
"""simple docstring"""





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_					:						Dict				= logging.get_logger(__name__)

UpperCamelCase_					:						Tuple				= {'''vocab_file''': '''sentencepiece.bpe.model'''}

UpperCamelCase_					:						Union[str, Any]				= {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
}

UpperCamelCase_					:						Dict				= {
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

UpperCamelCase_					:						Union[str, Any]				= '''▁'''



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				VOCAB_FILES_NAMES
			snake_case	      =				PRETRAINED_VOCAB_FILES_MAP
			snake_case	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case	      =				["input_ids", "attention_mask"]




			def __init__(				self						:					Tuple       ,   _snake_case						:					List[str]       ,   _snake_case						:					str="<s>"       ,   _snake_case						:					Optional[int]="</s>"       ,   _snake_case						:					Any="</s>"       ,   _snake_case						:					List[Any]="<s>"       ,   _snake_case						:					Any="<unk>"       ,   _snake_case						:					List[Any]="<pad>"       ,   _snake_case						:					Optional[Any]="<mask>"       ,   _snake_case						:					Optional[Dict[str, Any]] = None       ,   **_snake_case						:					Any       ,   )      -> None:







								"""simple docstring"""
								# Mask token behave like a normal word, i.e. include the space before it
								A_				 =						AddedToken(_snake_case       ,   lstrip=_snake_case       ,   rstrip=_snake_case	) if isinstance(_snake_case       ,   _snake_case	) else mask_token

								A_				 =						{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=_snake_case       ,   eos_token=_snake_case       ,   unk_token=_snake_case       ,   sep_token=_snake_case       ,   cls_token=_snake_case       ,   pad_token=_snake_case       ,   mask_token=_snake_case       ,   sp_model_kwargs=self.sp_model_kwargs       ,   **_snake_case       ,   )

								A_				 =						vocab_file
								A_				 =						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
								self.sp_model.Load(str(_snake_case	)	)

								A_				 =						{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

								A_				 =						len(self.sp_model	) - 1
								A_				 =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					List[int]       ,   _snake_case						:					Optional[List[int]] = None	)      -> List[int]:







								"""simple docstring"""

								if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								A_				 =						[self.cls_token_id]
								A_				 =						[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					List[int]       ,   _snake_case						:					Optional[List[int]] = None       ,   _snake_case						:					bool = False	)      -> List[int]:







								"""simple docstring"""
								if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_snake_case       ,   token_ids_a=_snake_case       ,   already_has_special_tokens=_snake_case	)

								if token_ids_a is None:
													return [1] + ([0] * len(_snake_case	)) + [1]
								return [1] + ([0] * len(_snake_case	)) + [1, 1] + ([0] * len(_snake_case	)) + [1]




			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					List[int]       ,   _snake_case						:					Optional[List[int]] = None	)      -> List[int]:







								"""simple docstring"""
								A_				 =						[self.sep_token_id]
								A_				 =						[self.cls_token_id]

								if token_ids_a is None:
													return len(cls + token_ids_a + sep	) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]




			@property
			def      lowerCamelCase__     (				self						:					List[str]	)      -> Optional[Any]:







								"""simple docstring"""
								return len(self.sp_model	)




			def      lowerCamelCase__     (				self						:					int	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						{self.convert_ids_to_tokens(_snake_case	): i for i in range(self.vocab_size	)}
								vocab.update(self.added_tokens_encoder	)
								return vocab




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str	)      -> List[str]:







								"""simple docstring"""
								return self.sp_model.encode(_snake_case       ,   out_type=_snake_case	)




			def      lowerCamelCase__     (				self						:					List[str]       ,   _snake_case						:					Any	)      -> Union[str, Any]:







								"""simple docstring"""
								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								A_				 =						self.sp_model.PieceToId(_snake_case	)

								return spm_id if spm_id else self.unk_token_id




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					List[Any]	)      -> Union[str, Any]:







								"""simple docstring"""
								if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(_snake_case	)




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					Any	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						[]
								A_				 =						""
								A_				 =						False
								for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																		if not prev_is_special:
																							out_string += " "
																		out_string += self.sp_model.decode(_snake_case	) + token
																		A_				 =						True
																		A_				 =						[]
													else:
																		current_sub_tokens.append(_snake_case	)
																		A_				 =						False
								out_string += self.sp_model.decode(_snake_case	)
								return out_string.strip()




			def __getstate__(				self						:					Optional[int]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.__dict__.copy()
								A_				 =						None
								return state




			def __setstate__(				self						:					Optional[Any]       ,   _snake_case						:					List[str]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						d

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

								A_				 =						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
								self.sp_model.Load(self.vocab_file	)






			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					str       ,   _snake_case						:					Optional[str] = None	)      -> Tuple[str]:







								"""simple docstring"""
								if not os.path.isdir(_snake_case	):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
													return
								A_				 =						os.path.join(
								    _snake_case       ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

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

								return (out_vocab_file,)






"""simple docstring"""





import os

import pytest
from attr import dataclass


UpperCamelCase_					:						str				= '''us-east-1'''  # defaults region



@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				42
			snake_case	      =				"arn:aws:iam::558105141721:role/sagemaker_execution_role"
			snake_case	      =				{
			    "task_name": "mnli",
			    "per_device_train_batch_size": 16,
			    "per_device_eval_batch_size": 16,
			    "do_train": True,
			    "do_eval": True,
			    "do_predict": True,
			    "output_dir": "/opt/ml/model",
			    "overwrite_output_dir": True,
			    "max_steps": 5_00,
			    "save_steps": 55_00,
			}
			snake_case	      =				{**hyperparameters, "max_steps": 10_00}




			@property
			def      lowerCamelCase__     (				self						:					int	)      -> str:







								"""simple docstring"""
								if self.framework == "pytorch":
													return [
													    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
													    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
													    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
													]
								else:
													return [
													    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
													    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
													    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
													]




			@property
			def      lowerCamelCase__     (				self						:					Any	)      -> str:







								"""simple docstring"""
								return F'{self.framework}-transfromers-test'




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> str:







								"""simple docstring"""
								return F'./tests/sagemaker/scripts/{self.framework}'






			@property
			def      lowerCamelCase__     (				self						:					Any	)      -> str:







								"""simple docstring"""
								if self.framework == "pytorch":
													return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
								else:
													return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"





@pytest.fixture(scope="class"				)
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						SageMakerTestEnvironment(framework=request.cls.framework				)




