





# 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.
import json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
						import botoa  # noqa: F401

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict     )      ->						Tuple:
		_lowercase							= botoa.client('iam'     )

		_lowercase							= {
		    'Version': '2012-10-17',
		    'Statement': [
		        {'Effect': 'Allow', 'Principal': {'Service': 'sagemaker.amazonaws.com'}, 'Action': 'sts:AssumeRole'}
		    ],
		}
		try:
				# create the role, associated with the chosen trust policy
				iam_client.create_role(
				    RoleName=snake_case__			,							AssumeRolePolicyDocument=json.dumps(snake_case__			,							indent=2     )     )
				_lowercase							= {
				    'Version': '2012-10-17',
				    'Statement': [
				        {
				            'Effect': 'Allow',
				            'Action': [
				                'sagemaker:*',
				                'ecr:GetDownloadUrlForLayer',
				                'ecr:BatchGetImage',
				                'ecr:BatchCheckLayerAvailability',
				                'ecr:GetAuthorizationToken',
				                'cloudwatch:PutMetricData',
				                'cloudwatch:GetMetricData',
				                'cloudwatch:GetMetricStatistics',
				                'cloudwatch:ListMetrics',
				                'logs:CreateLogGroup',
				                'logs:CreateLogStream',
				                'logs:DescribeLogStreams',
				                'logs:PutLogEvents',
				                'logs:GetLogEvents',
				                's3:CreateBucket',
				                's3:ListBucket',
				                's3:GetBucketLocation',
				                's3:GetObject',
				                's3:PutObject',
				            ],
				            'Resource': '*',
				        }
				    ],
				}
				# attach policy to role
				iam_client.put_role_policy(
				    RoleName=snake_case__			,							PolicyName=F"""{role_name}_policy_permission"""			,							PolicyDocument=json.dumps(snake_case__			,							indent=2     )			,							)
		except iam_client.exceptions.EntityAlreadyExistsException:
				print(F"""role {role_name} already exists. Using existing one"""     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[int]     )      ->						Tuple:
		_lowercase							= botoa.client('iam'     )
		return iam_client.get_role(RoleName=snake_case__     )["Role"]["Arn"]







def 							SCREAMING_SNAKE_CASE__       (  )      ->						List[str]:
		_lowercase							= _ask_options(
		    'How do you want to authorize?'			,							['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ']			,							snake_case__			,							)
		_lowercase							= None
		if credentials_configuration == 0:
				_lowercase							= _ask_field('Enter your AWS Profile name: [default] '			,							default='default'     )
				_lowercase							= aws_profile
		else:
				print(
				    'Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'
				    '`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`'     )
				_lowercase							= _ask_field('AWS Access Key ID: '     )
				_lowercase							= aws_access_key_id

				_lowercase							= _ask_field('AWS Secret Access Key: '     )
				_lowercase							= aws_secret_access_key

		_lowercase							= _ask_field('Enter your AWS Region: [us-east-1]'			,							default='us-east-1'     )
		_lowercase							= aws_region

		_lowercase							= _ask_options(
		    'Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?'			,							['Provide IAM Role name', 'Create new IAM role using credentials']			,							snake_case__			,							)
		if role_management == 0:
				_lowercase							= _ask_field('Enter your IAM role name: '     )
		else:
				_lowercase							= 'accelerate_sagemaker_execution_role'
				print(F"""Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"""     )
				_create_iam_role_for_sagemaker(snake_case__     )

		_lowercase							= _ask_field(
		    'Do you want to use custom Docker image? [yes/NO]: '			,							_convert_yes_no_to_bool			,							default=snake_case__			,							error_message='Please enter yes or no.'			,							)
		_lowercase							= None
		if is_custom_docker_image:
				_lowercase							= _ask_field('Enter your Docker image: '			,							lambda snake_case__     : str(snake_case__     ).lower()     )

