

import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

__snake_case							:     Tuple 						=	logging.get_logger(__name__)

__snake_case							:     List[str] 						=	{"""vocab_file""": """tokenizer.model"""}

__snake_case							:     Optional[int] 						=	{
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
__snake_case							:     Any 						=	{
    """hf-internal-testing/llama-tokenizer""": 20_48,
}
__snake_case							:     Optional[Any] 						=	"""▁"""

__snake_case						,				__snake_case							:     int 						=	"""[INST]""", """[/INST]"""
__snake_case						,				__snake_case							:     Optional[Any] 						=	"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
__snake_case							:     str 						=	"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""



class    A__(a_    ):
						"""simple docstring"""






						_A  :							Any   			=	VOCAB_FILES_NAMES
						_A  :							Optional[Any]   			=	PRETRAINED_VOCAB_FILES_MAP
						_A  :							Optional[int]   			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_A  :							Tuple   			=	['''input_ids''', '''attention_mask''']





						def __init__(  self       ,     _lowercase       ,     _lowercase="<unk>"       ,     _lowercase="<s>"       ,     _lowercase="</s>"       ,     _lowercase=None       ,     _lowercase = None       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=False       ,     _lowercase=True       ,     **_lowercase       ,     )      ->				List[Any]:
									a_		:		List[Any]				    =				{} if sp_model_kwargs is None else sp_model_kwargs
									a_		:		Union[str, Any]				    =				AddedToken(_lowercase       ,     lstrip=_lowercase       ,     rstrip=_lowercase  ) if isinstance(_lowercase       ,     _lowercase  ) else bos_token
									a_		:		List[Any]				    =				AddedToken(_lowercase       ,     lstrip=_lowercase       ,     rstrip=_lowercase  ) if isinstance(_lowercase       ,     _lowercase  ) else eos_token
									a_		:		Dict				    =				AddedToken(_lowercase       ,     lstrip=_lowercase       ,     rstrip=_lowercase  ) if isinstance(_lowercase       ,     _lowercase  ) else unk_token
									a_		:		str				    =				AddedToken(_lowercase       ,     lstrip=_lowercase       ,     rstrip=_lowercase  ) if isinstance(_lowercase       ,     _lowercase  ) else pad_token
									super().__init__(
									    bos_token=_lowercase       ,     eos_token=_lowercase       ,     unk_token=_lowercase       ,     pad_token=_lowercase       ,     add_bos_token=_lowercase       ,     add_eos_token=_lowercase       ,     sp_model_kwargs=self.sp_model_kwargs       ,     clean_up_tokenization_spaces=_lowercase       ,     legacy=_lowercase       ,     **_lowercase       ,     )
									if legacy:
												logger.warning_once(
												    F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
												    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""  )
									a_		:		Optional[Any]				    =				legacy
									a_		:		Dict				    =				vocab_file
									a_		:		List[str]				    =				add_bos_token
									a_		:		Optional[int]				    =				add_eos_token
									a_		:		int				    =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(_lowercase  )





						def __getstate__(  self  )      ->				int:
									a_		:		List[Any]				    =				self.__dict__.copy()
									a_		:		Optional[Any]				    =				None
									a_		:		Optional[Any]				    =				self.sp_model.serialized_model_proto()
									return state





						def __setstate__(  self       ,     _lowercase  )      ->				Optional[int]:
									a_		:		List[Any]				    =				d
									a_		:		Tuple				    =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )





						@property
						def 	UpperCamelCase__			(  self  )      ->				str:
									return self.sp_model.get_piece_size()





						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									a_		:		List[Any]				    =				{self.convert_ids_to_tokens(_lowercase  ): i for i in range(self.vocab_size  )}
									vocab.update(self.added_tokens_encoder  )
									return vocab





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     **_lowercase  )      ->				List[str]:
									# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
									# the beginning of the text
									if not self.legacy:
												a_		:		Dict				    =				SPIECE_UNDERLINE + text.replace(_lowercase       ,     """ """  )
									return super().tokenize(_lowercase       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				Optional[int]:
									if not self.legacy:
												a_		:		Optional[int]				    =				text.startswith(_lowercase  )
												if is_first:
															a_		:		int				    =				text[1:]

									a_		:		Optional[Any]				    =				self.sp_model.encode(_lowercase       ,     out_type=_lowercase  )

									if not self.legacy and not is_first and not text.startswith(""" """  ) and tokens[0].startswith(_lowercase  ):
												a_		:		Tuple				    =				([tokens[0][1:]] if len(tokens[0]  ) > 1 else []) + tokens[1:]
									return tokens





