


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 (							UpperCAmelCase       , unittest.TestCase     ):
		_a      :    List[Any]           =      ShapEImgaImgPipeline
		_a      :    Optional[Any]           =      ['image']
		_a      :    List[Any]           =      ['image']
		_a      :    Dict           =      [
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		_a      :    int           =      False
		@property
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									return 32
		@property
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									return 32
		@property
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									return self.time_input_dim * 4
		@property
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									return 8
		@property
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									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		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     CLIPImageProcessor(
									    crop_size=224     ,       do_center_crop=_SCREAMING_SNAKE_CASE     ,       do_normalize=_SCREAMING_SNAKE_CASE     ,       do_resize=_SCREAMING_SNAKE_CASE     ,       image_mean=[0.48145466, 0.4578275, 0.40821073]     ,       image_std=[0.26862954, 0.26130258, 0.27577711]     ,       resample=3     ,       size=224     ,       )

									return image_processor
		@property
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									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		)					->			Optional[Any]:




									"""simple docstring"""





									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		)					->			Any:




									"""simple docstring"""





									_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		)					->			Dict:




									"""simple docstring"""





									_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		)					->			Any:




									"""simple docstring"""





									_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(
									    [
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									    ]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									self._test_inference_batch_consistent(batch_sizes=[1, 2]		)
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_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		)					->			Union[str, Any]:




									"""simple docstring"""





									_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		)					->			Dict:




									"""simple docstring"""





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






		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_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		)




from typing import Any

def 			lowerCAmelCase__					(     a__:						list     ,    a__:						list     ,    a__:						dict     ,    a__:						dict     ,    a__:						dict     ,    )    ->						list:


							'''simple docstring'''





							_validation(
							    a__     ,    a__     ,    a__     ,    a__     ,    a__     ,    )
							# Creates data structures and fill initial step
							_UpperCAmelCase											=     {}
							_UpperCAmelCase											=     {}
							for state in states_space:
														_UpperCAmelCase											=     observations_space[0]
														_UpperCAmelCase											=     (
														    initial_probabilities[state] * emission_probabilities[state][observation]
														)
														_UpperCAmelCase											=     None

							# Fills the data structure with the probabilities of
							# different transitions and pointers to previous states
							for o in range(1     ,    len(a__			)			):
														_UpperCAmelCase											=     observations_space[o]
														_UpperCAmelCase											=     observations_space[o - 1]
														for state in states_space:
																					# Calculates the argmax for probability function
																					_UpperCAmelCase											=     ''
																					_UpperCAmelCase											=     -1
																					for k_state in states_space:
																												_UpperCAmelCase											=     (
																												    probabilities[(k_state, prior_observation)]
																												    * transition_probabilities[k_state][state]
																												    * emission_probabilities[state][observation]
																												)
																												if probability > max_probability:
																																			_UpperCAmelCase											=     probability
																																			_UpperCAmelCase											=     k_state

            # Update probabilities and pointers dicts
																					_UpperCAmelCase											=     (
																					    probabilities[(arg_max, prior_observation)]
																					    * transition_probabilities[arg_max][state]
																					    * emission_probabilities[state][observation]
																					)

																					_UpperCAmelCase											=     arg_max

    # The final observation
							_UpperCAmelCase											=     observations_space[len(a__			) - 1]

							# argmax for given final observation
							_UpperCAmelCase											=     ''
							_UpperCAmelCase											=     -1
							for k_state in states_space:
														_UpperCAmelCase											=     probabilities[(k_state, final_observation)]
														if probability > max_probability:
																					_UpperCAmelCase											=     probability
																					_UpperCAmelCase											=     k_state
							_UpperCAmelCase											=     arg_max

							# Process pointers backwards
							_UpperCAmelCase											=     last_state
							_UpperCAmelCase											=     []
							for o in range(len(a__			) - 1     ,    -1     ,    -1			):
														result.append(a__			)
														_UpperCAmelCase											=     pointers[previous, observations_space[o]]
							result.reverse()

							return result

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





							_validate_not_empty(
							    a__     ,    a__     ,    a__     ,    a__     ,    a__     ,    )
							_validate_lists(a__     ,    a__			)
							_validate_dicts(
							    a__     ,    a__     ,    a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





							if not all(
							    [
							        observations_space,
							        states_space,
							        initial_probabilities,
							        transition_probabilities,
							        emission_probabilities,
							    ]			):
														raise ValueError('There\'s an empty parameter'			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any			)    ->						None:


							'''simple docstring'''





							_validate_list(a__     ,    'observations_space'			)
							_validate_list(a__     ,    'states_space'			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str			)    ->						None:


							'''simple docstring'''





							if not isinstance(_object     ,    a__			):
														_UpperCAmelCase											=     F'''{var_name} must be a list'''
														raise ValueError(a__			)
							else:
														for x in _object:
																					if not isinstance(a__     ,    a__			):
																												_UpperCAmelCase											=     F'''{var_name} must be a list of strings'''
																												raise ValueError(a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						Any     ,    )    ->						None:


							'''simple docstring'''





							_validate_dict(a__     ,    'initial_probabilities'     ,    a__			)
							_validate_nested_dict(a__     ,    'transition_probabilities'			)
							_validate_nested_dict(a__     ,    'emission_probabilities'			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str			)    ->						None:


							'''simple docstring'''





							_validate_dict(_object     ,    a__     ,    a__			)
							for x in _object.values():
														_validate_dict(a__     ,    a__     ,    a__     ,    a__			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						str     ,    a__:						type     ,    a__:						bool = False			)    ->						None:


							'''simple docstring'''





							if not isinstance(_object     ,    a__			):
														_UpperCAmelCase											=     F'''{var_name} must be a dict'''
														raise ValueError(a__			)
							if not all(isinstance(a__     ,    a__			) for x in _object			):
														_UpperCAmelCase											=     F'''{var_name} all keys must be strings'''
														raise ValueError(a__			)
							if not all(isinstance(a__     ,    a__			) for x in _object.values()			):
														_UpperCAmelCase											=     'nested dictionary ' if nested else ''
														_UpperCAmelCase											=     F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
														raise ValueError(a__			)


if __name__ == "__main__":
		from doctest import testmod

		testmod()
