
"""simple docstring"""




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()




class 					UpperCamelCase     (	__UpperCAmelCase  ,				__UpperCAmelCase  ,				__UpperCAmelCase  ,				unittest.TestCase				):
				UpperCAmelCase_    :				Optional[int]						  =    StableDiffusionControlNetInpaintPipeline
				UpperCAmelCase_    :				List[str]						  =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				UpperCAmelCase_    :				List[str]						  =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				UpperCAmelCase_    :				List[Any]						  =    frozenset({"control_image"}				)  # skip `image` and `mask` for now, only test for control_image
				UpperCAmelCase_    :				Optional[Any]						  =    TEXT_TO_IMAGE_IMAGE_PARAMS


				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     UNetaDConditionModel(
									    block_out_channels=(32, 64)		,     layers_per_block=2		,     sample_size=32		,     in_channels=9		,     out_channels=4		,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,     cross_attention_dim=32		,     )
									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     ControlNetModel(
									    block_out_channels=(32, 64)		,     layers_per_block=2		,     in_channels=4		,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,     cross_attention_dim=32		,     conditioning_embedding_out_channels=(16, 32)		,     )
									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     DDIMScheduler(
									    beta_start=0.00_085		,     beta_end=0.012		,     beta_schedule='scaled_linear'		,     clip_sample=lowerCamelCase__		,     set_alpha_to_one=lowerCamelCase__		,     )
									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     AutoencoderKL(
									    block_out_channels=[32, 64]		,     in_channels=3		,     out_channels=3		,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,     latent_channels=4		,     )
									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     CLIPTextConfig(
									    bos_token_id=0		,     eos_token_id=2		,     hidden_size=32		,     intermediate_size=37		,     layer_norm_eps=1E-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=1000		,     )
									SCREAMING_SNAKE_CASE           =     CLIPTextModel(lowerCamelCase__      )
									SCREAMING_SNAKE_CASE           =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

									SCREAMING_SNAKE_CASE           =     {
									    'unet': unet,
									    'controlnet': controlnet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components


				def 							A (    self		,     lowercase__		,     lowercase__=0      )   ->   Dict:
									"""simple docstring"""







									if str(lowerCamelCase__      ).startswith('mps'      ):
														SCREAMING_SNAKE_CASE           =     torch.manual_seed(lowerCamelCase__      )
									else:
														SCREAMING_SNAKE_CASE           =     torch.Generator(device=lowerCamelCase__      ).manual_seed(lowerCamelCase__      )

									SCREAMING_SNAKE_CASE           =     2
									SCREAMING_SNAKE_CASE           =     randn_tensor(
									    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,     generator=lowerCamelCase__		,     device=torch.device(lowerCamelCase__      )		,     )
									SCREAMING_SNAKE_CASE           =     floats_tensor((1, 3, 32, 32)		,     rng=random.Random(lowerCamelCase__      )      ).to(lowerCamelCase__      )
									SCREAMING_SNAKE_CASE           =     init_image.cpu().permute(0		,     2		,     3		,     1      )[0]

									SCREAMING_SNAKE_CASE           =     Image.fromarray(np.uinta(lowerCamelCase__      )      ).convert('RGB'      ).resize((64, 64)      )
									SCREAMING_SNAKE_CASE           =     Image.fromarray(np.uinta(init_image + 4      )      ).convert('RGB'      ).resize((64, 64)      )

									SCREAMING_SNAKE_CASE           =     {
									    'prompt': 'A painting of a squirrel eating a burger',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									    'image': image,
									    'mask_image': mask_image,
									    'control_image': control_image,
									}

									return inputs


				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3      )


				@unittest.skipIf(
				    torch_device != 'cuda' or not is_xformers_available()		,     reason='XFormers attention is only available with CUDA and `xformers` installed'		,     )
				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )




				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									self._test_inference_batch_single_identical(expected_max_diff=2E-3      )






class 					UpperCamelCase     (	__UpperCAmelCase				):
				UpperCAmelCase_    :				str						  =    StableDiffusionControlNetInpaintPipeline
				UpperCAmelCase_    :				List[Any]						  =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				UpperCAmelCase_    :				List[str]						  =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				UpperCAmelCase_    :				List[Any]						  =    frozenset([]				)


