





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 SCREAMING_SNAKE_CASE_ (					__UpperCAmelCase							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    ShapEImgaImgPipeline
					A__				 =    ["image"]
					A__				 =    ["image"]
					A__				 =    [
					    "num_images_per_prompt",
					    "num_inference_steps",
					    "generator",
					    "latents",
					    "guidance_scale",
					    "frame_size",
					    "output_type",
					    "return_dict",
					]
					A__				 =    False





					@property
					def 					__magic_name__ ( self	):
										return 32





					@property
					def 					__magic_name__ ( self	):
										return 32





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





					@property
					def 					__magic_name__ ( self	):
										return 8





					@property
					def 					__magic_name__ ( self	):
										torch.manual_seed(0	)
										lowerCamelCase__       =       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	,  )

										lowerCamelCase__       =       CLIPVisionModel(_lowerCamelCase	)
										return model





					@property
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       CLIPImageProcessor(
										    crop_size=224	,  do_center_crop=_lowerCamelCase	,  do_normalize=_lowerCamelCase	,  do_resize=_lowerCamelCase	,  image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]	,  image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]	,  resample=3	,  size=224	,  )

										return image_processor





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

										lowerCamelCase__       =       {
										    "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,
										}

										lowerCamelCase__       =       PriorTransformer(**_lowerCamelCase	)
										return model





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

										lowerCamelCase__       =       {
										    "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,
										    ),
										}
										lowerCamelCase__       =       ShapERenderer(**_lowerCamelCase	)
										return model





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.dummy_prior
										lowerCamelCase__       =       self.dummy_image_encoder
										lowerCamelCase__       =       self.dummy_image_processor
										lowerCamelCase__       =       self.dummy_renderer

										lowerCamelCase__       =       HeunDiscreteScheduler(
										    beta_schedule="exp"	,  num_train_timesteps=1024	,  prediction_type="sample"	,  use_karras_sigmas=_lowerCamelCase	,  clip_sample=_lowerCamelCase	,  clip_sample_range=1.0	,  )
										lowerCamelCase__       =       {
										    "prior": prior,
										    "image_encoder": image_encoder,
										    "image_processor": image_processor,
										    "renderer": renderer,
										    "scheduler": scheduler,
										}

										return components





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase=0	):
										lowerCamelCase__       =       floats_tensor((1, 3, 64, 64)	,  rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)

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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       "cpu"

										lowerCamelCase__       =       self.get_dummy_components()

										lowerCamelCase__       =       self.pipeline_class(**_lowerCamelCase	)
										lowerCamelCase__       =       pipe.to(_lowerCamelCase	)

										pipe.set_progress_bar_config(disable=_lowerCamelCase	)

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

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

										lowerCamelCase__       =       np.array(
										    [
										        0.00_039_216,
										        0.00_039_216,
										        0.00_039_216,
										        0.00_039_216,
										        0.00_039_216,
										        0.00_039_216,
										        0.00_039_216,
										        0.00_039_216,
										        0.00_039_216,
										    ]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2





					def 					__magic_name__ ( 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 					__magic_name__ ( self	):
										lowerCamelCase__       =       torch_device == "cpu"
										lowerCamelCase__       =       True
										self._test_inference_batch_single_identical(
										    batch_size=2	,  test_max_difference=_lowerCamelCase	,  relax_max_difference=_lowerCamelCase	,  )






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_dummy_components()
										lowerCamelCase__       =       self.pipeline_class(**_lowerCamelCase	)
										lowerCamelCase__       =       pipe.to(_lowerCamelCase	)
										pipe.set_progress_bar_config(disable=_lowerCamelCase	)

										lowerCamelCase__       =       1
										lowerCamelCase__       =       2

										lowerCamelCase__       =       self.get_dummy_inputs(_lowerCamelCase	)

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

										lowerCamelCase__       =       pipe(**_lowerCamelCase	,  num_images_per_prompt=_lowerCamelCase	)[0]

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

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





					"""simple docstring"""


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






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"	)
										lowerCamelCase__       =       load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/shap_e/test_shap_e_img2img_out.npy"	)
										lowerCamelCase__       =       ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"	)
										lowerCamelCase__       =       pipe.to(_lowerCamelCase	)
										pipe.set_progress_bar_config(disable=_lowerCamelCase	)

										lowerCamelCase__       =       torch.Generator(device=_lowerCamelCase	).manual_seed(0	)

										lowerCamelCase__       =       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 typing import TYPE_CHECKING

from ...utils import _LazyModule


A_												=     {"tokenization_byt5": ["ByT5Tokenizer"]}


if TYPE_CHECKING:
						from .tokenization_byta import ByTaTokenizer
else:
						import sys

						A_												=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

