
'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowercase			:				Tuple    		=			logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase			:				str    		=			'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__=8						):




    '''simple docstring'''



    A       :   int					=						height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    A       :   Union[str, Any]					=						width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=512		,				snake_case__=512						):




    '''simple docstring'''



    A       :   Optional[Any]					=						pil_image.resize((w, h)		,				resample=Image.BICUBIC		,				reducing_gap=1						)
    A       :   str					=						np.array(pil_image.convert('''RGB'''						)						)
    A       :   Union[str, Any]					=						arr.astype(np.floataa						) / 1_27.5 - 1
    A       :   Tuple					=						np.transpose(snake_case__		,				[2, 0, 1]						)
    A       :   List[str]					=						torch.from_numpy(snake_case__						).unsqueeze(0						)
    return image







class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			) ->				Any:






         """simple docstring"""






         super().__init__()

         self.register_modules(
             unet=SCREAMING_SNAKE_CASE						,			scheduler=SCREAMING_SNAKE_CASE						,			movq=SCREAMING_SNAKE_CASE						,			)
         A       :   Dict					=						2 ** (len(self.movq.config.block_out_channels	) - 1)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Any					=						min(int(num_inference_steps * strength	)						,			SCREAMING_SNAKE_CASE	)

         A       :   int					=						max(num_inference_steps - init_timestep						,			0	)
         A       :   str					=						self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=None	) ->				List[Any]:






         """simple docstring"""






         if not isinstance(SCREAMING_SNAKE_CASE						,			(torch.Tensor, PIL.Image.Image, list)	):
             raise ValueError(
                 F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(SCREAMING_SNAKE_CASE	)}'	)

         A       :   str					=						image.to(device=SCREAMING_SNAKE_CASE						,			dtype=SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						batch_size * num_images_per_prompt

         if image.shape[1] == 4:
             A       :   Optional[int]					=						image

         else:
             if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) and len(SCREAMING_SNAKE_CASE	) != batch_size:
                 raise ValueError(
                     F'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE	)}, but requested an effective batch'
                     F' size of {batch_size}. Make sure the batch size matches the length of the generators.'	)

             elif isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
                 A       :   Union[str, Any]					=						[
                     self.movq.encode(image[i : i + 1]	).latent_dist.sample(generator[i]	) for i in range(SCREAMING_SNAKE_CASE	)
                 ]
                 A       :   Optional[int]					=						torch.cat(SCREAMING_SNAKE_CASE						,			dim=0	)
             else:
                 A       :   Optional[Any]					=						self.movq.encode(SCREAMING_SNAKE_CASE	).latent_dist.sample(SCREAMING_SNAKE_CASE	)

             A       :   str					=						self.movq.config.scaling_factor * init_latents

         A       :   Any					=						torch.cat([init_latents]						,			dim=0	)

         A       :   Tuple					=						init_latents.shape
         A       :   str					=						randn_tensor(SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			device=SCREAMING_SNAKE_CASE						,			dtype=SCREAMING_SNAKE_CASE	)

         # get latents
         A       :   str					=						self.scheduler.add_noise(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						init_latents

         return latents





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=0	) ->				List[Any]:






         """simple docstring"""






         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''	)

         A       :   List[Any]					=						torch.device(F'cuda:{gpu_id}'	)

         A       :   Optional[int]					=						[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                 cpu_offload(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=0	) ->				Tuple:






         """simple docstring"""






         if is_accelerate_available() and is_accelerate_version('''>='''						,			'''0.17.0.dev0'''	):
             from accelerate import cpu_offload_with_hook
         else:
             raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''	)

         A       :   Dict					=						torch.device(F'cuda:{gpu_id}'	)

         if self.device.type != "cpu":
             self.to('''cpu'''						,			silence_dtype_warnings=SCREAMING_SNAKE_CASE	)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         A       :   Union[str, Any]					=						None
         for cpu_offloaded_model in [self.unet, self.movq]:
             A,  A       :   str					=						cpu_offload_with_hook(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			prev_module_hook=SCREAMING_SNAKE_CASE	)

         # We'll offload the last model manually.
         A       :   List[str]					=						hook





     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         if not hasattr(self.unet						,			'''_hf_hook'''	):
             return self.device
         for module in self.unet.modules():
             if (
                 hasattr(SCREAMING_SNAKE_CASE						,			'''_hf_hook'''	)
                 and hasattr(module._hf_hook						,			'''execution_device'''	)
                 and module._hf_hook.execution_device is not None
             ):
                 return torch.device(module._hf_hook.execution_device	)
         return self.device