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     UNetaDConditionModel(
									    block_out_channels=(32, 64)		,     layers_per_block=2		,     sample_size=32		,     in_channels=4		,     out_channels=4		,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,     cross_attention_dim=32		,     )
									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     ControlNetModel(
									    block_out_channels=(32, 64)		,     layers_per_block=2		,     in_channels=4		,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,     cross_attention_dim=32		,     conditioning_embedding_out_channels=(16, 32)		,     )
									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     DDIMScheduler(
									    beta_start=0.00_085		,     beta_end=0.012		,     beta_schedule='scaled_linear'		,     clip_sample=lowerCamelCase__		,     set_alpha_to_one=lowerCamelCase__		,     )
									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     AutoencoderKL(
									    block_out_channels=[32, 64]		,     in_channels=3		,     out_channels=3		,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,     latent_channels=4		,     )
									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     CLIPTextConfig(
									    bos_token_id=0		,     eos_token_id=2		,     hidden_size=32		,     intermediate_size=37		,     layer_norm_eps=1E-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=1000		,     )
									SCREAMING_SNAKE_CASE           =     CLIPTextModel(lowerCamelCase__      )
									SCREAMING_SNAKE_CASE           =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

									SCREAMING_SNAKE_CASE           =     {
									    'unet': unet,
									    'controlnet': controlnet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components






class 					UpperCamelCase     (	__UpperCAmelCase  ,				__UpperCAmelCase  ,				unittest.TestCase				):
				UpperCAmelCase_    :				Dict						  =    StableDiffusionControlNetInpaintPipeline
				UpperCAmelCase_    :				str						  =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				UpperCAmelCase_    :				List[str]						  =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS


				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     UNetaDConditionModel(
									    block_out_channels=(32, 64)		,     layers_per_block=2		,     sample_size=32		,     in_channels=9		,     out_channels=4		,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,     cross_attention_dim=32		,     )
									torch.manual_seed(0      )

									def init_weights(lowercase__      ):
														if isinstance(lowerCamelCase__		,     torch.nn.Convad      ):
																			torch.nn.init.normal(m.weight      )
																			m.bias.data.fill_(1.0      )

									SCREAMING_SNAKE_CASE           =     ControlNetModel(
									    block_out_channels=(32, 64)		,     layers_per_block=2		,     in_channels=4		,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,     cross_attention_dim=32		,     conditioning_embedding_out_channels=(16, 32)		,     )
									controlneta.controlnet_down_blocks.apply(lowerCamelCase__      )

									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     ControlNetModel(
									    block_out_channels=(32, 64)		,     layers_per_block=2		,     in_channels=4		,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,     cross_attention_dim=32		,     conditioning_embedding_out_channels=(16, 32)		,     )
									controlneta.controlnet_down_blocks.apply(lowerCamelCase__      )

									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     DDIMScheduler(
									    beta_start=0.00_085		,     beta_end=0.012		,     beta_schedule='scaled_linear'		,     clip_sample=lowerCamelCase__		,     set_alpha_to_one=lowerCamelCase__		,     )
									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     AutoencoderKL(
									    block_out_channels=[32, 64]		,     in_channels=3		,     out_channels=3		,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,     latent_channels=4		,     )
									torch.manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     CLIPTextConfig(
									    bos_token_id=0		,     eos_token_id=2		,     hidden_size=32		,     intermediate_size=37		,     layer_norm_eps=1E-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=1000		,     )
									SCREAMING_SNAKE_CASE           =     CLIPTextModel(lowerCamelCase__      )
									SCREAMING_SNAKE_CASE           =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

									SCREAMING_SNAKE_CASE           =     MultiControlNetModel([controlneta, controlneta]      )

									SCREAMING_SNAKE_CASE           =     {
									    'unet': unet,
									    'controlnet': controlnet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components


				def 							A (    self		,     lowercase__		,     lowercase__=0      )   ->   int:
									"""simple docstring"""







									if str(lowerCamelCase__      ).startswith('mps'      ):
														SCREAMING_SNAKE_CASE           =     torch.manual_seed(lowerCamelCase__      )
									else:
														SCREAMING_SNAKE_CASE           =     torch.Generator(device=lowerCamelCase__      ).manual_seed(lowerCamelCase__      )

									SCREAMING_SNAKE_CASE           =     2

									SCREAMING_SNAKE_CASE           =     [
									    randn_tensor(
									        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,     generator=lowerCamelCase__		,     device=torch.device(lowerCamelCase__      )		,     ),
									    randn_tensor(
									        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,     generator=lowerCamelCase__		,     device=torch.device(lowerCamelCase__      )		,     ),
									]
									SCREAMING_SNAKE_CASE           =     floats_tensor((1, 3, 32, 32)		,     rng=random.Random(lowerCamelCase__      )      ).to(lowerCamelCase__      )
									SCREAMING_SNAKE_CASE           =     init_image.cpu().permute(0		,     2		,     3		,     1      )[0]

