


import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def 			lowerCAmelCase (		lowerCAmelCase_ )-> List[Any]:
				lowerCAmelCase_ :      List[str]    				=				[tensor.shape for tensor in tensor_list]
				return all(shape == shapes[0] for shape in shapes[1:] )







class 				snake_case__(							UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Union[str, Any]					=							StableDiffusionLatentUpscalePipeline
						SCREAMING_SNAKE_CASE__      :  List[Any]					=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    """height""",
						    """width""",
						    """cross_attention_kwargs""",
						    """negative_prompt_embeds""",
						    """prompt_embeds""",
						}
						SCREAMING_SNAKE_CASE__      :  Optional[Any]					=							PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
						SCREAMING_SNAKE_CASE__      :  Dict					=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						SCREAMING_SNAKE_CASE__      :  str					=							frozenset(
						    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						SCREAMING_SNAKE_CASE__      :  Tuple					=							frozenset([]							)

						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							True




						@property
						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      Optional[int]    				=				1
										lowerCAmelCase_ :      Any    				=				4
										lowerCAmelCase_ :      str    				=				(1_6, 1_6)

										lowerCAmelCase_ :      Dict    				=				floats_tensor((batch_size, num_channels) + sizes   ,						rng=random.Random(0							)							).to(__lowercase							)
										return image




						def 						lowercase_ (				self							)					->    List[str]:
										torch.manual_seed(0							)
										lowerCAmelCase_ :      List[Any]    				=				UNetaDConditionModel(
										    act_fn='''gelu'''   ,						attention_head_dim=8   ,						norm_num_groups=__lowercase   ,						block_out_channels=[3_2, 3_2, 6_4, 6_4]   ,						time_cond_proj_dim=1_6_0   ,						conv_in_kernel=1   ,						conv_out_kernel=1   ,						cross_attention_dim=3_2   ,						down_block_types=(
										        '''KDownBlock2D''',
										        '''KCrossAttnDownBlock2D''',
										        '''KCrossAttnDownBlock2D''',
										        '''KCrossAttnDownBlock2D''',
										    )   ,						in_channels=8   ,						mid_block_type=__lowercase   ,						only_cross_attention=__lowercase   ,						out_channels=5   ,						resnet_time_scale_shift='''scale_shift'''   ,						time_embedding_type='''fourier'''   ,						timestep_post_act='''gelu'''   ,						up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')   ,						)
										lowerCAmelCase_ :      int    				=				AutoencoderKL(
										    block_out_channels=[3_2, 3_2, 6_4, 6_4]   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=[
										        '''DownEncoderBlock2D''',
										        '''DownEncoderBlock2D''',
										        '''DownEncoderBlock2D''',
										        '''DownEncoderBlock2D''',
										    ]   ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,						latent_channels=4   ,						)
										lowerCAmelCase_ :      str    				=				EulerDiscreteScheduler(prediction_type='''sample'''							)
										lowerCAmelCase_ :      List[str]    				=				CLIPTextConfig(
										    bos_token_id=0   ,						eos_token_id=2   ,						hidden_size=3_2   ,						intermediate_size=3_7   ,						layer_norm_eps=1e-05   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						pad_token_id=1   ,						vocab_size=1_0_0_0   ,						hidden_act='''quick_gelu'''   ,						projection_dim=5_1_2   ,						)
										lowerCAmelCase_ :      str    				=				CLIPTextModel(__lowercase							)
										lowerCAmelCase_ :      Union[str, Any]    				=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										lowerCAmelCase_ :      List[str]    				=				{
										    '''unet''': model.eval(),
										    '''vae''': vae.eval(),
										    '''scheduler''': scheduler,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										}

										return components




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase=0							)					->    Union[str, Any]:
										if str(__lowercase							).startswith('''mps'''							):
														lowerCAmelCase_ :      Optional[int]    				=				torch.manual_seed(__lowercase							)
										else:
														lowerCAmelCase_ :      int    				=				torch.Generator(device=__lowercase							).manual_seed(__lowercase							)
										lowerCAmelCase_ :      Optional[int]    				=				{
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''image''': self.dummy_image.cpu(),
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''numpy''',
										}
										return inputs




