



"""simple docstring"""







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


lowerCAmelCase_   						=   logging.get_logger(__name__)

lowerCAmelCase_   						=   '▁'

lowerCAmelCase_   						=   {'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_   						=   {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

lowerCAmelCase_   						=   {
    'facebook/xglm-564M': 2_048,
}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[Any]   	=				VOCAB_FILES_NAMES
		lowerCAmelCase			:					Any   	=				PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase			:					str   	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase			:					int   	=				["input_ids", "attention_mask"]







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






				"""simple docstring"""



				lowercase__   :    Any				=			{} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				lowercase__   :    Any				=			7
				lowercase__   :    Optional[int]				=			[f"""<madeupword{i}>""" for i in range(self.num_madeup_words						)]

				lowercase__   :    Dict				=			kwargs.get('''additional_special_tokens'''			,[]						)
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

				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			,sp_model_kwargs=self.sp_model_kwargs			,**_snake_case			,)

				lowercase__   :    List[str]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.Load(str(_snake_case						)						)
				lowercase__   :    str				=			vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				lowercase__   :    Optional[int]				=			1

				# Mimic fairseq token-to-id alignment for the first 4 token
				lowercase__   :    Optional[int]				=			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

				lowercase__   :    List[str]				=			len(self.sp_model						)
				lowercase__   :    Tuple				=			{f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words						)}
				self.fairseq_tokens_to_ids.update(_snake_case						)

				lowercase__   :    Union[str, Any]				=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}







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






				"""simple docstring"""



				lowercase__   :    List[Any]				=			self.__dict__.copy()
				lowercase__   :    Optional[int]				=			None
				lowercase__   :    Any				=			self.sp_model.serialized_model_proto()
				return state







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






				"""simple docstring"""



				lowercase__   :    int				=			d

				# for backward compatibility
				if not hasattr(self			,'''sp_model_kwargs'''						):
						lowercase__   :    Dict				=			{}

				lowercase__   :    Optional[int]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)







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






				"""simple docstring"""



				if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
				lowercase__   :    Optional[Any]				=			[self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a







		def 					UpperCAmelCase    (	self						:  Any			,_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						))
				return [1] + ([0] * len(_snake_case						)) + [1, 1] + ([0] * len(_snake_case						))







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






				"""simple docstring"""



				lowercase__   :    List[Any]				=			[self.sep_token_id]

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







		@property
		def 					UpperCAmelCase    (	self						:  str						)					->		Tuple:






				"""simple docstring"""



				return len(self.sp_model						) + self.fairseq_offset + self.num_madeup_words







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			{self.convert_ids_to_tokens(_snake_case						): i for i in range(self.vocab_size						)}
				vocab.update(self.added_tokens_encoder						)
				return vocab







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






				"""simple docstring"""



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







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






				"""simple docstring"""



				if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
				lowercase__   :    Tuple				=			self.sp_model.PieceToId(_snake_case						)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







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






				"""simple docstring"""



				if index in self.fairseq_ids_to_tokens:
						return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset						)







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  Tuple						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			''''''.join(_snake_case						).replace(_snake_case			,''' '''						).strip()
				return out_string







		def 					UpperCAmelCase    (	self						:  Any			,_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
				lowercase__   :    Any				=			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:
								lowercase__   :    Dict				=			self.sp_model.serialized_model_proto()
								fi.write(_snake_case						)

				return (out_vocab_file,)








import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def __A    (      )->  Tuple:





						"""simple docstring"""



						raise RuntimeError('CUDA out of memory.'	)





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			nn.Linear(3       ,    4    )
							_UpperCAmelCase									=			nn.BatchNormad(4    )
							_UpperCAmelCase									=			nn.Linear(4       ,    5    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase    )    )    )





class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()

							mock_training_loop_function()
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()
													return batch_size, arga

							_UpperCAmelCase					, _UpperCAmelCase									=			mock_training_loop_function('hello'    )
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )
							self.assertListEqual([bs, arga]       ,    [8, 'hello']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=0    )
							def mock_training_loop_function(UpperCAmelCase    ):
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													if batch_size > 0:
																			raise_fake_out_of_memory()
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													if batch_size != 8:
																			raise raise_fake_out_of_memory()

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function(128       ,    'hello'       ,    'world'    )
													self.assertIn('Batch size was passed into `f`'       ,    cm.exception.args[0]    )
													self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													raise ValueError('Oops, we had an error!'    )

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('Oops, we had an error!'       ,    cm.exception.args[0]    )

	@require_cuda
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.cuda.memory_allocated()
							_UpperCAmelCase									=			ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			release_memory(UpperCAmelCase    )
							self.assertEqual(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