									SCREAMING_SNAKE_CASE           =     Image.fromarray(np.uinta(lowerCamelCase__      )      ).convert('RGB'      ).resize((64, 64)      )
									SCREAMING_SNAKE_CASE           =     Image.fromarray(np.uinta(init_image + 4      )      ).convert('RGB'      ).resize((64, 64)      )

									SCREAMING_SNAKE_CASE           =     {
									    'prompt': 'A painting of a squirrel eating a burger',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									    'image': image,
									    'mask_image': mask_image,
									    'control_image': control_image,
									}

									return inputs


				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_dummy_components()
									SCREAMING_SNAKE_CASE           =     self.pipeline_class(**lowerCamelCase__      )
									pipe.to(lowerCamelCase__      )

									SCREAMING_SNAKE_CASE           =     10.0
									SCREAMING_SNAKE_CASE           =     4

									SCREAMING_SNAKE_CASE           =     self.get_dummy_inputs(lowerCamelCase__      )
									SCREAMING_SNAKE_CASE           =     steps
									SCREAMING_SNAKE_CASE           =     scale
									SCREAMING_SNAKE_CASE           =     pipe(**lowerCamelCase__      )[0]

									SCREAMING_SNAKE_CASE           =     self.get_dummy_inputs(lowerCamelCase__      )
									SCREAMING_SNAKE_CASE           =     steps
									SCREAMING_SNAKE_CASE           =     scale
									SCREAMING_SNAKE_CASE           =     pipe(**lowerCamelCase__		,     control_guidance_start=0.1		,     control_guidance_end=0.2      )[0]

									SCREAMING_SNAKE_CASE           =     self.get_dummy_inputs(lowerCamelCase__      )
									SCREAMING_SNAKE_CASE           =     steps
									SCREAMING_SNAKE_CASE           =     scale
									SCREAMING_SNAKE_CASE           =     pipe(**lowerCamelCase__		,     control_guidance_start=[0.1, 0.3]		,     control_guidance_end=[0.2, 0.7]      )[0]

									SCREAMING_SNAKE_CASE           =     self.get_dummy_inputs(lowerCamelCase__      )
									SCREAMING_SNAKE_CASE           =     steps
									SCREAMING_SNAKE_CASE           =     scale
									SCREAMING_SNAKE_CASE           =     pipe(**lowerCamelCase__		,     control_guidance_start=0.4		,     control_guidance_end=[0.5, 0.8]      )[0]

									# make sure that all outputs are different
									assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3
									assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3
									assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3


				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3      )


				@unittest.skipIf(
				    torch_device != 'cuda' or not is_xformers_available()		,     reason='XFormers attention is only available with CUDA and `xformers` installed'		,     )
				def 							A (    self      )   ->   Optional[int]:
									"""simple docstring"""







									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )


				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									self._test_inference_batch_single_identical(expected_max_diff=2E-3      )




				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.get_dummy_components()
									SCREAMING_SNAKE_CASE           =     self.pipeline_class(**lowerCamelCase__      )
									pipe.to(lowerCamelCase__      )
									pipe.set_progress_bar_config(disable=lowerCamelCase__      )
									with tempfile.TemporaryDirectory() as tmpdir:
														try:
																			# save_pretrained is not implemented for Multi-ControlNet
																			pipe.save_pretrained(lowerCamelCase__      )
														except NotImplementedError:
																			pass






@slow
@require_torch_gpu
class 					UpperCamelCase     (	unittest.TestCase				):


				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'      )

									SCREAMING_SNAKE_CASE           =     StableDiffusionControlNetInpaintPipeline.from_pretrained(
									    'runwayml/stable-diffusion-inpainting'		,     safety_checker=lowerCamelCase__		,     controlnet=lowerCamelCase__      )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=lowerCamelCase__      )

									SCREAMING_SNAKE_CASE           =     torch.Generator(device='cpu'      ).manual_seed(0      )
									SCREAMING_SNAKE_CASE           =     load_image(
									    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'      ).resize((512, 512)      )

									SCREAMING_SNAKE_CASE           =     load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
									    '/stable_diffusion_inpaint/input_bench_mask.png'      ).resize((512, 512)      )

									SCREAMING_SNAKE_CASE           =     'pitch black hole'

									SCREAMING_SNAKE_CASE           =     load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'      ).resize((512, 512)      )

									SCREAMING_SNAKE_CASE           =     pipe(
									    lowerCamelCase__		,     image=lowerCamelCase__		,     mask_image=lowerCamelCase__		,     control_image=lowerCamelCase__		,     generator=lowerCamelCase__		,     output_type='np'		,     num_inference_steps=3		,     )

									SCREAMING_SNAKE_CASE           =     output.images[0]

									assert image.shape == (512, 512, 3)

									SCREAMING_SNAKE_CASE           =     load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'      )

									assert np.abs(expected_image - image      ).max() < 9E-2




				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'      )

									SCREAMING_SNAKE_CASE           =     StableDiffusionControlNetInpaintPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5'		,     safety_checker=lowerCamelCase__		,     controlnet=lowerCamelCase__      )
									SCREAMING_SNAKE_CASE           =     DDIMScheduler.from_config(pipe.scheduler.config      )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=lowerCamelCase__      )

									SCREAMING_SNAKE_CASE           =     torch.Generator(device='cpu'      ).manual_seed(33      )

									SCREAMING_SNAKE_CASE           =     load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'      )
									SCREAMING_SNAKE_CASE           =     init_image.resize((512, 512)      )

									SCREAMING_SNAKE_CASE           =     load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'      )
									SCREAMING_SNAKE_CASE           =     mask_image.resize((512, 512)      )

									SCREAMING_SNAKE_CASE           =     'a handsome man with ray-ban sunglasses'

									def make_inpaint_condition(lowercase__		,     lowercase__      ):
														SCREAMING_SNAKE_CASE           =     np.array(image.convert('RGB'      )      ).astype(np.floataa      ) / 255.0
														SCREAMING_SNAKE_CASE           =     np.array(image_mask.convert('L'      )      ).astype(np.floataa      ) / 255.0

														assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
														SCREAMING_SNAKE_CASE           =     -1.0  # set as masked pixel
														SCREAMING_SNAKE_CASE           =     np.expand_dims(lowerCamelCase__		,     0      ).transpose(0		,     3		,     1		,     2      )
														SCREAMING_SNAKE_CASE           =     torch.from_numpy(lowerCamelCase__      )
														return image

									SCREAMING_SNAKE_CASE           =     make_inpaint_condition(lowerCamelCase__		,     lowerCamelCase__      )

									SCREAMING_SNAKE_CASE           =     pipe(
									    lowerCamelCase__		,     image=lowerCamelCase__		,     mask_image=lowerCamelCase__		,     control_image=lowerCamelCase__		,     guidance_scale=9.0		,     eta=1.0		,     generator=lowerCamelCase__		,     num_inference_steps=20		,     output_type='np'		,     )
									SCREAMING_SNAKE_CASE           =     output.images[0]

									assert image.shape == (512, 512, 3)

									SCREAMING_SNAKE_CASE           =     load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'      )

									assert np.abs(expected_image - image      ).max() < 9E-2


"""simple docstring"""




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 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				torch.FloatTensor






class 					UpperCamelCase     (	__magic_name__  ,				__magic_name__				):

				"""simple docstring"""



				@register_to_config
				def __init__(    self		,     lowercase__ = 16		,     lowercase__ = 88		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = 1		,     lowercase__ = 0.0		,     lowercase__ = 32		,     lowercase__ = None		,     lowercase__ = False		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = "geglu"		,     lowercase__ = None		,     lowercase__ = False		,     lowercase__ = False		,     lowercase__ = False		,     lowercase__ = "layer_norm"		,     lowercase__ = True		,     )   ->   List[Any]:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =     use_linear_projection
									SCREAMING_SNAKE_CASE           =     num_attention_heads
									SCREAMING_SNAKE_CASE           =     attention_head_dim
									SCREAMING_SNAKE_CASE           =     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
									SCREAMING_SNAKE_CASE           =     (in_channels is not None) and (patch_size is None)
									SCREAMING_SNAKE_CASE           =     num_vector_embeds is not None
									SCREAMING_SNAKE_CASE           =     in_channels is not None and patch_size is not None

									if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
														SCREAMING_SNAKE_CASE           =     (
														    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'		,     lowercase__		,     standard_warn=lowercase__      )
														SCREAMING_SNAKE_CASE           =     '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:
														SCREAMING_SNAKE_CASE           =     in_channels

														SCREAMING_SNAKE_CASE           =     torch.nn.GroupNorm(num_groups=lowercase__		,     num_channels=lowercase__		,     eps=1E-6		,     affine=lowercase__      )
														if use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     nn.Convad(lowercase__		,     lowercase__		,     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"

														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     num_vector_embeds
														SCREAMING_SNAKE_CASE           =     self.height * self.width

														SCREAMING_SNAKE_CASE           =     ImagePositionalEmbeddings(
														    num_embed=lowercase__		,     embed_dim=lowercase__		,     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"

