




# 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 argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


SCREAMING_SNAKE_CASE__       :      Dict  =   'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'

def      A      (      )	-> Optional[Any]:
       lowerCamelCase   :      Union[str, Any]			 =					_ask_options(
           "In which compute environment are you running?" ,["This machine", "AWS (Amazon SageMaker)"] ,_convert_compute_environment ,)
       if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
              lowerCamelCase   :      Union[str, Any]			 =					get_sagemaker_input()
       else:
              lowerCamelCase   :      Tuple			 =					get_cluster_input()
       return config

def      A      (      _SCREAMING_SNAKE_CASE=None    )	-> str:
       if subparsers is not None:
              lowerCamelCase   :      int			 =					subparsers.add_parser("config" ,description=_SCREAMING_SNAKE_CASE    )
       else:
              lowerCamelCase   :      Optional[Any]			 =					argparse.ArgumentParser("Accelerate config command" ,description=_SCREAMING_SNAKE_CASE    )

       parser.add_argument(
           "--config_file" ,default=_SCREAMING_SNAKE_CASE ,help=(
               "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
               "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
               "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
               "with 'huggingface'."
           ) ,)

       if subparsers is not None:
              parser.set_defaults(func=_SCREAMING_SNAKE_CASE    )
       return parser

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Union[str, Any]:
       lowerCamelCase   :      int			 =					get_user_input()
       if args.config_file is not None:
              lowerCamelCase   :      Optional[int]			 =					args.config_file
       else:
              if not os.path.isdir(_SCREAMING_SNAKE_CASE    ):
                     os.makedirs(_SCREAMING_SNAKE_CASE    )
              lowerCamelCase   :      Tuple			 =					default_yaml_config_file

       if config_file.endswith(".json"    ):
              config.to_json_file(_SCREAMING_SNAKE_CASE    )
       else:
              config.to_yaml_file(_SCREAMING_SNAKE_CASE    )
       print(f'''accelerate configuration saved at {config_file}'''    )

def      A      (      )	-> List[str]:
       lowerCamelCase   :      int			 =					config_command_parser()
       lowerCamelCase   :      Tuple			 =					parser.parse_args()
       config_command(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()










"""simple docstring"""







import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
     from transformers.models.rag.configuration_rag import RagConfig
     from transformers.models.rag.tokenization_rag import RagTokenizer


@require_faiss
@require_torch
class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							tempfile.mkdtemp()
         lowerCAmelCase     :     Optional[int]								=							8

         # DPR tok
         lowerCAmelCase     :     Dict								=							[
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "[PAD]",
             "[MASK]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         lowerCAmelCase     :     List[str]								=							os.path.join(self.tmpdirname		,     "dpr_tokenizer"						)
         os.makedirs(snake_case__		,     exist_ok=snake_case__						)
         lowerCAmelCase     :     Dict								=							os.path.join(snake_case__		,     DPR_VOCAB_FILES_NAMES["vocab_file"]						)
         with open(self.vocab_file		,     "w"		,     encoding="utf-8"						) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)

         # BART tok
         lowerCAmelCase     :     Optional[int]								=							[
             "l",
             "o",
             "w",
             "e",
             "r",
             "s",
             "t",
             "i",
             "d",
             "n",
             "\u0120",
             "\u0120l",
             "\u0120n",
             "\u0120lo",
             "\u0120low",
             "er",
             "\u0120lowest",
             "\u0120newer",
             "\u0120wider",
             "<unk>",
         ]
         lowerCAmelCase     :     Optional[int]								=							dict(zip(snake_case__		,     range(len(snake_case__						)						)						)						)
         lowerCAmelCase     :     List[Any]								=							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
         lowerCAmelCase     :     str								=							{"unk_token": "<unk>"}

