code: 
  
  
  import multiprocessing
  import os
  from typing import BinaryIO, Optional, Union
  
  from .. import Dataset, Features, NamedSplit, config
  from ..formatting import query_table
  from ..packaged_modules.csv.csv import Csv
  from ..utils import logging
  from ..utils.typing import NestedDataStructureLike, PathLike
  from .abc import AbstractDatasetReader
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = None				,    **UpperCamelCase_				,    )		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__(
  								    UpperCamelCase_				,    split=UpperCamelCase_				,    features=UpperCamelCase_				,    cache_dir=UpperCamelCase_				,    keep_in_memory=UpperCamelCase_				,    streaming=UpperCamelCase_				,    num_proc=UpperCamelCase_				,    **UpperCamelCase_				,    )
  								a_       :   int			=	path_or_paths if isinstance(UpperCamelCase_				,    UpperCamelCase_							) else {self.split: path_or_paths}
  								a_       :   Dict			=	Csv(
  								    cache_dir=UpperCamelCase_				,    data_files=UpperCamelCase_				,    features=UpperCamelCase_				,    **UpperCamelCase_				,    )
  
  
  
  
  
  	def     A				(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.streaming:
  															a_       :   Dict			=	self.builder.as_streaming_dataset(split=self.split							)
  								# Build regular (map-style) dataset
  								else:
  															a_       :   Optional[int]			=	None
  															a_       :   Optional[Any]			=	None
  															a_       :   List[str]			=	None
  															a_       :   Dict			=	None
  
  															self.builder.download_and_prepare(
  															    download_config=UpperCamelCase_				,    download_mode=UpperCamelCase_				,    verification_mode=UpperCamelCase_				,    base_path=UpperCamelCase_				,    num_proc=self.num_proc				,    )
  															a_       :   Optional[int]			=	self.builder.as_dataset(
  															    split=self.split				,    verification_mode=UpperCamelCase_				,    in_memory=self.keep_in_memory							)
  								return dataset
  
  
  
  
  
  class 					snake_case__  :
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    **UpperCamelCase_				,    )		->			Optional[int]:
  								"""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_       :   int			=	dataset
  								a_       :   Dict			=	path_or_buf
  								a_       :   int			=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
  								a_       :   List[Any]			=	num_proc
  								a_       :   str			=	"""utf-8"""
  								a_       :   int			=	to_csv_kwargs
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	self.to_csv_kwargs.pop("""path_or_buf"""				,    UpperCamelCase_							)
  								a_       :   Dict			=	self.to_csv_kwargs.pop("""header"""				,    UpperCamelCase_							)
  								a_       :   int			=	self.to_csv_kwargs.pop("""index"""				,    UpperCamelCase_							)
  
  								if isinstance(self.path_or_buf				,    (str, bytes, os.PathLike)							):
  															with open(self.path_or_buf				,    """wb+"""							) as buffer:
  																						a_       :   Tuple			=	self._write(file_obj=UpperCamelCase_				,    header=UpperCamelCase_				,    index=UpperCamelCase_				,    **self.to_csv_kwargs							)
  								else:
  															a_       :   Optional[int]			=	self._write(file_obj=self.path_or_buf				,    header=UpperCamelCase_				,    index=UpperCamelCase_				,    **self.to_csv_kwargs							)
  								return written
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_		,				a_		,				a_       :   int			=	args
  
  								a_       :   Union[str, Any]			=	query_table(
  								    table=self.dataset.data				,    key=slice(UpperCamelCase_				,    offset + self.batch_size							)				,    indices=self.dataset._indices				,    )
  								a_       :   str			=	batch.to_pandas().to_csv(
  								    path_or_buf=UpperCamelCase_				,    header=header if (offset == 0) else False				,    index=UpperCamelCase_				,    **UpperCamelCase_							)
  								return csv_str.encode(self.encoding							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    **UpperCamelCase_							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	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 CSV from Arrow format"""				,    ):
  																						a_       :   List[Any]			=	self._batch_csv((offset, header, index, to_csv_kwargs)							)
  																						written += file_obj.write(UpperCamelCase_							)
  
  								else:
  															a_		,				a_       :   Dict			=	len(self.dataset							), self.batch_size
  															with multiprocessing.Pool(self.num_proc							) as pool:
  																						for csv_str in logging.tqdm(
  																						    pool.imap(
  																						        self._batch_csv				,    [(offset, header, index, to_csv_kwargs) for offset in range(0				,    UpperCamelCase_				,    UpperCamelCase_							)]				,    )				,    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 CSV from Arrow format"""				,    ):
  																													written += file_obj.write(UpperCamelCase_							)
  
  								return written
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from typing import Any, Dict, Optional
  
  import torch
  import torch.nn.functional as F
  from torch import nn
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..models.embeddings import ImagePositionalEmbeddings
  from ..utils import BaseOutput, deprecate
  from .attention import BasicTransformerBlock
  from .embeddings import PatchEmbed
  from .modeling_utils import ModelMixin
  
  
  
  
  
  @dataclass
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							torch.FloatTensor
  
  
  
  
  
  class 					snake_case__  ( __A      ,    __A       ):
  
