


'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch
@dataclass
class        lowercase     :

		"""simple docstring"""

		_a   			=    42  # [batch_size x 3]
		_a   			=    42  # [batch_size x 3]
		_a   			=    42  # [batch_size x 3]
		_a   			=    42  # [batch_size x 3]
		_a   			=    42
		_a   			=    42
		_a   			=    42
		_a   			=    42
		_a   			=    42




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
							assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
							assert len(self.x.shape							) == len(self.y.shape							) == len(self.z.shape							) == len(self.origin.shape							) == 2




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return torch.from_numpy(np.array([self.width, self.height]   ,       dtype=np.floataa							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return torch.from_numpy(np.array([self.x_fov, self.y_fov]   ,       dtype=np.floataa							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			torch.arange(self.height * self.width							)
							UpperCamelCase__       :List[Any]							 =			torch.stack(
							    [
							        pixel_indices % self.width,
							        torch.div(UpperCamelCase_   ,       self.width   ,       rounding_mode='''trunc'''							),
							    ]   ,       axis=1   ,       )
							return coords




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__ ,    *UpperCamelCase__       :Optional[int]							 =			self.shape
							UpperCamelCase__       :int							 =			int(np.prod(UpperCamelCase_							)							)

							UpperCamelCase__       :Dict							 =			self.get_image_coords()
							UpperCamelCase__       :Tuple							 =			torch.broadcast_to(coords.unsqueeze(0							)   ,       [batch_size * inner_batch_size, *coords.shape]							)
							UpperCamelCase__       :List[Any]							 =			self.get_camera_rays(UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			rays.view(UpperCamelCase_   ,       inner_batch_size * self.height * self.width   ,       2   ,       3							)

							return rays




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__ ,    *UpperCamelCase__ ,    UpperCamelCase__       :Tuple							 =			coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							UpperCamelCase__       :str							 =			coords.view(UpperCamelCase_   ,       -1   ,       2							)

							UpperCamelCase__       :List[str]							 =			self.resolution()
							UpperCamelCase__       :List[str]							 =			self.fov()

							UpperCamelCase__       :Dict							 =			(flat.float() / (res - 1)) * 2 - 1
							UpperCamelCase__       :Dict							 =			fracs * torch.tan(fov / 2							)

							UpperCamelCase__       :str							 =			fracs.view(UpperCamelCase_   ,       -1   ,       2							)
							UpperCamelCase__       :List[Any]							 =			(
							    self.z.view(UpperCamelCase_   ,       1   ,       3							)
							    + self.x.view(UpperCamelCase_   ,       1   ,       3							) * fracs[:, :, :1]
							    + self.y.view(UpperCamelCase_   ,       1   ,       3							) * fracs[:, :, 1:]
							)
							UpperCamelCase__       :Dict							 =			directions / directions.norm(dim=-1   ,       keepdim=UpperCamelCase_							)
							UpperCamelCase__       :str							 =			torch.stack(
							    [
							        torch.broadcast_to(self.origin.view(UpperCamelCase_   ,       1   ,       3							)   ,       [batch_size, directions.shape[1], 3]							),
							        directions,
							    ]   ,       dim=2   ,       )
							return rays.view(UpperCamelCase_   ,       *UpperCamelCase_   ,       2   ,       3							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							assert width * self.height == height * self.width, "The aspect ratio should not change."
							return DifferentiableProjectiveCamera(
							    origin=self.origin   ,       x=self.x   ,       y=self.y   ,       z=self.z   ,       width=UpperCamelCase_   ,       height=UpperCamelCase_   ,       x_fov=self.x_fov   ,       y_fov=self.y_fov   ,       )



def       a				(      __a       )  ->     DifferentiableProjectiveCamera:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			[]
					UpperCamelCase__       :str							 =			[]
					UpperCamelCase__       :Any							 =			[]
					UpperCamelCase__       :Union[str, Any]							 =			[]
					for theta in np.linspace(0			, 2 * np.pi			, num=20       ):
										UpperCamelCase__       :Optional[int]							 =			np.array([np.sin(__a       ), np.cos(__a       ), -0.5]       )
										z /= np.sqrt(np.sum(z**2       )       )
										UpperCamelCase__       :Tuple							 =			-z * 4
										UpperCamelCase__       :Any							 =			np.array([np.cos(__a       ), -np.sin(__a       ), 0.0]       )
										UpperCamelCase__       :List[str]							 =			np.cross(__a			, __a       )
										origins.append(__a       )
										xs.append(__a       )
										ys.append(__a       )
										zs.append(__a       )
					return DifferentiableProjectiveCamera(
					    origin=torch.from_numpy(np.stack(__a			, axis=0       )       ).float()			, x=torch.from_numpy(np.stack(__a			, axis=0       )       ).float()			, y=torch.from_numpy(np.stack(__a			, axis=0       )       ).float()			, z=torch.from_numpy(np.stack(__a			, axis=0       )       ).float()			, width=__a			, height=__a			, x_fov=0.7			, y_fov=0.7			, shape=(1, len(__a       ))			, )



