






import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
snake_case    				=		logging.getLogger()


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    "\n".join(lowercase				)
  Path(lowercase				).open("w"				).writelines(lowercase				)


snake_case    				=		"""patrickvonplaten/t5-tiny-random"""
snake_case    				=		"""sshleifer/bart-tiny-random"""
snake_case    				=		"""sshleifer/tiny-mbart"""

snake_case    				=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      int							=    Path(self.get_auto_remove_tmp_dir()      ) / "utest_input.source"
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    input_file_name.parent / "utest_output.txt"
       assert not output_file_name.exists()
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
       _dump_articles(UpperCAmelCase_					, UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    str(Path(self.get_auto_remove_tmp_dir()      ) / "scores.json"      )
       SCREAMING_SNAKE_CASE       :      Any							=    "translation_en_to_de" if model == T5_TINY else "summarization"
       SCREAMING_SNAKE_CASE       :      str							=    f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

       with patch.object(UpperCAmelCase_					, "argv"					, UpperCAmelCase_      ):
         run_generate()
         assert Path(UpperCAmelCase_      ).exists()
         # os.remove(Path(output_file_name))


     def 				_A       (				self       :	Any      ):
       self.run_eval_tester(UpperCAmelCase_      )


     @parameterized.expand([BART_TINY, MBART_TINY]      )
     @slow
     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Optional[int]      ):
       self.run_eval_tester(UpperCAmelCase_      )






     @parameterized.expand([T5_TINY, MBART_TINY]      )
     @slow
     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    Path(self.get_auto_remove_tmp_dir()      ) / "utest_input.source"
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    input_file_name.parent / "utest_output.txt"
       assert not output_file_name.exists()

       SCREAMING_SNAKE_CASE       :      Tuple							=    {
           "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
           "de": [
               "Maschinelles Lernen ist großartig, oder?",
               "Ich esse gerne Bananen",
               "Morgen ist wieder ein toller Tag!",
           ],
       }

       SCREAMING_SNAKE_CASE       :      Any							=    Path(self.get_auto_remove_tmp_dir()      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    str(tmp_dir / "scores.json"      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    str(tmp_dir / "val.target"      )
       _dump_articles(UpperCAmelCase_					, text["en"]      )
       _dump_articles(UpperCAmelCase_					, text["de"]      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    "translation_en_to_de" if model == T5_TINY else "summarization"
       SCREAMING_SNAKE_CASE       :      Any							=    f'''
            run_eval_search.py
            {model}
            {str(UpperCAmelCase_      )}
            {str(UpperCAmelCase_      )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
       testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]      )

       with patch.object(UpperCAmelCase_					, "argv"					, UpperCAmelCase_      ):
         with CaptureStdout() as cs:
           run_search()
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [" num_beams | length_penalty", model, "Best score args"]
         SCREAMING_SNAKE_CASE       :      List[Any]							=    ["Info"]
         if "translation" in task:
           expected_strings.append("bleu"      )
         else:
           expected_strings.extend(UpperCAmelCase_      )
         for w in expected_strings:
           assert w in cs.out
         for w in un_expected_strings:
           assert w not in cs.out
         assert Path(UpperCAmelCase_      ).exists()
         os.remove(Path(UpperCAmelCase_      )      )














# 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 pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


snake_case    				=		"""Create a default config file for Accelerate with only a few flags set."""


def   lowerCamelCase__			(     lowercase="no"						,     lowercase = default_json_config_file						,     lowercase = False				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Tuple							=    Path(lowercase				)
  path.parent.mkdir(parents=lowercase						,     exist_ok=lowercase				)
  if path.exists():
    print(
        F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''				)
    return False
  SCREAMING_SNAKE_CASE       :      int							=    mixed_precision.lower()
  if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
    raise ValueError(
        F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''				)
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {
      "compute_environment": "LOCAL_MACHINE",
      "mixed_precision": mixed_precision,
  }
  if torch.cuda.is_available():
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.cuda.device_count()
    SCREAMING_SNAKE_CASE       :      int							=    num_gpus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_gpus > 1:
      SCREAMING_SNAKE_CASE       :      Tuple							=    "MULTI_GPU"
    else:
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    "NO"
  elif is_xpu_available() and use_xpu:
    SCREAMING_SNAKE_CASE       :      List[str]							=    torch.xpu.device_count()
    SCREAMING_SNAKE_CASE       :      str							=    num_xpus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_xpus > 1:
      SCREAMING_SNAKE_CASE       :      Any							=    "MULTI_XPU"
    else:
      SCREAMING_SNAKE_CASE       :      str							=    "NO"
  elif is_npu_available():
    SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.npu.device_count()
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    num_npus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_npus > 1:
      SCREAMING_SNAKE_CASE       :      str							=    "MULTI_NPU"
    else:
      SCREAMING_SNAKE_CASE       :      int							=    "NO"
  else:
    SCREAMING_SNAKE_CASE       :      Dict							=    0
    SCREAMING_SNAKE_CASE       :      List[Any]							=    True
    SCREAMING_SNAKE_CASE       :      Dict							=    1
    SCREAMING_SNAKE_CASE       :      int							=    "NO"
  SCREAMING_SNAKE_CASE       :      Dict							=    ClusterConfig(**lowercase				)
  config.to_json_file(lowercase				)
  return path


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    parser.add_parser("default"						,     parents=lowercase						,     help=lowercase						,     formatter_class=lowercase				)
  parser.add_argument(
      "--config_file"						,     default=lowercase						,     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'."
      )						,     dest="save_location"						,     )

  parser.add_argument(
      "--mixed_precision"						,     choices=["no", "fp16", "bf16"]						,     type=lowercase						,     help="Whether or not to use mixed precision training. "
      "Choose between FP16 and BF16 (bfloat16) training. "
      "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."						,     default="no"						,     )
  parser.set_defaults(func=lowercase				)
  return parser


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[int]							=    write_basic_config(args.mixed_precision						,     args.save_location				)
  if config_file:
    print(F'''accelerate configuration saved at {config_file}'''				)






