code: 
  
  
  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   :							List[str]         = 42
  
  
  
  
  
  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				,    )		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__()
  								a_       :   str			=	use_linear_projection
  								a_       :   Tuple			=	num_attention_heads
  								a_       :   int			=	attention_head_dim
  								a_       :   int			=	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[str]			=	(in_channels is not None) and (patch_size is None)
  								a_       :   int			=	num_vector_embeds is not None
  								a_       :   Optional[int]			=	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_       :   Tuple			=	(
  															    f"""The configuration file of this model: {self.__class__} is outdated.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_       :   int			=	'''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_       :   str			=	in_channels
  
  															a_       :   Any			=	torch.nn.GroupNorm(num_groups=UpperCamelCase_				,    num_channels=UpperCamelCase_				,    eps=1e-6				,    affine=UpperCamelCase_							)
  															if use_linear_projection:
  																						a_       :   Optional[int]			=	nn.Linear(UpperCamelCase_				,    UpperCamelCase_							)
  															else:
  																						a_       :   Optional[int]			=	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_       :   List[Any]			=	sample_size
  															a_       :   Union[str, Any]			=	sample_size
  															a_       :   Union[str, Any]			=	num_vector_embeds
  															a_       :   List[str]			=	self.height * self.width
  
  															a_       :   List[str]			=	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_       :   Union[str, Any]			=	sample_size
  															a_       :   Union[str, Any]			=	sample_size
  
  															a_       :   List[Any]			=	patch_size
  															a_       :   Optional[Any]			=	PatchEmbed(
  															    height=UpperCamelCase_				,    width=UpperCamelCase_				,    patch_size=UpperCamelCase_				,    in_channels=UpperCamelCase_				,    embed_dim=UpperCamelCase_				,    )
  
  								# 3. Define transformers blocks
  								a_       :   Optional[int]			=	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_       :   Union[str, Any]			=	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[Any]			=	nn.Linear(UpperCamelCase_				,    UpperCamelCase_							)
  															else:
  																						a_       :   Any			=	nn.Convad(UpperCamelCase_				,    UpperCamelCase_				,    kernel_size=1				,    stride=1				,    padding=0							)
  								elif self.is_input_vectorized:
  															a_       :   Union[str, Any]			=	nn.LayerNorm(UpperCamelCase_							)
  															a_       :   Any			=	nn.Linear(UpperCamelCase_				,    self.num_vector_embeds - 1							)
  								elif self.is_input_patches:
  															a_       :   Optional[int]			=	nn.LayerNorm(UpperCamelCase_				,    elementwise_affine=UpperCamelCase_				,    eps=1e-6							)
  															a_       :   Optional[int]			=	nn.Linear(UpperCamelCase_				,    2 * inner_dim							)
  															a_       :   List[str]			=	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				,    )		->			List[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_       :   Union[str, Any]			=	(1 - attention_mask.to(hidden_states.dtype							)) * -1_0000.0
  															a_       :   Optional[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_       :   Dict			=	(1 - encoder_attention_mask.to(hidden_states.dtype							)) * -1_0000.0
  															a_       :   List[Any]			=	encoder_attention_mask.unsqueeze(1							)
  
  								# 1. Input
  								if self.is_input_continuous:
  															a_       :   Tuple			=	hidden_states.shape
  															a_       :   Optional[Any]			=	hidden_states
  
  															a_       :   List[Any]			=	self.norm(UpperCamelCase_							)
  															if not self.use_linear_projection:
  																						a_       :   List[Any]			=	self.proj_in(UpperCamelCase_							)
  																						a_       :   Any			=	hidden_states.shape[1]
  																						a_       :   Tuple			=	hidden_states.permute(0				,    2				,    3				,    1							).reshape(UpperCamelCase_				,    height * width				,    UpperCamelCase_							)
  															else:
  																						a_       :   List[Any]			=	hidden_states.shape[1]
  																						a_       :   List[str]			=	hidden_states.permute(0				,    2				,    3				,    1							).reshape(UpperCamelCase_				,    height * width				,    UpperCamelCase_							)
  																						a_       :   Optional[int]			=	self.proj_in(UpperCamelCase_							)
  								elif self.is_input_vectorized:
  															a_       :   Optional[int]			=	self.latent_image_embedding(UpperCamelCase_							)
  								elif self.is_input_patches:
  															a_       :   Optional[Any]			=	self.pos_embed(UpperCamelCase_							)
  