     @torch.no_grad()
     @replace_example_docstring(SCREAMING_SNAKE_CASE	)
     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = 512						,			SCREAMING_SNAKE_CASE = 512						,			SCREAMING_SNAKE_CASE = 100						,			SCREAMING_SNAKE_CASE = 4.0						,			SCREAMING_SNAKE_CASE = 0.3						,			SCREAMING_SNAKE_CASE = 1						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = "pil"						,			SCREAMING_SNAKE_CASE = True						,			) ->				List[str]:






         """simple docstring"""






         A       :   Tuple					=						self._execution_device

         A       :   Any					=						guidance_scale > 1.0

         if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   str					=						torch.cat(SCREAMING_SNAKE_CASE						,			dim=0	)
         A       :   str					=						image_embeds.shape[0]
         if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   Dict					=						torch.cat(SCREAMING_SNAKE_CASE						,			dim=0	)

         if do_classifier_free_guidance:
             A       :   Union[str, Any]					=						image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE						,			dim=0	)
             A       :   List[str]					=						negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE						,			dim=0	)

         A       :   Any					=						torch.cat([negative_image_embeds, image_embeds]						,			dim=0	).to(dtype=self.unet.dtype						,			device=SCREAMING_SNAKE_CASE	)

         if not isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   Dict					=						[image]
         if not all(isinstance(SCREAMING_SNAKE_CASE						,			(PIL.Image.Image, torch.Tensor)	) for i in image	):
             raise ValueError(
                 F'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE	) for i in image]}. Currently, we only support  PIL image and pytorch tensor'	)

         A       :   Tuple					=						torch.cat([prepare_image(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) for i in image]						,			dim=0	)
         A       :   Tuple					=						image.to(dtype=image_embeds.dtype						,			device=SCREAMING_SNAKE_CASE	)

         A       :   Tuple					=						self.movq.encode(SCREAMING_SNAKE_CASE	)['''latents''']
         A       :   int					=						latents.repeat_interleave(SCREAMING_SNAKE_CASE						,			dim=0	)
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE						,			device=SCREAMING_SNAKE_CASE	)
         A,  A       :   Optional[int]					=						self.get_timesteps(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         A       :   Any					=						timesteps[:1].repeat(batch_size * num_images_per_prompt	)
         A,  A       :   Optional[Any]					=						downscale_height_and_width(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			self.movq_scale_factor	)
         A       :   Optional[int]					=						self.prepare_latents(
             SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			image_embeds.dtype						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE	)	):
             # expand the latents if we are doing classifier free guidance
             A       :   str					=						torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

             A       :   Dict					=						{'''image_embeds''': image_embeds}
             A       :   Tuple					=						self.unet(
                 sample=SCREAMING_SNAKE_CASE						,			timestep=SCREAMING_SNAKE_CASE						,			encoder_hidden_states=SCREAMING_SNAKE_CASE						,			added_cond_kwargs=SCREAMING_SNAKE_CASE						,			return_dict=SCREAMING_SNAKE_CASE						,			)[0]

             if do_classifier_free_guidance:
                 A,  A       :   List[str]					=						noise_pred.split(latents.shape[1]						,			dim=1	)
                 A,  A       :   List[str]					=						noise_pred.chunk(2	)
                 A,  A       :   Tuple					=						variance_pred.chunk(2	)
                 A       :   Optional[int]					=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 A       :   Any					=						torch.cat([noise_pred, variance_pred_text]						,			dim=1	)

             if not (
                 hasattr(self.scheduler.config						,			'''variance_type'''	)
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                 A,  A       :   List[str]					=						noise_pred.split(latents.shape[1]						,			dim=1	)

             # compute the previous noisy sample x_t -> x_t-1
             A       :   List[Any]					=						self.scheduler.step(
                 SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			)[0]

         # post-processing
         A       :   Tuple					=						self.movq.decode(SCREAMING_SNAKE_CASE						,			force_not_quantize=SCREAMING_SNAKE_CASE	)['''sample''']

         if output_type not in ["pt", "np", "pil"]:
             raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'	)

         if output_type in ["np", "pil"]:
             A       :   Union[str, Any]					=						image * 0.5 + 0.5
             A       :   Dict					=						image.clamp(0						,			1	)
             A       :   Any					=						image.cpu().permute(0						,			2						,			3						,			1	).float().numpy()

         if output_type == "pil":
             A       :   int					=						self.numpy_to_pil(SCREAMING_SNAKE_CASE	)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE	)






'''simple docstring'''




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

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Union[str, Any]					=						[]
         A       :   Union[str, Any]					=						[]

         for i in range(self.num_layers	):
             A       :   Any					=						self.in_channels if i == 0 else self.out_channels

             A       :   Optional[Any]					=						FlaxResnetBlockaD(
                 in_channels=SCREAMING_SNAKE_CASE						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

