





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType



class    __lowerCAmelCase			:






			def __init__(self   ,				__magic_name__=2   ,				__magic_name__=3   ,				__magic_name__=64   ,				__magic_name__=None       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    np.random.default_rng(a__       )
							snake_case_		:       Dict           =    length
							snake_case_		:       List[str]           =    rng.normal(size=(length,)       ).astype(np.floataa       )
							snake_case_		:       List[Any]           =    a * self.x + b + rng.normal(scale=0.1   ,				size=(length,)       ).astype(np.floataa       )






			def __len__(self       )	->  int:


							'''simple docstring'''



							return self.length






			def __getitem__(self   ,				__magic_name__       )	->  str:


							'''simple docstring'''



							return {"x": self.x[i], "y": self.y[i]}




class    __lowerCAmelCase			(     torch.nn.Module	):






			def __init__(self   ,				__magic_name__=0   ,				__magic_name__=0   ,				__magic_name__=False       )	->  Union[str, Any]:


							'''simple docstring'''



							super().__init__()
							snake_case_		:       Union[str, Any]           =    torch.nn.Parameter(torch.tensor([2, 3]       ).float()       )
							snake_case_		:       Any           =    torch.nn.Parameter(torch.tensor([2, 3]       ).float()       )
							snake_case_		:       Optional[int]           =    True






			def      lowerCamelCase    (self   ,				__magic_name__=None       )	->  Tuple:


							'''simple docstring'''



							if self.first_batch:
											print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''       )
											snake_case_		:       Optional[Any]           =    False
							return x * self.a[0] + self.b[0]




class    __lowerCAmelCase			(     torch.nn.Module	):






			def __init__(self   ,				__magic_name__=0   ,				__magic_name__=0   ,				__magic_name__=False       )	->  Union[str, Any]:


							'''simple docstring'''



							super().__init__()
							snake_case_		:       str           =    torch.nn.Parameter(torch.tensor(a__       ).float()       )
							snake_case_		:       Union[str, Any]           =    torch.nn.Parameter(torch.tensor(a__       ).float()       )
							snake_case_		:       Optional[Any]           =    True






			def      lowerCamelCase    (self   ,				__magic_name__=None       )	->  List[Any]:


							'''simple docstring'''



							if self.first_batch:
											print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''       )
											snake_case_		:       Optional[int]           =    False
							return x * self.a + self.b






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase = 16					)						->		Dict:






				"""simple docstring"""


				from datasets import load_dataset
				from transformers import AutoTokenizer

				snake_case_		:       Tuple           =    AutoTokenizer.from_pretrained('''bert-base-cased'''					)
				snake_case_		:       Dict           =    {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
				snake_case_		:       str           =    load_dataset('''csv'''					,							data_files=snake_case__					)
				snake_case_		:       str           =    datasets["""train"""].unique('''label'''					)

				snake_case_		:       Tuple           =    {v: i for i, v in enumerate(snake_case__					)}

				def tokenize_function(_UpperCamelCase					):
								# max_length=None => use the model max length (it's actually the default)
								snake_case_		:       int           =    tokenizer(
								    examples['''sentence1''']					,							examples['''sentence2''']					,							truncation=snake_case__					,							max_length=snake_case__					,							padding='''max_length'''					)
								if "label" in examples:
												snake_case_		:       Optional[Any]           =    [label_to_id[l] for l in examples["""label"""]]
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				snake_case_		:       Union[str, Any]           =    datasets.map(
				    snake_case__					,							batched=snake_case__					,							remove_columns=['''sentence1''', '''sentence2''', '''label''']					,							)

				def collate_fn(_UpperCamelCase					):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(snake_case__					,							padding='''max_length'''					,							max_length=128					,							return_tensors='''pt'''					)
								return tokenizer.pad(snake_case__					,							padding='''longest'''					,							return_tensors='''pt'''					)

