


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

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		_a     =				StableDiffusionLDMaDPipeline
		_a     =				TEXT_TO_IMAGE_PARAMS
		_a     =				TEXT_TO_IMAGE_BATCH_PARAMS
		_a     =				TEXT_TO_IMAGE_IMAGE_PARAMS




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :int												= 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__   :Tuple												= 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__   :Union[str, Any]												= AutoencoderKL(
							    block_out_channels=[32, 64]    ,   in_channels=6    ,   out_channels=6    ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,   latent_channels=4    ,   )
							torch.manual_seed(0							)
							UpperCamelCase__   :Tuple												= 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    ,   )
							UpperCamelCase__   :Optional[int]												= CLIPTextModel(UpperCamelCase_							)
							UpperCamelCase__   :str												= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__   :Any												= {
							    '''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__   :List[str]												= {
							    '''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'''


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

							UpperCamelCase__   :List[Any]												= self.get_dummy_components()
							UpperCamelCase__   :Any												= StableDiffusionLDMaDPipeline(**UpperCamelCase_							)
							UpperCamelCase__   :str												= ldmad_pipe.to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :int												= self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__   :str												= ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= output.rgb, output.depth

							UpperCamelCase__   :List[Any]												= rgb[0, -3:, -3:, -1]
							UpperCamelCase__   :Optional[int]												= depth[0, -3:, -1]

							assert rgb.shape == (1, 64, 64, 3)
							assert depth.shape == (1, 64, 64)

							UpperCamelCase__   :Any												= np.array(
							    [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]							)
							UpperCamelCase__   :Optional[int]												= np.array([103.46727, 85.812004, 87.849236]							)

							assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb							).max() < 1e-2
							assert np.abs(image_slice_depth.flatten() - expected_slice_depth							).max() < 1e-2




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self.get_dummy_components()
							UpperCamelCase__   :Optional[Any]												= StableDiffusionLDMaDPipeline(**UpperCamelCase_							)
							UpperCamelCase__   :Dict												= ldmad_pipe.to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :str												= self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__   :Optional[Any]												= 3 * [inputs['''prompt''']]

							# forward
							UpperCamelCase__   :int												= ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__					,				UpperCamelCase__   :Tuple												= output.rgb, output.depth
							UpperCamelCase__   :Union[str, Any]												= rgb_slice_a[0, -3:, -3:, -1]
							UpperCamelCase__   :Union[str, Any]												= depth_slice_a[0, -3:, -1]

							UpperCamelCase__   :Dict												= self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__   :Optional[Any]												= 3 * [inputs.pop('''prompt'''							)]

							UpperCamelCase__   :str												= ldmad_pipe.tokenizer(
							    UpperCamelCase_    ,   padding='''max_length'''    ,   max_length=ldmad_pipe.tokenizer.model_max_length    ,   truncation=UpperCamelCase_    ,   return_tensors='''pt'''    ,   )
							UpperCamelCase__   :Optional[Any]												= text_inputs['''input_ids'''].to(UpperCamelCase_							)

							UpperCamelCase__   :str												= ldmad_pipe.text_encoder(UpperCamelCase_							)[0]

							UpperCamelCase__   :Optional[int]												= prompt_embeds

							# forward
							UpperCamelCase__   :Tuple												= ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__					,				UpperCamelCase__   :int												= output.rgb, output.depth
							UpperCamelCase__   :List[Any]												= rgb_slice_a[0, -3:, -3:, -1]
							UpperCamelCase__   :List[Any]												= depth_slice_a[0, -3:, -1]

							assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()							).max() < 1e-4
							assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()							).max() < 1e-4




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__   :List[str]												= self.get_dummy_components()
							UpperCamelCase__   :Tuple												= PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
							UpperCamelCase__   :List[str]												= StableDiffusionLDMaDPipeline(**UpperCamelCase_							)
							UpperCamelCase__   :int												= ldmad_pipe.to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :List[Any]												= self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__   :Optional[int]												= '''french fries'''
							UpperCamelCase__   :List[Any]												= ldmad_pipe(**UpperCamelCase_    ,   negative_prompt=UpperCamelCase_							)

							UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= output.rgb, output.depth
							UpperCamelCase__   :Tuple												= rgb[0, -3:, -3:, -1]
							UpperCamelCase__   :Any												= depth[0, -3:, -1]

							assert rgb.shape == (1, 64, 64, 3)
							assert depth.shape == (1, 64, 64)

