



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__snake_case							:						Any      =logging.get_logger(__name__)



class 			lowerCamelCase__     :




		'''simple docstring'''



		def __init__(self						,__lowerCamelCase						,__lowerCamelCase					)	->     List[str]:

						"""simple docstring"""






						lowerCAmelCase__	:							Optional[Any]				=	question_encoder
						lowerCAmelCase__	:							Union[str, Any]				=	generator
						lowerCAmelCase__	:							List[Any]				=	self.question_encoder



		def 						lowerCAmelCase__ (self						,__lowerCamelCase					)	->     Optional[Any]:

						"""simple docstring"""






						if os.path.isfile(UpperCamelCase__					):
										raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""					)
						os.makedirs(UpperCamelCase__						,exist_ok=UpperCamelCase__					)
						lowerCAmelCase__	:							Union[str, Any]				=	os.path.join(UpperCamelCase__						,'''question_encoder_tokenizer'''					)
						lowerCAmelCase__	:							str				=	os.path.join(UpperCamelCase__						,'''generator_tokenizer'''					)
						self.question_encoder.save_pretrained(UpperCamelCase__					)
						self.generator.save_pretrained(UpperCamelCase__					)



		@classmethod
		def 						lowerCAmelCase__ (cls						,__lowerCamelCase						,**__lowerCamelCase					)	->     str:

						"""simple docstring"""






						from ..auto.tokenization_auto import AutoTokenizer

						lowerCAmelCase__	:							Dict				=	kwargs.pop('''config'''						,UpperCamelCase__					)

						if config is None:
										lowerCAmelCase__	:							Any				=	RagConfig.from_pretrained(UpperCamelCase__					)

						lowerCAmelCase__	:							Optional[Any]				=	AutoTokenizer.from_pretrained(
						    UpperCamelCase__						,config=config.question_encoder						,subfolder='''question_encoder_tokenizer'''					)
						lowerCAmelCase__	:							Dict				=	AutoTokenizer.from_pretrained(
						    UpperCamelCase__						,config=config.generator						,subfolder='''generator_tokenizer'''					)
						return cls(question_encoder=UpperCamelCase__						,generator=UpperCamelCase__					)



		def __call__(self						,*__lowerCamelCase						,**__lowerCamelCase					)	->     Union[str, Any]:

						"""simple docstring"""






						return self.current_tokenizer(*UpperCamelCase__						,**UpperCamelCase__					)



		def 						lowerCAmelCase__ (self						,*__lowerCamelCase						,**__lowerCamelCase					)	->     Union[str, Any]:

						"""simple docstring"""






						return self.generator.batch_decode(*UpperCamelCase__						,**UpperCamelCase__					)



		def 						lowerCAmelCase__ (self						,*__lowerCamelCase						,**__lowerCamelCase					)	->     Union[str, Any]:

						"""simple docstring"""






						return self.generator.decode(*UpperCamelCase__						,**UpperCamelCase__					)



		def 						lowerCAmelCase__ (self					)	->     str:

						"""simple docstring"""






						lowerCAmelCase__	:							Union[str, Any]				=	self.question_encoder



		def 						lowerCAmelCase__ (self					)	->     List[Any]:

						"""simple docstring"""






						lowerCAmelCase__	:							List[Any]				=	self.generator



		def 						lowerCAmelCase__ (self						,__lowerCamelCase						,__lowerCamelCase = None						,__lowerCamelCase = None						,__lowerCamelCase = None						,__lowerCamelCase = "longest"						,__lowerCamelCase = None						,__lowerCamelCase = True						,**__lowerCamelCase						,)	->     BatchEncoding:

						"""simple docstring"""






						warnings.warn(
						    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
						    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
						    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
						    '''details'''						,UpperCamelCase__						,)
						if max_length is None:
										lowerCAmelCase__	:							List[str]				=	self.current_tokenizer.model_max_length
						lowerCAmelCase__	:							Dict				=	self(
						    UpperCamelCase__						,add_special_tokens=UpperCamelCase__						,return_tensors=UpperCamelCase__						,max_length=UpperCamelCase__						,padding=UpperCamelCase__						,truncation=UpperCamelCase__						,**UpperCamelCase__						,)
						if tgt_texts is None:
										return model_inputs
						# Process tgt_texts
						if max_target_length is None:
										lowerCAmelCase__	:							Dict				=	self.current_tokenizer.model_max_length
						lowerCAmelCase__	:							Tuple				=	self(
						    text_target=UpperCamelCase__						,add_special_tokens=UpperCamelCase__						,return_tensors=UpperCamelCase__						,padding=UpperCamelCase__						,max_length=UpperCamelCase__						,truncation=UpperCamelCase__						,**UpperCamelCase__						,)
						lowerCAmelCase__	:							Any				=	labels['''input_ids''']
						return model_inputs




'''simple docstring'''







# Copyright 2022 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 argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


__lowerCamelCase									=     '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def    UpperCAmelCase__     (   UpperCAmelCase__=None					)      ->      List[str]:
       if subparsers is not None:
              A_						       =   subparsers.add_parser("""tpu-config""",							description=_description					)
       else:
              A_						       =   argparse.ArgumentParser("""Accelerate tpu-config command""",							description=_description					)
       # Core arguments
       A_						       =   parser.add_argument_group(
           """Config Arguments""",							"""Arguments that can be configured through `accelerate config`."""					)
       config_args.add_argument(
           """--config_file""",							type=UpperCAmelCase__,							default=UpperCAmelCase__,							help="""Path to the config file to use for accelerate.""",							)
       config_args.add_argument(
           """--tpu_name""",							default=UpperCAmelCase__,							help="""The name of the TPU to use. If not specified, will use the TPU specified in the config file.""",							)
       config_args.add_argument(
           """--tpu_zone""",							default=UpperCAmelCase__,							help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file.""",							)
       A_						       =   parser.add_argument_group("""TPU Arguments""",							"""Arguments for options ran inside the TPU."""					)
       pod_args.add_argument(
           """--use_alpha""",							action="""store_true""",							help="""Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.""",							)
       pod_args.add_argument(
           """--command_file""",							default=UpperCAmelCase__,							help="""The path to the file containing the commands to run on the pod on startup.""",							)
       pod_args.add_argument(
           """--command""",							action="""append""",							nargs="""+""",							help="""A command to run on the pod. Can be passed multiple times.""",							)
       pod_args.add_argument(
           """--install_accelerate""",							action="""store_true""",							help="""Whether to install accelerate on the pod. Defaults to False.""",							)
       pod_args.add_argument(
           """--accelerate_version""",							default="""latest""",							help="""The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub.""",							)
       pod_args.add_argument(
           """--debug""",							action="""store_true""",							help="""If set, will print the command that would be run instead of running it."""					)

       if subparsers is not None:
              parser.set_defaults(func=UpperCAmelCase__					)
       return parser
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      List[Any]:
       A_						       =   None

       # Get the default from the config file if it exists.
       if args.config_file is not None or os.path.isfile(UpperCAmelCase__					):
              A_						       =   load_config_from_file(args.config_file					)
              if not args.command_file and defaults.command_file is not None and not args.command:
                     A_						       =   defaults.command_file
              if not args.command and defaults.commands is not None:
                     A_						       =   defaults.commands
              if not args.tpu_name:
                     A_						       =   defaults.tpu_name
              if not args.tpu_zone:
                     A_						       =   defaults.tpu_zone
       if args.accelerate_version == "dev":
              A_						       =   """git+https://github.com/huggingface/accelerate.git"""
       elif args.accelerate_version == "latest":
              A_						       =   """accelerate -U"""
       elif isinstance(parse(args.accelerate_version					),							UpperCAmelCase__					):
              A_						       =   F'''accelerate=={args.accelerate_version}'''

       if not args.command_file and not args.command:
              raise ValueError("""You must specify either a command file or a command to run on the pod."""					)

       if args.command_file:
              with open(args.command_file,							"""r"""					) as f:
                     A_						       =   [f.read().splitlines()]

    # To turn list of lists into list of strings
       if isinstance(args.command[0],							UpperCAmelCase__					):
              A_						       =   [line for cmd in args.command for line in cmd]
       # Default to the shared folder and install accelerate
       A_						       =   ["""cd /usr/share"""]
       if args.install_accelerate:
              new_cmd += [F'''pip install {args.accelerate_version}''']
       new_cmd += args.command
       A_						       =   """; """.join(UpperCAmelCase__					)

       # Then send it to gcloud
       # Eventually try to use google-api-core to do this instead of subprocess
       A_						       =   ["""gcloud"""]
       if args.use_alpha:
              cmd += ["alpha"]
       cmd += [
           "compute",
           "tpus",
           "tpu-vm",
           "ssh",
           args.tpu_name,
           "--zone",
           args.tpu_zone,
           "--command",
           args.command,
           "--worker",
           "all",
       ]
       if args.debug:
              print(F'''Running {" ".join(UpperCAmelCase__					)}'''					)
              return
       subprocess.run(UpperCAmelCase__					)
       print("""Successfully setup pod."""					)







def    UpperCAmelCase__     (   )      ->      int:
       A_						       =   tpu_command_parser()
       A_						       =   parser.parse_args()

       tpu_command_launcher(UpperCAmelCase__					)
