

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   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							torch.FloatTensor







class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_     ):




			"""simple docstring"""







			@register_to_config
			def __init__(			self  :					Optional[int]       ,						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       ,						):
									super().__init__()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	num_attention_heads
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	attention_head_dim
									SCREAMING_SNAKE_CASE_:			Any  		=	num_attention_heads * attention_head_dim

									SCREAMING_SNAKE_CASE_:			Tuple  		=	in_channels

									SCREAMING_SNAKE_CASE_:			Dict  		=	torch.nn.GroupNorm(num_groups=lowerCAmelCase__       ,						num_channels=lowerCAmelCase__       ,						eps=1E-6       ,						affine=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	nn.Linear(lowerCAmelCase__       ,						lowerCAmelCase__)

									# 3. Define transformers blocks
									SCREAMING_SNAKE_CASE_:			List[str]  		=	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__)
									    ])

									SCREAMING_SNAKE_CASE_:			List[str]  		=	nn.Linear(lowerCAmelCase__       ,						lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[str]       ,						lowerCAmelCase__  :					Optional[Any]=None       ,						lowerCAmelCase__  :					Tuple=None       ,						lowerCAmelCase__  :					Optional[Any]=None       ,						lowerCAmelCase__  :					Union[str, Any]=1       ,						lowerCAmelCase__  :					List[Any]=None       ,						lowerCAmelCase__  :					bool = True       ,						):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	hidden_states.shape
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	batch_frames // num_frames

									SCREAMING_SNAKE_CASE_:			List[str]  		=	hidden_states

									SCREAMING_SNAKE_CASE_:			Tuple  		=	hidden_states[None, :].reshape(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	hidden_states.permute(0       ,						2       ,						1       ,						3       ,						4)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.norm(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	hidden_states.permute(0       ,						3       ,						4       ,						2       ,						1).reshape(batch_size * height * width       ,						lowerCAmelCase__       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	self.proj_in(lowerCAmelCase__)

									# 2. Blocks
									for block in self.transformer_blocks:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	block(
															    lowerCAmelCase__       ,						encoder_hidden_states=lowerCAmelCase__       ,						timestep=lowerCAmelCase__       ,						cross_attention_kwargs=lowerCAmelCase__       ,						class_labels=lowerCAmelCase__       ,						)

									# 3. Output
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.proj_out(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	(
									    hidden_states[None, None, :]
									    .reshape(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
									    .permute(0       ,						3       ,						4       ,						1       ,						2)
									    .contiguous()
									)
									SCREAMING_SNAKE_CASE_:			int  		=	hidden_states.reshape(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	hidden_states + residual

									if not return_dict:
															return (output,)

									return TransformerTemporalModelOutput(sample=lowerCAmelCase__)









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCAmelCase							:  Optional[Any]            =						logging.get_logger(__name__)



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					['''pixel_values''']



			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Dict[str, int] = None       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Union[int, float] = 1 / 255       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					bool = True       ,						**lowerCAmelCase__  :					Tuple       ,						):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	size if size is not None else {"height": 384, "width": 384}
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			int  		=	do_resize
									SCREAMING_SNAKE_CASE_:			Dict  		=	size
									SCREAMING_SNAKE_CASE_:			int  		=	resample
									SCREAMING_SNAKE_CASE_:			str  		=	do_rescale
									SCREAMING_SNAKE_CASE_:			str  		=	rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	do_normalize
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_std if image_std is not None else OPENAI_CLIP_STD
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Dict[str, int]       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									if "height" not in size or "width" not in size:
															raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
									SCREAMING_SNAKE_CASE_:			int  		=	(size["height"], size["width"])
									return resize(lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[int, float]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return rescale(lowerCAmelCase__       ,						scale=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return normalize(lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					ImageInput       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Dict[str, int]] = None       ,						lowerCAmelCase__  :					PILImageResampling = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[float] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[str, TensorType]] = None       ,						lowerCAmelCase__  :					bool = None       ,						lowerCAmelCase__  :					ChannelDimension = ChannelDimension.FIRST       ,						**lowerCAmelCase__  :					Dict       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_:			Dict  		=	do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_:			Any  		=	rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE_:			str  		=	size if size is not None else self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	make_list_of_images(lowerCAmelCase__)

									if not valid_images(lowerCAmelCase__):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray.")

									if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True.")

									if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True.")

									if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("Image mean and std must be specified if do_normalize is True.")

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	[convert_to_rgb(lowerCAmelCase__) for image in images]

									# All transformations expect numpy arrays.
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[to_numpy_array(lowerCAmelCase__) for image in images]

									if do_resize:
															SCREAMING_SNAKE_CASE_:			int  		=	[self.resize(image=lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__) for image in images]

									if do_rescale:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[self.rescale(image=lowerCAmelCase__       ,						scale=lowerCAmelCase__) for image in images]

									if do_normalize:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.normalize(image=lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			str  		=	[to_channel_dimension_format(lowerCAmelCase__       ,						lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	BatchFeature(data={"pixel_values": images}       ,						tensor_type=lowerCAmelCase__)

									return encoded_outputs