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				Optional[int]:
									return self.sp_model.piece_to_id(_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				Union[str, Any]:
									a_		:		Dict				    =				self.sp_model.IdToPiece(_lowercase  )
									return token





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				List[Any]:
									a_		:		List[Any]				    =				[]
									a_		:		Union[str, Any]				    =				""""""
									a_		:		Optional[Any]				    =				False
									for i, token in enumerate(_lowercase  ):
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
															if not prev_is_special and i != 0:
																		out_string += " "
															out_string += self.sp_model.decode(_lowercase  ) + token
															a_		:		Union[str, Any]				    =				True
															a_		:		List[str]				    =				[]
												else:
															current_sub_tokens.append(_lowercase  )
															a_		:		Union[str, Any]				    =				False
									out_string += self.sp_model.decode(_lowercase  )
									return out_string





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None  )      ->				Tuple[str]:
									if not os.path.isdir(_lowercase  ):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
												return
									a_		:		int				    =				os.path.join(
									    _lowercase       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

									if os.path.abspath(self.vocab_file  ) != os.path.abspath(_lowercase  ) and os.path.isfile(self.vocab_file  ):
												copyfile(self.vocab_file       ,     _lowercase  )
									elif not os.path.isfile(self.vocab_file  ):
												with open(_lowercase       ,     """wb"""  ) as fi:
															a_		:		List[str]				    =				self.sp_model.serialized_model_proto()
															fi.write(_lowercase  )

									return (out_vocab_file,)





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase=None  )      ->				List[str]:
									a_		:		Tuple				    =				[self.bos_token_id] if self.add_bos_token else []
									a_		:		Tuple				    =				[self.eos_token_id] if self.add_eos_token else []

									a_		:		List[str]				    =				bos_token_id + token_ids_a + eos_token_id

									if token_ids_a is not None:
												a_		:		Any				    =				output + bos_token_id + token_ids_a + eos_token_id

									return output





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None       ,     _lowercase = False  )      ->				List[int]:
									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=_lowercase       ,     token_ids_a=_lowercase       ,     already_has_special_tokens=_lowercase  )

									a_		:		List[Any]				    =				[1] if self.add_bos_token else []
									a_		:		int				    =				[1] if self.add_eos_token else []

									if token_ids_a is None:
												return bos_token_id + ([0] * len(_lowercase  )) + eos_token_id
									return (
									    bos_token_id
									    + ([0] * len(_lowercase  ))
									    + eos_token_id
									    + bos_token_id
									    + ([0] * len(_lowercase  ))
									    + eos_token_id
									)





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None  )      ->				List[int]:
									a_		:		str				    =				[self.bos_token_id] if self.add_bos_token else []
									a_		:		Tuple				    =				[self.eos_token_id] if self.add_eos_token else []

									a_		:		Dict				    =				[0] * len(bos_token_id + token_ids_a + eos_token_id  )

									if token_ids_a is not None:
												output += [1] * len(bos_token_id + token_ids_a + eos_token_id  )

									return output





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				List[int]:
									a_		:		Tuple				    =				list(conversation.iter_texts()  )
									if not all([is_user for is_user, msg in dialogue[::2]]  ) or not all(
									    [not is_user for is_user, msg in dialogue[1::2]]  ):
												raise ValueError(
												    """The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"""  )

									a_		:		List[int]				    =				[]
									if len(conversation.past_user_inputs  ) > 0:
												if not conversation.past_user_inputs[0].startswith(_lowercase  ) or E_SYS not in conversation.past_user_inputs[0]:
															a_		:		Tuple				    =				(
															    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
															)
									elif not dialogue[0][1].startswith(_lowercase  ) or E_SYS not in dialogue[0][1]:
												a_		:		Dict				    =				(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

									dialog_tokens += sum(
									    [
									        [self.bos_token_id]
									        + self.encode(
									            F'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''       ,     add_special_tokens=_lowercase  )
									        + [self.eos_token_id]
									        for prompt, answer in zip(dialogue[::2]       ,     dialogue[1::2]  )
									    ]       ,     []       ,     )
									if not (dialogue[-1][0]):
												raise ValueError(F'''Last message must be from user, got {dialogue[-1]["role"]}'''  )
									dialog_tokens += [self.bos_token_id] + self.encode(
									    F'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''       ,     add_special_tokens=_lowercase  )
									return dialog_tokens





