





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_       (     UpperCamelCase__				,  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      _snake_case  (							self    :Optional[Any]		)   ->   str:



									"""simple docstring"""



									return 32


							@property
							def      _snake_case  (							self    :Dict		)   ->   str:



									"""simple docstring"""



									return 32


							@property
							def      _snake_case  (							self    :str		)   ->   Tuple:



									"""simple docstring"""



									return self.time_input_dim * 4


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



									"""simple docstring"""



									return 8


							@property
							def      _snake_case  (							self    :Any		)   ->   Dict:



									"""simple docstring"""



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


							@property
							def      _snake_case  (							self    :Tuple		)   ->   Dict:



									"""simple docstring"""



									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=      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(__A		)


							@property
							def      _snake_case  (							self    :str		)   ->   int:



									"""simple docstring"""



									torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=      {
									    """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,
									}

									SCREAMING_SNAKE_CASE__			=      PriorTransformer(**__A		)
									return model


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



									"""simple docstring"""



									torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=      {
									    """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,
									    ),
									}
									SCREAMING_SNAKE_CASE__			=      ShapERenderer(**__A		)
									return model


							def      _snake_case  (							self    :int		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.dummy_prior
									SCREAMING_SNAKE_CASE__			=      self.dummy_text_encoder
									SCREAMING_SNAKE_CASE__			=      self.dummy_tokenizer
									SCREAMING_SNAKE_CASE__			=      self.dummy_renderer

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

									return components


							def      _snake_case  (							self    :List[Any]							,				__A    :Tuple							,				__A    :List[str]=0		)   ->   Optional[int]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :str		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      """cpu"""

									SCREAMING_SNAKE_CASE__			=      self.get_dummy_components()

									SCREAMING_SNAKE_CASE__			=      self.pipeline_class(**__A		)
									SCREAMING_SNAKE_CASE__			=      pipe.to(__A		)

									pipe.set_progress_bar_config(disable=__A		)

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

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

									SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :Dict		)   ->   Any:



									"""simple docstring"""



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


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      torch_device == """cpu"""
									SCREAMING_SNAKE_CASE__			=      True

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


							def      _snake_case  (							self    :Tuple		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_dummy_components()
									SCREAMING_SNAKE_CASE__			=      self.pipeline_class(**__A		)
									SCREAMING_SNAKE_CASE__			=      pipe.to(__A		)
									pipe.set_progress_bar_config(disable=__A		)

									SCREAMING_SNAKE_CASE__			=      1
									SCREAMING_SNAKE_CASE__			=      2

									SCREAMING_SNAKE_CASE__			=      self.get_dummy_inputs(__A		)

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

									SCREAMING_SNAKE_CASE__			=      pipe(**__A							,				num_images_per_prompt=__A		)[0]

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




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


							def      _snake_case  (							self    :Optional[Any]		)   ->   Tuple:



									"""simple docstring"""



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


							def      _snake_case  (							self    :Union[str, Any]		)   ->   List[str]:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__			=      torch.Generator(device=__A		).manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=      pipe(
									    """a shark"""							,				generator=__A							,				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(__A							,				__A		)






import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_lowerCamelCase   				=			logging.get_logger(__name__)




class   UpperCamelCase_       (     UpperCamelCase__    ):


							def __init__(							self    :List[Any]							,				*__A    :Tuple							,				**__A    :Dict		)   ->   None:



									"""simple docstring"""



									warnings.warn(
									    """The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
									    """ Please use LayoutLMv2ImageProcessor instead."""							,				__A							,				)
									super().__init__(*__A							,				**__A		)