





import sys
from pathlib import Path


__lowercase  :   int							      =Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

__lowercase  :   int							      ={"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

__lowercase  :   Optional[int]							      ="""zero2"""
__lowercase  :   Tuple							      ="""zero3"""
__lowercase  :   Optional[Any]							      =[ZEROa, ZEROa]




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=parameterized.to_safe_name("_".join(str(__UpperCAmelCase  ) for x in param.args  )  )
   return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
__lowercase  :   Optional[int]							      =list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class      A       (	_snake_case						):



      @parameterized.expand(_lowerCAmelCase		,  name_func=_lowerCAmelCase       )
      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						Optional[int]		,  _lowerCAmelCase:						Tuple       )						->    Dict:
         '''simple docstring'''

         self.run_and_check(
             stage=_lowerCAmelCase		,  model=_lowerCAmelCase		,  distributed=_lowerCAmelCase		,  fpaa=_lowerCAmelCase		,  )



      @require_torch_multi_gpu
      @parameterized.expand(_lowerCAmelCase		,  name_func=_lowerCAmelCase       )
      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Dict       )						->    int:
         '''simple docstring'''

         self.run_and_check(
             stage=_lowerCAmelCase		,  model=_lowerCAmelCase		,  distributed=_lowerCAmelCase		,  fpaa=_lowerCAmelCase		,  )



      @parameterized.expand(_lowerCAmelCase		,  name_func=_lowerCAmelCase       )
      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						List[Any]       )						->    List[Any]:
         '''simple docstring'''

         self.run_and_check(
             stage=_lowerCAmelCase		,  model=_lowerCAmelCase		,  distributed=_lowerCAmelCase		,  fpaa=_lowerCAmelCase		,  )



      @require_torch_multi_gpu
      @parameterized.expand(_lowerCAmelCase		,  name_func=_lowerCAmelCase       )
      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Optional[Any]       )						->    List[str]:
         '''simple docstring'''

         self.run_and_check(
             stage=_lowerCAmelCase		,  model=_lowerCAmelCase		,  distributed=_lowerCAmelCase		,  fpaa=_lowerCAmelCase		,  )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Dict       )						->    List[str]:
         '''simple docstring'''

         pass



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						int = 10		,  _lowerCAmelCase:						bool = True		,  _lowerCAmelCase:						bool = True		,  _lowerCAmelCase:						bool = True		,  )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=models[model]

         UpperCAmelCase_											=self.run_trainer(
             stage=_lowerCAmelCase		,  model_name=_lowerCAmelCase		,  eval_steps=_lowerCAmelCase		,  num_train_epochs=1		,  distributed=_lowerCAmelCase		,  fpaa=_lowerCAmelCase		,  )

         self.do_checks(_lowerCAmelCase       )

         return output_dir



      def 	lowerCAmelCase__    (	self:						List[str]		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						int = 10		,  _lowerCAmelCase:						int = 1		,  _lowerCAmelCase:						bool = True		,  _lowerCAmelCase:						bool = True		,  )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.get_auto_remove_tmp_dir("./xxx"		,  after=_lowerCAmelCase       )
         UpperCAmelCase_											=F'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(_lowerCAmelCase       )}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

         if fpaa:
            args.extend(["--fp16"]       )

         # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
         # hence the separate config files
         UpperCAmelCase_											=F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
         UpperCAmelCase_											=[F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
         UpperCAmelCase_											=self.get_launcher(_lowerCAmelCase       )

         UpperCAmelCase_											=launcher + script + args + ds_args
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
         execute_subprocess_async(_lowerCAmelCase		,  env=self.get_env()       )

         return output_dir






      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						List[Any]=False       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=min(2		,  get_gpu_count()       ) if distributed else 1
         return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()











from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml

class      A       (	yaml.SafeLoader						):



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						int       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=[self.constructed_objects[key_node] for key_node, _ in node.value]
         UpperCAmelCase_											=[tuple(_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else key for key in keys]
         UpperCAmelCase_											=Counter(_lowerCAmelCase       )
         UpperCAmelCase_											=[key for key in counter if counter[key] > 1]
         if duplicate_keys:
            raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'       )






      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Optional[Any]=False       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=super().construct_mapping(_lowerCAmelCase		,  deep=_lowerCAmelCase       )
         self._check_no_duplicates_on_constructed_node(_lowerCAmelCase       )
         return mapping




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=list(readme_content.splitlines()  )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      UpperCAmelCase_											=full_content[1:].index("---"  ) + 1
      UpperCAmelCase_											="\n".join(full_content[1:sep_idx]  )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :]  )

   return None, "\n".join(lowercase__  )

class      A       (	__lowercase						):
      # class attributes
      _snake_case							     ={'''train_eval_index'''}  # train-eval-index in the YAML metadata



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Any		,  _lowerCAmelCase:						Path       )						->    "DatasetMetadata":
         '''simple docstring'''

         with open(_lowerCAmelCase		,  encoding="utf-8"       ) as readme_file:
            UpperCAmelCase_						,       UpperCAmelCase_											=_split_yaml_from_readme(readme_file.read()       )
         if yaml_string is not None:
            return cls.from_yaml_string(_lowerCAmelCase       )
         else:
            return cls()



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						Path       )						->    List[str]:
         '''simple docstring'''

         if path.exists():
            with open(_lowerCAmelCase		,  encoding="utf-8"       ) as readme_file:
               UpperCAmelCase_											=readme_file.read()
         else:
            UpperCAmelCase_											=None
         UpperCAmelCase_											=self._to_readme(_lowerCAmelCase       )
         with open(_lowerCAmelCase		,  "w"		,  encoding="utf-8"       ) as readme_file:
            readme_file.write(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						Optional[str] = None       )						->    str:
         '''simple docstring'''

         if readme_content is not None:
            UpperCAmelCase_						,       UpperCAmelCase_											=_split_yaml_from_readme(_lowerCAmelCase       )
            UpperCAmelCase_											="---\n" + self.to_yaml_string() + "---\n" + content
         else:
            UpperCAmelCase_											="---\n" + self.to_yaml_string() + "---\n"
         return full_content



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Optional[Any]		,  _lowerCAmelCase:						str       )						->    "DatasetMetadata":
         '''simple docstring'''

         UpperCAmelCase_											=yaml.load(_lowerCAmelCase		,  Loader=_NoDuplicateSafeLoader       ) or {}

         # Convert the YAML keys to DatasetMetadata fields
         UpperCAmelCase_											={
             (key.replace("-"		,  "_"       ) if key.replace("-"		,  "_"       ) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**_lowerCAmelCase       )






      def 	lowerCAmelCase__    (	self:						int       )						->    str:
         '''simple docstring'''

         return yaml.safe_dump(
             {
                 (key.replace("_"		,  "-"       ) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }		,  sort_keys=_lowerCAmelCase		,  allow_unicode=_lowerCAmelCase		,  encoding="utf-8"		,  ).decode("utf-8"       )

__lowercase  :   int						      ={
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
     from argparse import ArgumentParser

     __lowercase  :   List[Any]						      =ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
     ap.add_argument("""readme_filepath""")
     __lowercase  :   int						      =ap.parse_args()

     __lowercase  :   Optional[int]						      =Path(args.readme_filepath)
     __lowercase  :   Tuple						      =DatasetMetadata.from_readme(readme_filepath)
     print(dataset_metadata)
     dataset_metadata.to_readme(readme_filepath)




