





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

import sentencepiece as spm

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


if is_torch_available():
	import torch


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


lowerCAmelCase_    =    logging.get_logger(__name__)
lowerCAmelCase_    =    {'''vocab_file''': '''spiece.model'''}

lowerCAmelCase_    =    {
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

lowerCAmelCase_    =    {
    '''AI-Sweden/gpt-sw3-126m''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-350m''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-1.6b''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-6.7b''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-20b''': 2_0_4_8,
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Dict	      =				VOCAB_FILES_NAMES
			lowerCamelCase_					:					List[str]	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					str	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					Optional[int]	      =				['''input_ids''', '''attention_mask''']






			def __init__(self ,							__magic_name__ ,							__magic_name__=False ,							__magic_name__=False ,							__magic_name__=False ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       None:


							'''simple docstring'''



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

							snake_case_						:   Tuple           =  kwargs.get('''name_or_path'''   )
							if name_or_path is None:
											logger.warning(
											    '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
											    ''' you are testing the model, this can safely be ignored'''   )
											snake_case_						:   int           =  '''None'''

							# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
							snake_case_						:   Optional[Any]           =  '''<|endoftext|>''' if eos_token is None else eos_token
							snake_case_						:   List[str]           =  '''<unk>''' if unk_token is None else unk_token
							if "gpt-sw3-7b" in name_or_path:
											snake_case_						:   int           =  unk_token if pad_token is None else pad_token
											snake_case_						:   List[str]           =  eos_token if bos_token is None else bos_token
							else:
											snake_case_						:   Dict           =  '''<pad>''' if pad_token is None else pad_token
											snake_case_						:   List[Any]           =  '''<s>''' if bos_token is None else bos_token

							super().__init__(
							    do_lower_case=__magic_name__ ,							remove_space=__magic_name__ ,							keep_accents=__magic_name__ ,							bos_token=__magic_name__ ,							eos_token=__magic_name__ ,							unk_token=__magic_name__ ,							pad_token=__magic_name__ ,							sp_model_kwargs=self.sp_model_kwargs ,							**__magic_name__ ,							)

							snake_case_						:   Optional[int]           =  do_lower_case
							snake_case_						:   Union[str, Any]           =  remove_space
							snake_case_						:   List[str]           =  keep_accents
							snake_case_						:   Optional[int]           =  vocab_file

							snake_case_						:   Dict           =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(__magic_name__   )

							# Used for whitespace normalization in input texts
							# fmt : off
							snake_case_						:   Optional[int]           =  {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
							# fmt : on

							# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
							snake_case_						:   str           =  re.compile(
							    F'''[{"".join(map(__magic_name__ ,							list(range(0 ,							9   )   ) + list(range(11 ,							32   )   ) + list(range(127 ,							160   )   ) + [160, 173, 8203]   )   )}]'''   )






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


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.__dict__.copy()
							snake_case_						:   List[Any]           =  None
							return state






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


							'''simple docstring'''



							snake_case_						:   Any           =  d

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

							snake_case_						:   Any           =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(self.vocab_file   )






			@property
			# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return len(self.sp_model   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       str:


							'''simple docstring'''



							snake_case_						:   str           =  self.non_printing_characters_re.sub('''''' ,							__magic_name__   )

							# Normalize whitespaces
							snake_case_						:   Dict           =  ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]   )

							# NFC Unicode normalization
							snake_case_						:   Union[str, Any]           =  unicodedata.normalize('''NFC''' ,							__magic_name__   )
							return text






			def       lowerCamelCase  (self ,							__magic_name__ ,							**__magic_name__   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self.preprocess_text(__magic_name__   )
							return self.sp_model.encode(__magic_name__ ,							out_type=__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return self.sp_model.PieceToId(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       str:


							'''simple docstring'''



							return self.sp_model.IdToPiece(__magic_name__   )






			@staticmethod
			def       lowerCamelCase  (__magic_name__   )					->       str:


							'''simple docstring'''



							return out_string






			def       lowerCamelCase  (self ,							__magic_name__   )					->       str:


							'''simple docstring'''



							snake_case_						:   Dict           =  []
							snake_case_						:   str           =  ''''''
							snake_case_						:   Union[str, Any]           =  False
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
															if not prev_is_special:
																			out_string += " "

															out_string += self.sp_model.decode(__magic_name__   ) + token
															snake_case_						:   Optional[int]           =  True
															snake_case_						:   Optional[int]           =  []
											else:
															current_sub_tokens.append(__magic_name__   )
															snake_case_						:   List[str]           =  False
							out_string += self.sp_model.decode(__magic_name__   )

							return out_string






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


							'''simple docstring'''



							snake_case_						:   List[Any]           =  {self.convert_ids_to_tokens(__magic_name__   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       Tuple[str]:


							'''simple docstring'''



							if not os.path.isdir(__magic_name__   ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
											return
							snake_case_						:   Tuple           =  os.path.join(
							    __magic_name__ ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(__magic_name__   ) and os.path.isfile(self.vocab_file   ):
											copyfile(self.vocab_file ,							__magic_name__   )
							elif not os.path.isfile(self.vocab_file   ):
											with open(__magic_name__ ,							'''wb'''   ) as fi:
															snake_case_						:   Dict           =  self.sp_model.serialized_model_proto()
															fi.write(__magic_name__   )

							return (out_vocab_file,)






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = False   )					->       Union[List[int], List[List[int]], "torch.Tensor"]:


							'''simple docstring'''



							if isinstance(__magic_name__ ,							__magic_name__   ):
											snake_case_						:   Optional[Any]           =  self.preprocess_text(__magic_name__   )
											snake_case_						:   Dict           =  self.sp_model.encode(__magic_name__   )
							else:
											snake_case_						:   Union[str, Any]           =  [self.preprocess_text(__magic_name__   ) for t in text]
											snake_case_						:   List[Any]           =  self.sp_model.encode(__magic_name__   )

							if return_tensors is True or return_tensors == "pt":
											snake_case_						:   Union[str, Any]           =  torch.tensor(__magic_name__   )

							return token_ids






			def       lowerCamelCase  (self ,							__magic_name__   )					->       str:


							'''simple docstring'''



							return self.sp_model.decode(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       List[int]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
							snake_case_						:   Any           =  (
							    F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(__magic_name__   ) + F'''{self.bos_token}Bot:'''
							)
							return self.encode(text=__magic_name__   )







import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=False			)		->						int:






				"""simple docstring"""


				try:
								snake_case_						:   Optional[Any]           =  os.environ[key]
				except KeyError:
								# KEY isn't set, default to `default`.
								snake_case_						:   Tuple           =  default
				else:
								# KEY is set, convert it to True or False.
								try:
												snake_case_						:   Union[str, Any]           =  strtobool(_UpperCamelCase			)
								except ValueError:
												# More values are supported, but let's keep the message simple.
												raise ValueError(f'''If set, {key} must be yes or no.'''			)
				return _value


lowerCAmelCase_    =    parse_flag_from_env('''RUN_SLOW''', default=False)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skip('''Test was skipped'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				return unittest.skipUnless(_run_slow_tests			,    '''test is slow'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				return unittest.skipUnless(not torch.cuda.is_available()			,    '''test requires only a CPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.is_available()			,    '''test requires a GPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				return unittest.skipUnless(is_xpu_available()			,    '''test requires a XPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_mps_available()			,    '''test requires a `mps` backend support in `torch`'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				return unittest.skipUnless(
				    is_transformers_available() and is_datasets_available()			,    '''test requires the Hugging Face suite'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(is_bnb_available()			,    '''test requires the bitsandbytes library'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_tpu_available()			,    '''test requires TPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.device_count() == 1			,    '''test requires a GPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(torch.xpu.device_count() == 1			,    '''test requires a XPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.device_count() > 1			,    '''test requires multiple GPUs'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(torch.xpu.device_count() > 1			,    '''test requires multiple XPUs'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				return unittest.skipUnless(is_safetensors_available()			,    '''test requires safetensors'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(is_deepspeed_available()			,    '''test requires DeepSpeed'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(is_torch_version('''>='''			,    '''1.12.0'''			)			,    '''test requires torch version >= 1.12.0'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase=None			,    _UpperCamelCase=None			)		->						Tuple:






				"""simple docstring"""


				if test_case is None:
								return partial(_UpperCamelCase			,    version=_UpperCamelCase			)
				return unittest.skipUnless(is_torch_version('''>='''			,    _UpperCamelCase			)			,    f'''test requires torch version >= {version}'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(is_tensorboard_available()			,    '''test requires Tensorboard'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				return unittest.skipUnless(is_wandb_available()			,    '''test requires wandb'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_comet_ml_available()			,    '''test requires comet_ml'''			)(_UpperCamelCase			)


lowerCAmelCase_    =    (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				return unittest.skipUnless(
				    _atleast_one_tracker_available			,    '''test requires at least one tracker to be available and for `comet_ml` to not be installed'''			,    )(_UpperCamelCase			)



class  __lowerCAmelCase	(      unittest.TestCase					):
			lowerCamelCase_					:					str	      =				True






			@classmethod
			def       lowerCamelCase  (cls   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  tempfile.mkdtemp()






			@classmethod
			def       lowerCamelCase  (cls   )					->       List[Any]:


							'''simple docstring'''



							if os.path.exists(cls.tmpdir   ):
											shutil.rmtree(cls.tmpdir   )