  								# 2. Blocks
  								for block in self.transformer_blocks:
  															a_       :   Any			=	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_       :   Optional[int]			=	hidden_states.reshape(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							).permute(0				,    3				,    1				,    2							).contiguous()
  																						a_       :   Optional[int]			=	self.proj_out(UpperCamelCase_							)
  															else:
  																						a_       :   Union[str, Any]			=	self.proj_out(UpperCamelCase_							)
  																						a_       :   int			=	hidden_states.reshape(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							).permute(0				,    3				,    1				,    2							).contiguous()
  
  															a_       :   Union[str, Any]			=	hidden_states + residual
  								elif self.is_input_vectorized:
  															a_       :   int			=	self.norm_out(UpperCamelCase_							)
  															a_       :   List[Any]			=	self.out(UpperCamelCase_							)
  															# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
  															a_       :   Optional[Any]			=	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_       :   Tuple			=	self.transformer_blocks[0].norma.emb(
  															    UpperCamelCase_				,    UpperCamelCase_				,    hidden_dtype=hidden_states.dtype							)
  															a_       :   Any			=	self.proj_out_a(F.silu(UpperCamelCase_							)							).chunk(2				,    dim=1							)
  															a_       :   Any			=	self.norm_out(UpperCamelCase_							) * (1 + scale[:, None]) + shift[:, None]
  															a_       :   List[Any]			=	self.proj_out_a(UpperCamelCase_							)
  
  															# unpatchify
  															a_       :   int			=	int(hidden_states.shape[1] ** 0.5							)
  															a_       :   Optional[int]			=	hidden_states.reshape(
  															    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)							)
  															a_       :   Optional[int]			=	torch.einsum("""nhwpqc->nchpwq"""				,    UpperCamelCase_							)
  															a_       :   Dict			=	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_							)
style_context: 
  
  
  import itertools
  from dataclasses import dataclass
  from typing import Optional
  
  import pyarrow as pa
  
  import datasets
  from datasets.table import table_cast
  
  
  SCREAMING_SNAKE_CASE : int          =    datasets.utils.logging.get_logger(__name__)
  
  
  
  
  
  @dataclass
  class 					snake_case__  ( datasets.BuilderConfig       ):
  	UpperCAmelCase   :							Optional[datasets.Features]         = None
  
  
  
  
  
  class 					snake_case__  ( datasets.ArrowBasedBuilder       ):
  	UpperCAmelCase   :							Optional[int]         = ArrowConfig
  
  
  
  
  
  	def     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								return datasets.DatasetInfo(features=self.config.features							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								if not self.config.data_files:
  															raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
  								a_       :   List[str]			=	dl_manager.download_and_extract(self.config.data_files							)
  								if isinstance(UpperCamelCase_				,    (str, list, tuple)							):
  															a_       :   Dict			=	data_files
  															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  																						a_       :   Optional[Any]			=	[files]
  															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
  															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
  															return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={"""files""": files}							)]
  								a_       :   Any			=	[]
  								for split_name, files in data_files.items():
  															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  																						a_       :   Dict			=	[files]
  															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
  															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
  															# Infer features is they are stoed in the arrow schema
  															if self.info.features is None:
  																						for file in itertools.chain.from_iterable(UpperCamelCase_							):
  																													with open(UpperCamelCase_				,    """rb"""							) as f:
  																																				a_       :   List[str]			=	datasets.Features.from_arrow_schema(pa.ipc.open_stream(UpperCamelCase_							).schema							)
  																													break
  															splits.append(datasets.SplitGenerator(name=UpperCamelCase_				,    gen_kwargs={"""files""": files}							)							)
  								return splits
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			pa.Table:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.info.features is not None:
  															# more expensive cast to support nested features with keys in a different order
  															# allows str <-> int/float or str to Audio for example
  															a_       :   int			=	table_cast(UpperCamelCase_				,    self.info.features.arrow_schema							)
  								return pa_table
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_							)							):
  															with open(UpperCamelCase_				,    """rb"""							) as f:
  																						try:
  																													for batch_idx, record_batch in enumerate(pa.ipc.open_stream(UpperCamelCase_							)							):
  																																				a_       :   Tuple			=	pa.Table.from_batches([record_batch]							)
  																																				# Uncomment for debugging (will print the Arrow table size and elements)
  																																				# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
  																																				# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
  																																				yield f"""{file_idx}_{batch_idx}""", self._cast_table(UpperCamelCase_							)
  																						except ValueError as e:
  																													logger.error(f"""Failed to read file '{file}' with error {type(UpperCamelCase_							)}: {e}"""							)
  																													raise
  
  
  