  
  
  
  
  	@register_to_config
  	def __init__(  self				,    UpperCamelCase_ = 16				,    UpperCamelCase_ = 88				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 1				,    UpperCamelCase_ = 0.0				,    UpperCamelCase_ = 32				,    UpperCamelCase_ = None				,    UpperCamelCase_ = False				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = "geglu"				,    UpperCamelCase_ = None				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = "layer_norm"				,    UpperCamelCase_ = True				,    )		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__()
  								a_       :   Dict			=	use_linear_projection
  								a_       :   Tuple			=	num_attention_heads
  								a_       :   List[Any]			=	attention_head_dim
  								a_       :   Dict			=	num_attention_heads * attention_head_dim
  
  								# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
  								# Define whether input is continuous or discrete depending on configuration
  								a_       :   List[Any]			=	(in_channels is not None) and (patch_size is None)
  								a_       :   Dict			=	num_vector_embeds is not None
  								a_       :   Union[str, Any]			=	in_channels is not None and patch_size is not None
  
  								if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
  															a_       :   int			=	(
  															    f"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
  															    """ incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."""
  															    """ Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"""
  															    """ results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"""
  															    """ would be very nice if you could open a Pull request for the `transformer/config.json` file"""
  															)
  															deprecate("""norm_type!=num_embeds_ada_norm"""				,    """1.0.0"""				,    UpperCamelCase_				,    standard_warn=UpperCamelCase_							)
  															a_       :   List[Any]			=	"""ada_norm"""
  
  								if self.is_input_continuous and self.is_input_vectorized:
  															raise ValueError(
  															    f"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
  															    """ sure that either `in_channels` or `num_vector_embeds` is None."""							)
  								elif self.is_input_vectorized and self.is_input_patches:
  															raise ValueError(
  															    f"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
  															    """ sure that either `num_vector_embeds` or `num_patches` is None."""							)
  								elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
  															raise ValueError(
  															    f"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
  															    f""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."""							)
  
  								# 2. Define input layers
  								if self.is_input_continuous:
  															a_       :   List[Any]			=	in_channels
  
  															a_       :   List[str]			=	torch.nn.GroupNorm(num_groups=UpperCamelCase_				,    num_channels=UpperCamelCase_				,    eps=1e-6				,    affine=UpperCamelCase_							)
  															if use_linear_projection:
  																						a_       :   int			=	nn.Linear(UpperCamelCase_				,    UpperCamelCase_							)
  															else:
  																						a_       :   Optional[Any]			=	nn.Convad(UpperCamelCase_				,    UpperCamelCase_				,    kernel_size=1				,    stride=1				,    padding=0							)
  								elif self.is_input_vectorized:
  															assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
  															assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
  
  															a_       :   Optional[Any]			=	sample_size
  															a_       :   str			=	sample_size
  															a_       :   str			=	num_vector_embeds
  															a_       :   str			=	self.height * self.width
  
  															a_       :   int			=	ImagePositionalEmbeddings(
  															    num_embed=UpperCamelCase_				,    embed_dim=UpperCamelCase_				,    height=self.height				,    width=self.width							)
  								elif self.is_input_patches:
  															assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"
  
  															a_       :   Dict			=	sample_size
  															a_       :   List[Any]			=	sample_size
  
  															a_       :   Tuple			=	patch_size
  															a_       :   Optional[Any]			=	PatchEmbed(
  															    height=UpperCamelCase_				,    width=UpperCamelCase_				,    patch_size=UpperCamelCase_				,    in_channels=UpperCamelCase_				,    embed_dim=UpperCamelCase_				,    )
  
  								# 3. Define transformers blocks
  								a_       :   List[Any]			=	nn.ModuleList(
  								    [
  								        BasicTransformerBlock(
  								            UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    dropout=UpperCamelCase_				,    cross_attention_dim=UpperCamelCase_				,    activation_fn=UpperCamelCase_				,    num_embeds_ada_norm=UpperCamelCase_				,    attention_bias=UpperCamelCase_				,    only_cross_attention=UpperCamelCase_				,    upcast_attention=UpperCamelCase_				,    norm_type=UpperCamelCase_				,    norm_elementwise_affine=UpperCamelCase_				,    )
  								        for d in range(UpperCamelCase_							)
  								    ]							)
  
