'''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 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    StableDiffusionLDMaDPipeline
       SCREAMING_SNAKE_CASE							=    TEXT_TO_IMAGE_PARAMS
       SCREAMING_SNAKE_CASE							=    TEXT_TO_IMAGE_BATCH_PARAMS
       SCREAMING_SNAKE_CASE							=    TEXT_TO_IMAGE_IMAGE_PARAMS

       def _lowerCAmelCase(						self )  ->  List[Any]:
        torch.manual_seed(0 )
        lowercase__				:    Tuple  =					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	,   )
        lowercase__				:    List[Any]  =					DDIMScheduler(
            beta_start=0.0_0_0_8_5	,   beta_end=0.0_1_2	,   beta_schedule='''scaled_linear'''	,   clip_sample=__lowerCAmelCase	,   set_alpha_to_one=__lowerCAmelCase	,   )
        torch.manual_seed(0 )
        lowercase__				:    int  =					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 )
        lowercase__				:    Union[str, Any]  =					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	,   )
        lowercase__				:    Optional[int]  =					CLIPTextModel(__lowerCAmelCase )
        lowercase__				:    Any  =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

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

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=0 )  ->  int:
        if str(__lowerCAmelCase ).startswith('''mps''' ):
         lowercase__				:    Union[str, Any]  =					torch.manual_seed(__lowerCAmelCase )
        else:
         lowercase__				:    Union[str, Any]  =					torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
        lowercase__				:    List[Any]  =					{
            '''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 )  ->  Optional[Any]:
        lowercase__				:    Optional[Any]  =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

        lowercase__				:    Any  =					self.get_dummy_components()
        lowercase__				:    Union[str, Any]  =					StableDiffusionLDMaDPipeline(**__lowerCAmelCase )
        lowercase__				:    Tuple  =					ldmad_pipe.to(__lowerCAmelCase )
        ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    Any  =					self.get_dummy_inputs(__lowerCAmelCase )
        lowercase__				:    List[Any]  =					ldmad_pipe(**__lowerCAmelCase )
        lowercase__ ,							lowercase__				:    Dict  =					output.rgb, output.depth

        lowercase__				:    List[Any]  =					rgb[0, -3:, -3:, -1]
        lowercase__				:    str  =					depth[0, -3:, -1]

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

        lowercase__				:    Tuple  =					np.array(
            [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2] )
        lowercase__				:    Optional[Any]  =					np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6] )

        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 )  ->  List[str]:
        lowercase__				:    Tuple  =					self.get_dummy_components()
        lowercase__				:    Any  =					StableDiffusionLDMaDPipeline(**__lowerCAmelCase )
        lowercase__				:    Tuple  =					ldmad_pipe.to(__lowerCAmelCase )
        ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    Tuple  =					self.get_dummy_inputs(__lowerCAmelCase )
        lowercase__				:    str  =					3 * [inputs['''prompt''']]

        # forward
        lowercase__				:    Dict  =					ldmad_pipe(**__lowerCAmelCase )
        lowercase__ ,							lowercase__				:    List[Any]  =					output.rgb, output.depth
        lowercase__				:    Dict  =					rgb_slice_a[0, -3:, -3:, -1]
        lowercase__				:    Optional[int]  =					depth_slice_a[0, -3:, -1]

        lowercase__				:    List[Any]  =					self.get_dummy_inputs(__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					3 * [inputs.pop('''prompt''' )]

        lowercase__				:    Optional[int]  =					ldmad_pipe.tokenizer(
            __lowerCAmelCase	,   padding='''max_length'''	,   max_length=ldmad_pipe.tokenizer.model_max_length	,   truncation=__lowerCAmelCase	,   return_tensors='''pt'''	,   )
        lowercase__				:    Tuple  =					text_inputs['''input_ids'''].to(__lowerCAmelCase )

        lowercase__				:    int  =					ldmad_pipe.text_encoder(__lowerCAmelCase )[0]