'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class        lowercase     (							A__     ,		A__     ,		A__     ,		unittest.TestCase ):

		"""simple docstring"""

		_a   			=    AltDiffusionPipeline
		_a   			=    TEXT_TO_IMAGE_PARAMS
		_a   			=    TEXT_TO_IMAGE_BATCH_PARAMS
		_a   			=    TEXT_TO_IMAGE_IMAGE_PARAMS
		_a   			=    TEXT_TO_IMAGE_IMAGE_PARAMS




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__       :List[str]							 =			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   ,       )
							UpperCamelCase__       :int							 =			DDIMScheduler(
							    beta_start=0.00085   ,       beta_end=0.012   ,       beta_schedule='''scaled_linear'''   ,       clip_sample=UpperCamelCase_   ,       set_alpha_to_one=UpperCamelCase_   ,       )
							torch.manual_seed(0							)
							UpperCamelCase__       :List[str]							 =			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   ,       )

							# TODO: address the non-deterministic text encoder (fails for save-load tests)
							# torch.manual_seed(0)
							# text_encoder_config = RobertaSeriesConfig(
							#     hidden_size=32,
							#     project_dim=32,
							#     intermediate_size=37,
							#     layer_norm_eps=1e-05,
							#     num_attention_heads=4,
							#     num_hidden_layers=5,
							#     vocab_size=5002,
							# )
							# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

							torch.manual_seed(0							)
							UpperCamelCase__       :Union[str, Any]							 =			CLIPTextConfig(
							    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=32   ,       projection_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=5002   ,       )
							UpperCamelCase__       :Optional[Any]							 =			CLIPTextModel(UpperCamelCase_							)

							UpperCamelCase__       :int							 =			XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''							)
							UpperCamelCase__       :Dict							 =			77

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




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_=0							):

							'''simple docstring'''


							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__       :Dict							 =			torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__       :List[str]							 =			torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							super().test_inference_batch_single_identical(expected_max_diff=3e-3							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator

							UpperCamelCase__       :Tuple							 =			self.get_dummy_components()
							torch.manual_seed(0							)
							UpperCamelCase__       :Dict							 =			RobertaSeriesConfig(
							    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=5002   ,       )
							# TODO: remove after fixing the non-deterministic text encoder
							UpperCamelCase__       :Union[str, Any]							 =			RobertaSeriesModelWithTransformation(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			text_encoder

							UpperCamelCase__       :Any							 =			AltDiffusionPipeline(**UpperCamelCase_							)
							UpperCamelCase__       :List[Any]							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :int							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :Dict							 =			'''A photo of an astronaut'''
							UpperCamelCase__       :Union[str, Any]							 =			alt_pipe(**UpperCamelCase_							)
							UpperCamelCase__       :Any							 =			output.images
							UpperCamelCase__       :List[Any]							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase__       :List[Any]							 =			np.array(
							    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator

							UpperCamelCase__       :int							 =			self.get_dummy_components()
							UpperCamelCase__       :Union[str, Any]							 =			PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
							torch.manual_seed(0							)
							UpperCamelCase__       :Dict							 =			RobertaSeriesConfig(
							    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=5002   ,       )
							# TODO: remove after fixing the non-deterministic text encoder
							UpperCamelCase__       :str							 =			RobertaSeriesModelWithTransformation(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			text_encoder
							UpperCamelCase__       :Union[str, Any]							 =			AltDiffusionPipeline(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			alt_pipe(**UpperCamelCase_							)
							UpperCamelCase__       :str							 =			output.images
							UpperCamelCase__       :int							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase__       :List[Any]							 =			np.array(
							    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2



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

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       safety_checker=UpperCamelCase_							)
							UpperCamelCase__       :str							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :int							 =			'''A painting of a squirrel eating a burger'''
							UpperCamelCase__       :int							 =			torch.manual_seed(0							)
							UpperCamelCase__       :Optional[Any]							 =			alt_pipe([prompt]   ,       generator=UpperCamelCase_   ,       guidance_scale=6.0   ,       num_inference_steps=20   ,       output_type='''np'''							)

							UpperCamelCase__       :Union[str, Any]							 =			output.images

							UpperCamelCase__       :Optional[Any]							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__       :Union[str, Any]							 =			np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''   ,       subfolder='''scheduler'''							)

							UpperCamelCase__       :List[str]							 =			AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       scheduler=UpperCamelCase_   ,       safety_checker=UpperCamelCase_							)
							UpperCamelCase__       :Dict							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :List[Any]							 =			'''A painting of a squirrel eating a burger'''
							UpperCamelCase__       :str							 =			torch.manual_seed(0							)

							UpperCamelCase__       :Optional[int]							 =			alt_pipe([prompt]   ,       generator=UpperCamelCase_   ,       num_inference_steps=2   ,       output_type='''numpy'''							)
							UpperCamelCase__       :Any							 =			output.images

							UpperCamelCase__       :int							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__       :int							 =			np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2