
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  torch.FloatTensor




class 				_UpperCamelCase (    _A   ,				_A     ):






			'''simple docstring'''

			@register_to_config
			def __init__(				self       :				Union[str, Any]  ,		snake_case_       :				Optional[Union[int, Tuple[int, int]]] = None  ,		snake_case_       :				int = 3  ,		snake_case_       :				int = 3  ,		snake_case_       :				bool = False  ,		snake_case_       :				str = "positional"  ,		snake_case_       :				int = 0  ,		snake_case_       :				bool = True  ,		snake_case_       :				Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")  ,		snake_case_       :				Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")  ,		snake_case_       :				Tuple[int] = (224, 448, 672, 896)  ,		snake_case_       :				int = 2  ,		snake_case_       :				float = 1  ,		snake_case_       :				int = 1  ,		snake_case_       :				str = "conv"  ,		snake_case_       :				str = "conv"  ,		snake_case_       :				str = "silu"  ,		snake_case_       :				Optional[int] = 8  ,		snake_case_       :				int = 32  ,		snake_case_       :				float = 1e-5  ,		snake_case_       :				str = "default"  ,		snake_case_       :				bool = True  ,		snake_case_       :				Optional[str] = None  ,		snake_case_       :				Optional[int] = None  ,		):
							super().__init__()

							UpperCamelCase_:							int		=  sample_size
							UpperCamelCase_:							Dict		=  block_out_channels[0] * 4

							# Check inputs
							if len(snake_case_					) != len(snake_case_					):
											raise ValueError(
											    f'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'''					)

							if len(snake_case_					) != len(snake_case_					):
											raise ValueError(
											    f'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'''					)

							# input
							UpperCamelCase_:							List[str]		=  nn.Convad(snake_case_  ,		block_out_channels[0]  ,		kernel_size=3  ,		padding=(1, 1)					)

							# time
							if time_embedding_type == "fourier":
											UpperCamelCase_:							List[str]		=  GaussianFourierProjection(embedding_size=block_out_channels[0]  ,		scale=16					)
											UpperCamelCase_:							Union[str, Any]		=  2 * block_out_channels[0]
							elif time_embedding_type == "positional":
											UpperCamelCase_:							Dict		=  Timesteps(block_out_channels[0]  ,		snake_case_  ,		snake_case_					)
											UpperCamelCase_:							Dict		=  block_out_channels[0]

							UpperCamelCase_:							List[Any]		=  TimestepEmbedding(snake_case_  ,		snake_case_					)

							# class embedding
							if class_embed_type is None and num_class_embeds is not None:
											UpperCamelCase_:							Optional[int]		=  nn.Embedding(snake_case_  ,		snake_case_					)
							elif class_embed_type == "timestep":
											UpperCamelCase_:							Optional[int]		=  TimestepEmbedding(snake_case_  ,		snake_case_					)
							elif class_embed_type == "identity":
											UpperCamelCase_:							Optional[int]		=  nn.Identity(snake_case_  ,		snake_case_					)
							else:
											UpperCamelCase_:							Any		=  None

							UpperCamelCase_:							Union[str, Any]		=  nn.ModuleList([]					)
							UpperCamelCase_:							List[Any]		=  None
							UpperCamelCase_:							List[Any]		=  nn.ModuleList([]					)

							# down
							UpperCamelCase_:							str		=  block_out_channels[0]
							for i, down_block_type in enumerate(snake_case_					):
											UpperCamelCase_:							Optional[Any]		=  output_channel
											UpperCamelCase_:							str		=  block_out_channels[i]
											UpperCamelCase_:							str		=  i == len(snake_case_					) - 1

											UpperCamelCase_:							Any		=  get_down_block(
											    snake_case_  ,		num_layers=snake_case_  ,		in_channels=snake_case_  ,		out_channels=snake_case_  ,		temb_channels=snake_case_  ,		add_downsample=not is_final_block  ,		resnet_eps=snake_case_  ,		resnet_act_fn=snake_case_  ,		resnet_groups=snake_case_  ,		attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel  ,		downsample_padding=snake_case_  ,		resnet_time_scale_shift=snake_case_  ,		downsample_type=snake_case_  ,		)
											self.down_blocks.append(snake_case_					)

							# mid
							UpperCamelCase_:							Tuple		=  UNetMidBlockaD(
							    in_channels=block_out_channels[-1]  ,		temb_channels=snake_case_  ,		resnet_eps=snake_case_  ,		resnet_act_fn=snake_case_  ,		output_scale_factor=snake_case_  ,		resnet_time_scale_shift=snake_case_  ,		attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]  ,		resnet_groups=snake_case_  ,		add_attention=snake_case_  ,		)

