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      __lowercase					(    _A       ,     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        __a		(	self				:      int   )							->		Union[str, Any]:


									'''simple docstring'''

									return 32

						@property
						def        __a		(	self				:      str   )							->		Union[str, Any]:


									'''simple docstring'''

									return 32

						@property
						def        __a		(	self				:      List[Any]   )							->		Any:


									'''simple docstring'''

									return self.time_input_dim * 4

						@property
						def        __a		(	self				:      Union[str, Any]   )							->		Dict:


									'''simple docstring'''

									return 8

						@property
						def        __a		(	self				:      List[str]   )							->		Dict:


									'''simple docstring'''

									torch.manual_seed(0   )
									lowercase							=		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							,							)

									lowercase							=		CLIPVisionModel(__lowerCamelCase   )
									return model

						@property
						def        __a		(	self				:      str   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		CLIPImageProcessor(
									    crop_size=2_24							,							do_center_crop=__lowerCamelCase							,							do_normalize=__lowerCamelCase							,							do_resize=__lowerCamelCase							,							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=2_24							,							)

									return image_processor

						@property
						def        __a		(	self				:      List[str]   )							->		Any:


									'''simple docstring'''

									torch.manual_seed(0   )

									lowercase							=		{
									    '''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,
									}

									lowercase							=		PriorTransformer(**__lowerCamelCase   )
									return model

						@property
						def        __a		(	self				:      str   )							->		int:


									'''simple docstring'''

									torch.manual_seed(0   )

									lowercase							=		{
									    '''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,
									    ),
									}
									lowercase							=		ShapERenderer(**__lowerCamelCase   )
									return model

						def        __a		(	self				:      Any   )							->		int:


									'''simple docstring'''

									lowercase							=		self.dummy_prior
									lowercase							=		self.dummy_image_encoder
									lowercase							=		self.dummy_image_processor
									lowercase							=		self.dummy_renderer

									lowercase							=		HeunDiscreteScheduler(
									    beta_schedule='''exp'''							,							num_train_timesteps=10_24							,							prediction_type='''sample'''							,							use_karras_sigmas=__lowerCamelCase							,							clip_sample=__lowerCamelCase							,							clip_sample_range=1.0							,							)
									lowercase							=		{
									    '''prior''': prior,
									    '''image_encoder''': image_encoder,
									    '''image_processor''': image_processor,
									    '''renderer''': renderer,
									    '''scheduler''': scheduler,
									}

									return components

						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Any=0   )							->		int:


									'''simple docstring'''

									lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(__lowerCamelCase   )   ).to(__lowerCamelCase   )

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

						def        __a		(	self				:      Any   )							->		Any:


									'''simple docstring'''

									lowercase							=		'''cpu'''

									lowercase							=		self.get_dummy_components()

									lowercase							=		self.pipeline_class(**__lowerCamelCase   )
									lowercase							=		pipe.to(__lowerCamelCase   )

									pipe.set_progress_bar_config(disable=__lowerCamelCase   )

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

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

									lowercase							=		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        __a		(	self				:      List[Any]   )							->		List[Any]:


									'''simple docstring'''

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

						def        __a		(	self				:      int   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		torch_device == '''cpu'''
									lowercase							=		True
									self._test_inference_batch_single_identical(
									    batch_size=2							,							test_max_difference=__lowerCamelCase							,							relax_max_difference=__lowerCamelCase							,							)



						def        __a		(	self				:      Optional[int]   )							->		str:


									'''simple docstring'''

									lowercase							=		self.get_dummy_components()
									lowercase							=		self.pipeline_class(**__lowerCamelCase   )
									lowercase							=		pipe.to(__lowerCamelCase   )
									pipe.set_progress_bar_config(disable=__lowerCamelCase   )

									lowercase							=		1
									lowercase							=		2

									lowercase							=		self.get_dummy_inputs(__lowerCamelCase   )

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

									lowercase							=		pipe(**__lowerCamelCase							,							num_images_per_prompt=__lowerCamelCase   )[0]

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






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

						def        __a		(	self				:      List[str]   )							->		Any:


									'''simple docstring'''

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



						def        __a		(	self				:      Tuple   )							->		int:


