
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor







class 				lowercase		(					unittest.TestCase		):




							def __init__(				self   ,A__   ,A__=7   ,A__=3   ,A__=1_8   ,A__=3_0   ,A__=4_0_0   ,A__=True   ,A__=None   ,A__=True   ,A__=None   ,):
												lowercase							=						size if size is not None else {'''shortest_edge''': 2_0}
												lowercase							=						crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
												lowercase							=						parent
												lowercase							=						batch_size
												lowercase							=						num_channels
												lowercase							=						image_size
												lowercase							=						min_resolution
												lowercase							=						max_resolution
												lowercase							=						do_resize
												lowercase							=						size
												lowercase							=						do_center_crop
												lowercase							=						crop_size





							def  A__      (				self):
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												}







@require_torch
@require_vision
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Tuple       					=MobileNetVaImageProcessor if is_vision_available() else None




							def  A__      (				self):
												lowercase							=						MobileNetVaImageProcessingTester(self)




							@property
							def  A__      (				self):
												return self.image_processor_tester.prepare_image_processor_dict()




							def  A__      (				self):
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(A__   ,'''do_resize'''))
												self.assertTrue(hasattr(A__   ,'''size'''))
												self.assertTrue(hasattr(A__   ,'''do_center_crop'''))
												self.assertTrue(hasattr(A__   ,'''crop_size'''))




							def  A__      (				self):
												lowercase							=						self.image_processing_class.from_dict(self.image_processor_dict)
												self.assertEqual(image_processor.size   ,{'''shortest_edge''': 2_0})
												self.assertEqual(image_processor.crop_size   ,{'''height''': 1_8, '''width''': 1_8})

												lowercase							=						self.image_processing_class.from_dict(self.image_processor_dict   ,size=4_2   ,crop_size=8_4)
												self.assertEqual(image_processor.size   ,{'''shortest_edge''': 4_2})
												self.assertEqual(image_processor.crop_size   ,{'''height''': 8_4, '''width''': 8_4})




							def  A__      (				self):
												pass




							def  A__      (				self):
												# Initialize image_processing
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												lowercase							=						prepare_image_inputs(self.image_processor_tester   ,equal_resolution=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__   ,Image.Image)

												# Test not batched input
												lowercase							=						image_processing(image_inputs[0]   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)

												# Test batched
												lowercase							=						image_processing(A__   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)




							def  A__      (				self):
												# Initialize image_processing
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												# create random numpy tensors
												lowercase							=						prepare_image_inputs(self.image_processor_tester   ,equal_resolution=A__   ,numpify=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__   ,np.ndarray)

												# Test not batched input
												lowercase							=						image_processing(image_inputs[0]   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)

												# Test batched
												lowercase							=						image_processing(A__   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)





							def  A__      (				self):
												# Initialize image_processing
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												lowercase							=						prepare_image_inputs(self.image_processor_tester   ,equal_resolution=A__   ,torchify=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__   ,torch.Tensor)

												# Test not batched input
												lowercase							=						image_processing(image_inputs[0]   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)

												# Test batched
												lowercase							=						image_processing(A__   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Tuple       					=ShapEPipeline
							lowercase_       :       List[Any]       					=['''prompt''']
							lowercase_       :       int       					=['''prompt''']
							lowercase_       :       Union[str, Any]       					=[
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       Optional[int]       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




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




							@property
							def  A__      (				self):
												return 8




							@property
							def  A__      (				self):
												lowercase							=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
												return tokenizer




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						CLIPTextConfig(
												    bos_token_id=0   ,eos_token_id=2   ,hidden_size=self.text_embedder_hidden_size   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,)
												return CLIPTextModelWithProjection(A__)




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

												lowercase							=						{
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 1_6,
												    '''embedding_dim''': self.time_input_dim,
												    '''num_embeddings''': 3_2,
												    '''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''',
												    '''encoder_hid_proj_type''': None,
												    '''added_emb_type''': None,
												}

												lowercase							=						PriorTransformer(**A__)
												return model




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

												lowercase							=						{
												    '''param_shapes''': (
												        (self.renderer_dim, 9_3),
												        (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''': 1_2,
												    '''background''': (
												        0.1,
												        0.1,
												        0.1,
												    ),
												}
												lowercase							=						ShapERenderer(**A__)
												return model




							def  A__      (				self):
												lowercase							=						self.dummy_prior
												lowercase							=						self.dummy_text_encoder
												lowercase							=						self.dummy_tokenizer
												lowercase							=						self.dummy_renderer

												lowercase							=						HeunDiscreteScheduler(
												    beta_schedule='''exp'''   ,num_train_timesteps=1_0_2_4   ,prediction_type='''sample'''   ,use_karras_sigmas=A__   ,clip_sample=A__   ,clip_sample_range=1.0   ,)
												lowercase							=						{
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''frame_size''': 3_2,
												    '''output_type''': '''np''',
												}
												return inputs




							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=A__)

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

												assert image.shape == (2_0, 3_2, 3_2, 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  A__      (				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  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						True

												self._test_inference_batch_single_identical(
												    batch_size=2   ,test_max_difference=A__   ,relax_max_difference=A__   ,)





							def  A__      (				self):
												lowercase							=						self.get_dummy_components()
												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						1
												lowercase							=						2

												lowercase							=						self.get_dummy_inputs(A__)

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

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

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







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




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





							def  A__      (				self):
												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/shap_e/test_shap_e_np_out.npy''')
												lowercase							=						ShapEPipeline.from_pretrained('''openai/shap-e''')
												lowercase							=						pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

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

												lowercase							=						pipe(
												    '''a shark'''   ,generator=A__   ,guidance_scale=15.0   ,num_inference_steps=6_4   ,frame_size=6_4   ,output_type='''np'''   ,).images[0]

												assert images.shape == (2_0, 6_4, 6_4, 3)

												assert_mean_pixel_difference(A__   ,A__)