        lowercase__				:    Union[str, Any]  =					prompt_embeds

        # forward
        lowercase__				:    Optional[Any]  =					ldmad_pipe(**__lowerCAmelCase )
        lowercase__ ,							lowercase__				:    List[str]  =					output.rgb, output.depth
        lowercase__				:    List[Any]  =					rgb_slice_a[0, -3:, -3:, -1]
        lowercase__				:    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 )  ->  Union[str, Any]:
        lowercase__				:    Optional[int]  =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        lowercase__				:    List[Any]  =					self.get_dummy_components()
        lowercase__				:    Optional[Any]  =					PNDMScheduler(skip_prk_steps=__lowerCAmelCase )
        lowercase__				:    List[Any]  =					StableDiffusionLDMaDPipeline(**__lowerCAmelCase )
        lowercase__				:    Dict  =					ldmad_pipe.to(__lowerCAmelCase )
        ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    int  =					self.get_dummy_inputs(__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					'''french fries'''
        lowercase__				:    Dict  =					ldmad_pipe(**__lowerCAmelCase	,   negative_prompt=__lowerCAmelCase )

        lowercase__ ,							lowercase__				:    Optional[Any]  =					output.rgb, output.depth
        lowercase__				:    List[Any]  =					rgb[0, -3:, -3:, -1]
        lowercase__				:    Any  =					depth[0, -3:, -1]

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

        lowercase__				:    Any  =					np.array(
            [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7] )
        lowercase__				:    Dict  =					np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5] )
        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 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase="cpu"	,   __lowerCAmelCase=torch.floataa	,   __lowerCAmelCase=0 )  ->  Optional[Any]:
        lowercase__				:    List[Any]  =					torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
        lowercase__				:    Optional[int]  =					np.random.RandomState(__lowerCAmelCase ).standard_normal((1, 4, 64, 64) )
        lowercase__				:    Union[str, Any]  =					torch.from_numpy(__lowerCAmelCase ).to(device=__lowerCAmelCase	,   dtype=__lowerCAmelCase )
        lowercase__				:    List[str]  =					{
            '''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 )  ->  int:
        lowercase__				:    List[str]  =					StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' )
        lowercase__				:    int  =					ldmad_pipe.to(__lowerCAmelCase )
        ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    List[str]  =					self.get_inputs(__lowerCAmelCase )
        lowercase__				:    List[str]  =					ldmad_pipe(**__lowerCAmelCase )
        lowercase__ ,							lowercase__				:    List[str]  =					output.rgb, output.depth
        lowercase__				:    Dict  =					rgb[0, -3:, -3:, -1].flatten()
        lowercase__				:    Dict  =					rgb[0, -3:, -1].flatten()

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

        lowercase__				:    List[str]  =					np.array(
            [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6] )
        lowercase__				:    Optional[Any]  =					np.array(
            [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6] )
        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 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       def _lowerCAmelCase(						self )  ->  Tuple:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase="cpu"	,   __lowerCAmelCase=torch.floataa	,   __lowerCAmelCase=0 )  ->  Tuple:
        lowercase__				:    Dict  =					torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
        lowercase__				:    Tuple  =					np.random.RandomState(__lowerCAmelCase ).standard_normal((1, 4, 64, 64) )
        lowercase__				:    Tuple  =					torch.from_numpy(__lowerCAmelCase ).to(device=__lowerCAmelCase	,   dtype=__lowerCAmelCase )
        lowercase__				:    Any  =					{
            '''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 )  ->  Union[str, Any]:
        lowercase__				:    Optional[Any]  =					StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' ).to(__lowerCAmelCase )
        ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    List[str]  =					self.get_inputs(__lowerCAmelCase )
        lowercase__				:    str  =					ldmad_pipe(**__lowerCAmelCase )
        lowercase__ ,							lowercase__				:    Union[str, Any]  =					output.rgb, output.depth

        lowercase__				:    Optional[int]  =					0.4_9_5_5_8_6
        lowercase__				:    Optional[Any]  =					0.3_3_7_9_5_5_1_5
        lowercase__				:    Optional[Any]  =					1_1_2.4_8_5_1_8
        lowercase__				:    List[Any]  =					9_8.4_8_9_7_4_6
        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 )  ->  Optional[int]:
        lowercase__				:    List[Any]  =					StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c''' ).to(__lowerCAmelCase )
        ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    Any  =					self.get_inputs(__lowerCAmelCase )
        lowercase__				:    Dict  =					ldmad_pipe(**__lowerCAmelCase )
        lowercase__ ,							lowercase__				:    Optional[Any]  =					output.rgb, output.depth

        lowercase__				:    Union[str, Any]  =					0.4_1_9_4_1_2_7
        lowercase__				:    Any  =					0.3_5_3_7_5_5_8_6
        lowercase__				:    Tuple  =					0.5_6_3_8_5_0_2
        lowercase__				:    Any  =					0.3_4_6_8_6_1_0_3

