'''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			)  ->  Optional[Any]:
        torch.manual_seed(0			)
        lowercase__    :   Optional[Any]						= 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[str]						= 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__    :   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     ,       )
        lowercase__    :   int						= CLIPTextModel(__lowerCAmelCase			)
        lowercase__    :   Dict						= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

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

        lowercase__    :   int						= self.get_dummy_components()
        lowercase__    :   Optional[int]						= StableDiffusionLDMaDPipeline(**__lowerCAmelCase			)
        lowercase__    :   str						= ldmad_pipe.to(__lowerCAmelCase			)
        ldmad_pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

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

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

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

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

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

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

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

        lowercase__    :   Any						= ldmad_pipe.tokenizer(
            __lowerCAmelCase     ,       padding='''max_length'''     ,       max_length=ldmad_pipe.tokenizer.model_max_length     ,       truncation=__lowerCAmelCase     ,       return_tensors='''pt'''     ,       )
        lowercase__    :   Union[str, Any]						= text_inputs['''input_ids'''].to(__lowerCAmelCase			)

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

        lowercase__    :   Dict						= prompt_embeds

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

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

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

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

        lowercase__    :   Union[str, 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__    :   Any						= 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			)  ->  List[Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

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

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

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

        lowercase__    :   Optional[Any]						= 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__    :   List[str]						= 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			)  ->  Union[str, Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

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

        lowercase__    :   List[str]						= self.get_inputs(__lowerCAmelCase			)
        lowercase__    :   Tuple						= ldmad_pipe(**__lowerCAmelCase			)
        lowercase__			,							lowercase__    :   Optional[int]						= output.rgb, output.depth

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

        lowercase__    :   str						= self.get_inputs(__lowerCAmelCase			)
        lowercase__    :   Any						= ldmad_pipe(**__lowerCAmelCase			)
        lowercase__			,							lowercase__    :   int						= output.rgb, output.depth

        lowercase__    :   Any						= 0.4_1_9_4_1_2_7
        lowercase__    :   List[Any]						= 0.3_5_3_7_5_5_8_6
        lowercase__    :   Optional[int]						= 0.5_6_3_8_5_0_2
        lowercase__    :   int						= 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'''

def       __UpperCamelCase	(       UpperCAmelCase					):
 if not all(x.isalpha() for x in string					):
  raise ValueError('''String must only contain alphabetic characters.'''					)

 lowercase__    :   Tuple						= sorted(string.lower()					)
 return len(UpperCAmelCase					) == len(set(UpperCAmelCase					)					)


if __name__ == "__main__":
  __a:			Union[str, Any]	       =       input("""Enter a string """).strip()

  __a:			Tuple	       =       is_isogram(input_str)
  print(F'{input_str} is {"an" if isogram else "not an"} isogram.')

