






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    __lowercase   (						lowercase_      ,   unittest.TestCase	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					ShapEImgaImgPipeline
     SCREAMING_SNAKE_CASE     =					["image"]
     SCREAMING_SNAKE_CASE     =					["image"]
     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 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         return 32




     @property
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         return 32




     @property
     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         return self.time_input_dim * 4




     @property
     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         return 8




     @property
     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         torch.manual_seed(0					)
         __A       =						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			,						)

         __A       =						CLIPVisionModel(UpperCamelCase_					)
         return model




     @property
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         __A       =						CLIPImageProcessor(
             crop_size=224			,						do_center_crop=UpperCamelCase_			,						do_normalize=UpperCamelCase_			,						do_resize=UpperCamelCase_			,						image_mean=[0.48145466, 0.4578275, 0.40821073]			,						image_std=[0.26862954, 0.26130258, 0.27577711]			,						resample=3			,						size=224			,						)

         return image_processor




     @property
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         torch.manual_seed(0					)

         __A       =						{
             """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,
         }

         __A       =						PriorTransformer(**UpperCamelCase_					)
         return model




     @property
     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         torch.manual_seed(0					)

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




     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         __A       =						self.dummy_prior
         __A       =						self.dummy_image_encoder
         __A       =						self.dummy_image_processor
         __A       =						self.dummy_renderer

         __A       =						HeunDiscreteScheduler(
             beta_schedule="""exp"""			,						num_train_timesteps=1_024			,						prediction_type="""sample"""			,						use_karras_sigmas=UpperCamelCase_			,						clip_sample=UpperCamelCase_			,						clip_sample_range=1.0			,						)
         __A       =						{
             """prior""": prior,
             """image_encoder""": image_encoder,
             """image_processor""": image_processor,
             """renderer""": renderer,
             """scheduler""": scheduler,
         }

         return components




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      Optional[int]=0					):


         """simple docstring"""



         __A       =						floats_tensor((1, 3, 64, 64)			,						rng=random.Random(UpperCamelCase_					)					).to(UpperCamelCase_					)

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




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						"""cpu"""

         __A       =						self.get_dummy_components()

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

         pipe.set_progress_bar_config(disable=UpperCamelCase_					)

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

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

         __A       =						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 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						torch_device == """cpu"""
         __A       =						True
         self._test_inference_batch_single_identical(
             batch_size=2			,						test_max_difference=UpperCamelCase_			,						relax_max_difference=UpperCamelCase_			,						)





     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



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

         __A       =						1
         __A       =						2

         __A       =						self.get_dummy_inputs(UpperCamelCase_					)

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

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

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


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







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



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





     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



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

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

         __A       =						pipe(
             UpperCamelCase_			,						generator=UpperCamelCase_			,						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(UpperCamelCase_			,						UpperCamelCase_					)












import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__a   :							Any						= logging.get_logger(__name__)


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Tuple			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



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




