import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




def       _snake_case	(   lowerCAmelCase							:   Features	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    np.inf

		def set_batch_size(lowerCAmelCase							:   FeatureType	) -> None:
				nonlocal batch_size
				if isinstance(lowerCAmelCase   , lowerCAmelCase	):
						SCREAMING_SNAKE_CASE_  :		int				      =    min(lowerCAmelCase   , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS	)
				elif isinstance(lowerCAmelCase   , lowerCAmelCase	):
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    min(lowerCAmelCase   , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS	)
				elif isinstance(lowerCAmelCase   , lowerCAmelCase	) and feature.dtype == "binary":
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    min(lowerCAmelCase   , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS	)

		_visit(lowerCAmelCase   , lowerCAmelCase	)

		return None if batch_size is np.inf else batch_size






class 		a__						(					A__					):

					def __init__(    self      :       List[str],_A      :       NestedDataStructureLike[PathLike],_A      :       Optional[NamedSplit] = None,_A      :       Optional[Features] = None,_A      :       str = None,_A      :       bool = False,_A      :       bool = False,_A      :       Optional[int] = None,**_A      :       int,):







							"""simple docstring"""






							super().__init__(
							    _A,split=_A,features=_A,cache_dir=_A,keep_in_memory=_A,streaming=_A,num_proc=_A,**_A,)
							SCREAMING_SNAKE_CASE_  :		int				      =    path_or_paths if isinstance(_A,_A    ) else {self.split: path_or_paths}
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    _PACKAGED_DATASETS_MODULES["parquet"][1]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    Parquet(
							    cache_dir=_A,data_files=_A,features=_A,hash=_A,**_A,)






					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							if self.streaming:
									SCREAMING_SNAKE_CASE_  :		str				      =    self.builder.as_streaming_dataset(split=self.split    )
							# Build regular (map-style) dataset
							else:
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    None
									SCREAMING_SNAKE_CASE_  :		int				      =    None
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    None
									SCREAMING_SNAKE_CASE_  :		Any				      =    None

									self.builder.download_and_prepare(
									    download_config=_A,download_mode=_A,verification_mode=_A,base_path=_A,num_proc=self.num_proc,)
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.builder.as_dataset(
									    split=self.split,verification_mode=_A,in_memory=self.keep_in_memory    )
							return dataset







class 		a__						:

					def __init__(    self      :       int,_A      :       Dataset,_A      :       Union[PathLike, BinaryIO],_A      :       Optional[int] = None,**_A      :       str,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    dataset
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    path_or_buf
							SCREAMING_SNAKE_CASE_  :		int				      =    batch_size or get_writer_batch_size(dataset.features    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    parquet_writer_kwargs

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

							if isinstance(self.path_or_buf,(str, bytes, os.PathLike)    ):
									with open(self.path_or_buf,"wb+"    ) as buffer:
											SCREAMING_SNAKE_CASE_  :		Any				      =    self._write(file_obj=_A,batch_size=_A,**self.parquet_writer_kwargs    )
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    self._write(file_obj=self.path_or_buf,batch_size=_A,**self.parquet_writer_kwargs    )
							return written






					def 				__UpperCamelCase   (    self      :       str,_A      :       BinaryIO,_A      :       int,**_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    0
							SCREAMING_SNAKE_CASE_  :		Any				      =    parquet_writer_kwargs.pop("path_or_buf",_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.dataset.features.arrow_schema

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    pq.ParquetWriter(_A,schema=_A,**_A    )

							for offset in logging.tqdm(
							    range(0,len(self.dataset    ),_A    ),unit="ba",disable=not logging.is_progress_bar_enabled(),desc="Creating parquet from Arrow format",):
									SCREAMING_SNAKE_CASE_  :		Any				      =    query_table(
									    table=self.dataset._data,key=slice(_A,offset + batch_size    ),indices=self.dataset._indices if self.dataset._indices is not None else None,)
									writer.write_table(_A    )
									written += batch.nbytes
							writer.close()
							return written

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

import yaml






class 		a__						(					yaml.SafeLoader					):

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str]    ):







							"""simple docstring"""






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






					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Dict,_A      :       List[Any]=False    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    super().construct_mapping(_A,deep=_A    )
							self._check_no_duplicates_on_constructed_node(_A    )
							return mapping







def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







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

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






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

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Any,_A      :       Path    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Path    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       Optional[str] = None    ):







							"""simple docstring"""






							if readme_content is not None:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    _split_yaml_from_readme(_A    )
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    "---\n" + self.to_yaml_string() + "---\n" + content
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    "---\n" + self.to_yaml_string() + "---\n"
							return full_content

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Dict,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    yaml.load(_A,Loader=_NoDuplicateSafeLoader    ) or {}

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






					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""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=_A,allow_unicode=_A,encoding="utf-8",).decode("utf-8"    )







__lowerCamelCase					:	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

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

		__lowerCamelCase					:	List[Any]       =  Path(args.readme_filepath)
		__lowerCamelCase					:	Optional[int]       =  DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)
