

'''simple docstring'''



from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_lowercase      :      Union[str, Any]            =					logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase	)
class 	UpperCamelCase__(       lowerCAmelCase	):




				def __init__(		self      :			Tuple				,   *lowerCAmelCase      :			Optional[int]				,   **lowerCAmelCase      :			int				)->					Dict:

										"""simple docstring"""




										super().__init__(*lowerCAmelCase				,   **lowerCAmelCase				)
										requires_backends(self				,   '''vision'''				)
										self.check_model_type(lowerCAmelCase				)




				def __call__(		self      :			List[Any]				,   lowerCAmelCase      :			Union[str, List[str], "Image.Image", List["Image.Image"]]				,   **lowerCAmelCase      :			Optional[int]				)->					Optional[int]:

										"""simple docstring"""




										return super().__call__(lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   **lowerCAmelCase      :			int				)->					Dict:

										"""simple docstring"""




										return {}, {}, {}




				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			List[str]				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     load_image(lowerCAmelCase				)
										UpperCAmelCase					    =     image.size
										UpperCAmelCase					    =     self.image_processor(images=lowerCAmelCase				,   return_tensors=self.framework				)
										return model_inputs




				def 					a__(		self      :			str				,   lowerCAmelCase      :			Optional[Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.model(**lowerCAmelCase				)
										return model_outputs





				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			List[str]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     model_outputs.predicted_depth
										UpperCAmelCase					    =     torch.nn.functional.interpolate(
										    predicted_depth.unsqueeze(1				)				,   size=self.image_size[::-1]				,   mode='''bicubic'''				,   align_corners=lowerCAmelCase				)
										UpperCAmelCase					    =     prediction.squeeze().cpu().numpy()
										UpperCAmelCase					    =     (output * 255 / np.max(lowerCAmelCase				)).astype('''uint8'''				)
										UpperCAmelCase					    =     Image.fromarray(lowerCAmelCase				)
										UpperCAmelCase					    =     {}
										UpperCAmelCase					    =     predicted_depth
										UpperCAmelCase					    =     depth
										return output_dict









'''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 	UpperCamelCase__(       lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				Any  		=	ShapEPipeline
				__magic_name__    :				Tuple  		=	["prompt"]
				__magic_name__    :				Optional[int]  		=	["prompt"]
				__magic_name__    :				Dict  		=	[
				    "num_images_per_prompt",
				    "num_inference_steps",
				    "generator",
				    "latents",
				    "guidance_scale",
				    "frame_size",
				    "output_type",
				    "return_dict",
				]
				__magic_name__    :				Optional[int]  		=	False




				@property
				def 					a__(		self      :			Optional[Any]				)->					Dict:

										"""simple docstring"""




										return 32




				@property
				def 					a__(		self      :			Dict				)->					Dict:

										"""simple docstring"""




										return 32




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

										"""simple docstring"""




										return self.time_input_dim * 4




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

										"""simple docstring"""




										return 8




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

										"""simple docstring"""




										UpperCAmelCase					    =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
										return tokenizer




				@property
				def 					a__(		self      :			Tuple				)->					Optional[int]:

										"""simple docstring"""




										torch.manual_seed(0				)
										UpperCAmelCase					    =     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 					a__(		self      :			str				)->					List[str]:

										"""simple docstring"""




										torch.manual_seed(0				)

										UpperCAmelCase					    =     {
										    '''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,
										}

										UpperCAmelCase					    =     PriorTransformer(**lowerCAmelCase				)
										return model




				@property
				def 					a__(		self      :			List[Any]				)->					List[Any]:

										"""simple docstring"""




										torch.manual_seed(0				)

										UpperCAmelCase					    =     {
										    '''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,
										    ),
										}
										UpperCAmelCase					    =     ShapERenderer(**lowerCAmelCase				)
										return model




				def 					a__(		self      :			Any				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     self.dummy_prior
										UpperCAmelCase					    =     self.dummy_text_encoder
										UpperCAmelCase					    =     self.dummy_tokenizer
										UpperCAmelCase					    =     self.dummy_renderer

										UpperCAmelCase					    =     HeunDiscreteScheduler(
										    beta_schedule='''exp'''				,   num_train_timesteps=1024				,   prediction_type='''sample'''				,   use_karras_sigmas=lowerCAmelCase				,   clip_sample=lowerCAmelCase				,   clip_sample_range=1.0				,   )
										UpperCAmelCase					    =     {
										    '''prior''': prior,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''renderer''': renderer,
										    '''scheduler''': scheduler,
										}

										return components




				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			List[Any]=0				)->					Optional[Any]:

										"""simple docstring"""




										if str(lowerCAmelCase				).startswith('''mps'''				):
																UpperCAmelCase					    =     torch.manual_seed(lowerCAmelCase				)
										else:
																UpperCAmelCase					    =     torch.Generator(device=lowerCAmelCase				).manual_seed(lowerCAmelCase				)
										UpperCAmelCase					    =     {
										    '''prompt''': '''horse''',
										    '''generator''': generator,
										    '''num_inference_steps''': 1,
										    '''frame_size''': 32,
										    '''output_type''': '''np''',
										}
										return inputs




				def 					a__(		self      :			int				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     '''cpu'''

										UpperCAmelCase					    =     self.get_dummy_components()

										UpperCAmelCase					    =     self.pipeline_class(**lowerCAmelCase				)
										UpperCAmelCase					    =     pipe.to(lowerCAmelCase				)

										pipe.set_progress_bar_config(disable=lowerCAmelCase				)

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

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

										UpperCAmelCase					    =     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      :			Union[str, Any]				)->					Optional[int]:

										"""simple docstring"""




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




				def 					a__(		self      :			Optional[int]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     torch_device == '''cpu'''
										UpperCAmelCase					    =     True

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





				def 					a__(		self      :			int				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_dummy_components()
										UpperCAmelCase					    =     self.pipeline_class(**lowerCAmelCase				)
										UpperCAmelCase					    =     pipe.to(lowerCAmelCase				)
										pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     2

										UpperCAmelCase					    =     self.get_dummy_inputs(lowerCAmelCase				)

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

										UpperCAmelCase					    =     pipe(**lowerCAmelCase				,   num_images_per_prompt=lowerCAmelCase				)[0]

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







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




				def 					a__(		self      :			Dict				)->					Union[str, Any]:

										"""simple docstring"""




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





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

										"""simple docstring"""




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

										UpperCAmelCase					    =     torch.Generator(device=lowerCAmelCase				).manual_seed(0				)

										UpperCAmelCase					    =     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				)





