
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        _SCREAMING_SNAKE_CASE	( snake_case_     ,			unittest.TestCase		):
	lowerCAmelCase__   =					ShapEPipeline
	lowerCAmelCase__   =					['prompt']
	lowerCAmelCase__   =					['prompt']
	lowerCAmelCase__   =					[
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	lowerCAmelCase__   =					False



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					return 32



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



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



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					return 8



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					lowerCamelCase_         =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
					return tokenizer



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					torch.manual_seed(0					)
					lowerCamelCase_         =     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=37		,    layer_norm_eps=1e-05		,    num_attention_heads=4		,    num_hidden_layers=5		,    pad_token_id=1		,    vocab_size=1000		,    )
					return CLIPTextModelWithProjection(lowercase					)



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					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",
					    "encoder_hid_proj_type": None,
					    "added_emb_type": None,
					}

					lowerCamelCase_         =     PriorTransformer(**lowercase					)
					return model



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					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(**lowercase					)
					return model



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					lowerCamelCase_         =     self.dummy_prior
					lowerCamelCase_         =     self.dummy_text_encoder
					lowerCamelCase_         =     self.dummy_tokenizer
					lowerCamelCase_         =     self.dummy_renderer

					lowerCamelCase_         =     HeunDiscreteScheduler(
					    beta_schedule="exp"		,    num_train_timesteps=1024		,    prediction_type="sample"		,    use_karras_sigmas=lowercase		,    clip_sample=lowercase		,    clip_sample_range=1.0		,    )
					lowerCamelCase_         =     {
					    "prior": prior,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "renderer": renderer,
					    "scheduler": scheduler,
					}

					return components



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=0					)							->			Optional[int]:
					if str(lowercase					).startswith("mps"					):
									lowerCamelCase_         =     torch.manual_seed(lowercase					)
					else:
									lowerCamelCase_         =     torch.Generator(device=lowercase					).manual_seed(lowercase					)
					lowerCamelCase_         =     {
					    "prompt": "horse",
					    "generator": generator,
					    "num_inference_steps": 1,
					    "frame_size": 32,
					    "output_type": "np",
					}
					return inputs



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					lowerCamelCase_         =     "cpu"

					lowerCamelCase_         =     self.get_dummy_components()

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

					pipe.set_progress_bar_config(disable=lowercase					)

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

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

					lowerCamelCase_         =     np.array(
					    [
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					    ]					)

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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
					self._test_inference_batch_consistent(batch_sizes=[1, 2]					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					lowerCamelCase_         =     torch_device == "cpu"
					lowerCamelCase_         =     True

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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					lowerCamelCase_         =     self.get_dummy_components()
					lowerCamelCase_         =     self.pipeline_class(**lowercase					)
					lowerCamelCase_         =     pipe.to(lowercase					)
					pipe.set_progress_bar_config(disable=lowercase					)

					lowerCamelCase_         =     1
					lowerCamelCase_         =     2

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)

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

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

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




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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					lowerCamelCase_         =     load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/shap_e/test_shap_e_np_out.npy"					)
					lowerCamelCase_         =     ShapEPipeline.from_pretrained("openai/shap-e"					)
					lowerCamelCase_         =     pipe.to(lowercase					)
					pipe.set_progress_bar_config(disable=lowercase					)

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

					lowerCamelCase_         =     pipe(
					    "a shark"		,    generator=lowercase		,    guidance_scale=1_5.0		,    num_inference_steps=64		,    frame_size=64		,    output_type="np"		,    ).images[0]

					assert images.shape == (20, 64, 64, 3)

					assert_mean_pixel_difference(lowercase		,    lowercase					)




import re





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]"      ,					str_       )]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     split_input(str_       )
				return "".join(
				    ["".join([char.capitalize() for char in sub_str]       ) for sub_str in string_split]       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     split_input(lowerCamelCase__       )
								if upper:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.upper() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								else:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.lower() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								return res_str
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return to_simple_case(lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     to_simple_case(lowerCamelCase__       )
								return res_str[0].lower() + res_str[1:]
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"_"       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"-"       )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


