






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 						_a       (  UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   ShapEImgaImgPipeline
  A_						=   ["""image"""]
  A_						=   ["""image"""]
  A_						=   [
      """num_images_per_prompt""",
      """num_inference_steps""",
      """generator""",
      """latents""",
      """guidance_scale""",
      """frame_size""",
      """output_type""",
      """return_dict""",
  ]
  A_						=   False




  @property
  def    _UpperCAmelCase     (     self )			-> List[Any]:
     return 32




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




  @property
  def    _UpperCAmelCase     (     self )			-> int:
     return self.time_input_dim * 4




  @property
  def    _UpperCAmelCase     (     self )			-> Tuple:
     return 8




  @property
  def    _UpperCAmelCase     (     self )			-> List[Any]:
     torch.manual_seed(0 )
     UpperCamelCase_									=     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		,		)

     UpperCamelCase_									=     CLIPVisionModel(_UpperCAmelCase )
     return model




  @property
  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     CLIPImageProcessor(
         crop_size=224		,		do_center_crop=_UpperCAmelCase		,		do_normalize=_UpperCAmelCase		,		do_resize=_UpperCAmelCase		,		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=224		,		)

     return image_processor




  @property
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     torch.manual_seed(0 )

     UpperCamelCase_									=     {
         '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,
     }

     UpperCamelCase_									=     PriorTransformer(**_UpperCAmelCase )
     return model




  @property
  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     torch.manual_seed(0 )

     UpperCamelCase_									=     {
         '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,
         ),
     }
     UpperCamelCase_									=     ShapERenderer(**_UpperCAmelCase )
     return model




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.dummy_prior
     UpperCamelCase_									=     self.dummy_image_encoder
     UpperCamelCase_									=     self.dummy_image_processor
     UpperCamelCase_									=     self.dummy_renderer

     UpperCamelCase_									=     HeunDiscreteScheduler(
         beta_schedule='exp'		,		num_train_timesteps=1024		,		prediction_type='sample'		,		use_karras_sigmas=_UpperCAmelCase		,		clip_sample=_UpperCAmelCase		,		clip_sample_range=1.0		,		)
     UpperCamelCase_									=     {
         'prior': prior,
         'image_encoder': image_encoder,
         'image_processor': image_processor,
         'renderer': renderer,
         'scheduler': scheduler,
     }

     return components




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=0 )			-> List[str]:
     UpperCamelCase_									=     floats_tensor((1, 3, 64, 64)		,		rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )

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




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     'cpu'

     UpperCamelCase_									=     self.get_dummy_components()

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

     pipe.set_progress_bar_config(disable=_UpperCAmelCase )

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

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

     UpperCamelCase_									=     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    _UpperCAmelCase     (     self )			-> List[str]:
     # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
     self._test_inference_batch_consistent(batch_sizes=[1, 2] )




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     torch_device == 'cpu'
     UpperCamelCase_									=     True
     self._test_inference_batch_single_identical(
         batch_size=2		,		test_max_difference=_UpperCAmelCase		,		relax_max_difference=_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.get_dummy_components()
     UpperCamelCase_									=     self.pipeline_class(**_UpperCAmelCase )
     UpperCamelCase_									=     pipe.to(_UpperCAmelCase )
     pipe.set_progress_bar_config(disable=_UpperCAmelCase )

     UpperCamelCase_									=     1
     UpperCamelCase_									=     2

     UpperCamelCase_									=     self.get_dummy_inputs(_UpperCAmelCase )

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

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

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




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






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> str:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




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

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

     UpperCamelCase_									=     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 os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


snake_case__      :	Optional[int]       	=      pytest.mark.integration






@pytest.mark.parametrize('path'    ,      ['paws', 'csv'])
def     _snake_case    (__lowercase    ,      __lowercase):
   inspect_dataset(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     path + '.py'
   assert script_name in os.listdir(__lowercase)
   assert "__pycache__" not in os.listdir(__lowercase)






@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning')
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning')
@pytest.mark.parametrize('path'    ,      ['accuracy'])
def     _snake_case    (__lowercase    ,      __lowercase):
   inspect_metric(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     path + '.py'
   assert script_name in os.listdir(__lowercase)
   assert "__pycache__" not in os.listdir(__lowercase)






@pytest.mark.parametrize(
    'path, config_name, expected_splits'    ,      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_config_info(__lowercase    ,      config_name=__lowercase)
   assert info.config_name == config_name
   assert list(info.splits.keys()) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'    ,      [
        ('paws', None, ValueError),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   with pytest.raises(__lowercase):
      get_dataset_config_info(__lowercase    ,      config_name=__lowercase)






@pytest.mark.parametrize(
    'path, expected'    ,      [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_config_names(__lowercase)
   assert expected in config_names






@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'    ,      [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_infos(__lowercase)
   assert list(infos.keys()) == expected_configs
   UpperCamelCase_									=     expected_configs[0]
   assert expected_config in infos
   UpperCamelCase_									=     infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits_in_first_config






@pytest.mark.parametrize(
    'path, expected_config, expected_splits'    ,      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_infos(__lowercase)
   assert expected_config in infos
   UpperCamelCase_									=     infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'    ,      [
        ('paws', None, ValueError),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   with pytest.raises(__lowercase):
      get_dataset_split_names(__lowercase    ,      config_name=__lowercase)