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Dict    				=				'''cpu'''

										lowerCAmelCase_ :      str    				=				self.get_dummy_components()
										lowerCAmelCase_ :      int    				=				self.pipeline_class(**__lowercase							)
										pipe.to(__lowercase							)
										pipe.set_progress_bar_config(disable=__lowercase							)

										lowerCAmelCase_ :      Optional[Any]    				=				self.get_dummy_inputs(__lowercase							)
										lowerCAmelCase_ :      Any    				=				pipe(**__lowercase							).images
										lowerCAmelCase_ :      int    				=				image[0, -3:, -3:, -1]

										self.assertEqual(image.shape   ,						(1, 2_5_6, 2_5_6, 3)							)
										lowerCAmelCase_ :      Dict    				=				np.array(
										    [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]							)
										lowerCAmelCase_ :      Tuple    				=				np.abs(image_slice.flatten() - expected_slice							).max()
										self.assertLessEqual(__lowercase   ,						1e-3							)




						def 						lowercase_ (				self							)					->    int:
										super().test_attention_slicing_forward_pass(expected_max_diff=7e-3							)




						def 						lowercase_ (				self							)					->    Optional[Any]:
										super().test_cpu_offload_forward_pass(expected_max_diff=3e-3							)




						def 						lowercase_ (				self							)					->    str:
										super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3							)




						def 						lowercase_ (				self							)					->    Optional[int]:
										super().test_inference_batch_single_identical(expected_max_diff=7e-3							)




						def 						lowercase_ (				self							)					->    int:
										super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3							)




						def 						lowercase_ (				self							)					->    str:
										super().test_save_load_local(expected_max_difference=3e-3							)




						def 						lowercase_ (				self							)					->    List[Any]:
										super().test_save_load_optional_components(expected_max_difference=3e-3							)




						def 						lowercase_ (				self							)					->    Dict:
										lowerCAmelCase_ :      Any    				=				[
										    '''DDIMScheduler''',
										    '''DDPMScheduler''',
										    '''PNDMScheduler''',
										    '''HeunDiscreteScheduler''',
										    '''EulerAncestralDiscreteScheduler''',
										    '''KDPM2DiscreteScheduler''',
										    '''KDPM2AncestralDiscreteScheduler''',
										    '''DPMSolverSDEScheduler''',
										]
										lowerCAmelCase_ :      Any    				=				self.get_dummy_components()
										lowerCAmelCase_ :      Any    				=				self.pipeline_class(**__lowercase							)

										# make sure that PNDM does not need warm-up
										pipe.scheduler.register_to_config(skip_prk_steps=__lowercase							)

										pipe.to(__lowercase							)
										pipe.set_progress_bar_config(disable=__lowercase							)
										lowerCAmelCase_ :      Optional[Any]    				=				self.get_dummy_inputs(__lowercase							)
										lowerCAmelCase_ :      Tuple    				=				2

										lowerCAmelCase_ :      Optional[int]    				=				[]
										for scheduler_enum in KarrasDiffusionSchedulers:
														if scheduler_enum.name in skip_schedulers:
																		# no sigma schedulers are not supported
																		# no schedulers
																		continue

														lowerCAmelCase_ :      str    				=				getattr(__lowercase   ,						scheduler_enum.name							)
														lowerCAmelCase_ :      int    				=				scheduler_cls.from_config(pipe.scheduler.config							)
														lowerCAmelCase_ :      str    				=				pipe(**__lowercase							)[0]
														outputs.append(__lowercase							)

										assert check_same_shape(__lowercase							)







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







						'''simple docstring'''