							# up
							UpperCamelCase_:							Dict		=  list(reversed(snake_case_					)					)
							UpperCamelCase_:							Any		=  reversed_block_out_channels[0]
							for i, up_block_type in enumerate(snake_case_					):
											UpperCamelCase_:							Dict		=  output_channel
											UpperCamelCase_:							Tuple		=  reversed_block_out_channels[i]
											UpperCamelCase_:							List[str]		=  reversed_block_out_channels[min(i + 1  ,		len(snake_case_					) - 1					)]

											UpperCamelCase_:							int		=  i == len(snake_case_					) - 1

											UpperCamelCase_:							List[str]		=  get_up_block(
											    snake_case_  ,		num_layers=layers_per_block + 1  ,		in_channels=snake_case_  ,		out_channels=snake_case_  ,		prev_output_channel=snake_case_  ,		temb_channels=snake_case_  ,		add_upsample=not is_final_block  ,		resnet_eps=snake_case_  ,		resnet_act_fn=snake_case_  ,		resnet_groups=snake_case_  ,		attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel  ,		resnet_time_scale_shift=snake_case_  ,		upsample_type=snake_case_  ,		)
											self.up_blocks.append(snake_case_					)
											UpperCamelCase_:							Dict		=  output_channel

							# out
							UpperCamelCase_:							int		=  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4  ,		32					)
							UpperCamelCase_:							int		=  nn.GroupNorm(num_channels=block_out_channels[0]  ,		num_groups=snake_case_  ,		eps=snake_case_					)
							UpperCamelCase_:							Optional[Any]		=  nn.SiLU()
							UpperCamelCase_:							str		=  nn.Convad(block_out_channels[0]  ,		snake_case_  ,		kernel_size=3  ,		padding=1					)




			def 					lowerCAmelCase__		(				self       :				int  ,		snake_case_       :				torch.FloatTensor  ,		snake_case_       :				Union[torch.Tensor, float, int]  ,		snake_case_       :				Optional[torch.Tensor] = None  ,		snake_case_       :				bool = True  ,		):
							# 0. center input if necessary
							if self.config.center_input_sample:
											UpperCamelCase_:							Tuple		=  2 * sample - 1.0

							# 1. time
							UpperCamelCase_:							Optional[int]		=  timestep
							if not torch.is_tensor(snake_case_					):
											UpperCamelCase_:							Optional[Any]		=  torch.tensor([timesteps]  ,		dtype=torch.long  ,		device=sample.device					)
							elif torch.is_tensor(snake_case_					) and len(timesteps.shape					) == 0:
											UpperCamelCase_:							List[Any]		=  timesteps[None].to(sample.device					)

							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							UpperCamelCase_:							Optional[Any]		=  timesteps * torch.ones(sample.shape[0]  ,		dtype=timesteps.dtype  ,		device=timesteps.device					)

							UpperCamelCase_:							Dict		=  self.time_proj(snake_case_					)

							# timesteps does not contain any weights and will always return f32 tensors
							# but time_embedding might actually be running in fp16. so we need to cast here.
							# there might be better ways to encapsulate this.
							UpperCamelCase_:							Any		=  t_emb.to(dtype=self.dtype					)
							UpperCamelCase_:							Dict		=  self.time_embedding(snake_case_					)

							if self.class_embedding is not None:
											if class_labels is None:
															raise ValueError("""class_labels should be provided when doing class conditioning"""					)

											if self.config.class_embed_type == "timestep":
															UpperCamelCase_:							List[str]		=  self.time_proj(snake_case_					)

											UpperCamelCase_:							Any		=  self.class_embedding(snake_case_					).to(dtype=self.dtype					)
											UpperCamelCase_:							Optional[int]		=  emb + class_emb

							# 2. pre-process
							UpperCamelCase_:							str		=  sample
							UpperCamelCase_:							Tuple		=  self.conv_in(snake_case_					)

							# 3. down
							UpperCamelCase_:							Union[str, Any]		=  (sample,)
							for downsample_block in self.down_blocks:
											if hasattr(snake_case_  ,		"""skip_conv"""					):
															UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							str		=  downsample_block(
															    hidden_states=snake_case_  ,		temb=snake_case_  ,		skip_sample=snake_case_					)
											else:
															UpperCamelCase_,      UpperCamelCase_:							Any		=  downsample_block(hidden_states=snake_case_  ,		temb=snake_case_					)

											down_block_res_samples += res_samples

							# 4. mid
							UpperCamelCase_:							List[Any]		=  self.mid_block(snake_case_  ,		snake_case_					)

							# 5. up
							UpperCamelCase_:							List[str]		=  None
							for upsample_block in self.up_blocks:
											UpperCamelCase_:							List[Any]		=  down_block_res_samples[-len(upsample_block.resnets					) :]
											UpperCamelCase_:							Optional[int]		=  down_block_res_samples[: -len(upsample_block.resnets					)]

											if hasattr(snake_case_  ,		"""skip_conv"""					):
															UpperCamelCase_,      UpperCamelCase_:							int		=  upsample_block(snake_case_  ,		snake_case_  ,		snake_case_  ,		snake_case_					)
											else:
															UpperCamelCase_:							Optional[Any]		=  upsample_block(snake_case_  ,		snake_case_  ,		snake_case_					)