             A       :   Optional[int]					=						FlaxTransformeraDModel(
                 in_channels=self.out_channels						,			n_heads=self.num_attention_heads						,			d_head=self.out_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			only_cross_attention=self.only_cross_attention						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						resnets
         A       :   Union[str, Any]					=						attentions

         if self.add_downsample:
             A       :   int					=						FlaxDownsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						()

         for resnet, attn in zip(self.resnets						,			self.attentions	):
             A       :   int					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Dict					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         if self.add_downsample:
             A       :   Optional[Any]					=						self.downsamplers_a(SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         return hidden_states, output_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						[]

         for i in range(self.num_layers	):
             A       :   Optional[Any]					=						self.in_channels if i == 0 else self.out_channels

             A       :   List[str]					=						FlaxResnetBlockaD(
                 in_channels=SCREAMING_SNAKE_CASE						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						resnets

         if self.add_downsample:
             A       :   Dict					=						FlaxDownsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Optional[Any]:






         """simple docstring"""






         A       :   str					=						()

         for resnet in self.resnets:
             A       :   Optional[int]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         if self.add_downsample:
             A       :   Optional[int]					=						self.downsamplers_a(SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         return hidden_states, output_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Optional[Any]					=						[]
         A       :   Optional[int]					=						[]

         for i in range(self.num_layers	):
             A       :   str					=						self.in_channels if (i == self.num_layers - 1) else self.out_channels
             A       :   Dict					=						self.prev_output_channel if i == 0 else self.out_channels

             A       :   List[str]					=						FlaxResnetBlockaD(
                 in_channels=resnet_in_channels + res_skip_channels						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

             A       :   int					=						FlaxTransformeraDModel(
                 in_channels=self.out_channels						,			n_heads=self.num_attention_heads						,			d_head=self.out_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			only_cross_attention=self.only_cross_attention						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						resnets
         A       :   Optional[Any]					=						attentions

         if self.add_upsample:
             A       :   Optional[int]					=						FlaxUpsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Optional[int]:






         """simple docstring"""






         for resnet, attn in zip(self.resnets						,			self.attentions	):
             # pop res hidden states
             A       :   List[str]					=						res_hidden_states_tuple[-1]
             A       :   int					=						res_hidden_states_tuple[:-1]
             A       :   List[str]					=						jnp.concatenate((hidden_states, res_hidden_states)						,			axis=-1	)

             A       :   Union[str, Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Tuple					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         if self.add_upsample:
             A       :   Dict					=						self.upsamplers_a(SCREAMING_SNAKE_CASE	)

         return hidden_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   int					=						[]

         for i in range(self.num_layers	):
             A       :   List[Any]					=						self.in_channels if (i == self.num_layers - 1) else self.out_channels
             A       :   List[str]					=						self.prev_output_channel if i == 0 else self.out_channels

             A       :   str					=						FlaxResnetBlockaD(
                 in_channels=resnet_in_channels + res_skip_channels						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

         A       :   List[Any]					=						resnets

         if self.add_upsample:
             A       :   Optional[Any]					=						FlaxUpsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Tuple:






         """simple docstring"""






         for resnet in self.resnets:
             # pop res hidden states
             A       :   Optional[int]					=						res_hidden_states_tuple[-1]
             A       :   Optional[Any]					=						res_hidden_states_tuple[:-1]
             A       :   List[Any]					=						jnp.concatenate((hidden_states, res_hidden_states)						,			axis=-1	)

             A       :   Optional[Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         if self.add_upsample:
             A       :   List[str]					=						self.upsamplers_a(SCREAMING_SNAKE_CASE	)

         return hidden_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   str					=						[
             FlaxResnetBlockaD(
                 in_channels=self.in_channels						,			out_channels=self.in_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
         ]

         A       :   List[Any]					=						[]

         for _ in range(self.num_layers	):
             A       :   int					=						FlaxTransformeraDModel(
                 in_channels=self.in_channels						,			n_heads=self.num_attention_heads						,			d_head=self.in_channels // self.num_attention_heads						,			depth=1						,			use_linear_projection=self.use_linear_projection						,			use_memory_efficient_attention=self.use_memory_efficient_attention						,			dtype=self.dtype						,			)
             attentions.append(SCREAMING_SNAKE_CASE	)

             A       :   Union[str, Any]					=						FlaxResnetBlockaD(
                 in_channels=self.in_channels						,			out_channels=self.in_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

         A       :   List[str]					=						resnets
         A       :   List[str]					=						attentions





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Dict:






         """simple docstring"""






         A       :   Optional[Any]					=						self.resnets[0](SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         for attn, resnet in zip(self.attentions						,			self.resnets[1:]	):
             A       :   Optional[int]					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Union[str, Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         return hidden_states




