




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

from accelerate.utils.dataclasses import DistributedType




class       a__	:







     def __init__(     self  ,		A=2  ,		A=3  ,		A=64  ,		A=None			)  ->   List[Any]:



          '''simple docstring'''



          a     =				np.random.default_rng(A			)
          a     =				length
          a     =				rng.normal(size=(length,)			).astype(np.floataa			)
          a     =				a * self.x + b + rng.normal(scale=0.1  ,		size=(length,)			).astype(np.floataa			)







     def __len__(     self			)  ->   Dict:



          '''simple docstring'''



          return self.length







     def __getitem__(     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



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







class       a__	( torch.nn.Module				):







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



          '''simple docstring'''



          super().__init__()
          a     =				torch.nn.Parameter(torch.tensor([2, 3]			).float()			)
          a     =				torch.nn.Parameter(torch.tensor([2, 3]			).float()			)
          a     =				True







     def       lowerCAmelCase_    (     self  ,		A=None			)  ->   str:



          '''simple docstring'''



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







class       a__	( torch.nn.Module				):







     def __init__(     self  ,		A=0  ,		A=0  ,		A=False			)  ->   List[Any]:



          '''simple docstring'''



          super().__init__()
          a     =				torch.nn.Parameter(torch.tensor(A			).float()			)
          a     =				torch.nn.Parameter(torch.tensor(A			).float()			)
          a     =				True







     def       lowerCAmelCase_    (     self  ,		A=None			)  ->   str:



          '''simple docstring'''



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







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase = 16)      ->					Tuple:
     from datasets import load_dataset
     from transformers import AutoTokenizer

     a     =				AutoTokenizer.from_pretrained("bert-base-cased")
     a     =				{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
     a     =				load_dataset("csv"		, data_files=__UpperCamelCase)
     a     =				datasets["train"].unique("label")

     a     =				{v: i for i, v in enumerate(__UpperCamelCase)}

     def tokenize_function(__UpperCamelCase):
          # max_length=None => use the model max length (it's actually the default)
          a     =				tokenizer(
              examples["sentence1"]		, examples["sentence2"]		, truncation=__UpperCamelCase		, max_length=__UpperCamelCase		, padding="max_length")
          if "label" in examples:
               a     =				[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
     a     =				datasets.map(
         __UpperCamelCase		, batched=__UpperCamelCase		, 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(__UpperCamelCase		, padding="max_length"		, max_length=1_28		, return_tensors="pt")
          return tokenizer.pad(__UpperCamelCase		, padding="longest"		, return_tensors="pt")

     # Instantiate dataloaders.
     a     =				DataLoader(tokenized_datasets["train"]		, shuffle=__UpperCamelCase		, collate_fn=__UpperCamelCase		, batch_size=2)
     a     =				DataLoader(tokenized_datasets["validation"]		, shuffle=__UpperCamelCase		, collate_fn=__UpperCamelCase		, batch_size=1)

     return train_dataloader, eval_dataloader







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


lowercase__				:      int	   = logging.get_logger(__name__)


lowercase__				:      Dict	   = "▁"

lowercase__				:      Union[str, Any]	   = {"vocab_file": "spiece.model"}

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

lowercase__				:      Tuple	   = {
    "google/reformer-crime-and-punishment": 524_288,
}




class       a__	( UpperCamelCase__				):
     a		:      List[Any]		 =     VOCAB_FILES_NAMES
     a		:      List[Any]		 =     PRETRAINED_VOCAB_FILES_MAP
     a		:      Any		 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a		:      Dict		 =     ["""input_ids""", """attention_mask"""]







     def __init__(     self  ,		A  ,		A="</s>"  ,		A="<unk>"  ,		A=[]  ,		A = None  ,		**A  ,		)  ->   None:



          '''simple docstring'''



          a     =				{} if sp_model_kwargs is None else sp_model_kwargs

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

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







     @property
     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          return self.sp_model.get_piece_size()







     def       lowerCAmelCase_    (     self			)  ->   Dict[str, int]:



          '''simple docstring'''



          a     =				{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			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.__dict__.copy()
          a     =				None
          return state







     def __setstate__(     self  ,		A			)  ->   Union[str, 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  ,		A			)  ->   List[str]:



          '''simple docstring'''



          return self.sp_model.encode(A  ,		out_type=A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.sp_model.piece_to_id(A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[int]:



          '''simple docstring'''



          if index < self.sp_model.get_piece_size():
               a     =				self.sp_model.IdToPiece(A			)
          return token







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				[]
          a     =				""
          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(A			) + token
                    a     =				[]
               else:
                    current_sub_tokens.append(A			)
          out_string += self.sp_model.decode(A			)
          return out_string.strip()







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None			)  ->   Tuple[str]:



          '''simple docstring'''



          if not os.path.isdir(A			):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
               return
          a     =				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			) and os.path.isfile(self.vocab_file			):
               copyfile(self.vocab_file  ,		A			)
          elif not os.path.isfile(self.vocab_file			):
               with open(A  ,		"wb"			) as fi:
                    a     =				self.sp_model.serialized_model_proto()
                    fi.write(A			)

          return (out_vocab_file,)

