
"""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__						(      a_,			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 			__magic_name__			(			self				):
          return 32
   @property
   def 			__magic_name__			(			self				):
          return 32
   @property
   def 			__magic_name__			(			self				):
          return self.time_input_dim * 4
   @property
   def 			__magic_name__			(			self				):
          return 8
   @property
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)
          return tokenizer
   @property
   def 			__magic_name__			(			self				):
          torch.manual_seed(0				)
          lowercase    :				Any       		=							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=1_000	,    )
          return CLIPTextModelWithProjection(_a				)
   @property
   def 			__magic_name__			(			self				):
          torch.manual_seed(0				)

          lowercase    :				Any       		=							{
              "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,
          }

          lowercase    :				Tuple       		=							PriorTransformer(**_a				)
          return model
   @property
   def 			__magic_name__			(			self				):
          torch.manual_seed(0				)

          lowercase    :				Optional[int]       		=							{
              "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,
              ),
          }
          lowercase    :				Dict       		=							ShapERenderer(**_a				)
          return model
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.dummy_prior
          lowercase    :				Optional[int]       		=							self.dummy_text_encoder
          lowercase    :				Optional[int]       		=							self.dummy_tokenizer
          lowercase    :				str       		=							self.dummy_renderer

          lowercase    :				Dict       		=							HeunDiscreteScheduler(
              beta_schedule="exp"	,    num_train_timesteps=1_024	,    prediction_type="sample"	,    use_karras_sigmas=_a	,    clip_sample=_a	,    clip_sample_range=1.0	,    )
          lowercase    :				Any       		=							{
              "prior": prior,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "renderer": renderer,
              "scheduler": scheduler,
          }

          return components
   def 			__magic_name__			(			self	,    _a	,    _a=0				):
          if str(_a				).startswith("mps"				):
                 lowercase    :				str       		=							torch.manual_seed(_a				)
          else:
                 lowercase    :				List[str]       		=							torch.Generator(device=_a				).manual_seed(_a				)
          lowercase    :				Tuple       		=							{
              "prompt": "horse",
              "generator": generator,
              "num_inference_steps": 1,
              "frame_size": 32,
              "output_type": "np",
          }
          return inputs
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							"cpu"

          lowercase    :				str       		=							self.get_dummy_components()

          lowercase    :				Dict       		=							self.pipeline_class(**_a				)
          lowercase    :				int       		=							pipe.to(_a				)

          pipe.set_progress_bar_config(disable=_a				)

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

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

          lowercase    :				Any       		=							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 			__magic_name__			(			self				):
          # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
          self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							torch_device == "cpu"
          lowercase    :				List[str]       		=							True

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

   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.get_dummy_components()
          lowercase    :				List[str]       		=							self.pipeline_class(**_a				)
          lowercase    :				List[str]       		=							pipe.to(_a				)
          pipe.set_progress_bar_config(disable=_a				)

          lowercase    :				Optional[Any]       		=							1
          lowercase    :				Tuple       		=							2

          lowercase    :				Any       		=							self.get_dummy_inputs(_a				)

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

          lowercase    :				Optional[int]       		=							pipe(**_a	,    num_images_per_prompt=_a				)[0]

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


@slow
@require_torch_gpu
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()

   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/shap_e/test_shap_e_np_out.npy"				)
          lowercase    :				Tuple       		=							ShapEPipeline.from_pretrained("openai/shap-e"				)
          lowercase    :				Tuple       		=							pipe.to(_a				)
          pipe.set_progress_bar_config(disable=_a				)

          lowercase    :				Optional[int]       		=							torch.Generator(device=_a				).manual_seed(0				)

          lowercase    :				Any       		=							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				)




"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=False  ) ->				Tuple:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       if display:
              print(yaml.dump(OmegaConf.to_container(__snake_case  )  )  )
       return config


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=None     , __snake_case				:       Union[str, Any]=None  ) ->				Tuple:
       if conf_path is None:
              lowercase    :				List[Any]       		=							"./model_checkpoints/vqgan_only.yaml"
       lowercase    :				Tuple       		=							load_config(__snake_case     , display=__snake_case  )
       lowercase    :				List[Any]       		=							VQModel(**config.model.params  )
       if ckpt_path is None:
              lowercase    :				List[str]       		=							"./model_checkpoints/vqgan_only.pt"
       lowercase    :				Optional[int]       		=							torch.load(__snake_case     , map_location=__snake_case  )
       if ".ckpt" in ckpt_path:
              lowercase    :				str       		=							sd["state_dict"]
       model.load_state_dict(__snake_case     , strict=__snake_case  )
       model.to(__snake_case  )
       del sd
       return model


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase		,    lowercase		,    lowercase    :				List[Any]       		=							model.encode(__snake_case  )
       print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""  )
       lowercase    :				str       		=							model.decode(__snake_case  )
       return xrec


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[int]=False  ) ->				int:
       lowercase		,    lowercase    :				Union[str, Any]       		=							string.rsplit("."     , 1  )
       if reload:
              lowercase    :				Any       		=							importlib.import_module(__snake_case  )
              importlib.reload(__snake_case  )
       return getattr(importlib.import_module(__snake_case     , package=__snake_case  )     , cls  )


def   __magic_name__	(      __snake_case				:       str  ) ->				List[str]:
       if "target" not in config:
              raise KeyError("Expected key `target` to instantiate."  )
       return get_obj_from_str(config["target"]  )(**config.get("params"     , {}  )  )


def   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       int     , __snake_case				:       List[Any]=True     , __snake_case				:       Dict=True  ) ->				str:
       lowercase    :				Optional[int]       		=							instantiate_from_config(__snake_case  )
       if sd is not None:
              model.load_state_dict(__snake_case  )
       if gpu:
              model.cuda()
       if eval_mode:
              model.eval()
       return {"model": model}


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Any     , __snake_case				:       Optional[int]     , __snake_case				:       List[str]  ) ->				Any:
       # load the specified checkpoint
       if ckpt:
              lowercase    :				Dict       		=							torch.load(__snake_case     , map_location="cpu"  )
              lowercase    :				List[Any]       		=							pl_sd["global_step"]
              print(f"""loaded model from global step {global_step}."""  )
       else:
              lowercase    :				int       		=							{"state_dict": None}
              lowercase    :				Optional[Any]       		=							None
       lowercase    :				List[Any]       		=							load_model_from_config(config.model     , pl_sd["state_dict"]     , gpu=__snake_case     , eval_mode=__snake_case  )["model"]
       return model, global_step


