


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







       @property
       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            return 32







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            return 32







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            return self.time_input_dim * 4







       @property
       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            return 8







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






            '''simple docstring'''




            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(A_					)
            return model







       @property
       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			CLIPImageProcessor(
                crop_size=224,do_center_crop=A_,do_normalize=A_,do_resize=A_,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 		snake_case_	(			self: int					):






            '''simple docstring'''




            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(**A_					)
            return model







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






            '''simple docstring'''




            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(**A_					)
            return model







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __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=A_,clip_sample=A_,clip_sample_range=1.0,)
            __UpperCamelCase =			{
                'prior': prior,
                'image_encoder': image_encoder,
                'image_processor': image_processor,
                'renderer': renderer,
                'scheduler': scheduler,
            }

            return components







       def 		snake_case_	(			self: Union[str, Any],A_: Optional[Any],A_: str=0					):






            '''simple docstring'''




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

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







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			'cpu'

            __UpperCamelCase =			self.get_dummy_components()

            __UpperCamelCase =			self.pipeline_class(**A_					)
            __UpperCamelCase =			pipe.to(A_					)

            pipe.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			pipe(**self.get_dummy_inputs(A_					)					)
            __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 		snake_case_	(			self: Any					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			torch_device == 'cpu'
            __UpperCamelCase =			True
            self._test_inference_batch_single_identical(
                batch_size=2,test_max_difference=A_,relax_max_difference=A_,)







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






            '''simple docstring'''




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

            __UpperCamelCase =			1
            __UpperCamelCase =			2

            __UpperCamelCase =			self.get_dummy_inputs(A_					)

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

            __UpperCamelCase =			pipe(**A_,num_images_per_prompt=A_					)[0]

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



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







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __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(A_					)
            pipe.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			torch.Generator(device=A_					).manual_seed(0					)

            __UpperCamelCase =			pipe(
                A_,generator=A_,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(A_,A_					)






import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__snake_case   							=						'''src/diffusers'''

# Matches is_xxx_available()
__snake_case   							=						re.compile(r'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
__snake_case   							=						re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')


__snake_case   							=						'''
{0} = None
'''

__snake_case   							=						'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
'''


__snake_case   							=						'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			_re_backend.findall(_lowercase )
     if len(_lowercase ) == 0:
          return None

     return "_and_".join(_lowercase )






def 		_A		(		)					-> Tuple:


     """simple docstring"""


     with open(os.path.join(_lowercase					,  '__init__.py' )					,  'r'					,  encoding='utf-8'					,  newline='\n' ) as f:
          __UpperCamelCase =			f.readlines()

     # Get to the point we do the actual imports for type checking
     __UpperCamelCase =			0
     __UpperCamelCase =			{}
     # Go through the end of the file
     while line_index < len(_lowercase ):
          # If the line contains is_backend_available, we grab all objects associated with the `else` block
          __UpperCamelCase =			find_backend(lines[line_index] )
          if backend is not None:
               while not lines[line_index].startswith('else:' ):
                    line_index += 1
               line_index += 1
               __UpperCamelCase =			[]
               # Until we unindent, add backend objects to the list
               while line_index < len(_lowercase ) and len(lines[line_index] ) > 1:
                    __UpperCamelCase =			lines[line_index]
                    __UpperCamelCase =			_re_single_line_import.search(_lowercase )
                    if single_line_import_search is not None:
                         objects.extend(single_line_import_search.groups()[0].split(', ' ) )
                    elif line.startswith(' ' * 8 ):
                         objects.append(line[8:-2] )
                    line_index += 1

               if len(_lowercase ) > 0:
                    __UpperCamelCase =			objects
          else:
               line_index += 1

     return backend_specific_objects






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


     """simple docstring"""


     if name.isupper():
          return DUMMY_CONSTANT.format(_lowercase )
     elif name.islower():
          return DUMMY_FUNCTION.format(_lowercase					,  _lowercase )
     else:
          return DUMMY_CLASS.format(_lowercase					,  _lowercase )






def 		_A		(		_lowercase=None )					-> Optional[Any]:


     """simple docstring"""


     if backend_specific_objects is None:
          __UpperCamelCase =			read_init()
     # For special correspondence backend to module name as used in the function requires_modulename
     __UpperCamelCase =			{}

     for backend, objects in backend_specific_objects.items():
          __UpperCamelCase =			'[' + ', '.join(f'''"{b}"''' for b in backend.split('_and_' ) ) + ']'
          __UpperCamelCase =			'# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
          dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
          dummy_file += "\n".join([create_dummy_object(_lowercase					,  _lowercase ) for o in objects] )
          __UpperCamelCase =			dummy_file

     return dummy_files






def 		_A		(		_lowercase=False )					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			create_dummy_files()
     # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
     __UpperCamelCase =			{'torch': 'pt'}

     # Locate actual dummy modules and read their content.
     __UpperCamelCase =			os.path.join(_lowercase					,  'utils' )
     __UpperCamelCase =			{
         backend: os.path.join(_lowercase					,  f'''dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py''' )
         for backend in dummy_files.keys()
     }

     __UpperCamelCase =			{}
     for backend, file_path in dummy_file_paths.items():
          if os.path.isfile(_lowercase ):
               with open(_lowercase					,  'r'					,  encoding='utf-8'					,  newline='\n' ) as f:
                    __UpperCamelCase =			f.read()
          else:
               __UpperCamelCase =			''

     for backend in dummy_files.keys():
          if dummy_files[backend] != actual_dummies[backend]:
               if overwrite:
                    print(
                        f'''Updating diffusers.utils.dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py as the main '''
                        '__init__ has new objects.' )
                    with open(dummy_file_paths[backend]					,  'w'					,  encoding='utf-8'					,  newline='\n' ) as f:
                         f.write(dummy_files[backend] )
               else:
                    raise ValueError(
                        'The main __init__ has objects that are not present in '
                        f'''diffusers.utils.dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py. Run `make fix-copies` '''
                        'to fix this.' )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    __snake_case   							=						parser.parse_args()

    check_dummies(args.fix_and_overwrite)


