




'''simple docstring'''



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_   (			A					,  unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						ShapEImgaImgPipeline
   lowerCamelCase_      =						['''image''']
   lowerCamelCase_      =						['''image''']
   lowerCamelCase_      =						[
       '''num_images_per_prompt''',
       '''num_inference_steps''',
       '''generator''',
       '''latents''',
       '''guidance_scale''',
       '''frame_size''',
       '''output_type''',
       '''return_dict''',
   ]
   lowerCamelCase_      =						False



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

      """simple docstring"""
      return 3_2



   @property
   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      return 3_2



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

      """simple docstring"""
      return self.time_input_dim * 4



   @property
   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      return 8



   @property
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size  ,				image_size=6_4  ,				projection_dim=self.text_embedder_hidden_size  ,				intermediate_size=3_7  ,				num_attention_heads=4  ,				num_channels=3  ,				num_hidden_layers=5  ,				patch_size=1  ,				)

      _SCREAMING_SNAKE_CASE	=				CLIPVisionModel(__lowerCamelCase   )
      return model



   @property
   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				CLIPImageProcessor(
          crop_size=2_2_4  ,				do_center_crop=__lowerCamelCase  ,				do_normalize=__lowerCamelCase  ,				do_resize=__lowerCamelCase  ,				image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]  ,				image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]  ,				resample=3  ,				size=2_2_4  ,				)

      return image_processor



   @property
   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      torch.manual_seed(0   )

      _SCREAMING_SNAKE_CASE	=				{
          "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",
          "embedding_proj_norm_type": "layer",
          "encoder_hid_proj_type": None,
          "added_emb_type": None,
      }

      _SCREAMING_SNAKE_CASE	=				PriorTransformer(**__lowerCamelCase   )
      return model



   @property
   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      torch.manual_seed(0   )

      _SCREAMING_SNAKE_CASE	=				{
          "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,
          ),
      }
      _SCREAMING_SNAKE_CASE	=				ShapERenderer(**__lowerCamelCase   )
      return model



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.dummy_prior
      _SCREAMING_SNAKE_CASE	=				self.dummy_image_encoder
      _SCREAMING_SNAKE_CASE	=				self.dummy_image_processor
      _SCREAMING_SNAKE_CASE	=				self.dummy_renderer

      _SCREAMING_SNAKE_CASE	=				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  ,				)
      _SCREAMING_SNAKE_CASE	=				{
          "prior": prior,
          "image_encoder": image_encoder,
          "image_processor": image_processor,
          "renderer": renderer,
          "scheduler": scheduler,
      }

      return components



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Dict=0   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				floats_tensor((1, 3, 6_4, 6_4)  ,				rng=random.Random(__lowerCamelCase   )   ).to(__lowerCamelCase   )

      if str(__lowerCamelCase   ).startswith("mps"   ):
         _SCREAMING_SNAKE_CASE	=				torch.manual_seed(__lowerCamelCase   )
      else:
         _SCREAMING_SNAKE_CASE	=				torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				{
          "image": input_image,
          "generator": generator,
          "num_inference_steps": 1,
          "frame_size": 3_2,
          "output_type": "np",
      }
      return inputs



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"cpu"

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_components()

      _SCREAMING_SNAKE_CASE	=				self.pipeline_class(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				pipe.to(__lowerCamelCase   )

      pipe.set_progress_bar_config(disable=__lowerCamelCase   )

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

      assert image.shape == (2_0, 3_2, 3_2, 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 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
      self._test_inference_batch_consistent(batch_sizes=[1, 2]   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch_device == "cpu"
      _SCREAMING_SNAKE_CASE	=				True
      self._test_inference_batch_single_identical(
          batch_size=2  ,				test_max_difference=__lowerCamelCase  ,				relax_max_difference=__lowerCamelCase  ,				)



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.get_dummy_components()
      _SCREAMING_SNAKE_CASE	=				self.pipeline_class(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				pipe.to(__lowerCamelCase   )
      pipe.set_progress_bar_config(disable=__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				1
      _SCREAMING_SNAKE_CASE	=				2

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_inputs(__lowerCamelCase   )

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

      _SCREAMING_SNAKE_CASE	=				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       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_      (   self :     Dict   ):

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

      _SCREAMING_SNAKE_CASE	=				torch.Generator(device=__lowerCamelCase   ).manual_seed(0   )

      _SCREAMING_SNAKE_CASE	=				pipe(
          __lowerCamelCase  ,				generator=__lowerCamelCase  ,				guidance_scale=3.0  ,				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   )







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_			=						{
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'CTRLForSequenceClassification',
     'CTRLLMHeadModel',
     'CTRLModel',
     'CTRLPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFCTRLForSequenceClassification',
     'TFCTRLLMHeadModel',
     'TFCTRLModel',
     'TFCTRLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
 from .tokenization_ctrl import CTRLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ctrl import (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
      CTRLPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ctrl import (
      TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCTRLForSequenceClassification,
      TFCTRLLMHeadModel,
      TFCTRLModel,
      TFCTRLPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