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin


def 			_UpperCAmelCase							(		a__=2       ,					a__=True       ,					a__=False):

			'''simple docstring'''



			a_		:		Optional[Any]				    =				Accelerator(step_scheduler_with_optimizer=a__       ,					split_batches=a__)
			a_		:		Dict				    =				torch.nn.Linear(2       ,					4)
			a_		:		int				    =				torch.optim.AdamW(model.parameters()       ,					lr=1.0)
			a_		:		Optional[int]				    =				torch.optim.lr_scheduler.OneCycleLR(a__       ,					max_lr=0.01       ,					steps_per_epoch=2       ,					epochs=1)
			a_    ,			a_    ,			a_		:		List[Any]				    =				accelerator.prepare(a__       ,					a__       ,					a__)

			# Optimizer has stepped
			scheduler.step()
			if step_scheduler_with_optimizer or (num_processes == 1):
						assert (
						    scheduler.scheduler.last_epoch == num_processes
						), f'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
			else:
						assert (
						    scheduler.scheduler.last_epoch != num_processes
						), f'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''


def 			_UpperCAmelCase							(		a__=2       ,					a__=True       ,					a__=False):

			'''simple docstring'''



			a_		:		int				    =				Accelerator(step_scheduler_with_optimizer=a__       ,					split_batches=a__)
			a_		:		Tuple				    =				torch.nn.Linear(2       ,					4)
			a_		:		str				    =				torch.optim.AdamW(model.parameters()       ,					lr=1.0)
			a_		:		Optional[int]				    =				torch.optim.lr_scheduler.LambdaLR(a__       ,					lr_lambda=lambda a__: 1 - n / 1_0)
			a_    ,			a_    ,			a_		:		List[str]				    =				accelerator.prepare(a__       ,					a__       ,					a__)

			# Optimizer has stepped
			a_		:		Optional[Any]				    =				False
			scheduler.step()
			a_		:		Union[str, Any]				    =				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), f'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

			# Optimizer has not stepped
			a_		:		Any				    =				True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						a_		:		Any				    =				1 - 2 / 1_0
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), f'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''


def 			_UpperCAmelCase							(		a__ = 2):

			'''simple docstring'''



			from transformers import get_linear_schedule_with_warmup

			a_		:		int				    =				[1, 2, 4]
			for num_steps in steps:
						a_		:		List[str]				    =				GradientAccumulationPlugin(num_steps=a__       ,					adjust_scheduler=num_steps > 1)
						a_		:		List[str]				    =				Accelerator(gradient_accumulation_plugin=a__)
						a_		:		int				    =				torch.nn.Linear(2       ,					4)
						a_		:		Any				    =				torch.optim.AdamW(model.parameters()       ,					lr=10.0)
						a_		:		List[Any]				    =				get_linear_schedule_with_warmup(optimizer=a__       ,					num_warmup_steps=0       ,					num_training_steps=2_0)

						a_    ,			a_    ,			a_		:		int				    =				accelerator.prepare(a__       ,					a__       ,					a__)

						for i in range(1_0 * num_steps):
									with accelerator.accumulate(a__):
												optimizer.step()
												scheduler.step()

									if i == (1_0 * num_steps - 2):
												assert (
												    scheduler.get_last_lr()[0] != 0
												), f'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
						assert (
						    scheduler.get_last_lr()[0] == 0
						), f'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
						GradientState._reset_state()



@require_cpu
class    A__(unittest.TestCase    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				str:
									debug_launcher(partial(_lowercase       ,     num_processes=1  )       ,     num_processes=1  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									debug_launcher(partial(_lowercase       ,     num_processes=1  )       ,     num_processes=1  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     step_scheduler_with_optimizer=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     step_scheduler_with_optimizer=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     step_scheduler_with_optimizer=_lowercase  )  )





						def 	UpperCamelCase__			(  self  )      ->				str:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     step_scheduler_with_optimizer=_lowercase  )  )





						@require_huggingface_suite
						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1  )  )
									debug_launcher(_lowercase  )


