






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    Any			     =							ShapEImgaImgPipeline
			_lowercase		:    Tuple			     =							['''image''']
			_lowercase		:    Optional[Any]			     =							['''image''']
			_lowercase		:    List[str]			     =							[
			    '''num_images_per_prompt''',
			    '''num_inference_steps''',
			    '''generator''',
			    '''latents''',
			    '''guidance_scale''',
			    '''frame_size''',
			    '''output_type''',
			    '''return_dict''',
			]
			_lowercase		:    Optional[Any]			     =							False






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						return 32






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						return 32






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						return self.time_input_dim * 4






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						return 8






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size		,					image_size=64		,					projection_dim=self.text_embedder_hidden_size		,					intermediate_size=37		,					num_attention_heads=4		,					num_channels=3		,					num_hidden_layers=5		,					patch_size=1		,					)

						SCREAMING_SNAKE_CASE						= CLIPVisionModel(a)
						return model






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= CLIPImageProcessor(
						    crop_size=224		,					do_center_crop=a		,					do_normalize=a		,					do_resize=a		,					image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]		,					image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]		,					resample=3		,					size=224		,					)

						return image_processor






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						torch.manual_seed(0)

						SCREAMING_SNAKE_CASE						= {
						    'num_attention_heads': 2,
						    'attention_head_dim': 16,
						    'embedding_dim': self.time_input_dim,
						    'num_embeddings': 32,
						    'embedding_proj_dim': self.text_embedder_hidden_size,
						    'time_embed_dim': self.time_embed_dim,
						    'num_layers': 1,
						    'clip_embed_dim': self.time_input_dim * 2,
						    'additional_embeddings': 0,
						    'time_embed_act_fn': 'gelu',
						    'norm_in_type': 'layer',
						    'embedding_proj_norm_type': 'layer',
						    'encoder_hid_proj_type': None,
						    'added_emb_type': None,
						}

						SCREAMING_SNAKE_CASE						= PriorTransformer(**a)
						return model






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						torch.manual_seed(0)

						SCREAMING_SNAKE_CASE						= {
						    'param_shapes': (
						        (self.renderer_dim, 93),
						        (self.renderer_dim, 8),
						        (self.renderer_dim, 8),
						        (self.renderer_dim, 8),
						    ),
						    'd_latent': self.time_input_dim,
						    'd_hidden': self.renderer_dim,
						    'n_output': 12,
						    'background': (
						        0.1,
						        0.1,
						        0.1,
						    ),
						}
						SCREAMING_SNAKE_CASE						= ShapERenderer(**a)
						return model






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= self.dummy_prior
						SCREAMING_SNAKE_CASE						= self.dummy_image_encoder
						SCREAMING_SNAKE_CASE						= self.dummy_image_processor
						SCREAMING_SNAKE_CASE						= self.dummy_renderer

						SCREAMING_SNAKE_CASE						= HeunDiscreteScheduler(
						    beta_schedule='exp'		,					num_train_timesteps=1024		,					prediction_type='sample'		,					use_karras_sigmas=a		,					clip_sample=a		,					clip_sample_range=1.0		,					)
						SCREAMING_SNAKE_CASE						= {
						    'prior': prior,
						    'image_encoder': image_encoder,
						    'image_processor': image_processor,
						    'renderer': renderer,
						    'scheduler': scheduler,
						}

						return components






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=0)      ->       Any:
						SCREAMING_SNAKE_CASE						= floats_tensor((1, 3, 64, 64)		,					rng=random.Random(a)).to(a)

						if str(a).startswith('mps'):
									SCREAMING_SNAKE_CASE						= torch.manual_seed(a)
						else:
									SCREAMING_SNAKE_CASE						= torch.Generator(device=a).manual_seed(a)
						SCREAMING_SNAKE_CASE						= {
						    'image': input_image,
						    'generator': generator,
						    'num_inference_steps': 1,
						    'frame_size': 32,
						    'output_type': 'np',
						}
						return inputs






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE						= 'cpu'

						SCREAMING_SNAKE_CASE						= self.get_dummy_components()

						SCREAMING_SNAKE_CASE						= self.pipeline_class(**a)
						SCREAMING_SNAKE_CASE						= pipe.to(a)

						pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE						= pipe(**self.get_dummy_inputs(a))
						SCREAMING_SNAKE_CASE						= output.images[0]
						SCREAMING_SNAKE_CASE						= image[0, -3:, -3:, -1]

						assert image.shape == (20, 32, 32, 3)

						SCREAMING_SNAKE_CASE						= np.array(
						    [
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						    ])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
						self._test_inference_batch_consistent(batch_sizes=[1, 2])






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= torch_device == 'cpu'
						SCREAMING_SNAKE_CASE						= True
						self._test_inference_batch_single_identical(
						    batch_size=2		,					test_max_difference=a		,					relax_max_difference=a		,					)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.get_dummy_components()
						SCREAMING_SNAKE_CASE						= self.pipeline_class(**a)
						SCREAMING_SNAKE_CASE						= pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE						= 1
						SCREAMING_SNAKE_CASE						= 2

						SCREAMING_SNAKE_CASE						= self.get_dummy_inputs(a)

						for key in inputs.keys():
									if key in self.batch_params:
												SCREAMING_SNAKE_CASE						= batch_size * [inputs[key]]

						SCREAMING_SNAKE_CASE						= pipe(**a		,					num_images_per_prompt=a)[0]

						assert images.shape[0] == batch_size * num_images_per_prompt



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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png')
						SCREAMING_SNAKE_CASE						= load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/shap_e/test_shap_e_img2img_out.npy')
						SCREAMING_SNAKE_CASE						= ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img')
						SCREAMING_SNAKE_CASE						= pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE						= torch.Generator(device=a).manual_seed(0)

						SCREAMING_SNAKE_CASE						= pipe(
						    a		,					generator=a		,					guidance_scale=3.0		,					num_inference_steps=64		,					frame_size=64		,					output_type='np'		,					).images[0]

						assert images.shape == (20, 64, 64, 3)

						assert_mean_pixel_difference(a		,					a)












a_		:						Tuple             =    {
    'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
    'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
    'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
    'V': '...-', 'W': '.--', 'X': '-..-', 'Y': '-.--', 'Z': '--..', '1': '.----',
    '2': '..---', '3': '...--', '4': '....-', '5': '.....', '6': '-....', '7': '--...',
    '8': '---..', '9': '----.', '0': '-----', '&': '.-...', '@': '.--.-.',
    ':': '---...', ',': '--..--', '.': '.-.-.-', '\'': '.----.', '"': '.-..-.',
    '?': '..--..', '/': '-..-.', '=': '-...-', '+': '.-.-.', '-': '-....-',
    '(': '-.--.', ')': '-.--.-', '!': '-.-.--', ' ': '/'
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
a_		:						List[Any]             =    {value: key for key, value in MORSE_CODE_DICT.items()}






def 				lowerCamelCase__				(_UpperCAmelCase):
			return " ".join(MORSE_CODE_DICT[char] for char in message.upper())






def 				lowerCamelCase__				(_UpperCAmelCase):
			return "".join(REVERSE_DICT[char] for char in message.split())






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= 'Morse code here!'
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= encrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= decrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)


if __name__ == "__main__":
				main()




