

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a    :Dict      =			{'configuration_xglm': ['XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XGLMConfig']}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :Dict      =			['XGLMTokenizer']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :str      =			['XGLMTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :Tuple      =			[
          'XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
          'XGLMForCausalLM',
          'XGLMModel',
          'XGLMPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :List[Any]      =			[
          'FlaxXGLMForCausalLM',
          'FlaxXGLMModel',
          'FlaxXGLMPreTrainedModel',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :Any      =			[
          'TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFXGLMForCausalLM',
          'TFXGLMModel',
          'TFXGLMPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xglm import XGLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xglm_fast import XGLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_xglm import (
                TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFXGLMForCausalLM,
                TFXGLMModel,
                TFXGLMPreTrainedModel,
            )


else:
      import sys

      __a    :List[Any]      =			_LazyModule(__name__, globals()['__file__'], _import_structure)



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

import yaml



class 						_UpperCAmelCase      (     yaml.SafeLoader							):






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[self.constructed_objects[key_node] for key_node, _ in node.value]
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[tuple(UpperCAmelCase) if isinstance(UpperCAmelCase   ,  UpperCAmelCase) else key for key in keys]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						Counter(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:str				     =						[key for key in counter if counter[key] > 1]
				if duplicate_keys:
						raise TypeError(F"Got duplicate yaml keys: {duplicate_keys}")







		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Union[str, Any]=False):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						super().construct_mapping(UpperCAmelCase   ,  deep=UpperCAmelCase)
				self._check_no_duplicates_on_constructed_node(UpperCAmelCase)
				return mapping




def     lowercase	(    a ):



		'''simple docstring'''




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

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



class 						_UpperCAmelCase      (     lowercase							):
		# class attributes
		lowerCamelCase_	:			List[Any]       			=  {"""train_eval_index"""}  # train-eval-index in the YAML metadata






		@classmethod
		def _snake_case		(						cls				:     Optional[int]   ,  UpperCAmelCase				:     Path):
				with open(UpperCAmelCase   ,  encoding="utf-8") as readme_file:
						SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						_split_yaml_from_readme(readme_file.read())
				if yaml_string is not None:
						return cls.from_yaml_string(UpperCAmelCase)
				else:
						return cls()






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






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     Optional[str] = None):
				if readme_content is not None:
						SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[str]				     =						_split_yaml_from_readme(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:int				     =						"---\n" + self.to_yaml_string() + "---\n" + content
				else:
						SCREAMING_SNAKE_CASE_				:Any				     =						"---\n" + self.to_yaml_string() + "---\n"
				return full_content






		@classmethod
		def _snake_case		(						cls				:     List[str]   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						yaml.load(UpperCAmelCase   ,  Loader=_NoDuplicateSafeLoader) or {}

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







		def _snake_case		(						self				:     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=UpperCAmelCase   ,  allow_unicode=UpperCAmelCase   ,  encoding="utf-8"   ,  ).decode("utf-8")




SCREAMING_SNAKE_CASE__     					=			{
    "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

		SCREAMING_SNAKE_CASE__     					=			ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
		ap.add_argument("readme_filepath")
		SCREAMING_SNAKE_CASE__     					=			ap.parse_args()

		SCREAMING_SNAKE_CASE__     					=			Path(args.readme_filepath)
		SCREAMING_SNAKE_CASE__     					=			DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)






