import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
      from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
      from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
      from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


A     :  List[str]					     =       DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

A     :  Dict					     =       "main"
# Default branch name
A     :  Dict					     =       "f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
A     :  Union[str, Any]					     =       "aaaaaaa"
# This commit does not exist, so we should 404.

A     :  Tuple					     =       "d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
A     :  int					     =       "4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"


@contextlib.contextmanager
def      a__ (		):
      print("Welcome!"				)
      yield
      print("Bye!"				)


@contextlib.contextmanager
def      a__ (		):
      print("Bonjour!"				)
      yield
      print("Au revoir!"				)


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Union[str, Any]   )  ->	int:
        # If the spec is missing, importlib would not be able to import the module dynamically.
        assert transformers.__spec__ is not None
        assert importlib.util.find_spec("transformers"   ) is not None



class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @unittest.mock.patch("sys.stdout"  ,     new_callable=io.StringIO   )
  def    __A     (							self :       List[Any]  ,     __magic_name__ :       Union[str, Any]   )  ->	List[str]:
        with ContextManagers([]   ):
              print("Transformers are awesome!"   )
        # The print statement adds a new line at the end of the output
        self.assertEqual(mock_stdout.getvalue()  ,     "Transformers are awesome!\n"   )







  @unittest.mock.patch("sys.stdout"  ,     new_callable=io.StringIO   )
  def    __A     (							self :       List[str]  ,     __magic_name__ :       str   )  ->	Dict:
        with ContextManagers([context_en()]   ):
              print("Transformers are awesome!"   )
        # The output should be wrapped with an English welcome and goodbye
        self.assertEqual(mock_stdout.getvalue()  ,     "Welcome!\nTransformers are awesome!\nBye!\n"   )







  @unittest.mock.patch("sys.stdout"  ,     new_callable=io.StringIO   )
  def    __A     (							self :       List[Any]  ,     __magic_name__ :       Tuple   )  ->	Any:
        with ContextManagers([context_fr(), context_en()]   ):
              print("Transformers are awesome!"   )
        # The output should be wrapped with an English and French welcome and goodbye
        self.assertEqual(mock_stdout.getvalue()  ,     "Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"   )







  @require_torch
  def    __A     (							self :       List[str]   )  ->	Optional[Any]:
        self.assertEqual(find_labels(__magic_name__   )  ,     ["labels"]   )
        self.assertEqual(find_labels(__magic_name__   )  ,     ["labels", "next_sentence_label"]   )
        self.assertEqual(find_labels(__magic_name__   )  ,     ["start_positions", "end_positions"]   )


        class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
          """simple docstring"""







          pass



        self.assertEqual(find_labels(__magic_name__   )  ,     ["labels"]   )







  @require_tf
  def    __A     (							self :       Optional[int]   )  ->	Any:
        self.assertEqual(find_labels(__magic_name__   )  ,     ["labels"]   )
        self.assertEqual(find_labels(__magic_name__   )  ,     ["labels", "next_sentence_label"]   )
        self.assertEqual(find_labels(__magic_name__   )  ,     ["start_positions", "end_positions"]   )


        class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
          """simple docstring"""







          pass



        self.assertEqual(find_labels(__magic_name__   )  ,     ["labels"]   )







  @require_flax
  def    __A     (							self :       List[Any]   )  ->	List[Any]:
        # Flax models don't have labels
        self.assertEqual(find_labels(__magic_name__   )  ,     []   )
        self.assertEqual(find_labels(__magic_name__   )  ,     []   )
        self.assertEqual(find_labels(__magic_name__   )  ,     []   )


        class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
          """simple docstring"""







          pass



        self.assertEqual(find_labels(__magic_name__   )  ,     []   )



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

import yaml


class   lowerCamelCase					(yaml.SafeLoader					):
  """simple docstring"""







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







  def    __A     (							self :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]=False   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  super().construct_mapping(__magic_name__  ,     deep=__magic_name__   )
        self._check_no_duplicates_on_constructed_node(__magic_name__   )
        return mapping



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

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


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						{'''train_eval_index'''}  # train-eval-index in the YAML metadata







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       Path   )  ->	"DatasetMetadata":
        with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(readme_file.read()   )
        if yaml_string is not None:
              return cls.from_yaml_string(__magic_name__   )
        else:
              return cls()







  def    __A     (							self :       str  ,     __magic_name__ :       Path   )  ->	List[str]:
        if path.exists():
              with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
                    SCREAMING_SNAKE_CASE_          =  readme_file.read()
        else:
              SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  self._to_readme(__magic_name__   )
        with open(__magic_name__  ,     "w"  ,     encoding="utf-8"   ) as readme_file:
              readme_file.write(__magic_name__   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Optional[str] = None   )  ->	str:
        if readme_content is not None:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n" + content
        else:
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n"
        return full_content







  @classmethod
  def    __A     (							cls :       List[Any]  ,     __magic_name__ :       str   )  ->	"DatasetMetadata":
        SCREAMING_SNAKE_CASE_          =  yaml.load(__magic_name__  ,     Loader=_NoDuplicateSafeLoader   ) or {}

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







  def    __A     (							self :       Optional[Any]   )  ->	str:
        return yaml.safe_dump(
            {
                (key.replace("_"  ,     "-"   ) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }  ,     sort_keys=__magic_name__  ,     allow_unicode=__magic_name__  ,     encoding="utf-8"  ,     ).decode("utf-8"   )



A     :  List[Any]					     =       {
    "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

      A     :  Optional[Any]					     =       ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
      ap.add_argument("readme_filepath")
      A     :  Union[str, Any]					     =       ap.parse_args()

      A     :  Union[str, Any]					     =       Path(args.readme_filepath)
      A     :  List[Any]					     =       DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)


