
'''simple docstring'''







import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     Tuple							,_a      :     NestedDataStructureLike[PathLike]							,_a      :     Optional[NamedSplit] = None							,_a      :     Optional[Features] = None							,_a      :     str = None							,_a      :     bool = False							,_a      :     bool = False							,_a      :     Optional[str] = None							,_a      :     Optional[int] = None							,**_a      :     Union[str, Any]							,):


     '''simple docstring'''






     super().__init__(
         _a							,split=_a							,features=_a							,cache_dir=_a							,keep_in_memory=_a							,streaming=_a							,num_proc=_a							,**_a							,)
     _a				:	List[Any]			=   field
     _a				:	List[Any]			=   path_or_paths if isinstance(_a							,_a       ) else {self.split: path_or_paths}
     _a				:	int			=   Json(
         cache_dir=_a							,data_files=_a							,features=_a							,field=_a							,**_a							,)


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     if self.streaming:
        _a				:	Tuple			=   self.builder.as_streaming_dataset(split=self.split       )
     # Build regular (map-style) dataset
     else:
        _a				:	Any			=   None
        _a				:	Optional[Any]			=   None
        _a				:	Dict			=   None
        _a				:	Dict			=   None

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







class UpperCAmelCase__	:





  """simple docstring"""

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


     '''simple docstring'''






     if num_proc is not None and num_proc <= 0:
        raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""       )

     _a				:	Union[str, Any]			=   dataset
     _a				:	Optional[Any]			=   path_or_buf
     _a				:	Optional[int]			=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     _a				:	Any			=   num_proc
     _a				:	Optional[Any]			=   'utf-8'
     _a				:	Any			=   to_json_kwargs


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	List[str]			=   self.to_json_kwargs.pop('path_or_buf'							,_a       )
     _a				:	List[str]			=   self.to_json_kwargs.pop('orient'							,'records'       )
     _a				:	Dict			=   self.to_json_kwargs.pop('lines'							,True if orient == 'records' else False       )
     _a				:	Optional[Any]			=   self.to_json_kwargs.pop('index'							,False if orient in ['split', 'table'] else True       )
     _a				:	Union[str, Any]			=   self.to_json_kwargs.pop('compression'							,_a       )

     if compression not in [None, "infer", "gzip", "bz2", "xz"]:
        raise NotImplementedError(F"""`datasets` currently does not support {compression} compression"""       )

     if isinstance(self.path_or_buf							,(str, bytes, os.PathLike)       ):
        with fsspec.open(self.path_or_buf							,'wb'							,compression=_a       ) as buffer:
           _a				:	str			=   self._write(file_obj=_a							,orient=_a							,lines=_a							,index=_a							,**self.to_json_kwargs       )
     else:
        if compression:
           raise NotImplementedError(
               F"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
               ' was passed. Please provide a local path instead.'       )
        _a				:	List[Any]			=   self._write(
            file_obj=self.path_or_buf							,orient=_a							,lines=_a							,index=_a							,**self.to_json_kwargs       )
     return written


  def   __lowercase		(		self      :     Dict							,_a      :     Optional[Any]       ):


     '''simple docstring'''






     _a,  _a,  _a,  _a,  _a				:	Optional[Any]			=   args

     _a				:	Any			=   query_table(
         table=self.dataset.data							,key=slice(_a							,offset + self.batch_size       )							,indices=self.dataset._indices							,)
     _a				:	List[Any]			=   batch.to_pandas().to_json(
         path_or_buf=_a							,orient=_a							,lines=_a							,index=_a							,**_a       )
     if not json_str.endswith('\n'       ):
        json_str += "\n"
     return json_str.encode(self.encoding       )


  def   __lowercase		(		self      :     Any							,_a      :     BinaryIO							,_a      :     Optional[int]							,_a      :     Any							,_a      :     Union[str, Any]							,**_a      :     str							,):


     '''simple docstring'''






     _a				:	Dict			=   0

     if self.num_proc is None or self.num_proc == 1:
        for offset in logging.tqdm(
            range(0							,len(self.dataset       )							,self.batch_size       )							,unit='ba'							,disable=not logging.is_progress_bar_enabled()							,desc='Creating json from Arrow format'							,):
           _a				:	Any			=   self._batch_json((offset, orient, lines, index, to_json_kwargs)       )
           written += file_obj.write(_a       )
     else:
        _a,  _a				:	Optional[int]			=   len(self.dataset       ), self.batch_size
        with multiprocessing.Pool(self.num_proc       ) as pool:
           for json_str in logging.tqdm(
               pool.imap(
                   self._batch_json							,[(offset, orient, lines, index, to_json_kwargs) for offset in range(0							,_a							,_a       )]							,)							,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size							,unit='ba'							,disable=not logging.is_progress_bar_enabled()							,desc='Creating json from Arrow format'							,):
              written += file_obj.write(_a       )

     return written





'''simple docstring'''







import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


__lowerCAmelCase         =    HUGGINGFACE_HUB_CACHE


__lowerCAmelCase         =    """config.json"""
__lowerCAmelCase         =    """diffusion_pytorch_model.bin"""
__lowerCAmelCase         =    """diffusion_flax_model.msgpack"""
__lowerCAmelCase         =    """model.onnx"""
__lowerCAmelCase         =    """diffusion_pytorch_model.safetensors"""
__lowerCAmelCase         =    """weights.pb"""
__lowerCAmelCase         =    """https://huggingface.co"""
__lowerCAmelCase         =    default_cache_path
__lowerCAmelCase         =    """diffusers_modules"""
__lowerCAmelCase         =    os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
__lowerCAmelCase         =    ["""fp16""", """non-ema"""]
__lowerCAmelCase         =    """.self_attn"""