        # 6. post-process
							UpperCamelCase_:							int		=  self.conv_norm_out(snake_case_					)
							UpperCamelCase_:							List[str]		=  self.conv_act(snake_case_					)
							UpperCamelCase_:							Optional[Any]		=  self.conv_out(snake_case_					)

							if skip_sample is not None:
											sample += skip_sample

							if self.config.time_embedding_type == "fourier":
											UpperCamelCase_:							List[str]		=  timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]					)))					)
											UpperCamelCase_:							Optional[Any]		=  sample / timesteps

							if not return_dict:
											return (sample,)

							return UNetaDOutput(sample=snake_case_					)








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor

class 				_UpperCamelCase (    unittest.TestCase     ):






			'''simple docstring'''

			def __init__(				self       :				Optional[int]  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				List[Any]=7  ,		snake_case_       :				int=3  ,		snake_case_       :				Tuple=18  ,		snake_case_       :				List[Any]=30  ,		snake_case_       :				Tuple=400  ,		snake_case_       :				Dict=True  ,		snake_case_       :				List[str]=None  ,		snake_case_       :				Any=True  ,		snake_case_       :				str=[0.5, 0.5, 0.5]  ,		snake_case_       :				Optional[Any]=[0.5, 0.5, 0.5]  ,		):
							UpperCamelCase_:							int		=  size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:							Any		=  parent
							UpperCamelCase_:							List[str]		=  batch_size
							UpperCamelCase_:							Union[str, Any]		=  num_channels
							UpperCamelCase_:							str		=  image_size
							UpperCamelCase_:							Optional[int]		=  min_resolution
							UpperCamelCase_:							Optional[Any]		=  max_resolution
							UpperCamelCase_:							Dict		=  do_resize
							UpperCamelCase_:							Union[str, Any]		=  size
							UpperCamelCase_:							Optional[Any]		=  do_normalize
							UpperCamelCase_:							str		=  image_mean
							UpperCamelCase_:							int		=  image_std




			def 					lowerCAmelCase__		(				self       :				Tuple					):
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}




@require_torch
@require_vision
class 				_UpperCamelCase (    _A   ,				unittest.TestCase     ):






			'''simple docstring'''

			__UpperCamelCase					:  str    					=       ViTImageProcessor if is_vision_available() else None

			def 					lowerCAmelCase__		(				self       :				Any					):
							UpperCamelCase_:							Tuple		=  ViTImageProcessingTester(self					)

			@property
			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							return self.image_processor_tester.prepare_image_processor_dict()

			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							Union[str, Any]		=  self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_mean"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_std"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""size"""					)					)

			def 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							UpperCamelCase_:							Optional[Any]		=  self.image_processing_class.from_dict(self.image_processor_dict					)
							self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18}					)

							UpperCamelCase_:							Union[str, Any]		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42					)
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42}					)

			def 					lowerCAmelCase__		(				self       :				int					):
							pass

			def 					lowerCAmelCase__		(				self       :				int					):
							# Initialize image_processing
							UpperCamelCase_:							List[str]		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							UpperCamelCase_:							List[Any]		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		Image.Image					)

							# Test not batched input
							UpperCamelCase_:							Optional[Any]		=  image_processing(image_inputs[0]  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							List[str]		=  image_processing(snake_case_  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

			def 					lowerCAmelCase__		(				self       :				str					):
							# Initialize image_processing
							UpperCamelCase_:							List[str]		=  self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							UpperCamelCase_:							Tuple		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_  ,		numpify=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		np.ndarray					)

							# Test not batched input
							UpperCamelCase_:							Any		=  image_processing(image_inputs[0]  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							Dict		=  image_processing(snake_case_  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)




			def 					lowerCAmelCase__		(				self       :				Any					):
							# Initialize image_processing
							UpperCamelCase_:							int		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCamelCase_:							Dict		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_  ,		torchify=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		torch.Tensor					)

							# Test not batched input
							UpperCamelCase_:							List[str]		=  image_processing(image_inputs[0]  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							Any		=  image_processing(snake_case_  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)






