




from math import asin, atan, cos, radians, sin, sqrt, tan

UpperCamelCase								=	6_378_137.0
UpperCamelCase								=	6_356_752.314_245
UpperCamelCase								=	637_8137



def 				_A  (		lowerCAmelCase_	:   float  ,							lowerCAmelCase_	:   float  ,							lowerCAmelCase_	:   float  ,							lowerCAmelCase_	:   float     ):
     """simple docstring"""

     lowerCAmelCase__            =					(AXIS_A - AXIS_B) / AXIS_A
     lowerCAmelCase__            =					atan((1 - flattening) * tan(radians(lowerCAmelCase_     )     )     )
     lowerCAmelCase__            =					atan((1 - flattening) * tan(radians(lowerCAmelCase_     )     )     )
     lowerCAmelCase__            =					radians(lowerCAmelCase_     )
     lowerCAmelCase__            =					radians(lowerCAmelCase_     )
     # Equation
     lowerCAmelCase__            =					sin((phi_a - phi_a) / 2     )
     lowerCAmelCase__            =					sin((lambda_a - lambda_a) / 2     )
     # Square both values
     sin_sq_phi *= sin_sq_phi
     sin_sq_lambda *= sin_sq_lambda
     lowerCAmelCase__            =					sqrt(sin_sq_phi + (cos(lowerCAmelCase_     ) * cos(lowerCAmelCase_     ) * sin_sq_lambda)     )
     return 2 * RADIUS * asin(lowerCAmelCase_     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''simple docstring'''
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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Dict							     =  [tensor.shape for tensor in tensor_list]
   return all(shape == shapes[0] for shape in shapes[1:]				)


class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Union[str, Any]									=     StableDiffusionLatentUpscalePipeline
    __lowerCamelCase: List[Any]									=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        'height',
        'width',
        'cross_attention_kwargs',
        'negative_prompt_embeds',
        'prompt_embeds',
    }
    __lowerCamelCase: List[Any]									=     PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
    __lowerCamelCase: List[Any]									=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowerCamelCase: Union[str, Any]									=     frozenset(
        []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __lowerCamelCase: List[Any]									=     frozenset([]      )

    __lowerCamelCase: Dict									=     True




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  1
       lowercase_						:     Any							     =  4
       lowercase_						:     int							     =  (1_6, 1_6)

       lowercase_						:     List[Any]							     =  floats_tensor((batch_size, num_channels) + sizes	,  rng=random.Random(0      )      ).to(a      )
       return image




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       torch.manual_seed(0      )
       lowercase_						:     int							     =  UNetaDConditionModel(
           act_fn="gelu"	,  attention_head_dim=8	,  norm_num_groups=a	,  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=a	,  only_cross_attention=a	,  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_						:     List[str]							     =  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	,  )
       lowercase_						:     Optional[int]							     =  EulerDiscreteScheduler(prediction_type="sample"      )
       lowercase_						:     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	,  )
       lowercase_						:     Optional[int]							     =  CLIPTextModel(a      )
       lowercase_						:     List[str]							     =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

       lowercase_						:     int							     =  {
           "unet": model.eval(),
           "vae": vae.eval(),
           "scheduler": scheduler,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
       }

       return components




    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							int	,  a	:							List[str]=0      ):






       '''simple docstring'''






       if str(a      ).startswith("mps"      ):
          lowercase_						:     Optional[int]							     =  torch.manual_seed(a      )
       else:
          lowercase_						:     Optional[int]							     =  torch.Generator(device=a      ).manual_seed(a      )
       lowercase_						:     Optional[Any]							     =  {
           "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 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  "cpu"

       lowercase_						:     Any							     =  self.get_dummy_components()
       lowercase_						:     Tuple							     =  self.pipeline_class(**a      )
       pipe.to(a      )
       pipe.set_progress_bar_config(disable=a      )

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

       self.assertEqual(image.shape	,  (1, 2_5_6, 2_5_6, 3)      )
       lowercase_						:     int							     =  np.array(
           [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]      )
       lowercase_						:     int							     =  np.abs(image_slice.flatten() - expected_slice      ).max()
       self.assertLessEqual(a	,  1e-3      )




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       super().test_attention_slicing_forward_pass(expected_max_diff=7e-3      )




    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3      )




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       super().test_inference_batch_single_identical(expected_max_diff=7e-3      )




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       super().test_save_load_local(expected_max_difference=3e-3      )




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






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





    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  [
           "DDIMScheduler",
           "DDPMScheduler",
           "PNDMScheduler",
           "HeunDiscreteScheduler",
           "EulerAncestralDiscreteScheduler",
           "KDPM2DiscreteScheduler",
           "KDPM2AncestralDiscreteScheduler",
           "DPMSolverSDEScheduler",
       ]
       lowercase_						:     Optional[int]							     =  self.get_dummy_components()
       lowercase_						:     int							     =  self.pipeline_class(**a      )

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

       pipe.to(a      )
       pipe.set_progress_bar_config(disable=a      )
       lowercase_						:     Dict							     =  self.get_dummy_inputs(a      )
       lowercase_						:     int							     =  2

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

          lowercase_						:     Optional[int]							     =  getattr(a	,  scheduler_enum.name      )
          lowercase_						:     Union[str, Any]							     =  scheduler_cls.from_config(pipe.scheduler.config      )
          lowercase_						:     Tuple							     =  pipe(**a      )[0]
          outputs.append(a      )

       assert check_same_shape(a      )


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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     str							     =  torch.manual_seed(3_3      )

       lowercase_						:     Union[str, Any]							     =  StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"	,  torch_dtype=torch.floataa      )
       pipe.to("cuda"      )

       lowercase_						:     List[Any]							     =  StableDiffusionLatentUpscalePipeline.from_pretrained(
           "stabilityai/sd-x2-latent-upscaler"	,  torch_dtype=torch.floataa      )
       upscaler.to("cuda"      )

       lowercase_						:     Dict							     =  "a photo of an astronaut high resolution, unreal engine, ultra realistic"

       lowercase_						:     Optional[Any]							     =  pipe(a	,  generator=a	,  output_type="latent"      ).images

       lowercase_						:     Tuple							     =  upscaler(
           prompt=a	,  image=a	,  num_inference_steps=2_0	,  guidance_scale=0	,  generator=a	,  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 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  torch.manual_seed(3_3      )

       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_						:     str							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"      )

       lowercase_						:     int							     =  upscaler(
           prompt=a	,  image=a	,  num_inference_steps=2_0	,  guidance_scale=0	,  generator=a	,  output_type="np"	,  ).images[0]

       lowercase_						:     Union[str, 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