									'''simple docstring'''

									lowercase							=		load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''   )
									lowercase							=		load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/shap_e/test_shap_e_img2img_out.npy'''   )
									lowercase							=		ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''   )
									lowercase							=		pipe.to(__lowerCamelCase   )
									pipe.set_progress_bar_config(disable=__lowerCamelCase   )

									lowercase							=		torch.Generator(device=__lowerCamelCase   ).manual_seed(0   )

									lowercase							=		pipe(
									    __lowerCamelCase							,							generator=__lowerCamelCase							,							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(__lowerCamelCase							,							__lowerCamelCase   )






from __future__ import annotations

from collections.abc import MutableSequence






class      __lowercase					:

						def __init__(	self				:      Optional[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      MutableSequence[float]   )							->		None:


									'''simple docstring'''

									if len(__lowerCamelCase   ) != degree + 1:
												raise ValueError(
												    '''The number of coefficients should be equal to the degree + 1.'''   )

									lowercase							=		list(__lowerCamelCase   )
									lowercase							=		degree

						def __add__(	self				:      Any							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									if self.degree > polynomial_a.degree:
												lowercase							=		self.coefficients[:]
												for i in range(polynomial_a.degree + 1   ):
															coefficients[i] += polynomial_a.coefficients[i]
												return Polynomial(self.degree							,							__lowerCamelCase   )
									else:
												lowercase							=		polynomial_a.coefficients[:]
												for i in range(self.degree + 1   ):
															coefficients[i] += self.coefficients[i]
												return Polynomial(polynomial_a.degree							,							__lowerCamelCase   )

						def __sub__(	self				:      str							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									return self + polynomial_a * Polynomial(0							,							[-1]   )

						def __neg__(	self				:      str   )							->		Polynomial:


									'''simple docstring'''

									return Polynomial(self.degree							,							[-c for c in self.coefficients]   )

						def __mul__(	self				:      List[str]							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									lowercase							=		[0] * (self.degree + polynomial_a.degree + 1)
									for i in range(self.degree + 1   ):
												for j in range(polynomial_a.degree + 1   ):
															coefficients[i + j] += (
															    self.coefficients[i] * polynomial_a.coefficients[j]
															)

									return Polynomial(self.degree + polynomial_a.degree							,							__lowerCamelCase   )

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      int | float   )							->		int | float:


									'''simple docstring'''

									lowercase							=		0
									for i in range(self.degree + 1   ):
												result += self.coefficients[i] * (substitution**i)
									return result

						def __str__(	self				:      str   )							->		str:


									'''simple docstring'''

									lowercase							=		''''''
									for i in range(self.degree							,							-1							,							-1   ):
												if self.coefficients[i] == 0:
															continue
												elif self.coefficients[i] > 0:
															if polynomial:
																		polynomial += " + "
												else:
															polynomial += " - "

												if i == 0:
															polynomial += str(abs(self.coefficients[i]   )   )
												elif i == 1:
															polynomial += str(abs(self.coefficients[i]   )   ) + "x"
												else:
															polynomial += str(abs(self.coefficients[i]   )   ) + "x^" + str(__lowerCamelCase   )

									return polynomial

						def __repr__(	self				:      Tuple   )							->		str:


									'''simple docstring'''

									return self.__str__()

						def        __a		(	self				:      Union[str, Any]   )							->		Polynomial:


									'''simple docstring'''

									lowercase							=		[0] * self.degree
									for i in range(self.degree   ):
												lowercase							=		self.coefficients[i + 1] * (i + 1)
									return Polynomial(self.degree - 1							,							__lowerCamelCase   )

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      int | float = 0   )							->		Polynomial:


									'''simple docstring'''

									lowercase							=		[0] * (self.degree + 2)
									lowercase							=		constant
									for i in range(self.degree + 1   ):
												lowercase							=		self.coefficients[i] / (i + 1)
									return Polynomial(self.degree + 1							,							__lowerCamelCase   )

						def __eq__(	self				:      Tuple							,							__lowerCamelCase				:      object   )							->		bool:


									'''simple docstring'''

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return False

									if self.degree != polynomial_a.degree:
												return False

									for i in range(self.degree + 1   ):
												if self.coefficients[i] != polynomial_a.coefficients[i]:
															return False

									return True



						def __ne__(	self				:      Tuple							,							__lowerCamelCase				:      object   )							->		bool:


									'''simple docstring'''

									return not self.__eq__(__lowerCamelCase   )