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


							'''simple docstring'''



							if self.clear_on_setup:
											for path in Path(self.tmpdir   ).glob('''**/*'''   ):
															if path.is_file():
																			path.unlink()
															elif path.is_dir():
																			shutil.rmtree(__magic_name__   )




class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							super().tearDown()
							# Reset the state of the AcceleratorState singleton.
							AcceleratorState._reset_state()
							PartialState._reset_state()




class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   str           =  mocks if isinstance(__magic_name__ ,							(tuple, list)   ) else [mocks]
							for m in self.mocks:
											m.start()
											self.addCleanup(m.stop   )






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  AcceleratorState()
				snake_case_						:   List[str]           =  tensor[None].clone().to(state.device			)
				snake_case_						:   Optional[Any]           =  gather(_UpperCamelCase			).cpu()
				snake_case_						:   Optional[Any]           =  tensor[0].cpu()
				for i in range(tensors.shape[0]			):
								if not torch.equal(tensors[i]			,    _UpperCamelCase			):
												return False
				return True



class  __lowerCAmelCase	:






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  returncode
							snake_case_						:   List[Any]           =  stdout
							snake_case_						:   Tuple           =  stderr






async def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				while True:
								snake_case_						:   Tuple           =  await stream.readline()
								if line:
												callback(_UpperCamelCase			)
								else:
												break






async def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=False			,    _UpperCamelCase=False			)		->						_RunOutput:






				"""simple docstring"""


				if echo:
								print('''\nRunning: '''			,    ''' '''.join(_UpperCamelCase			)			)

				snake_case_						:   List[str]           =  await asyncio.create_subprocess_exec(
				    cmd[0]			,    *cmd[1:]			,    stdin=_UpperCamelCase			,    stdout=asyncio.subprocess.PIPE			,    stderr=asyncio.subprocess.PIPE			,    env=_UpperCamelCase			,    )

				# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
				# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
				#
				# If it starts hanging, will need to switch to the following code. The problem is that no data
				# will be seen until it's done and if it hangs for example there will be no debug info.
				# out, err = await p.communicate()
				# return _RunOutput(p.returncode, out, err)

				snake_case_						:   List[Any]           =  []
				snake_case_						:   List[Any]           =  []

				def tee(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=""			):
								snake_case_						:   Union[str, Any]           =  line.decode('''utf-8'''			).rstrip()
								sink.append(_UpperCamelCase			)
								if not quiet:
												print(_UpperCamelCase			,    _UpperCamelCase			,    file=_UpperCamelCase			)

    # XXX: the timeout doesn't seem to make any difference here
				await asyncio.wait(
				    [
				        asyncio.create_task(_read_stream(p.stdout			,    lambda _UpperCamelCase			: tee(_UpperCamelCase			,    _UpperCamelCase			,    sys.stdout			,    label='''stdout:'''			)			)			),
				        asyncio.create_task(_read_stream(p.stderr			,    lambda _UpperCamelCase			: tee(_UpperCamelCase			,    _UpperCamelCase			,    sys.stderr			,    label='''stderr:'''			)			)			),
				    ]			,    timeout=_UpperCamelCase			,    )
				return _RunOutput(await p.wait()			,    _UpperCamelCase			,    _UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=180			,    _UpperCamelCase=False			,    _UpperCamelCase=True			)		->						_RunOutput:






				"""simple docstring"""


				snake_case_						:   List[str]           =  asyncio.get_event_loop()
				snake_case_						:   List[Any]           =  loop.run_until_complete(
				    _stream_subprocess(_UpperCamelCase			,    env=_UpperCamelCase			,    stdin=_UpperCamelCase			,    timeout=_UpperCamelCase			,    quiet=_UpperCamelCase			,    echo=_UpperCamelCase			)			)

				snake_case_						:   Optional[int]           =  ''' '''.join(_UpperCamelCase			)
				if result.returncode > 0:
								snake_case_						:   Union[str, Any]           =  '''\n'''.join(result.stderr			)
								raise RuntimeError(
								    f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
								    f'''The combined stderr from workers follows:\n{stderr}'''			)

				return result



class  __lowerCAmelCase	(      _a					):
			pass






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=False			)		->						List[Any]:






				"""simple docstring"""


				try:
								snake_case_						:   List[str]           =  subprocess.check_output(_UpperCamelCase			,    stderr=subprocess.STDOUT			)
								if return_stdout:
												if hasattr(_UpperCamelCase			,    '''decode'''			):
																snake_case_						:   Tuple           =  output.decode('''utf-8'''			)
												return output
				except subprocess.CalledProcessError as e:
								raise SubprocessCallException(
								    f'''Command `{" ".join(_UpperCamelCase			)}` failed with the following error:\n\n{e.output.decode()}'''			) from e
