



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
							from ...tokenization_utils_base import TextInput
from ...utils import logging


lowercase_								=     logging.get_logger(__name__)

lowercase_								=     {"""vocab_file""": """spiece.model"""}

lowercase_								=     {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowercase_								=     {
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

lowercase_								=     """▁"""
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					List[Any]     		=VOCAB_FILES_NAMES
				UpperCamelCase__				:					Optional[Any]     		=PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__				:					Dict     		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__				:					Dict     		=["""input_ids""", """attention_mask"""]




				def __init__(						self					:						Optional[Any],					__lowercase					:						List[Any],					__lowercase					:						List[str]="</s>",					__lowercase					:						str="<unk>",					__lowercase					:						List[Any]="<pad>",					__lowercase					:						Optional[Any]=100,					__lowercase					:						List[Any]=None,					__lowercase					:						Optional[Dict[str, Any]] = None,					__lowercase					:						Optional[Any]=True,					**__lowercase					:						Optional[Any],					):
							# Add extra_ids to the special token list
							if extra_ids > 0 and additional_special_tokens is None:
										lowercase__					  =				[F'''<extra_id_{i}>''' for i in range(__lowercase						)]
							elif extra_ids > 0 and additional_special_tokens is not None:
										# Check that we have the right number of extra_id special tokens
										lowercase__					  =				len(set(filter(lambda __lowercase						: bool("extra_id" in str(__lowercase						)						),					__lowercase						)						)						)
										if extra_tokens != extra_ids:
													raise ValueError(
													    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
													    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
													    " tokens"						)
							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"						)

							lowercase__					  =				legacy
							lowercase__					  =				{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=__lowercase,					unk_token=__lowercase,					pad_token=__lowercase,					extra_ids=__lowercase,					additional_special_tokens=__lowercase,					sp_model_kwargs=self.sp_model_kwargs,					legacy=__lowercase,					**__lowercase,					)

							lowercase__					  =				vocab_file
							lowercase__					  =				extra_ids

							lowercase__					  =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(__lowercase						)




				@staticmethod
				def 							A__      (						__lowercase					:						List[Any],					__lowercase					:						Union[str, Any],					__lowercase					:						str						):
							if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
										lowercase__					  =				TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
										if init_max_model_length is not None and init_max_model_length != max_model_length:
													return init_max_model_length
										elif init_max_model_length is None:
													warnings.warn(
													    "This tokenizer was incorrectly instantiated with a model max length of"
													    F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
													    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
													    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
													    F''' {pretrained_model_name_or_path} automatically truncating your input to'''
													    F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
													    F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
													    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
													    " instantiate this tokenizer with `model_max_length` set to your preferred value.",					__lowercase,					)

							return max_model_length




				@property
				def 							A__      (						self					:						List[str]						):
							return self.sp_model.get_piece_size() + self._extra_ids




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				{self.convert_ids_to_tokens(__lowercase						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab




				def 							A__      (						self					:						Optional[Any],					__lowercase					:						List[int],					__lowercase					:						Optional[List[int]] = None,					__lowercase					:						bool = False						):
							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=__lowercase,					token_ids_a=__lowercase,					already_has_special_tokens=__lowercase						)

							# normal case: some special tokens
							if token_ids_a is None:
										return ([0] * len(__lowercase						)) + [1]
							return ([0] * len(__lowercase						)) + [1] + ([0] * len(__lowercase						)) + [1]




				def 							A__      (						self					:						Union[str, Any]						):
							return list(
							    set(filter(lambda __lowercase						: bool(re.search(R"<extra_id_\d+>",					__lowercase						)						) is not None,					self.additional_special_tokens						)						)						)




				def 							A__      (						self					:						str						):
							return [self._convert_token_to_id(__lowercase						) for token in self.get_sentinel_tokens()]




				def 							A__      (						self					:						Any,					__lowercase					:						List[int]						):
							if len(__lowercase						) > 0 and token_ids[-1] == self.eos_token_id:
										warnings.warn(
										    F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
										    " eos tokens being added."						)
										return token_ids
							else:
										return token_ids + [self.eos_token_id]




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						List[int],					__lowercase					:						Optional[List[int]] = None						):
							lowercase__					  =				[self.eos_token_id]

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




				def 							A__      (						self					:						str,					__lowercase					:						List[int],					__lowercase					:						Optional[List[int]] = None						):
							lowercase__					  =				self._add_eos_if_not_present(__lowercase						)
							if token_ids_a is None:
										return token_ids_a
							else:
										lowercase__					  =				self._add_eos_if_not_present(__lowercase						)
										return token_ids_a + token_ids_a




				def __getstate__(						self					:						Tuple						):
							lowercase__					  =				self.__dict__.copy()
							lowercase__					  =				None
							return state




				def __setstate__(						self					:						Dict,					__lowercase					:						List[str]						):
							lowercase__					  =				d

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

							lowercase__					  =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(self.vocab_file						)




				def 							A__      (						self					:						List[str],					__lowercase					:						"TextInput",					**__lowercase					:						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:
										lowercase__					  =				SPIECE_UNDERLINE + text.replace(__lowercase,					" "						)
							return super().tokenize(__lowercase,					**__lowercase						)




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						Optional[Any],					**__lowercase					:						List[Any]						):
							if not self.legacy:
										lowercase__					  =				text.startswith(__lowercase						)
										if is_first:
													lowercase__					  =				text[1:]

							lowercase__					  =				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						):
										lowercase__					  =				([tokens[0][1:]] if len(tokens[0]						) > 1 else []) + tokens[1:]
							return tokens




				def 							A__      (						self					:						Tuple,					__lowercase					:						Tuple						):
							if token.startswith("<extra_id_"						):
										lowercase__					  =				re.match(R"<extra_id_(\d+)>",					__lowercase						)
										lowercase__					  =				int(match.group(1						)						)
										return self.vocab_size - num - 1
							return self.sp_model.piece_to_id(__lowercase						)




				def 							A__      (						self					:						Optional[Any],					__lowercase					:						Optional[int]						):
							if index < self.sp_model.get_piece_size():
										lowercase__					  =				self.sp_model.IdToPiece(__lowercase						)
							else:
										lowercase__					  =				F'''<extra_id_{self.vocab_size - 1 - index}>'''
							return token




				def 							A__      (						self					:						Optional[int],					__lowercase					:						Tuple						):
							lowercase__					  =				[]
							lowercase__					  =				""
							lowercase__					  =				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(__lowercase						) + token
													lowercase__					  =				True
													lowercase__					  =				[]
										else:
													current_sub_tokens.append(__lowercase						)
													lowercase__					  =				False
							out_string += self.sp_model.decode(__lowercase						)
							return out_string.strip()




				def 							A__      (						self					:						List[str],					__lowercase					:						str,					__lowercase					:						Optional[str] = None						):
							if not os.path.isdir(__lowercase						):
										logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
										return
							lowercase__					  =				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:
													lowercase__					  =				self.sp_model.serialized_model_proto()
													fi.write(__lowercase						)

							return (out_vocab_file,)









# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def __lowerCAmelCase (     ):
			lowercase__					  =				ArgumentParser("Accelerate CLI tool"						,  usage="accelerate <command> [<args>]"						,  allow_abbrev=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.add_subparsers(help="accelerate command helpers"  )

			# Register commands
			get_config_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			env_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			launch_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			tpu_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			test_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )

			# Let's go
			lowercase__					  =				parser.parse_args()

			if not hasattr(SCREAMING_SNAKE_CASE_						,  "func"  ):
						parser.print_help()
						exit(1  )

			# Run
			args.func(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
							main()