        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 Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__a: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    ["pixel_values"]

       def __init__(						self	,   __lowerCAmelCase = True	,   __lowerCAmelCase = None	,   __lowerCAmelCase = PILImageResampling.BICUBIC	,   __lowerCAmelCase = True	,   __lowerCAmelCase = 1 / 255	,   __lowerCAmelCase = True	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = True	,   **__lowerCAmelCase	,   )  ->  None:
        super().__init__(**__lowerCAmelCase )
        lowercase__				:    Optional[int]  =					size if size is not None else {'''height''': 384, '''width''': 384}
        lowercase__				:    Optional[Any]  =					get_size_dict(__lowerCAmelCase	,   default_to_square=__lowerCAmelCase )

        lowercase__				:    Dict  =					do_resize
        lowercase__				:    int  =					size
        lowercase__				:    int  =					resample
        lowercase__				:    Tuple  =					do_rescale
        lowercase__				:    int  =					rescale_factor
        lowercase__				:    int  =					do_normalize
        lowercase__				:    Optional[int]  =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowercase__				:    Optional[int]  =					image_std if image_std is not None else OPENAI_CLIP_STD
        lowercase__				:    Tuple  =					do_convert_rgb

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = PILImageResampling.BICUBIC	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  np.ndarray:
        lowercase__				:    Union[str, Any]  =					get_size_dict(__lowerCAmelCase	,   default_to_square=__lowerCAmelCase )
        if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
        lowercase__				:    Any  =					(size['''height'''], size['''width'''])
        return resize(__lowerCAmelCase	,   size=__lowerCAmelCase	,   resample=__lowerCAmelCase	,   data_format=__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  str:
        return rescale(__lowerCAmelCase	,   scale=__lowerCAmelCase	,   data_format=__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  np.ndarray:
        return normalize(__lowerCAmelCase	,   mean=__lowerCAmelCase	,   std=__lowerCAmelCase	,   data_format=__lowerCAmelCase	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = ChannelDimension.FIRST	,   **__lowerCAmelCase	,   )  ->  PIL.Image.Image:
        lowercase__				:    Union[str, Any]  =					do_resize if do_resize is not None else self.do_resize
        lowercase__				:    Any  =					resample if resample is not None else self.resample
        lowercase__				:    int  =					do_rescale if do_rescale is not None else self.do_rescale
        lowercase__				:    List[Any]  =					rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase__				:    List[Any]  =					do_normalize if do_normalize is not None else self.do_normalize
        lowercase__				:    Dict  =					image_mean if image_mean is not None else self.image_mean
        lowercase__				:    Dict  =					image_std if image_std is not None else self.image_std
        lowercase__				:    Dict  =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowercase__				:    Optional[int]  =					size if size is not None else self.size
        lowercase__				:    int  =					get_size_dict(__lowerCAmelCase	,   default_to_square=__lowerCAmelCase )

        lowercase__				:    str  =					make_list_of_images(__lowerCAmelCase )

        if not valid_images(__lowerCAmelCase ):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

        if do_resize and size is None or resample is None:
         raise ValueError('''Size and resample must be specified if do_resize is True.''' )

        if do_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

        if do_normalize and (image_mean is None or image_std is None):
         raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
         lowercase__				:    Optional[Any]  =					[convert_to_rgb(__lowerCAmelCase ) for image in images]

        # All transformations expect numpy arrays.
        lowercase__				:    Any  =					[to_numpy_array(__lowerCAmelCase ) for image in images]

        if do_resize:
         lowercase__				:    Tuple  =					[self.resize(image=__lowerCAmelCase	,   size=__lowerCAmelCase	,   resample=__lowerCAmelCase ) for image in images]

        if do_rescale:
         lowercase__				:    List[str]  =					[self.rescale(image=__lowerCAmelCase	,   scale=__lowerCAmelCase ) for image in images]

        if do_normalize:
         lowercase__				:    Tuple  =					[self.normalize(image=__lowerCAmelCase	,   mean=__lowerCAmelCase	,   std=__lowerCAmelCase ) for image in images]

        lowercase__				:    List[str]  =					[to_channel_dimension_format(__lowerCAmelCase	,   __lowerCAmelCase ) for image in images]

        lowercase__				:    Optional[Any]  =					BatchFeature(data={'''pixel_values''': images}	,   tensor_type=__lowerCAmelCase )

        return encoded_outputs

