


'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class   lowercase      :

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_=2       ,  UpperCamelCase_=3       ,  UpperCamelCase_=64       ,  UpperCamelCase_=None				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					np.random.default_rng(A__				)
							UpperCamelCase__  :Any				   =					length
							UpperCamelCase__  :List[Any]				   =					rng.normal(size=(length,)				).astype(np.floataa				)
							UpperCamelCase__  :Tuple				   =					a * self.x + b + rng.normal(scale=0.1       ,  size=(length,)				).astype(np.floataa				)




		def __len__(						self				):

							'''simple docstring'''


							return self.length




		def __getitem__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


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



class   lowercase      (				torch.nn.Module   ):

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_=0       ,  UpperCamelCase_=0       ,  UpperCamelCase_=False				):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__  :int				   =					torch.nn.Parameter(torch.tensor([2, 3]				).float()				)
							UpperCamelCase__  :Dict				   =					torch.nn.Parameter(torch.tensor([2, 3]				).float()				)
							UpperCamelCase__  :Dict				   =					True




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=None				):

							'''simple docstring'''


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



class   lowercase      (				torch.nn.Module   ):

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_=0       ,  UpperCamelCase_=0       ,  UpperCamelCase_=False				):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__  :Dict				   =					torch.nn.Parameter(torch.tensor(A__				).float()				)
							UpperCamelCase__  :Dict				   =					torch.nn.Parameter(torch.tensor(A__				).float()				)
							UpperCamelCase__  :int				   =					True




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=None				):

							'''simple docstring'''


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



def      a (     __a					,   __a = 16  )    ->      int:



					'''simple docstring'''






					from datasets import load_dataset
					from transformers import AutoTokenizer

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

					UpperCamelCase__  :str				   =					{v: i for i, v in enumerate(lowercase_  )}

					def tokenize_function(__a  ):
										# max_length=None => use the model max length (it's actually the default)
										UpperCamelCase__  :List[Any]				   =					tokenizer(
										    examples['''sentence1''']					,   examples['''sentence2''']					,   truncation=lowercase_					,   max_length=lowercase_					,   padding='''max_length'''  )
										if "label" in examples:
															UpperCamelCase__  :List[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
					UpperCamelCase__  :Tuple				   =					datasets.map(
					    lowercase_					,   batched=lowercase_					,   remove_columns=['''sentence1''', '''sentence2''', '''label''']					,   )

					def collate_fn(__a  ):
										# 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(lowercase_					,   padding='''max_length'''					,   max_length=128					,   return_tensors='''pt'''  )
										return tokenizer.pad(lowercase_					,   padding='''longest'''					,   return_tensors='''pt'''  )

					# Instantiate dataloaders.
					UpperCamelCase__  :str				   =					DataLoader(tokenized_datasets['''train''']					,   shuffle=lowercase_					,   collate_fn=lowercase_					,   batch_size=2  )
					UpperCamelCase__  :str				   =					DataLoader(tokenized_datasets['''validation''']					,   shuffle=lowercase_					,   collate_fn=lowercase_					,   batch_size=1  )

					return train_dataloader, eval_dataloader


import os
import re
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


A_			:					Tuple    			=     logging.get_logger(__name__)

A_			:					Tuple    			=     {'vocab_file': 'spiece.model'}

A_			:					Optional[Any]    			=     {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

A_			:					Tuple    			=     {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  Optional[int]    				=			VOCAB_FILES_NAMES
					UpperCAmelCase__:  str    				=			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:  List[str]    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:  str    				=			['''input_ids''', '''attention_mask''']
					UpperCAmelCase__:  List[int]    				=			[]






					def __init__(					self       ,			A__       ,			A__="<unk>"       ,			A__="<s>"       ,			A__="</s>"       ,			A__="<pad>"       ,			A__="[SEP]"       ,			A__="[MASK]"       ,			A__="[CLS]"       ,			A__ = None       ,			**A__       ,			):
						A__				:       Tuple	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else bos_token
						A__				:       int	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else eos_token
						A__				:       Tuple	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else unk_token
						A__				:       str	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else pad_token
						A__				:       Dict	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else cls_token
						A__				:       Any	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						A__				:       Optional[Any]	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else mask_token

						A__				:       Optional[Any]	  =						{} if sp_model_kwargs is None else sp_model_kwargs

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

						A__				:       Any	  =						vocab_file

						A__				:       Optional[int]	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(A__     )






					@property
					def     __A     (					self     ):
						return self.sp_model.get_piece_size()






					def     __A     (					self     ):
						A__				:       List[str]	  =						{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     ):
						A__				:       Optional[Any]	  =						self.__dict__.copy()
						A__				:       Optional[Any]	  =						None
						return state






					def __setstate__(					self       ,			A__     ):
						A__				:       Dict	  =						d

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

						A__				:       Tuple	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(self.vocab_file     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.encode(A__       ,			out_type=A__     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.piece_to_id(A__     )






					def     __A     (					self       ,			A__     ):
						A__				:       Dict	  =						self.sp_model.IdToPiece(A__     )
						return token






					def     __A     (					self       ,			A__     ):
						A__				:       str	  =						[]
						A__				:       Optional[int]	  =						""""""
						A__				:       Any	  =						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(A__     ) + token
								A__				:       Union[str, Any]	  =						True
								A__				:       Union[str, Any]	  =						[]
							else:
								current_sub_tokens.append(A__     )
								A__				:       Any	  =						False
						out_string += self.sp_model.decode(A__     )
						return out_string.strip()






					def     __A     (					self       ,			A__       ,			A__ = False       ,			A__ = None       ,			A__ = True       ,			**A__       ,			):
						A__				:       List[str]	  =						kwargs.pop("""use_source_tokenizer"""       ,			A__     )

						A__				:       Union[str, Any]	  =						self.convert_ids_to_tokens(A__       ,			skip_special_tokens=A__     )

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						A__				:       Dict	  =						[]
						A__				:       List[Any]	  =						[]
						for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
								continue
							if token in self.added_tokens_encoder:
								if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(A__     )     )
									A__				:       Optional[Any]	  =						[]
								sub_texts.append(A__     )
							else:
								current_sub_text.append(A__     )
						if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(A__     )     )

						# Mimic the behavior of the Rust tokenizer:
						# No space before [MASK] and [SEP]
						if spaces_between_special_tokens:
							A__				:       List[Any]	  =						re.sub(r""" (\[(MASK|SEP)\])"""       ,			r"""\1"""       ,			""" """.join(A__     )     )
						else:
							A__				:       Optional[Any]	  =						"""""".join(A__     )

						A__				:       Union[str, Any]	  =						(
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
							A__				:       Union[str, Any]	  =						self.clean_up_tokenization(A__     )
							return clean_text
						else:
							return text






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if not os.path.isdir(A__     ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
							return
						A__				:       str	  =						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__				:       Dict	  =						self.sp_model.serialized_model_proto()
								fi.write(A__     )

						return (out_vocab_file,)






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A__				:       List[Any]	  =						[self.cls_token_id]
						A__				:       List[Any]	  =						[self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep






					def     __A     (					self       ,			A__       ,			A__ = None       ,			A__ = False     ):
						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=A__       ,			token_ids_a=A__       ,			already_has_special_tokens=A__     )

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






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						A__				:       Any	  =						[self.sep_token_id]
						A__				:       List[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]



