





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 __a (   _snake_case			):





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

      '''simple docstring'''

      super().__init__(
          lowerCamelCase				,split=lowerCamelCase				,features=lowerCamelCase				,cache_dir=lowerCamelCase				,keep_in_memory=lowerCamelCase				,streaming=lowerCamelCase				,num_proc=lowerCamelCase				,**lowerCamelCase				,)
      __SCREAMING_SNAKE_CASE      							=       field
      __SCREAMING_SNAKE_CASE      							=       path_or_paths if isinstance(lowerCamelCase				,lowerCamelCase    ) else {self.split: path_or_paths}
      __SCREAMING_SNAKE_CASE      							=       Json(
          cache_dir=lowerCamelCase				,data_files=lowerCamelCase				,features=lowerCamelCase				,field=lowerCamelCase				,**lowerCamelCase				,)





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

      '''simple docstring'''

      if self.streaming:
         __SCREAMING_SNAKE_CASE      							=       self.builder.as_streaming_dataset(split=self.split    )
      # Build regular (map-style) dataset
      else:
         __SCREAMING_SNAKE_CASE      							=       None
         __SCREAMING_SNAKE_CASE      							=       None
         __SCREAMING_SNAKE_CASE      							=       None
         __SCREAMING_SNAKE_CASE      							=       None

         self.builder.download_and_prepare(
             download_config=lowerCamelCase				,download_mode=lowerCamelCase				,verification_mode=lowerCamelCase				,base_path=lowerCamelCase				,num_proc=self.num_proc				,)
         __SCREAMING_SNAKE_CASE      							=       self.builder.as_dataset(
             split=self.split				,verification_mode=lowerCamelCase				,in_memory=self.keep_in_memory    )
      return dataset







class __a :





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

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       dataset
      __SCREAMING_SNAKE_CASE      							=       path_or_buf
      __SCREAMING_SNAKE_CASE      							=       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
      __SCREAMING_SNAKE_CASE      							=       num_proc
      __SCREAMING_SNAKE_CASE      							=       """utf-8"""
      __SCREAMING_SNAKE_CASE      							=       to_json_kwargs





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.to_json_kwargs.pop("""path_or_buf"""				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.to_json_kwargs.pop("""orient"""				,"""records"""    )
      __SCREAMING_SNAKE_CASE      							=       self.to_json_kwargs.pop("""lines"""				,True if orient == """records""" else False    )
      __SCREAMING_SNAKE_CASE      							=       self.to_json_kwargs.pop("""index"""				,False if orient in ["""split""", """table"""] else True    )
      __SCREAMING_SNAKE_CASE      							=       self.to_json_kwargs.pop("""compression"""				,lowerCamelCase    )

      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=lowerCamelCase    ) as buffer:
            __SCREAMING_SNAKE_CASE      							=       self._write(file_obj=lowerCamelCase				,orient=lowerCamelCase				,lines=lowerCamelCase				,index=lowerCamelCase				,**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."""    )
         __SCREAMING_SNAKE_CASE      							=       self._write(
             file_obj=self.path_or_buf				,orient=lowerCamelCase				,lines=lowerCamelCase				,index=lowerCamelCase				,**self.to_json_kwargs    )
      return written





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,lowerCamelCase				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       args

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





   def      UpperCAmelCase__     (					self				:			Dict				,lowerCamelCase				:			BinaryIO				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Any				,lowerCamelCase				:			Union[str, Any]				,**lowerCamelCase				:			Any				,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       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"""				,):
            __SCREAMING_SNAKE_CASE      							=       self._batch_json((offset, orient, lines, index, to_json_kwargs)    )
            written += file_obj.write(lowerCamelCase    )
      else:
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       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				,lowerCamelCase				,lowerCamelCase    )]				,)				,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(lowerCamelCase    )

      return written












'''simple docstring'''

import flax.linen as nn
import jax
import jax.numpy as jnp







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[Any]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       hidden_states.shape
      __SCREAMING_SNAKE_CASE      							=       jax.image.resize(
          lowerCamelCase				,shape=(batch, height * 2, width * 2, channels)				,method="""nearest"""				,)
      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(2, 2)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : int	      =	None
   __UpperCamelCase   : float	      =	0.0
   __UpperCamelCase   : bool	      =	None
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.in_channels if self.out_channels is None else self.out_channels

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       nn.Dense(lowerCamelCase				,dtype=self.dtype    )

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Dropout(self.dropout_prob    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

      __SCREAMING_SNAKE_CASE      							=       None
      if use_nin_shortcut:
         __SCREAMING_SNAKE_CASE      							=       nn.Conv(
             lowerCamelCase				,kernel_size=(1, 1)				,strides=(1, 1)				,padding="""VALID"""				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			Union[str, Any]=True    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       hidden_states
      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       self.time_emb_proj(nn.swish(lowerCamelCase    )    )
      __SCREAMING_SNAKE_CASE      							=       jnp.expand_dims(jnp.expand_dims(lowerCamelCase				,1    )				,1    )
      __SCREAMING_SNAKE_CASE      							=       hidden_states + temb

      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.dropout(lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      if self.conv_shortcut is not None:
         __SCREAMING_SNAKE_CASE      							=       self.conv_shortcut(lowerCamelCase    )

      return hidden_states + residual





