






from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       42



class       _UpperCAmelCase					(       A__		,A__	):

				"""simple docstring"""




				@register_to_config
				def __init__(     self						:    Union[str, Any],  lowerCamelCase						:    int = 16,  lowerCamelCase						:    int = 88,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    int = 1,  lowerCamelCase						:    float = 0.0,  lowerCamelCase						:    int = 32,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    str = "geglu",  lowerCamelCase						:    bool = True,  lowerCamelCase						:    bool = True,  ):

								'''simple docstring'''
								super().__init__()
								lowercase__    =			num_attention_heads
								lowercase__    =			attention_head_dim
								lowercase__    =			num_attention_heads * attention_head_dim

								lowercase__    =			in_channels

								lowercase__    =			torch.nn.GroupNorm(num_groups=lowerCamelCase,  num_channels=lowerCamelCase,  eps=1E-6,  affine=lowerCamelCase  )
								lowercase__    =			nn.Linear(lowerCamelCase,  lowerCamelCase  )

								# 3. Define transformers blocks
								lowercase__    =			nn.ModuleList(
								    [
								        BasicTransformerBlock(
								            lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  dropout=lowerCamelCase,  cross_attention_dim=lowerCamelCase,  activation_fn=lowerCamelCase,  attention_bias=lowerCamelCase,  double_self_attention=lowerCamelCase,  norm_elementwise_affine=lowerCamelCase,  )
								        for d in range(lowerCamelCase  )
								    ]  )

								lowercase__    =			nn.Linear(lowerCamelCase,  lowerCamelCase  )




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    int,  lowerCamelCase						:    int=None,  lowerCamelCase						:    List[str]=None,  lowerCamelCase						:    Tuple=None,  lowerCamelCase						:    List[str]=1,  lowerCamelCase						:    Dict=None,  lowerCamelCase						:    bool = True,  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__    =			hidden_states.shape
								lowercase__    =			batch_frames // num_frames

								lowercase__    =			hidden_states

								lowercase__    =			hidden_states[None, :].reshape(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )
								lowercase__    =			hidden_states.permute(0,  2,  1,  3,  4  )

								lowercase__    =			self.norm(lowerCamelCase  )
								lowercase__    =			hidden_states.permute(0,  3,  4,  2,  1  ).reshape(batch_size * height * width,  lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			self.proj_in(lowerCamelCase  )

								# 2. Blocks
								for block in self.transformer_blocks:
												lowercase__    =			block(
												    lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  timestep=lowerCamelCase,  cross_attention_kwargs=lowerCamelCase,  class_labels=lowerCamelCase,  )

								# 3. Output
								lowercase__    =			self.proj_out(lowerCamelCase  )
								lowercase__    =			(
								    hidden_states[None, None, :]
								    .reshape(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )
								    .permute(0,  3,  4,  1,  2  )
								    .contiguous()
								)
								lowercase__    =			hidden_states.reshape(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			hidden_states + residual

								if not return_dict:
												return (output,)

								return TransformerTemporalModelOutput(sample=lowerCamelCase  )












from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Dict,  lowerCamelCase						:    pyspark.sql.DataFrame,  lowerCamelCase						:    Optional[NamedSplit] = None,  lowerCamelCase						:    Optional[Features] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = "arrow",  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(
								    split=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  keep_in_memory=lowerCamelCase,  streaming=lowerCamelCase,  **lowerCamelCase,  )
								lowercase__    =			load_from_cache_file
								lowercase__    =			file_format
								lowercase__    =			Spark(
								    df=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  working_dir=lowerCamelCase,  **lowerCamelCase,  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								if self.streaming:
												return self.builder.as_streaming_dataset(split=self.split  )
								lowercase__    =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
								self.builder.download_and_prepare(
								    download_mode=lowerCamelCase,  file_format=self._file_format,  )
								return self.builder.as_dataset(split=self.split  )




