

'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)







def   a__			(    a__ ,       a__=False		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []

     # fmt: off
     # stem:
     rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")		)
     rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")		)

     rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")		)
     rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")		)

     # backbone
     rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")		)
     rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")		)
     rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")		)

     for stage_idx in range(len(config.backbone_config.depths		)		):
          for layer_idx in range(config.backbone_config.depths[stage_idx]		):
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')		)
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')		)
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')		)
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')		)
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')		)
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')		)
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')		)
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')		)
               rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')		)

          rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')		)
          rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')		)
          rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')		)

     # transformer encoder
     for i in range(config.num_hidden_layers		):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight')		)
          rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias')		)
          rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight')		)
          rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias')		)
          rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight')		)
          rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias')		)
          rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight')		)
          rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias')		)
          rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight')		)
          rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias')		)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ("""norm.weight""", """layernorm.weight"""),
                  ("""norm.bias""", """layernorm.bias"""),
                  ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                  ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
              ]		)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          __SCREAMING_SNAKE_CASE      = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""		) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("""norm.weight""", """vit.layernorm.weight"""),
                  ("""norm.bias""", """vit.layernorm.bias"""),
                  ("""head.weight""", """classifier.weight"""),
                  ("""head.bias""", """classifier.bias"""),
              ]		)
     # fmt: on

     return rename_keys







def   a__			(    a__ ,       a__ ,       a__=False		):




     """simple docstring"""


     for i in range(config.num_hidden_layers		):
          if base_model:
               __SCREAMING_SNAKE_CASE      = """"""
          else:
               __SCREAMING_SNAKE_CASE      = """vit."""
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          __SCREAMING_SNAKE_CASE      = state_dict.pop(F'blocks.{i}.attn.qkv.weight'		)
          __SCREAMING_SNAKE_CASE      = state_dict.pop(F'blocks.{i}.attn.qkv.bias'		)
          # next, add query, keys and values (in that order) to the state dict
          __SCREAMING_SNAKE_CASE      = in_proj_weight[
              : config.hidden_size, :
          ]
          __SCREAMING_SNAKE_CASE      = in_proj_bias[: config.hidden_size]
          __SCREAMING_SNAKE_CASE      = in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __SCREAMING_SNAKE_CASE      = in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __SCREAMING_SNAKE_CASE      = in_proj_weight[
              -config.hidden_size :, :
          ]
          __SCREAMING_SNAKE_CASE      = in_proj_bias[-config.hidden_size :]







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = ["""head.weight""", """head.bias"""]
     for k in ignore_keys:
          state_dict.pop(a__ ,       a__		)







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = dct.pop(a__		)
     __SCREAMING_SNAKE_CASE      = val







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = """http://images.cocodataset.org/val2017/000000039769.jpg"""
     __SCREAMING_SNAKE_CASE      = Image.open(requests.get(a__ ,       stream=a__		).raw		)
     return im







@torch.no_grad()
def   a__			(    a__ ,       a__ ,       a__=False		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = BitConfig(
         global_padding="""same""" ,       layer_type="""bottleneck""" ,       depths=(3, 4, 9) ,       out_features=["""stage3"""] ,       embedding_dynamic_padding=a__ ,       )
     __SCREAMING_SNAKE_CASE      = ViTHybridConfig(backbone_config=a__ ,       image_size=3_84 ,       num_labels=10_00		)
     __SCREAMING_SNAKE_CASE      = False

     # load original model from timm
     __SCREAMING_SNAKE_CASE      = timm.create_model(a__ ,       pretrained=a__		)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     __SCREAMING_SNAKE_CASE      = timm_model.state_dict()
     if base_model:
          remove_classification_head_(a__		)
     __SCREAMING_SNAKE_CASE      = create_rename_keys(a__ ,       a__		)
     for src, dest in rename_keys:
          rename_key(a__ ,       a__ ,       a__		)
     read_in_q_k_v(a__ ,       a__ ,       a__		)

     __SCREAMING_SNAKE_CASE      = """huggingface/label-files"""
     __SCREAMING_SNAKE_CASE      = """imagenet-1k-id2label.json"""
     __SCREAMING_SNAKE_CASE      = json.load(open(hf_hub_download(a__ ,       a__ ,       repo_type="""dataset"""		) ,       """r"""		)		)
     __SCREAMING_SNAKE_CASE      = {int(a__		): v for k, v in idalabel.items()}
     __SCREAMING_SNAKE_CASE      = idalabel
     __SCREAMING_SNAKE_CASE      = {v: k for k, v in idalabel.items()}

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          __SCREAMING_SNAKE_CASE      = ViTHybridModel(a__		).eval()
     else:
          __SCREAMING_SNAKE_CASE      = ViTHybridForImageClassification(a__		).eval()
     model.load_state_dict(a__		)

     # create image processor
     __SCREAMING_SNAKE_CASE      = create_transform(**resolve_data_config({} ,       model=a__		)		)
     __SCREAMING_SNAKE_CASE      = transform.transforms

     __SCREAMING_SNAKE_CASE      = {
         """bilinear""": PILImageResampling.BILINEAR,
         """bicubic""": PILImageResampling.BICUBIC,
         """nearest""": PILImageResampling.NEAREST,
     }

     __SCREAMING_SNAKE_CASE      = ViTHybridImageProcessor(
         do_resize=a__ ,       size={"""shortest_edge""": timm_transforms[0].size} ,       resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,       do_center_crop=a__ ,       crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} ,       do_normalize=a__ ,       image_mean=timm_transforms[-1].mean.tolist() ,       image_std=timm_transforms[-1].std.tolist() ,       )

     __SCREAMING_SNAKE_CASE      = prepare_img()
     __SCREAMING_SNAKE_CASE      = transform(a__		).unsqueeze(0		)
     __SCREAMING_SNAKE_CASE      = processor(a__ ,       return_tensors="""pt"""		).pixel_values

     # verify pixel values
     assert torch.allclose(a__ ,       a__		)

     # verify logits
     with torch.no_grad():
          __SCREAMING_SNAKE_CASE      = model(a__		)
          __SCREAMING_SNAKE_CASE      = outputs.logits

     print("""Predicted class:""" ,       logits.argmax(-1		).item()		)
     if base_model:
          __SCREAMING_SNAKE_CASE      = timm_model.forward_features(a__		)
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(a__ ,       outputs.pooler_output ,       atol=1E-3		)
     else:
          __SCREAMING_SNAKE_CASE      = timm_model(a__		)
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(a__ ,       outputs.logits ,       atol=1E-3		)
     print("""Looks ok!"""		)

     if pytorch_dump_folder_path is not None:
          Path(a__		).mkdir(exist_ok=a__		)
          print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'		)
          model.save_pretrained(a__		)
          print(F'Saving processor to {pytorch_dump_folder_path}'		)
          processor.save_pretrained(a__		)

     if push_to_hub:
          print(F'Pushing model and processor to the hub {vit_name}'		)
          model.push_to_hub(F'ybelkada/{vit_name}'		)
          processor.push_to_hub(F'ybelkada/{vit_name}'		)


if __name__ == "__main__":
  UpperCAmelCase					:			Dict       =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--vit_name',
      default='vit_base_r50_s16_384',
      type=str,
      help='Name of the hybrid ViT timm model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )
  parser.add_argument(
      '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
  )

  UpperCAmelCase					:			Optional[int]       =	parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''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 		lowerCAmelCase__  ( 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  UpperCAmelCase__						(						self   :			Tuple     )    ->					Tuple:


      """simple docstring"""




      return 32
 @property
 def  UpperCAmelCase__						(						self   :			str     )    ->					int:


      """simple docstring"""




      return 32
 @property
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					Union[str, Any]:


      """simple docstring"""




      return self.time_input_dim * 4
 @property
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      return 8
 @property
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Optional[Any]:


      """simple docstring"""




      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = 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   ,				)

      __SCREAMING_SNAKE_CASE      = CLIPVisionModel(__SCREAMING_SNAKE_CASE     )
      return model
 @property
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = CLIPImageProcessor(
          crop_size=224   ,				do_center_crop=__SCREAMING_SNAKE_CASE   ,				do_normalize=__SCREAMING_SNAKE_CASE   ,				do_resize=__SCREAMING_SNAKE_CASE   ,				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  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Tuple:


      """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""",
          """embedding_proj_norm_type""": """layer""",
          """encoder_hid_proj_type""": None,
          """added_emb_type""": None,
      }

      __SCREAMING_SNAKE_CASE      = PriorTransformer(**__SCREAMING_SNAKE_CASE     )
      return model
 @property
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """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(**__SCREAMING_SNAKE_CASE     )
      return model
 def  UpperCAmelCase__						(						self   :			str     )    ->					Tuple:


      """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_024   ,				prediction_type="""sample"""   ,				use_karras_sigmas=__SCREAMING_SNAKE_CASE   ,				clip_sample=__SCREAMING_SNAKE_CASE   ,				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  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			str=0     )    ->					Tuple:


      """simple docstring"""




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

      if str(__SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
           __SCREAMING_SNAKE_CASE      = torch.manual_seed(__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = {
          """image""": input_image,
          """generator""": generator,
          """num_inference_steps""": 1,
          """frame_size""": 32,
          """output_type""": """np""",
      }
      return inputs
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """cpu"""

      __SCREAMING_SNAKE_CASE      = self.get_dummy_components()

      __SCREAMING_SNAKE_CASE      = self.pipeline_class(**__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = pipe.to(__SCREAMING_SNAKE_CASE     )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE     )     )
      __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(
          [
              0.00039216,
              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  UpperCAmelCase__						(						self   :			str     )    ->					Dict:


      """simple docstring"""




      self._test_inference_batch_consistent(batch_sizes=[1, 2]     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




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


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


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = 1
      __SCREAMING_SNAKE_CASE      = 2

      __SCREAMING_SNAKE_CASE      = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE     )

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

      __SCREAMING_SNAKE_CASE      = pipe(**__SCREAMING_SNAKE_CASE   ,				num_images_per_prompt=__SCREAMING_SNAKE_CASE     )[0]

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







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



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			int     )    ->					Dict:


      """simple docstring"""




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


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					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(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(0     )

      __SCREAMING_SNAKE_CASE      = pipe(
          __SCREAMING_SNAKE_CASE   ,				generator=__SCREAMING_SNAKE_CASE   ,				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(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )






