
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      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  ShapEImgaImgPipeline
				lowercase			    =  ['image']
				lowercase			    =  ['image']
				lowercase			    =  [
				    'num_images_per_prompt',
				    'num_inference_steps',
				    'generator',
				    'latents',
				    'guidance_scale',
				    'frame_size',
				    'output_type',
				    'return_dict',
				]
				lowercase			    =  False






				@property
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							return 32






				@property
				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							return 32






				@property
				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							return self.time_input_dim * 4






				@property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return 8






				@property
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							torch.manual_seed(0   )
							lowerCAmelCase__      :      Optional[Any]         =		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    ,     )

							lowerCAmelCase__      :      Any         =		CLIPVisionModel(a   )
							return model






				@property
				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		CLIPImageProcessor(
							    crop_size=224    ,     do_center_crop=a    ,     do_normalize=a    ,     do_resize=a    ,     image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,     image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,     resample=3    ,     size=224    ,     )

							return image_processor






				@property
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							torch.manual_seed(0   )

							lowerCAmelCase__      :      int         =		{
							    '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,
							}

							lowerCAmelCase__      :      Tuple         =		PriorTransformer(**a   )
							return model






				@property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							torch.manual_seed(0   )

							lowerCAmelCase__      :      List[str]         =		{
							    '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,
							    ),
							}
							lowerCAmelCase__      :      List[Any]         =		ShapERenderer(**a   )
							return model






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		self.dummy_prior
							lowerCAmelCase__      :      List[str]         =		self.dummy_image_encoder
							lowerCAmelCase__      :      int         =		self.dummy_image_processor
							lowerCAmelCase__      :      Tuple         =		self.dummy_renderer

							lowerCAmelCase__      :      int         =		HeunDiscreteScheduler(
							    beta_schedule='exp'    ,     num_train_timesteps=1_024    ,     prediction_type='sample'    ,     use_karras_sigmas=a    ,     clip_sample=a    ,     clip_sample_range=1.0    ,     )
							lowerCAmelCase__      :      List[Any]         =		{
							    'prior': prior,
							    'image_encoder': image_encoder,
							    'image_processor': image_processor,
							    'renderer': renderer,
							    'scheduler': scheduler,
							}

							return components






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      Union[str, Any]    ,     a					:      Optional[Any]=0   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		floats_tensor((1, 3, 64, 64)    ,     rng=random.Random(a   )   ).to(a   )

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






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		'cpu'

							lowerCAmelCase__      :      str         =		self.get_dummy_components()

							lowerCAmelCase__      :      Optional[Any]         =		self.pipeline_class(**a   )
							lowerCAmelCase__      :      int         =		pipe.to(a   )

							pipe.set_progress_bar_config(disable=a   )

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

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

							lowerCAmelCase__      :      Tuple         =		np.array(
							    [
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							        0.0_0_0_3_9_2_1_6,
							    ]   )

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






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							self._test_inference_batch_consistent(batch_sizes=[1, 2]   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		torch_device == 'cpu'
							lowerCAmelCase__      :      List[Any]         =		True
							self._test_inference_batch_single_identical(
							    batch_size=2    ,     test_max_difference=a    ,     relax_max_difference=a    ,     )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		self.get_dummy_components()
							lowerCAmelCase__      :      Union[str, Any]         =		self.pipeline_class(**a   )
							lowerCAmelCase__      :      int         =		pipe.to(a   )
							pipe.set_progress_bar_config(disable=a   )

							lowerCAmelCase__      :      Optional[int]         =		1
							lowerCAmelCase__      :      Union[str, Any]         =		2

							lowerCAmelCase__      :      int         =		self.get_dummy_inputs(a   )

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

							lowerCAmelCase__      :      Optional[Any]         =		pipe(**a    ,     num_images_per_prompt=a   )[0]

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







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






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'   )
							lowerCAmelCase__      :      Any         =		load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/shap_e/test_shap_e_img2img_out.npy'   )
							lowerCAmelCase__      :      Tuple         =		ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'   )
							lowerCAmelCase__      :      Optional[int]         =		pipe.to(a   )
							pipe.set_progress_bar_config(disable=a   )

							lowerCAmelCase__      :      Any         =		torch.Generator(device=a   ).manual_seed(0   )

							lowerCAmelCase__      :      Any         =		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   )

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct







class      A__							:






				def __init__(						self					:      Dict    ,     a					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		data
							lowerCAmelCase__      :      List[Any]         =		[0X67_452_301, 0Xef_cda_b89, 0X98_bad_cfe, 0X10_325_476, 0Xc3_d2e_1f0]






				@staticmethod
				def 							_lowerCamelCase		(						a					:      int    ,     a					:      List[str]   ):






							'''simple docstring'''







							return ((n << b) | (n >> (32 - b))) & 0Xff_fff_fff






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		b'\x80' + b'\x00' * (63 - (len(self.data   ) + 8) % 64)
							lowerCAmelCase__      :      List[str]         =		self.data + padding + struct.pack('>Q'    ,     8 * len(self.data   )   )
							return padded_data






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							return [
							    self.padded_data[i : i + 64] for i in range(0    ,     len(self.padded_data   )    ,     64   )
							]






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		list(struct.unpack('>16L'    ,     a   )   ) + [0] * 64
							for i in range(16    ,     80   ):
										lowerCAmelCase__      :      int         =		self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])    ,     1   )
							return w






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.padding()
							lowerCAmelCase__      :      List[Any]         =		self.split_blocks()
							for block in self.blocks:
										lowerCAmelCase__      :      str         =		self.expand_block(a   )
										lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		self.h
										for i in range(0    ,     80   ):
													if 0 <= i < 20:
																lowerCAmelCase__      :      Tuple         =		(b & c) | ((~b) & d)
																lowerCAmelCase__      :      int         =		0X5a_827_999
													elif 20 <= i < 40:
																lowerCAmelCase__      :      List[str]         =		b ^ c ^ d
																lowerCAmelCase__      :      Any         =		0X6e_d9e_ba1
													elif 40 <= i < 60:
																lowerCAmelCase__      :      Tuple         =		(b & c) | (b & d) | (c & d)
																lowerCAmelCase__      :      Tuple         =		0X8f_1bb_cdc
													elif 60 <= i < 80:
																lowerCAmelCase__      :      List[Any]         =		b ^ c ^ d
																lowerCAmelCase__      :      int         =		0Xca_62c_1d6
													lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      :      Dict         =		(
													    self.rotate(a    ,     5   ) + f + e + k + expanded_block[i] & 0Xff_fff_fff,
													    a,
													    self.rotate(a    ,     30   ),
													    c,
													    d,
													)
										lowerCAmelCase__      :      Optional[Any]         =		(
										    self.h[0] + a & 0Xff_fff_fff,
										    self.h[1] + b & 0Xff_fff_fff,
										    self.h[2] + c & 0Xff_fff_fff,
										    self.h[3] + d & 0Xff_fff_fff,
										    self.h[4] + e & 0Xff_fff_fff,
										)
							return ("{:08x}" * 5).format(*self.h   )






def  lowerCAmelCase__			(  )				->		Tuple:
			lowerCAmelCase__      :      Optional[int]         =		b'Test String'
			assert SHAaHash(SCREAMING_SNAKE_CASE_     ).final_hash() == hashlib.shaa(SCREAMING_SNAKE_CASE_     ).hexdigest()  # noqa: S324
def  lowerCAmelCase__			(  )				->		Any:
			lowerCAmelCase__      :      str         =		argparse.ArgumentParser(description='Process some strings or files'     )
			parser.add_argument(
			    '--string'       ,							dest='input_string'       ,							default='Hello World!! Welcome to Cryptography'       ,							help='Hash the string'       ,							)
			parser.add_argument('--file'       ,							dest='input_file'       ,							help='Hash contents of a file'     )
			lowerCAmelCase__      :      Dict         =		parser.parse_args()
			lowerCAmelCase__      :      Tuple         =		args.input_string
			# In any case hash input should be a bytestring
			if args.input_file:
						with open(args.input_file       ,							'rb'     ) as f:
									lowerCAmelCase__      :      List[Any]         =		f.read()
			else:
						lowerCAmelCase__      :      Tuple         =		bytes(SCREAMING_SNAKE_CASE_       ,							'utf-8'     )
			print(SHAaHash(SCREAMING_SNAKE_CASE_     ).final_hash()     )


if __name__ == "__main__":
					main()
					import doctest

					doctest.testmod()