


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   __lowercase				(  _A ,					unittest.TestCase  ):
      UpperCamelCase								=       ShapEPipeline
      UpperCamelCase								=       ['''prompt''']
      UpperCamelCase								=       ['''prompt''']
      UpperCamelCase								=       [
          '''num_images_per_prompt''',
          '''num_inference_steps''',
          '''generator''',
          '''latents''',
          '''guidance_scale''',
          '''frame_size''',
          '''output_type''',
          '''return_dict''',
      ]
      UpperCamelCase								=       False





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






         """simple docstring"""







         return 3_2





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






         """simple docstring"""







         return 3_2





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






         """simple docstring"""







         return self.time_input_dim * 4





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






         """simple docstring"""







         return 8





      @property
      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Tuple:






         """simple docstring"""







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





      @property
      def        _lowercase				(		self						:					Dict       )    ->   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=3_7					,       layer_norm_eps=1e-0_5					,       num_attention_heads=4					,       num_hidden_layers=5					,       pad_token_id=1					,       vocab_size=1_0_0_0					,       )
         return CLIPTextModelWithProjection(__lowerCamelCase       )





      @property
      def        _lowercase				(		self						:					Any       )    ->   str:






         """simple docstring"""







         torch.manual_seed(0       )

         UpperCAmelCase							=     {
             "num_attention_heads": 2,
             "attention_head_dim": 1_6,
             "embedding_dim": self.time_input_dim,
             "num_embeddings": 3_2,
             "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        _lowercase				(		self						:					str       )    ->   List[str]:






         """simple docstring"""







         torch.manual_seed(0       )

         UpperCAmelCase							=     {
             "param_shapes": (
                 (self.renderer_dim, 9_3),
                 (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": 1_2,
             "background": (
                 0.1,
                 0.1,
                 0.1,
             ),
         }
         UpperCAmelCase							=     ShapERenderer(**__lowerCamelCase       )
         return model





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Optional[int]:






         """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=1_0_2_4					,       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        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					Optional[int]=0       )    ->   List[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": 3_2,
             "output_type": "np",
         }
         return inputs





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






         """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 == (2_0, 3_2, 3_2, 3)

         UpperCAmelCase							=     np.array(
             [
                 0.00_039_216,
                 0.00_039_216,
                 0.00_039_216,
                 0.00_039_216,
                 0.00_039_216,
                 0.00_039_216,
                 0.00_039_216,
                 0.00_039_216,
                 0.00_039_216,
             ]       )

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





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






         """simple docstring"""







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





      def        _lowercase				(		self						:					str       )    ->   Tuple:






         """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        _lowercase				(		self						:					int       )    ->   Optional[int]:






         """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   __lowercase				(  unittest.TestCase  ):





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






         """simple docstring"""







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





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






         """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=6_4					,       frame_size=6_4					,       output_type="""np"""					,       ).images[0]

         assert images.shape == (2_0, 6_4, 6_4, 3)

         assert_mean_pixel_difference(__lowerCamelCase					,       __lowerCamelCase       )







import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu






@require_cpu
class   __lowercase				(  unittest.TestCase  ):





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






         """simple docstring"""







         UpperCAmelCase							=     torch.nn.Linear(1_0					,       1_0       )
         UpperCAmelCase							=     torch.optim.SGD(model.parameters()					,       0.1       )
         UpperCAmelCase							=     Accelerator()
         UpperCAmelCase							=     accelerator.prepare(__lowerCamelCase       )
         try:
            pickle.loads(pickle.dumps(__lowerCamelCase       )       )
         except Exception as e:
            self.fail(F"""Accelerated optimizer pickling failed with {e}"""       )
         AcceleratorState._reset_state()