		_lowercase							= _ask_field(
		    'Do you want to provide SageMaker input channels with data locations? [yes/NO]: '			,							_convert_yes_no_to_bool			,							default=snake_case__			,							error_message='Please enter yes or no.'			,							)
		_lowercase							= None
		if is_sagemaker_inputs_enabled:
				_lowercase							= _ask_field(
				    'Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): '			,							lambda snake_case__     : str(snake_case__     ).lower()			,							)

		_lowercase							= _ask_field(
		    'Do you want to enable SageMaker metrics? [yes/NO]: '			,							_convert_yes_no_to_bool			,							default=snake_case__			,							error_message='Please enter yes or no.'			,							)
		_lowercase							= None
		if is_sagemaker_metrics_enabled:
				_lowercase							= _ask_field(
				    'Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): '			,							lambda snake_case__     : str(snake_case__     ).lower()			,							)

		_lowercase							= _ask_options(
		    'What is the distributed mode?'			,							['No distributed training', 'Data parallelism']			,							_convert_sagemaker_distributed_mode			,							)
		_lowercase							= {}
		_lowercase							= _ask_field(
		    'Do you wish to optimize your script with torch dynamo?[yes/NO]:'			,							_convert_yes_no_to_bool			,							default=snake_case__			,							error_message='Please enter yes or no.'			,							)
		if use_dynamo:
				_lowercase							= 'dynamo_'
				_lowercase							= _ask_options(
				    'Which dynamo backend would you like to use?'			,							[x.lower() for x in DYNAMO_BACKENDS]			,							_convert_dynamo_backend			,							default=2			,							)
				_lowercase							= _ask_field(
				    'Do you want to customize the defaults sent to torch.compile? [yes/NO]: '			,							_convert_yes_no_to_bool			,							default=snake_case__			,							error_message='Please enter yes or no.'			,							)

				if use_custom_options:
						_lowercase							= _ask_options(
						    'Which mode do you want to use?'			,							snake_case__			,							lambda snake_case__     : TORCH_DYNAMO_MODES[int(snake_case__     )]			,							default='default'			,							)
						_lowercase							= _ask_field(
						    'Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: '			,							_convert_yes_no_to_bool			,							default=snake_case__			,							error_message='Please enter yes or no.'			,							)
						_lowercase							= _ask_field(
						    'Do you want to enable dynamic shape tracing? [yes/NO]: '			,							_convert_yes_no_to_bool			,							default=snake_case__			,							error_message='Please enter yes or no.'			,							)
		_lowercase							= 'Which EC2 instance type you want to use for your training?'
		if distributed_type != SageMakerDistributedType.NO:
				_lowercase							= _ask_options(
				    snake_case__			,							snake_case__			,							lambda snake_case__     : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(snake_case__     )]     )
		else:
				eca_instance_query += "? [ml.p3.2xlarge]:"
				_lowercase							= _ask_field(snake_case__			,							lambda snake_case__     : str(snake_case__     ).lower()			,							default='ml.p3.2xlarge'     )

		_lowercase							= 1
		if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
				_lowercase							= _ask_field(
				    'How many machines do you want use? [1]: '			,							snake_case__			,							default=1			,							)

		_lowercase							= _ask_options(
		    'Do you wish to use FP16 or BF16 (mixed precision)?'			,							['no', 'fp16', 'bf16', 'fp8']			,							_convert_mixed_precision			,							)

		if use_dynamo and mixed_precision == "no":
				print(
				    'Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.'     )

		return SageMakerConfig(
		    image_uri=snake_case__			,							compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER			,							distributed_type=snake_case__			,							use_cpu=snake_case__			,							dynamo_config=snake_case__			,							eca_instance_type=snake_case__			,							profile=snake_case__			,							region=snake_case__			,							iam_role_name=snake_case__			,							mixed_precision=snake_case__			,							num_machines=snake_case__			,							sagemaker_inputs_file=snake_case__			,							sagemaker_metrics_file=snake_case__			,							)






