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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_xlnet import XLNetTokenizer
else:
      UpperCamelCase__											=					None


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__											=					{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

UpperCamelCase__											=					{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

UpperCamelCase__											=					'▁'

# Segments (not really needed)
UpperCamelCase__											=					0
UpperCamelCase__											=					1
UpperCamelCase__											=					2
UpperCamelCase__											=					3
UpperCamelCase__											=					4





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : int      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : List[str]      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : int      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : str      =			'left'
   __UpperCAmelCase : List[str]      =			XLNetTokenizer


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Optional[int]=None       ,  __UpperCAmelCase     :						List[str]=None       ,  __UpperCAmelCase     :						List[Any]=False       ,  __UpperCAmelCase     :						Union[str, Any]=True       ,  __UpperCAmelCase     :						Union[str, Any]=False       ,  __UpperCAmelCase     :						str="<s>"       ,  __UpperCAmelCase     :						Tuple="</s>"       ,  __UpperCAmelCase     :						List[Any]="<unk>"       ,  __UpperCAmelCase     :						int="<sep>"       ,  __UpperCAmelCase     :						Optional[Any]="<pad>"       ,  __UpperCAmelCase     :						str="<cls>"       ,  __UpperCAmelCase     :						Union[str, Any]="<mask>"       ,  __UpperCAmelCase     :						Optional[int]=["<eop>", "<eod>"]       ,  **__UpperCAmelCase     :						Any       ,  )						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase       ,  lstrip=__UpperCAmelCase       ,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		) else mask_token

         super().__init__(
             vocab_file=__UpperCAmelCase       ,  tokenizer_file=__UpperCAmelCase       ,  do_lower_case=__UpperCAmelCase       ,  remove_space=__UpperCAmelCase       ,  keep_accents=__UpperCAmelCase       ,  bos_token=__UpperCAmelCase       ,  eos_token=__UpperCAmelCase       ,  unk_token=__UpperCAmelCase       ,  sep_token=__UpperCAmelCase       ,  pad_token=__UpperCAmelCase       ,  cls_token=__UpperCAmelCase       ,  mask_token=__UpperCAmelCase       ,  additional_special_tokens=__UpperCAmelCase       ,  **__UpperCAmelCase       ,  )

         UpperCAmelCase__								=  3
         UpperCAmelCase__								=  do_lower_case
         UpperCAmelCase__								=  remove_space
         UpperCAmelCase__								=  keep_accents
         UpperCAmelCase__								=  vocab_file
         UpperCAmelCase__								=  False if not self.vocab_file else True


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None		)						->				List[int]:
         """simple docstring"""



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


   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None		)						->				List[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [self.sep_token_id]
         UpperCAmelCase__								=  [2]

         if token_ids_a is None:
               return len(token_ids_a + sep		) * [0] + cls_segment_id
         return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id





   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str] = None		)						->				Tuple[str]:
         """simple docstring"""



         if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

         if not os.path.isdir(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  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		):
               copyfile(self.vocab_file       ,  __UpperCAmelCase		)

         return (out_vocab_file,)











"""simple docstring"""


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

from accelerate.utils.dataclasses import DistributedType


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     Optional[int],       _lowerCamelCase :     Optional[int]=2,       _lowerCamelCase :     Optional[int]=3,       _lowerCamelCase :     int=64,       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     __A									= np.random.default_rng(_lowerCamelCase  )
     __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 :     str  ):

     '''simple docstring'''
     return self.length






   def __getitem__(	self :     Dict,       _lowerCamelCase :     Optional[int]  ):

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





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Tuple=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     Optional[Any]=False  ):

     '''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 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     Optional[Any]=None  ):

     '''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 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     str,       _lowerCamelCase :     Optional[Any]=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     List[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     List[str]=None  ):

     '''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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase = 1_6 ):




  """simple docstring"""







  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_2_8		,       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




