


"""simple docstring"""






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     A__     (						lowerCAmelCase__						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			ShapEPipeline
	SCREAMING_SNAKE_CASE					   =			["prompt"]
	SCREAMING_SNAKE_CASE					   =			["prompt"]
	SCREAMING_SNAKE_CASE					   =			[
	    "num_images_per_prompt",
	    "num_inference_steps",
	    "generator",
	    "latents",
	    "guidance_scale",
	    "frame_size",
	    "output_type",
	    "return_dict",
	]
	SCREAMING_SNAKE_CASE					   =			False

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Dict:

				"""simple docstring"""

				return 32

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

				"""simple docstring"""

				return 32

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> List[str]:

				"""simple docstring"""

				return self.time_input_dim * 4

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

				"""simple docstring"""

				return 8

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
				return tokenizer

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

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase   :						Optional[int]				= 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(_lowerCamelCase)

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

				"""simple docstring"""

				torch.manual_seed(0)

				__lowerCAmelCase   :						int				= {
				    "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   :						Dict				= PriorTransformer(**_lowerCamelCase)
				return model

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

				"""simple docstring"""

				torch.manual_seed(0)

				__lowerCAmelCase   :						Any				= {
				    "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   :						Optional[int]				= ShapERenderer(**_lowerCamelCase)
				return model

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.dummy_prior
				__lowerCAmelCase   :						Optional[int]				= self.dummy_text_encoder
				__lowerCAmelCase   :						List[str]				= self.dummy_tokenizer
				__lowerCAmelCase   :						Union[str, Any]				= self.dummy_renderer

				__lowerCAmelCase   :						Optional[int]				= HeunDiscreteScheduler(
				    beta_schedule="exp" ,    num_train_timesteps=1024 ,    prediction_type="sample" ,    use_karras_sigmas=_lowerCamelCase ,    clip_sample=_lowerCamelCase ,    clip_sample_range=1.0 ,    )
				__lowerCAmelCase   :						List[str]				= {
				    "prior": prior,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "renderer": renderer,
				    "scheduler": scheduler,
				}

				return components

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	str=0)       -> Union[str, Any]:

				"""simple docstring"""

				if str(_lowerCamelCase).startswith("mps"):
							__lowerCAmelCase   :						Dict				= torch.manual_seed(_lowerCamelCase)
				else:
							__lowerCAmelCase   :						int				= torch.Generator(device=_lowerCamelCase).manual_seed(_lowerCamelCase)
				__lowerCAmelCase   :						Any				= {
				    "prompt": "horse",
				    "generator": generator,
				    "num_inference_steps": 1,
				    "frame_size": 32,
				    "output_type": "np",
				}
				return inputs

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= "cpu"

				__lowerCAmelCase   :						Union[str, Any]				= self.get_dummy_components()

				__lowerCAmelCase   :						Optional[int]				= self.pipeline_class(**_lowerCamelCase)
				__lowerCAmelCase   :						Dict				= pipe.to(_lowerCamelCase)

				pipe.set_progress_bar_config(disable=_lowerCamelCase)

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

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

				__lowerCAmelCase   :						List[Any]				= np.array(
				    [
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				    ])

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> str:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= torch_device == "cpu"
				__lowerCAmelCase   :						Dict				= True

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







	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= self.get_dummy_components()
				__lowerCAmelCase   :						Union[str, Any]				= self.pipeline_class(**_lowerCamelCase)
				__lowerCAmelCase   :						Optional[int]				= pipe.to(_lowerCamelCase)
				pipe.set_progress_bar_config(disable=_lowerCamelCase)

				__lowerCAmelCase   :						List[str]				= 1
				__lowerCAmelCase   :						Optional[int]				= 2

				__lowerCAmelCase   :						List[str]				= self.get_dummy_inputs(_lowerCamelCase)

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

				__lowerCAmelCase   :						Dict				= pipe(**_lowerCamelCase ,    num_images_per_prompt=_lowerCamelCase)[0]

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






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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Any:

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/shap_e/test_shap_e_np_out.npy")
				__lowerCAmelCase   :						Union[str, Any]				= ShapEPipeline.from_pretrained("openai/shap-e")
				__lowerCAmelCase   :						Dict				= pipe.to(_lowerCamelCase)
				pipe.set_progress_bar_config(disable=_lowerCamelCase)

				__lowerCAmelCase   :						str				= torch.Generator(device=_lowerCamelCase).manual_seed(0)

				__lowerCAmelCase   :						Any				= pipe(
				    "a shark" ,    generator=_lowerCamelCase ,    guidance_scale=15.0 ,    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)




"""simple docstring"""






import re

from filelock import FileLock


try:
	import nltk

	__snake_case							:     Any   =					True
except (ImportError, ModuleNotFoundError):
	__snake_case							:     Optional[int]   =					False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)


def     _lowercase       (						__snake_case			)		->     str:
			re.sub("<n>"      ,""      ,__snake_case			)  # remove pegasus newline char
			assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
			return "\n".join(nltk.sent_tokenize(__snake_case			)			)