




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				(		__lowercase       ,     unittest.TestCase     ):
		__a					    =       ShapEImgaImgPipeline
		__a					    =       ["""image"""]
		__a					    =       ["""image"""]
		__a					    =       [
		    """num_images_per_prompt""",
		    """num_inference_steps""",
		    """generator""",
		    """latents""",
		    """guidance_scale""",
		    """frame_size""",
		    """output_type""",
		    """return_dict""",
		]
		__a					    =       False






		@property
		def 				UpperCAmelCase (    self			):
								return 32






		@property
		def 				UpperCAmelCase (    self			):
								return 32






		@property
		def 				UpperCAmelCase (    self			):
								return self.time_input_dim * 4






		@property
		def 				UpperCAmelCase (    self			):
								return 8






		@property
		def 				UpperCAmelCase (    self			):
								torch.manual_seed(0			)
								_UpperCAmelCase      =			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						,		)

								_UpperCAmelCase      =			CLIPVisionModel(_SCREAMING_SNAKE_CASE			)
								return model






		@property
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			CLIPImageProcessor(
								    crop_size=224						,		do_center_crop=_SCREAMING_SNAKE_CASE						,		do_normalize=_SCREAMING_SNAKE_CASE						,		do_resize=_SCREAMING_SNAKE_CASE						,		image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]						,		image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,		resample=3						,		size=224						,		)

								return image_processor






		@property
		def 				UpperCAmelCase (    self			):
								torch.manual_seed(0			)

								_UpperCAmelCase      =			{
								    """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,
								}

								_UpperCAmelCase      =			PriorTransformer(**_SCREAMING_SNAKE_CASE			)
								return model






		@property
		def 				UpperCAmelCase (    self			):
								torch.manual_seed(0			)

								_UpperCAmelCase      =			{
								    """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,
								    ),
								}
								_UpperCAmelCase      =			ShapERenderer(**_SCREAMING_SNAKE_CASE			)
								return model






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.dummy_prior
								_UpperCAmelCase      =			self.dummy_image_encoder
								_UpperCAmelCase      =			self.dummy_image_processor
								_UpperCAmelCase      =			self.dummy_renderer

								_UpperCAmelCase      =			HeunDiscreteScheduler(
								    beta_schedule="""exp"""						,		num_train_timesteps=1024						,		prediction_type="""sample"""						,		use_karras_sigmas=_SCREAMING_SNAKE_CASE						,		clip_sample=_SCREAMING_SNAKE_CASE						,		clip_sample_range=1.0						,		)
								_UpperCAmelCase      =			{
								    """prior""": prior,
								    """image_encoder""": image_encoder,
								    """image_processor""": image_processor,
								    """renderer""": renderer,
								    """scheduler""": scheduler,
								}

								return components






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=0			):
								_UpperCAmelCase      =			floats_tensor((1, 3, 64, 64)						,		rng=random.Random(_SCREAMING_SNAKE_CASE			)			).to(_SCREAMING_SNAKE_CASE			)

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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			"""cpu"""

								_UpperCAmelCase      =			self.get_dummy_components()

								_UpperCAmelCase      =			self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			pipe.to(_SCREAMING_SNAKE_CASE			)

								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

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

								_UpperCAmelCase      =			np.array(
								    [
								        0.0003_9216,
								        0.0003_9216,
								        0.0003_9216,
								        0.0003_9216,
								        0.0003_9216,
								        0.0003_9216,
								        0.0003_9216,
								        0.0003_9216,
								        0.0003_9216,
								    ]			)

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






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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			torch_device == """cpu"""
								_UpperCAmelCase      =			True
								self._test_inference_batch_single_identical(
								    batch_size=2						,		test_max_difference=_SCREAMING_SNAKE_CASE						,		relax_max_difference=_SCREAMING_SNAKE_CASE						,		)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.get_dummy_components()
								_UpperCAmelCase      =			self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			pipe.to(_SCREAMING_SNAKE_CASE			)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			1
								_UpperCAmelCase      =			2

								_UpperCAmelCase      =			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)

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

								_UpperCAmelCase      =			pipe(**_SCREAMING_SNAKE_CASE						,		num_images_per_prompt=_SCREAMING_SNAKE_CASE			)[0]

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




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






		def 				UpperCAmelCase (    self			):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""			)
								_UpperCAmelCase      =			load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/shap_e/test_shap_e_img2img_out.npy"""			)
								_UpperCAmelCase      =			ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""			)
								_UpperCAmelCase      =			pipe.to(_SCREAMING_SNAKE_CASE			)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(0			)

								_UpperCAmelCase      =			pipe(
								    _SCREAMING_SNAKE_CASE						,		generator=_SCREAMING_SNAKE_CASE						,		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(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)





import itertools
import string
from collections.abc import Generator, Iterable




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Generator[tuple[str, ...], None, None]:
						_UpperCAmelCase      =			iter(snake_case       )
						while True:
												_UpperCAmelCase      =			tuple(itertools.islice(snake_case			,     snake_case       )       )
												if not chunk:
																		return
												yield chunk




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				str:
						_UpperCAmelCase      =			"""""".join([c.upper() for c in dirty if c in string.ascii_letters]       )
						_UpperCAmelCase      =			""""""

						if len(snake_case       ) < 2:
												return dirty

						for i in range(len(snake_case       ) - 1       ):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(snake_case       ) & 1:
												clean += "X"

						return clean




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				list[str]:
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						_UpperCAmelCase      =			"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						_UpperCAmelCase      =			[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(snake_case       )

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(snake_case       )

						return table




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			generate_table(snake_case       )
						_UpperCAmelCase      =			prepare_input(snake_case       )
						_UpperCAmelCase      =			""""""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(snake_case			,     2       ):
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			generate_table(snake_case       )
						_UpperCAmelCase      =			""""""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(snake_case			,     2       ):
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext