


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 				UpperCamelCase_(      lowerCamelCase_ )    ->						Tuple:
    _lowercase       :							Dict      		=		[tensor.shape for tensor in tensor_list]
    return all(shape == shapes[0] for shape in shapes[1:] )






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

       lowercase_ :	Dict     =			True
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							int      		=		1
           _lowercase       :							List[str]      		=		4
           _lowercase       :							Optional[Any]      		=		(16, 16)

           _lowercase       :							List[Any]      		=		floats_tensor((batch_size, num_channels) + sizes,      rng=random.Random(0)).to(lowerCamelCase)
           return image
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Optional[Any]      		=		UNetaDConditionModel(
               act_fn='gelu',      attention_head_dim=8,      norm_num_groups=lowerCamelCase,      block_out_channels=[32, 32, 64, 64],      time_cond_proj_dim=1_60,      conv_in_kernel=1,      conv_out_kernel=1,      cross_attention_dim=32,      down_block_types=(
                   'KDownBlock2D',
                   'KCrossAttnDownBlock2D',
                   'KCrossAttnDownBlock2D',
                   'KCrossAttnDownBlock2D',
               ),      in_channels=8,      mid_block_type=lowerCamelCase,      only_cross_attention=lowerCamelCase,      out_channels=5,      resnet_time_scale_shift='scale_shift',      time_embedding_type='fourier',      timestep_post_act='gelu',      up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D'),      )
           _lowercase       :							Dict      		=		AutoencoderKL(
               block_out_channels=[32, 32, 64, 64],      in_channels=3,      out_channels=3,      down_block_types=[
                   'DownEncoderBlock2D',
                   'DownEncoderBlock2D',
                   'DownEncoderBlock2D',
                   'DownEncoderBlock2D',
               ],      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D'],      latent_channels=4,      )
           _lowercase       :							Optional[Any]      		=		EulerDiscreteScheduler(prediction_type='sample')
           _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=10_00,      hidden_act='quick_gelu',      projection_dim=5_12,      )
           _lowercase       :							Optional[Any]      		=		CLIPTextModel(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _lowercase       :							Optional[int]      		=		{
               'unet': model.eval(),
               'vae': vae.eval(),
               'scheduler': scheduler,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
           }

           return components
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=0)       ->   Union[str, Any]:




           """simple docstring"""

           if str(lowerCamelCase).startswith('mps'):
               _lowercase       :							List[Any]      		=		torch.manual_seed(lowerCamelCase)
           else:
               _lowercase       :							Optional[Any]      		=		torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase       :							Tuple      		=		{
               '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 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		'cpu'

           _lowercase       :							int      		=		self.get_dummy_components()
           _lowercase       :							Optional[Any]      		=		self.pipeline_class(**lowerCamelCase)
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							int      		=		self.get_dummy_inputs(lowerCamelCase)
           _lowercase       :							List[Any]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							List[Any]      		=		image[0, -3:, -3:, -1]

           self.assertEqual(image.shape,      (1, 2_56, 2_56, 3))
           _lowercase       :							Union[str, Any]      		=		np.array(
               [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5])
           _lowercase       :							List[Any]      		=		np.abs(image_slice.flatten() - expected_slice).max()
           self.assertLessEqual(lowerCamelCase,      1E-3)
       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           super().test_attention_slicing_forward_pass(expected_max_diff=7E-3)
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           super().test_cpu_offload_forward_pass(expected_max_diff=3E-3)
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           super().test_inference_batch_single_identical(expected_max_diff=7E-3)
       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3)
       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           super().test_save_load_local(expected_max_difference=3E-3)
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           super().test_save_load_optional_components(expected_max_difference=3E-3)




       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		[
               'DDIMScheduler',
               'DDPMScheduler',
               'PNDMScheduler',
               'HeunDiscreteScheduler',
               'EulerAncestralDiscreteScheduler',
               'KDPM2DiscreteScheduler',
               'KDPM2AncestralDiscreteScheduler',
               'DPMSolverSDEScheduler',
           ]
           _lowercase       :							List[Any]      		=		self.get_dummy_components()
           _lowercase       :							Dict      		=		self.pipeline_class(**lowerCamelCase)

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

           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           _lowercase       :							str      		=		self.get_dummy_inputs(lowerCamelCase)
           _lowercase       :							List[Any]      		=		2

           _lowercase       :							List[str]      		=		[]
           for scheduler_enum in KarrasDiffusionSchedulers:
               if scheduler_enum.name in skip_schedulers:
                   # no sigma schedulers are not supported
                   # no schedulers
                   continue

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

           assert check_same_shape(lowerCamelCase)






@require_torch_gpu
@slow
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							Tuple      		=		torch.manual_seed(33)

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

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

           _lowercase       :							List[str]      		=		'a photo of an astronaut high resolution, unreal engine, ultra realistic'

           _lowercase       :							Any      		=		pipe(lowerCamelCase,      generator=lowerCamelCase,      output_type='latent').images

           _lowercase       :							List[Any]      		=		upscaler(
               prompt=lowerCamelCase,      image=lowerCamelCase,      num_inference_steps=20,      guidance_scale=0,      generator=lowerCamelCase,      output_type='np',      ).images[0]

           _lowercase       :							Any      		=		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 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		torch.manual_seed(33)

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

           _lowercase       :							Any      		=		'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

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

           _lowercase       :							int      		=		upscaler(
               prompt=lowerCamelCase,      image=lowerCamelCase,      num_inference_steps=20,      guidance_scale=0,      generator=lowerCamelCase,      output_type='np',      ).images[0]

           _lowercase       :							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






def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						float:
    _lowercase       :							Tuple      		=		(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
    # formula for sum of series
    return total



def 				UpperCamelCase_(      )    ->						Optional[int]:
    print(sum_of_series(1							, 1							, 10 ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


