
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	(      SCREAMING_SNAKE_CASE__ ,						unittest.TestCase	):
							lowercase_ : List[str]       =ShapEImgaImgPipeline
							lowercase_ : str       =['''image''']
							lowercase_ : Any       =['''image''']
							lowercase_ : Union[str, Any]       =[
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_ : Union[str, Any]       =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):
												torch.manual_seed(0)
												lowercase					=		CLIPVisionConfig(
												    hidden_size=self.text_embedder_hidden_size     ,image_size=6_4     ,projection_dim=self.text_embedder_hidden_size     ,intermediate_size=3_7     ,num_attention_heads=4     ,num_channels=3     ,num_hidden_layers=5     ,patch_size=1     ,)

												lowercase					=		CLIPVisionModel(A__)
												return model




							@property
							def 				A__   (       self):
												lowercase					=		CLIPImageProcessor(
												    crop_size=2_2_4     ,do_center_crop=A__     ,do_normalize=A__     ,do_resize=A__     ,image_mean=[0.48145466, 0.4578275, 0.40821073]     ,image_std=[0.26862954, 0.26130258, 0.27577711]     ,resample=3     ,size=2_2_4     ,)

												return image_processor




							@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''',
												    '''embedding_proj_norm_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_image_encoder
												lowercase					=		self.dummy_image_processor
												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,
												    '''image_encoder''': image_encoder,
												    '''image_processor''': image_processor,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components




							def 				A__   (       self     ,A__     ,A__=0):
												lowercase					=		floats_tensor((1, 3, 6_4, 6_4)     ,rng=random.Random(A__)).to(A__)

												if str(A__).startswith('''mps'''):
																	lowercase					=		torch.manual_seed(A__)
												else:
																	lowercase					=		torch.Generator(device=A__).manual_seed(A__)
												lowercase					=		{
												    '''image''': input_image,
												    '''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_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(A__)
												pipe.set_progress_bar_config(disable=A__)

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

												lowercase					=		pipe(
												    A__     ,generator=A__     ,guidance_scale=3.0     ,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__)






import logging

from transformers import PretrainedConfig


lowercase__	:int										=					logging.getLogger(__name__)


lowercase__	:Dict										=					{
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Optional[int]       ='''bertabs'''




							def __init__(       self     ,A__=3_0_5_2_2     ,A__=5_1_2     ,A__=6     ,A__=5_1_2     ,A__=8     ,A__=5_1_2     ,A__=0.2     ,A__=6     ,A__=7_6_8     ,A__=8     ,A__=2_0_4_8     ,A__=0.2     ,**A__     ,):
												super().__init__(**A__)

												lowercase					=		vocab_size
												lowercase					=		max_pos

												lowercase					=		enc_layers
												lowercase					=		enc_hidden_size
												lowercase					=		enc_heads
												lowercase					=		enc_ff_size
												lowercase					=		enc_dropout

												lowercase					=		dec_layers
												lowercase					=		dec_hidden_size
												lowercase					=		dec_heads
												lowercase					=		dec_ff_size
												lowercase					=		dec_dropout