				# Instantiate dataloaders.
				snake_case_		:       Dict           =    DataLoader(tokenized_datasets['''train''']					,							shuffle=snake_case__					,							collate_fn=snake_case__					,							batch_size=2					)
				snake_case_		:       Tuple           =    DataLoader(tokenized_datasets['''validation''']					,							shuffle=snake_case__					,							collate_fn=snake_case__					,							batch_size=1					)

				return train_dataloader, eval_dataloader








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

import sentencepiece as spm

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


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {"""vocab_file""": """sentencepiece.model"""}

lowerCamelCase__       		=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCamelCase__       		=  {
    """google/rembert""": 256,
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			VOCAB_FILES_NAMES
       lowerCamelCase				:					Tuple								=			PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase				:					Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



       def __init__(				self      ,    a__      ,    a__=False      ,    a__=True      ,    a__=True      ,    a__="[CLS]"      ,    a__="[SEP]"      ,    a__="[UNK]"      ,    a__="[SEP]"      ,    a__="[PAD]"      ,    a__="[CLS]"      ,    a__="[MASK]"      ,    **a__      ,    )			->   List[str]:






            '''simple docstring'''





            super().__init__(
                do_lower_case=a__      ,    remove_space=a__      ,    keep_accents=a__      ,    bos_token=a__      ,    eos_token=a__      ,    unk_token=a__      ,    sep_token=a__      ,    pad_token=a__      ,    cls_token=a__      ,    mask_token=a__      ,    **a__      ,    )

            __snake_case      :List[Any] 							=    do_lower_case
            __snake_case      :Dict 							=    remove_space
            __snake_case      :int 							=    keep_accents
            __snake_case      :Optional[int] 							=    vocab_file

            __snake_case      :int 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(a__     )



       @property
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            return len(self.sp_model     )



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






            '''simple docstring'''





            __snake_case      :int 							=    {self.convert_ids_to_tokens(a__     ): i for i in range(self.vocab_size     )}
            vocab.update(self.added_tokens_encoder     )
            return vocab



       def __getstate__(				self     )			->   str:






            '''simple docstring'''





            __snake_case      :str 							=    self.__dict__.copy()
            __snake_case      :Dict 							=    None
            return state



       def __setstate__(				self      ,    a__     )			->   str:






            '''simple docstring'''





            __snake_case      :Dict 							=    d
            __snake_case      :Dict 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(self.vocab_file     )



       def        __lowercase    (				self      ,    a__      ,    a__=False     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.sp_model.EncodeAsPieces(a__     )
            return pieces



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            return self.sp_model.PieceToId(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Any:






            '''simple docstring'''





            return self.sp_model.IdToPiece(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self.sp_model.decode_pieces(a__     )
            return out_string



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    [self.sep_token_id]
            __snake_case      :List[Any] 							=    [self.cls_token_id]
            if token_ids_a is None:
                 return cls + token_ids_a + sep
            return cls + token_ids_a + sep + token_ids_a + sep



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = False     )			->   List[int]:






            '''simple docstring'''





            if already_has_special_tokens:
                 if token_ids_a is not None:
                      raise ValueError(
                          """You should not supply a second sequence if the provided sequence of """
                          """ids is already formatted with special tokens for the model."""     )
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

            if token_ids_a is not None:
                 return [1] + ([0] * len(a__     )) + [1] + ([0] * len(a__     )) + [1]
            return [1] + ([0] * len(a__     )) + [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Any 							=    [self.sep_token_id]
            __snake_case      :Any 							=    [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     ) * [0] + len(token_ids_a + sep     ) * [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   Tuple[str]:






            '''simple docstring'''





            if not os.path.isdir(a__     ):
                 logger.error("""Vocabulary path ({}) should be a directory""".format(a__     )     )
                 return
            __snake_case      :Optional[int] 							=    os.path.join(
                a__      ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

            if os.path.abspath(self.vocab_file     ) != os.path.abspath(a__     ):
                 copyfile(self.vocab_file      ,    a__     )

            return (out_vocab_file,)