														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     sample_size

														SCREAMING_SNAKE_CASE           =     patch_size
														SCREAMING_SNAKE_CASE           =     PatchEmbed(
														    height=lowercase__		,     width=lowercase__		,     patch_size=lowercase__		,     in_channels=lowercase__		,     embed_dim=lowercase__		,     )

									# 3. Define transformers blocks
									SCREAMING_SNAKE_CASE           =     nn.ModuleList(
									    [
									        BasicTransformerBlock(
									            lowercase__		,     lowercase__		,     lowercase__		,     dropout=lowercase__		,     cross_attention_dim=lowercase__		,     activation_fn=lowercase__		,     num_embeds_ada_norm=lowercase__		,     attention_bias=lowercase__		,     only_cross_attention=lowercase__		,     upcast_attention=lowercase__		,     norm_type=lowercase__		,     norm_elementwise_affine=lowercase__		,     )
									        for d in range(lowercase__      )
									    ]      )

									# 4. Define output layers
									SCREAMING_SNAKE_CASE           =     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:
																			SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     nn.Convad(lowercase__		,     lowercase__		,     kernel_size=1		,     stride=1		,     padding=0      )
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     nn.LayerNorm(lowercase__      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     self.num_vector_embeds - 1      )
									elif self.is_input_patches:
														SCREAMING_SNAKE_CASE           =     nn.LayerNorm(lowercase__		,     elementwise_affine=lowercase__		,     eps=1E-6      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     2 * inner_dim      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     patch_size * patch_size * self.out_channels      )




				def 							A (    self		,     lowercase__		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = True		,     )   ->   List[Any]:
									"""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)
														SCREAMING_SNAKE_CASE           =     (1 - attention_mask.to(hidden_states.dtype      )) * -10_000.0
														SCREAMING_SNAKE_CASE           =     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:
														SCREAMING_SNAKE_CASE           =     (1 - encoder_attention_mask.to(hidden_states.dtype      )) * -10_000.0
														SCREAMING_SNAKE_CASE           =     encoder_attention_mask.unsqueeze(1      )

									# 1. Input
									if self.is_input_continuous:
														SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     hidden_states.shape
														SCREAMING_SNAKE_CASE           =     hidden_states

														SCREAMING_SNAKE_CASE           =     self.norm(lowercase__      )
														if not self.use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     self.proj_in(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     hidden_states.shape[1]
																			SCREAMING_SNAKE_CASE           =     hidden_states.permute(0		,     2		,     3		,     1      ).reshape(lowercase__		,     height * width		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     hidden_states.shape[1]
																			SCREAMING_SNAKE_CASE           =     hidden_states.permute(0		,     2		,     3		,     1      ).reshape(lowercase__		,     height * width		,     lowercase__      )
																			SCREAMING_SNAKE_CASE           =     self.proj_in(lowercase__      )
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     self.latent_image_embedding(lowercase__      )
									elif self.is_input_patches:
														SCREAMING_SNAKE_CASE           =     self.pos_embed(lowercase__      )

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

									# 3. Output
									if self.is_input_continuous:
														if not self.use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     hidden_states.reshape(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      ).permute(0		,     3		,     1		,     2      ).contiguous()
																			SCREAMING_SNAKE_CASE           =     self.proj_out(lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     self.proj_out(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     hidden_states.reshape(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      ).permute(0		,     3		,     1		,     2      ).contiguous()

														SCREAMING_SNAKE_CASE           =     hidden_states + residual
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     self.norm_out(lowercase__      )
														SCREAMING_SNAKE_CASE           =     self.out(lowercase__      )
														# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
														SCREAMING_SNAKE_CASE           =     logits.permute(0		,     2		,     1      )

														# log(p(x_0))
														SCREAMING_SNAKE_CASE           =     F.log_softmax(logits.double()		,     dim=1      ).float()
									elif self.is_input_patches:
														# TODO: cleanup!
														SCREAMING_SNAKE_CASE           =     self.transformer_blocks[0].norma.emb(
														    lowercase__		,     lowercase__		,     hidden_dtype=hidden_states.dtype      )
														SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.proj_out_a(F.silu(lowercase__      )      ).chunk(2		,     dim=1      )
														SCREAMING_SNAKE_CASE           =     self.norm_out(lowercase__      ) * (1 + scale[:, None]) + shift[:, None]
														SCREAMING_SNAKE_CASE           =     self.proj_out_a(lowercase__      )

														# unpatchify
														SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     int(hidden_states.shape[1] ** 0.5      )
														SCREAMING_SNAKE_CASE           =     hidden_states.reshape(
														    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)      )
														SCREAMING_SNAKE_CASE           =     torch.einsum('nhwpqc->nchpwq'		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     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=lowercase__      )
