





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_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		ShapEImgaImgPipeline
	__UpperCamelCase   =		['''image''']
	__UpperCamelCase   =		['''image''']
	__UpperCamelCase   =		[
	    '''num_images_per_prompt''',
	    '''num_inference_steps''',
	    '''generator''',
	    '''latents''',
	    '''guidance_scale''',
	    '''frame_size''',
	    '''output_type''',
	    '''return_dict''',
	]
	__UpperCamelCase   =		False




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

			'''simple docstring'''




			return 32




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

			'''simple docstring'''




			return 32




	@property
	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Optional[Any]:

			'''simple docstring'''




			return self.time_input_dim * 4




	@property
	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> List[Any]:

			'''simple docstring'''




			return 8




	@property
	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> Union[str, Any]:

			'''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    UpperCAmelCase			(						self							:     Optional[int]   )       -> str:

			'''simple docstring'''




			__lowercase											=     CLIPImageProcessor(
			    crop_size=224					,      do_center_crop=__lowerCamelCase					,      do_normalize=__lowerCamelCase					,      do_resize=__lowerCamelCase					,      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							:     Optional[int]   )       -> List[str]:

			'''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    UpperCAmelCase			(						self							:     str   )       -> Union[str, Any]:

			'''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    UpperCAmelCase			(						self							:     str   )       -> Tuple:

			'''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=1_024					,      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    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     Dict=0   )       -> Any:

			'''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    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Optional[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(
			    [
			        0.00039216,
			        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[Any]   )       -> Tuple:

			'''simple docstring'''




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




	def    UpperCAmelCase			(						self							:     List[str]   )       -> Union[str, Any]:

			'''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    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> Dict:

			'''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 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




	def    UpperCAmelCase			(						self							:     str   )       -> Dict:

			'''simple docstring'''




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






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

			'''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   )











import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Optional[int]:
		return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     _TestCommandArgs(dataset=snake_case						,   all_configs=snake_case						,   save_infos=snake_case							)
		__lowercase											=     TestCommand(*snake_case							)
		test_command.run()
		__lowercase											=     os.path.join(snake_case						,   'README.md'							)
		assert os.path.exists(snake_case							)
		__lowercase											=     DatasetInfosDict.from_directory(snake_case							)
		__lowercase											=     DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'							)							),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']							)							),
		                    'langs': Sequence(Value('string'							)							),
		                    'spans': Sequence(Value('string'							)							),
		                }							)						,   splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_351_563,
		                    'num_examples': 10_000,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 238_418,
		                    'num_examples': 1_000,
		                },
		            ]						,   download_size=3_940_680						,   dataset_size=2_589_981						,   )
		    }							)
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				__lowercase						,      __lowercase											=     getattr(dataset_infos['default']						,   snake_case							), getattr(expected_dataset_infos['default']						,   snake_case							)
				if key == "num_bytes":
						assert is_apercent_close(snake_case						,   snake_case							)
				elif key == "splits":
						assert list(snake_case							) == list(snake_case							)
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,   expected[split].num_bytes							)
				else:
						result == expected