         lowerCAmelCase     :     int								=							os.path.join(self.tmpdirname		,     "bart_tokenizer"						)
         os.makedirs(snake_case__		,     exist_ok=snake_case__						)
         lowerCAmelCase     :     int								=							os.path.join(snake_case__		,     BART_VOCAB_FILES_NAMES["vocab_file"]						)
         lowerCAmelCase     :     Dict								=							os.path.join(snake_case__		,     BART_VOCAB_FILES_NAMES["merges_file"]						)
         with open(self.vocab_file		,     "w"		,     encoding="utf-8"						) as fp:
           fp.write(json.dumps(snake_case__						) + "\n"						)
         with open(self.merges_file		,     "w"		,     encoding="utf-8"						) as fp:
           fp.write("\n".join(snake_case__						)						)

       def        lowercase__							(						self						):






         """simple docstring"""





         return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,     "dpr_tokenizer"						)						)

       def        lowercase__							(						self						):






         """simple docstring"""





         return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,     "bart_tokenizer"						)						)

       def        lowercase__							(						self						):






         """simple docstring"""





         shutil.rmtree(self.tmpdirname						)

       @require_tokenizers
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							os.path.join(self.tmpdirname		,     "rag_tokenizer"						)
         lowerCAmelCase     :     List[Any]								=							RagConfig(question_encoder=DPRConfig().to_dict()		,     generator=BartConfig().to_dict()						)
         lowerCAmelCase     :     Optional[Any]								=							RagTokenizer(question_encoder=self.get_dpr_tokenizer()		,     generator=self.get_bart_tokenizer()						)
         rag_config.save_pretrained(snake_case__						)
         rag_tokenizer.save_pretrained(snake_case__						)
         lowerCAmelCase     :     List[str]								=							RagTokenizer.from_pretrained(snake_case__		,     config=snake_case__						)
         self.assertIsInstance(new_rag_tokenizer.question_encoder		,     snake_case__						)
         self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()		,     rag_tokenizer.question_encoder.get_vocab()						)
         self.assertIsInstance(new_rag_tokenizer.generator		,     snake_case__						)
         self.assertEqual(new_rag_tokenizer.generator.get_vocab()		,     rag_tokenizer.generator.get_vocab()						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							RagTokenizer.from_pretrained("facebook/rag-token-nq"						)
         lowerCAmelCase     :     Dict								=							[
             "who got the first nobel prize in physics",
             "when is the next deadpool movie being released",
             "which mode is used for short wave broadcast service",
             "who is the owner of reading football club",
             "when is the next scandal episode coming out",
             "when is the last time the philadelphia won the superbowl",
             "what is the most current adobe flash player version",
             "how many episodes are there in dragon ball z",
             "what is the first step in the evolution of the eye",
             "where is gall bladder situated in human body",
             "what is the main mineral in lithium batteries",
             "who is the president of usa right now",
             "where do the greasers live in the outsiders",
             "panda is a national animal of which country",
             "what is the name of manchester united stadium",
         ]
         lowerCAmelCase     :     Union[str, Any]								=							tokenizer(snake_case__						)
         self.assertIsNotNone(snake_case__						)







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							RagTokenizer.from_pretrained("facebook/rag-sequence-nq"						)
         lowerCAmelCase     :     List[str]								=							[
             "who got the first nobel prize in physics",
             "when is the next deadpool movie being released",
             "which mode is used for short wave broadcast service",
             "who is the owner of reading football club",
             "when is the next scandal episode coming out",
             "when is the last time the philadelphia won the superbowl",
             "what is the most current adobe flash player version",
             "how many episodes are there in dragon ball z",
             "what is the first step in the evolution of the eye",
             "where is gall bladder situated in human body",
             "what is the main mineral in lithium batteries",
             "who is the president of usa right now",
             "where do the greasers live in the outsiders",
             "panda is a national animal of which country",
             "what is the name of manchester united stadium",
         ]
         lowerCAmelCase     :     str								=							tokenizer(snake_case__						)
         self.assertIsNotNone(snake_case__						)