							UpperCamelCase__   :Union[str, Any]												= np.array(
							    [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]							)
							UpperCamelCase__   :List[str]												= np.array([107.84738, 84.62802, 89.962135]							)
							assert np.abs(rgb_slice.flatten() - expected_slice_rgb							).max() < 1e-2
							assert np.abs(depth_slice.flatten() - expected_slice_depth							).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    ,   UpperCamelCase_    ,   UpperCamelCase_="cpu"    ,   UpperCamelCase_=torch.floataa    ,   UpperCamelCase_=0							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__   :int												= np.random.RandomState(UpperCamelCase_							).standard_normal((1, 4, 64, 64)							)
							UpperCamelCase__   :Any												= torch.from_numpy(UpperCamelCase_							).to(device=UpperCamelCase_    ,   dtype=UpperCamelCase_							)
							UpperCamelCase__   :Optional[Any]												= {
							    '''prompt''': '''a photograph of an astronaut riding a horse''',
							    '''latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''							)
							UpperCamelCase__   :int												= ldmad_pipe.to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :List[Any]												= self.get_inputs(UpperCamelCase_							)
							UpperCamelCase__   :Tuple												= ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__					,				UpperCamelCase__   :List[str]												= output.rgb, output.depth
							UpperCamelCase__   :List[str]												= rgb[0, -3:, -3:, -1].flatten()
							UpperCamelCase__   :Optional[int]												= rgb[0, -3:, -1].flatten()

							assert rgb.shape == (1, 512, 512, 3)
							assert depth.shape == (1, 512, 512)

							UpperCamelCase__   :List[str]												= np.array(
							    [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]							)
							UpperCamelCase__   :Optional[int]												= np.array(
							    [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]							)
							assert np.abs(rgb_slice - expected_slice_rgb							).max() < 3e-3
							assert np.abs(depth_slice - expected_slice_depth							).max() < 3e-3



@nightly
@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    ,   UpperCamelCase_    ,   UpperCamelCase_="cpu"    ,   UpperCamelCase_=torch.floataa    ,   UpperCamelCase_=0							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__   :List[str]												= np.random.RandomState(UpperCamelCase_							).standard_normal((1, 4, 64, 64)							)
							UpperCamelCase__   :Any												= torch.from_numpy(UpperCamelCase_							).to(device=UpperCamelCase_    ,   dtype=UpperCamelCase_							)
							UpperCamelCase__   :List[str]												= {
							    '''prompt''': '''a photograph of an astronaut riding a horse''',
							    '''latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 50,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''							).to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :Any												= self.get_inputs(UpperCamelCase_							)
							UpperCamelCase__   :Optional[int]												= ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= output.rgb, output.depth

							UpperCamelCase__   :Union[str, Any]												= 0.495586
							UpperCamelCase__   :List[str]												= 0.33795515
							UpperCamelCase__   :Tuple												= 112.48518
							UpperCamelCase__   :int												= 98.489746
							assert np.abs(expected_rgb_mean - rgb.mean()							) < 1e-3
							assert np.abs(expected_rgb_std - rgb.std()							) < 1e-3
							assert np.abs(expected_depth_mean - depth.mean()							) < 1e-3
							assert np.abs(expected_depth_std - depth.std()							) < 1e-3




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''							).to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :Dict												= self.get_inputs(UpperCamelCase_							)
							UpperCamelCase__   :Any												= ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__					,				UpperCamelCase__   :Dict												= output.rgb, output.depth

							UpperCamelCase__   :int												= 0.4194127
							UpperCamelCase__   :Optional[int]												= 0.35375586
							UpperCamelCase__   :int												= 0.5638502
							UpperCamelCase__   :Optional[int]												= 0.34686103

							assert rgb.shape == (1, 512, 512, 3)
							assert depth.shape == (1, 512, 512, 1)
							assert np.abs(expected_rgb_mean - rgb.mean()							) < 1e-3
							assert np.abs(expected_rgb_std - rgb.std()							) < 1e-3
							assert np.abs(expected_depth_mean - depth.mean()							) < 1e-3
							assert np.abs(expected_depth_std - depth.std()							) < 1e-3



'''simple docstring'''
from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(
							    features=UpperCamelCase_    ,   cache_dir=UpperCamelCase_    ,   keep_in_memory=UpperCamelCase_    ,   streaming=UpperCamelCase_    ,   num_proc=UpperCamelCase_    ,   **UpperCamelCase_    ,   )
							UpperCamelCase__   :Any												= Generator(
							    cache_dir=UpperCamelCase_    ,   features=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   gen_kwargs=UpperCamelCase_    ,   **UpperCamelCase_    ,   )




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self.streaming:
												UpperCamelCase__   :Optional[Any]												= self.builder.as_streaming_dataset(split='''train'''							)
							# Build regular (map-style) dataset
							else:
												UpperCamelCase__   :Optional[int]												= None
												UpperCamelCase__   :int												= None
												UpperCamelCase__   :Any												= None
												UpperCamelCase__   :Any												= None

												self.builder.download_and_prepare(
												    download_config=UpperCamelCase_    ,   download_mode=UpperCamelCase_    ,   verification_mode=UpperCamelCase_    ,   base_path=UpperCamelCase_    ,   num_proc=self.num_proc    ,   )
												UpperCamelCase__   :List[Any]												= self.builder.as_dataset(
												    split='''train'''    ,   verification_mode=UpperCamelCase_    ,   in_memory=self.keep_in_memory							)
							return dataset