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




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Optional[int]    				=				torch.manual_seed(3_3							)

										lowerCAmelCase_ :      List[str]    				=				StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''   ,						torch_dtype=torch.floataa							)
										pipe.to('''cuda'''							)

										lowerCAmelCase_ :      List[str]    				=				StableDiffusionLatentUpscalePipeline.from_pretrained(
										    '''stabilityai/sd-x2-latent-upscaler'''   ,						torch_dtype=torch.floataa							)
										upscaler.to('''cuda'''							)

										lowerCAmelCase_ :      Dict    				=				'''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

										lowerCAmelCase_ :      Optional[Any]    				=				pipe(__lowercase   ,						generator=__lowercase   ,						output_type='''latent'''							).images

										lowerCAmelCase_ :      Optional[Any]    				=				upscaler(
										    prompt=__lowercase   ,						image=__lowercase   ,						num_inference_steps=2_0   ,						guidance_scale=0   ,						generator=__lowercase   ,						output_type='''np'''   ,						).images[0]

										lowerCAmelCase_ :      int    				=				load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''							)
										assert np.abs((expected_image - image).mean()							) < 5e-2




						def 						lowercase_ (				self							)					->    List[str]:
										lowerCAmelCase_ :      str    				=				torch.manual_seed(3_3							)

										lowerCAmelCase_ :      Optional[Any]    				=				StableDiffusionLatentUpscalePipeline.from_pretrained(
										    '''stabilityai/sd-x2-latent-upscaler'''   ,						torch_dtype=torch.floataa							)
										upscaler.to('''cuda'''							)

										lowerCAmelCase_ :      List[str]    				=				'''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

										lowerCAmelCase_ :      int    				=				load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''							)

										lowerCAmelCase_ :      List[Any]    				=				upscaler(
										    prompt=__lowercase   ,						image=__lowercase   ,						num_inference_steps=2_0   ,						guidance_scale=0   ,						generator=__lowercase   ,						output_type='''np'''   ,						).images[0]

										lowerCAmelCase_ :      Any    				=				load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''							)
										assert np.abs((expected_image - image).max()							) < 5e-2



_UpperCAmelCase			:  int     =frozenset(
    [
        """prompt""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

_UpperCAmelCase			:  List[Any]     =frozenset(["""prompt""", """negative_prompt"""])

_UpperCAmelCase			:  Dict     =frozenset([])

_UpperCAmelCase			:  int     =frozenset(["""image"""])

_UpperCAmelCase			:  Tuple     =frozenset(
    [
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

_UpperCAmelCase			:  int     =frozenset(["""image"""])

_UpperCAmelCase			:  str     =frozenset(
    [
        """prompt""",
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

_UpperCAmelCase			:  int     =frozenset(["""prompt""", """image""", """negative_prompt"""])

_UpperCAmelCase			:  Optional[int]     =frozenset(
    [
        # Text guided image variation with an image mask
        """prompt""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

_UpperCAmelCase			:  Optional[int]     =frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])

_UpperCAmelCase			:  Optional[Any]     =frozenset(
    [
        # image variation with an image mask
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

_UpperCAmelCase			:  Optional[Any]     =frozenset(["""image""", """mask_image"""])

_UpperCAmelCase			:  Union[str, Any]     =frozenset(
    [
        """example_image""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

_UpperCAmelCase			:  Tuple     =frozenset(["""example_image""", """image""", """mask_image"""])

_UpperCAmelCase			:  Any     =frozenset(["""class_labels"""])

_UpperCAmelCase			:  List[Any]     =frozenset(["""class_labels"""])

_UpperCAmelCase			:  int     =frozenset(["""batch_size"""])

_UpperCAmelCase			:  str     =frozenset([])

_UpperCAmelCase			:  str     =frozenset(["""batch_size"""])

_UpperCAmelCase			:  Optional[Any]     =frozenset([])

_UpperCAmelCase			:  Tuple     =frozenset(
    [
        """prompt""",
        """audio_length_in_s""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

_UpperCAmelCase			:  Tuple     =frozenset(["""prompt""", """negative_prompt"""])
_UpperCAmelCase			:  List[str]     =frozenset(["""input_tokens"""])

_UpperCAmelCase			:  Optional[Any]     =frozenset(["""input_tokens"""])