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


snake_case 				=       logging.get_logger(__name__)
snake_case 				=       {"""vocab_file""": """spiece.model"""}

snake_case 				=       {
    """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""",
    }
}

snake_case 				=       {
    """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 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Any      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_      : str      =  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_      : List[Any]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_      : Optional[Any]      =  ['''input_ids''', '''attention_mask''']







       def __init__(    self	:	Any						,__A	:	Tuple						,__A	:	Any=False						,__A	:	int=False						,__A	:	List[str]=False						,__A	:	List[str]=None						,__A	:	Dict=None						,__A	:	Dict=None						,__A	:	Union[str, Any]=None						,__A	:	Optional[Dict[str, Any]] = None						,**__A	:	Tuple						,)						->					None:
         _lowercase							= {} if sp_model_kwargs is None else sp_model_kwargs

         _lowercase							= 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'  )
           _lowercase							= 'None'

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

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

         _lowercase							= do_lower_case
         _lowercase							= remove_space
         _lowercase							= keep_accents
         _lowercase							= vocab_file

         _lowercase							= spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.Load(__A  )

         # Used for whitespace normalization in input texts
         # fmt : off
         _lowercase							= {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
         # fmt : on

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







       def __getstate__(    self	:	List[Any]  )						->					List[str]:
         _lowercase							= self.__dict__.copy()
         _lowercase							= None
         return state







       def __setstate__(    self	:	Optional[Any]						,__A	:	Dict  )						->					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  )







       @property
       # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
       def    __UpperCAmelCase (    self	:	List[Any]  )						->					int:
         return len(self.sp_model  )







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	str  )						->					str:
         _lowercase							= self.non_printing_characters_re.sub(''						,__A  )

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

         # NFC Unicode normalization
         _lowercase							= unicodedata.normalize('NFC'						,__A  )
         return text







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	str						,**__A	:	Optional[int]  )						->					List[str]:
         _lowercase							= self.preprocess_text(__A  )
         return self.sp_model.encode(__A						,out_type=__A  )







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	str  )						->					int:
         return self.sp_model.PieceToId(__A  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	int  )						->					str:
         return self.sp_model.IdToPiece(__A  )







       @staticmethod
       def    __UpperCAmelCase (    __A	:	str  )						->					str:
         return out_string







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	List[str]  )						->					str:
         _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:
             # 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(__A  ) + token
             _lowercase							= True
             _lowercase							= []
           else:
             current_sub_tokens.append(__A  )
             _lowercase							= False
         out_string += self.sp_model.decode(__A  )

         return out_string







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Dict[str, int]:
         _lowercase							= {self.convert_ids_to_tokens(__A  ): i for i in range(self.vocab_size  )}
         vocab.update(self.added_tokens_encoder  )
         return vocab







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	str						,__A	:	Optional[str] = None  )						->					Tuple[str]:
         if not os.path.isdir(__A  ):
           logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
           return
         _lowercase							= 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:
             _lowercase							= self.sp_model.serialized_model_proto()
             fi.write(__A  )

         return (out_vocab_file,)







       def    __UpperCAmelCase (    self	:	str						,__A	:	Union[str, List[str]]						,__A	:	Union[str, bool] = False  )						->					Union[List[int], List[List[int]], "torch.Tensor"]:

         if isinstance(__A						,__A  ):
           _lowercase							= self.preprocess_text(__A  )
           _lowercase							= self.sp_model.encode(__A  )
         else:
           _lowercase							= [self.preprocess_text(__A  ) for t in text]
           _lowercase							= self.sp_model.encode(__A  )

         if return_tensors is True or return_tensors == "pt":
           _lowercase							= torch.tensor(__A  )

         return token_ids







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	Union[int, List[int]]  )						->					str:
         return self.sp_model.decode(__A  )







       def    __UpperCAmelCase (    self	:	str						,__A	:	"Conversation"  )						->					List[int]:
         _lowercase							= [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
         _lowercase							= (
             F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__A  ) + F"""{self.bos_token}Bot:"""
         )
         return self.encode(text=__A  )