  								# 4. Define output layers
  								a_       :   str			=	in_channels if out_channels is None else out_channels
  								if self.is_input_continuous:
  															# TODO: should use out_channels for continuous projections
  															if use_linear_projection:
  																						a_       :   Optional[int]			=	nn.Linear(UpperCamelCase_				,    UpperCamelCase_							)
  															else:
  																						a_       :   Any			=	nn.Convad(UpperCamelCase_				,    UpperCamelCase_				,    kernel_size=1				,    stride=1				,    padding=0							)
  								elif self.is_input_vectorized:
  															a_       :   Optional[int]			=	nn.LayerNorm(UpperCamelCase_							)
  															a_       :   str			=	nn.Linear(UpperCamelCase_				,    self.num_vector_embeds - 1							)
  								elif self.is_input_patches:
  															a_       :   int			=	nn.LayerNorm(UpperCamelCase_				,    elementwise_affine=UpperCamelCase_				,    eps=1e-6							)
  															a_       :   str			=	nn.Linear(UpperCamelCase_				,    2 * inner_dim							)
  															a_       :   Optional[int]			=	nn.Linear(UpperCamelCase_				,    patch_size * patch_size * self.out_channels							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    UpperCamelCase_ = True				,    )		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								if attention_mask is not None and attention_mask.ndim == 2:
  															# assume that mask is expressed as:
  															#   (1 = keep,      0 = discard)
  															# convert mask into a bias that can be added to attention scores:
  															#       (keep = +0,     discard = -10000.0)
  															a_       :   str			=	(1 - attention_mask.to(hidden_states.dtype							)) * -1_0000.0
  															a_       :   Union[str, Any]			=	attention_mask.unsqueeze(1							)
  
  								# convert encoder_attention_mask to a bias the same way we do for attention_mask
  								if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
  															a_       :   Any			=	(1 - encoder_attention_mask.to(hidden_states.dtype							)) * -1_0000.0
  															a_       :   List[str]			=	encoder_attention_mask.unsqueeze(1							)
  
  								# 1. Input
  								if self.is_input_continuous:
  															a_		,				a_		,				a_		,				a_       :   str			=	hidden_states.shape
  															a_       :   Optional[Any]			=	hidden_states
  
  															a_       :   Optional[int]			=	self.norm(UpperCamelCase_							)
  															if not self.use_linear_projection:
  																						a_       :   int			=	self.proj_in(UpperCamelCase_							)
  																						a_       :   str			=	hidden_states.shape[1]
  																						a_       :   List[str]			=	hidden_states.permute(0				,    2				,    3				,    1							).reshape(UpperCamelCase_				,    height * width				,    UpperCamelCase_							)
  															else:
  																						a_       :   int			=	hidden_states.shape[1]
  																						a_       :   Optional[int]			=	hidden_states.permute(0				,    2				,    3				,    1							).reshape(UpperCamelCase_				,    height * width				,    UpperCamelCase_							)
  																						a_       :   Tuple			=	self.proj_in(UpperCamelCase_							)
  								elif self.is_input_vectorized:
  															a_       :   List[str]			=	self.latent_image_embedding(UpperCamelCase_							)
  								elif self.is_input_patches:
  															a_       :   Optional[int]			=	self.pos_embed(UpperCamelCase_							)
  
  								# 2. Blocks
  								for block in self.transformer_blocks:
  															a_       :   int			=	block(
  															    UpperCamelCase_				,    attention_mask=UpperCamelCase_				,    encoder_hidden_states=UpperCamelCase_				,    encoder_attention_mask=UpperCamelCase_				,    timestep=UpperCamelCase_				,    cross_attention_kwargs=UpperCamelCase_				,    class_labels=UpperCamelCase_				,    )
  
  								# 3. Output
  								if self.is_input_continuous:
  															if not self.use_linear_projection:
  																						a_       :   Dict			=	hidden_states.reshape(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							).permute(0				,    3				,    1				,    2							).contiguous()
  																						a_       :   List[Any]			=	self.proj_out(UpperCamelCase_							)
  															else:
  																						a_       :   Optional[int]			=	self.proj_out(UpperCamelCase_							)
  																						a_       :   int			=	hidden_states.reshape(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							).permute(0				,    3				,    1				,    2							).contiguous()
  
  															a_       :   int			=	hidden_states + residual
  								elif self.is_input_vectorized:
  															a_       :   str			=	self.norm_out(UpperCamelCase_							)
  															a_       :   str			=	self.out(UpperCamelCase_							)
  															# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
  															a_       :   int			=	logits.permute(0				,    2				,    1							)
  
  															# log(p(x_0))
  															a_       :   int			=	F.log_softmax(logits.double()				,    dim=1							).float()
  								elif self.is_input_patches:
  															# TODO: cleanup!
  															a_       :   Union[str, Any]			=	self.transformer_blocks[0].norma.emb(
  															    UpperCamelCase_				,    UpperCamelCase_				,    hidden_dtype=hidden_states.dtype							)
  															a_		,				a_       :   int			=	self.proj_out_a(F.silu(UpperCamelCase_							)							).chunk(2				,    dim=1							)
  															a_       :   Union[str, Any]			=	self.norm_out(UpperCamelCase_							) * (1 + scale[:, None]) + shift[:, None]
  															a_       :   Union[str, Any]			=	self.proj_out_a(UpperCamelCase_							)
  
  															# unpatchify
  															a_       :   Any			=	int(hidden_states.shape[1] ** 0.5							)
  															a_       :   str			=	hidden_states.reshape(
  															    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)							)
  															a_       :   Union[str, Any]			=	torch.einsum("""nhwpqc->nchpwq"""				,    UpperCamelCase_							)
  															a_       :   List[Any]			=	hidden_states.reshape(
  															    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)							)
  
  								if not return_dict:
  															return (output,)
  
  								return TransformeraDModelOutput(sample=UpperCamelCase_							)
  
  
  