       ,				snake_case_ :							Union[str, Any]         =				self.remove_language_code(_SCREAMING_SNAKE_CASE )
								snake_case_ :							str         =				self.current_spm.encode(_SCREAMING_SNAKE_CASE						, out_type=_SCREAMING_SNAKE_CASE )
								return code + pieces






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> str:
								return self.decoder.get(_SCREAMING_SNAKE_CASE						, self.unk_token )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE )       -> Tuple:
								return super().batch_decode(_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE )       -> str:
								return super().decode(_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> str:
								snake_case_ :							int         =				self.spm_source if self._decode_use_source_tokenizer else self.spm_target
								snake_case_ :							str         =				[]
								snake_case_ :							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 += sp_model.decode_pieces(_SCREAMING_SNAKE_CASE ) + token + " "
												snake_case_ :							Optional[int]         =				[]
										else:
												current_sub_tokens.append(_SCREAMING_SNAKE_CASE )
								out_string += sp_model.decode_pieces(_SCREAMING_SNAKE_CASE )
								return out_string.strip()






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE=None )       -> List[int]:
								if token_ids_a is None:
										return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]






						def 					_lowerCAmelCase				(		self )       -> Tuple:
								snake_case_ :							Dict         =				self.spm_source
								snake_case_ :							str         =				self.encoder






						def 					_lowerCAmelCase				(		self )       -> Union[str, Any]:
								snake_case_ :							Optional[Any]         =				self.spm_target
								if self.separate_vocabs:
										snake_case_ :							Optional[int]         =				self.target_encoder






						@property
						def 					_lowerCAmelCase				(		self )       -> int:
								return len(self.encoder )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = None )       -> Tuple[str]:
								if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
										logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
										return
								snake_case_ :							List[str]         =				[]

								if self.separate_vocabs:
										snake_case_ :							List[str]         =				os.path.join(
										    _SCREAMING_SNAKE_CASE						, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]						, )
										snake_case_ :							Optional[int]         =				os.path.join(
										    _SCREAMING_SNAKE_CASE						, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]						, )
										save_json(self.encoder						, _SCREAMING_SNAKE_CASE )
										save_json(self.target_encoder						, _SCREAMING_SNAKE_CASE )
										saved_files.append(_SCREAMING_SNAKE_CASE )
										saved_files.append(_SCREAMING_SNAKE_CASE )
								else:
										snake_case_ :							int         =				os.path.join(
										    _SCREAMING_SNAKE_CASE						, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"] )
										save_json(self.encoder						, _SCREAMING_SNAKE_CASE )
										saved_files.append(_SCREAMING_SNAKE_CASE )

								for spm_save_filename, spm_orig_path, spm_model in zip(
								    [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]						, self.spm_files						, [self.spm_source, self.spm_target]						, ):
										snake_case_ :							Any         =				os.path.join(
										    _SCREAMING_SNAKE_CASE						, (filename_prefix + "-" if filename_prefix else "") + spm_save_filename )
										if os.path.abspath(_SCREAMING_SNAKE_CASE ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(_SCREAMING_SNAKE_CASE ):
												copyfile(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )
												saved_files.append(_SCREAMING_SNAKE_CASE )
										elif not os.path.isfile(_SCREAMING_SNAKE_CASE ):
												with open(_SCREAMING_SNAKE_CASE						, "wb" ) as fi:
														snake_case_ :							int         =				spm_model.serialized_model_proto()
														fi.write(_SCREAMING_SNAKE_CASE )
												saved_files.append(_SCREAMING_SNAKE_CASE )

								return tuple(_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> Dict:
								return self.get_src_vocab()






						def 					_lowerCAmelCase				(		self )       -> int:
								return dict(self.encoder						, **self.added_tokens_encoder )






						def 					_lowerCAmelCase				(		self )       -> Tuple:
								return dict(self.target_encoder						, **self.added_tokens_decoder )






						def __getstate__(		self )       -> Dict:
								snake_case_ :							Tuple         =				self.__dict__.copy()
								state.update(
								    {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]} )
								return state






						def __setstate__(		self						, _SCREAMING_SNAKE_CASE )       -> None:
								snake_case_ :							Union[str, Any]         =				d

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

								snake_case_       ,				snake_case_ :							Optional[Any]         =				(load_spm(_SCREAMING_SNAKE_CASE						, self.sp_model_kwargs ) for f in self.spm_files)
								snake_case_ :							List[str]         =				self.spm_source
								self._setup_normalizer()






						def 					_lowerCAmelCase				(		self						, *_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE )       -> Any:
								return 1






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> Tuple:
								snake_case_ :							int         =				set(self.all_special_ids )  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special
								return [1 if x in all_special_ids else 0 for x in seq]






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = False )       -> List[int]:
								if already_has_special_tokens:
										return self._special_token_mask(_SCREAMING_SNAKE_CASE )
								elif token_ids_a is None:
										return self._special_token_mask(_SCREAMING_SNAKE_CASE ) + [1]
								else:
										return self._special_token_mask(token_ids_a + token_ids_a ) + [1]




def    lowerCAmelCase__		(			_a				:	str     ,					_a				:	Dict[str, Any]			):
		snake_case_ :							Any         =				sentencepiece.SentencePieceProcessor(**_a			)
		spm.Load(_a			)
		return spm

def    lowerCAmelCase__		(			_a				:	List[Any]     ,					_a				:	str			):
		with open(_a     ,					"w"			) as f:
				json.dump(_a     ,					_a     ,					indent=2			)

def    lowerCAmelCase__		(			_a				:	str			):
		with open(_a     ,					"r"			) as f:
				return json.load(_a			)








import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
							lowercase     :							List[Any]	     =			[]

							lowercase     :							Optional[int]	     =			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
							lowercase     :							Optional[Any]	     =			glob.glob(pattern)
							# TODO: deal with TF/Flax too
							lowercase     :							Tuple	     =			[
							    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
							]

							for test_file in test_files:
														lowercase     :							Union[str, Any]	     =			get_tester_classes(test_file)
														for tester_class in tester_classes:
																					# A few tester classes don't have `parent` parameter in `__init__`.
																					# TODO: deal this better
																					try:
																												lowercase     :							Union[str, Any]	     =			tester_class(parent=None)
																					except Exception:
																												continue
																					if hasattr(tester, '''get_config'''):
																												lowercase     :							Tuple	     =			tester.get_config()
																												for k, v in config.to_dict().items():
																																			if isinstance(v, int):
																																										lowercase     :							Tuple	     =			None
																																										if k in ["vocab_size"]:
																																																	lowercase     :							str	     =			1_00
																																										elif k in ["max_position_embeddings"]:
																																																	lowercase     :							List[str]	     =			1_28
																																										elif k in ["hidden_size", "d_model"]:
																																																	lowercase     :							Union[str, Any]	     =			40
																																										elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																																	lowercase     :							List[str]	     =			5
																																										if target is not None and v > target:
																																																	failures.append(
																																																	    F"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
																																																	    F""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
																																																	    F""" than {target}."""
																																																	)

							if len(failures) > 0:
														raise Exception(F"""There were {len(failures)} failures:\n""" + '''\n'''.join(failures))
