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        UpperCamelCase						(  SCREAMING_SNAKE_CASE_      ,   unittest.TestCase   ):
					__UpperCamelCase							= ShapEImgaImgPipeline
					__UpperCamelCase							= ["""image"""]
					__UpperCamelCase							= ["""image"""]
					__UpperCamelCase							= [
					    """num_images_per_prompt""",
					    """num_inference_steps""",
					    """generator""",
					    """latents""",
					    """guidance_scale""",
					    """frame_size""",
					    """output_type""",
					    """return_dict""",
					]
					__UpperCamelCase							= False




					@property
					def 				UpperCamelCase_    (			self			:       Tuple    ):


							"""simple docstring"""




							return 32




					@property
					def 				UpperCamelCase_    (			self			:       str    ):


							"""simple docstring"""




							return 32




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


							"""simple docstring"""




							return self.time_input_dim * 4




					@property
					def 				UpperCamelCase_    (			self			:       Dict    ):


							"""simple docstring"""




							return 8




					@property
					def 				UpperCamelCase_    (			self			:       int    ):


							"""simple docstring"""




							torch.manual_seed(0    )
							__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   ,)

							__snake_case									=  CLIPVisionModel(_lowerCAmelCase    )
							return model




					@property
					def 				UpperCamelCase_    (			self			:       Dict    ):


							"""simple docstring"""




							__snake_case									=  CLIPImageProcessor(
							    crop_size=224   ,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=224   ,)

							return image_processor




					@property
					def 				UpperCamelCase_    (			self			:       int    ):


							"""simple docstring"""




							torch.manual_seed(0    )

							__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,
							}

							__snake_case									=  PriorTransformer(**_lowerCAmelCase    )
							return model




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


							"""simple docstring"""




							torch.manual_seed(0    )

							__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,
							    ),
							}
							__snake_case									=  ShapERenderer(**_lowerCAmelCase    )
							return model




					def 				UpperCamelCase_    (			self			:       Any    ):


							"""simple docstring"""




							__snake_case									=  self.dummy_prior
							__snake_case									=  self.dummy_image_encoder
							__snake_case									=  self.dummy_image_processor
							__snake_case									=  self.dummy_renderer

							__snake_case									=  HeunDiscreteScheduler(
							    beta_schedule="exp"   ,num_train_timesteps=1_024   ,prediction_type="sample"   ,use_karras_sigmas=_lowerCAmelCase   ,clip_sample=_lowerCAmelCase   ,clip_sample_range=1.0   ,)
							__snake_case									=  {
							    'prior': prior,
							    'image_encoder': image_encoder,
							    'image_processor': image_processor,
							    'renderer': renderer,
							    'scheduler': scheduler,
							}

							return components




					def 				UpperCamelCase_    (			self			:       Tuple   ,_lowerCAmelCase			:       Tuple   ,_lowerCAmelCase			:       str=0    ):


							"""simple docstring"""




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

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




					def 				UpperCamelCase_    (			self			:       Dict    ):


							"""simple docstring"""




							__snake_case									=  'cpu'

							__snake_case									=  self.get_dummy_components()

							__snake_case									=  self.pipeline_class(**_lowerCAmelCase    )
							__snake_case									=  pipe.to(_lowerCAmelCase    )

							pipe.set_progress_bar_config(disable=_lowerCAmelCase    )

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

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

							__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 				UpperCamelCase_    (			self			:       Optional[Any]    ):


							"""simple docstring"""




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




					def 				UpperCamelCase_    (			self			:       Dict    ):


							"""simple docstring"""




							__snake_case									=  torch_device == 'cpu'
							__snake_case									=  True
							self._test_inference_batch_single_identical(
							    batch_size=2   ,test_max_difference=_lowerCAmelCase   ,relax_max_difference=_lowerCAmelCase   ,)







					def 				UpperCamelCase_    (			self			:       List[Any]    ):


							"""simple docstring"""




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

							__snake_case									=  1
							__snake_case									=  2

							__snake_case									=  self.get_dummy_inputs(_lowerCAmelCase    )

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

							__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        UpperCamelCase						(  unittest.TestCase   ):




					def 				UpperCamelCase_    (			self			:       List[Any]    ):


							"""simple docstring"""




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







					def 				UpperCamelCase_    (			self			:       List[Any]    ):


							"""simple docstring"""




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

							__snake_case									=  torch.Generator(device=_lowerCAmelCase    ).manual_seed(0    )

							__snake_case									=  pipe(
							    _lowerCAmelCase   ,generator=_lowerCAmelCase   ,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(_lowerCAmelCase   ,_lowerCAmelCase    )










"""simple docstring"""







# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


a_							=    re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>\d+)$""")







@total_ordering
@dataclass
class 					A_:







			"""simple docstring"""



			a_       :			str
			a_       :			Optional[str]												=			None
			a_       :			Optional[Union[str, int]]												=			None
			a_       :			Optional[Union[str, int]]												=			None
			a_       :			Optional[Union[str, int]]												=			None
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase					:					List[str]	       =    _str_to_version_tuple(self.version_str							)
			def __repr__(	self							):
						return F"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"
			@property
			def    _lowerCAmelCase		(	self							):
						return self.major, self.minor, self.patch
			def    _lowerCAmelCase		(	self				,   A							):
						if isinstance(A				,   A							):
									return Version(A							)
						elif isinstance(A				,   A							):
									return other
						raise TypeError(F"{other} (type {type(A							)}) cannot be compared to version."							)
			def __eq__(	self				,   A							):
						try:
									_lowerCamelCase					:					Dict	       =    self._validate_operand(A							)
						except (TypeError, ValueError):
									return False
						else:
									return self.tuple == other.tuple
			def __lt__(	self				,   A							):
						_lowerCamelCase					:					Any	       =    self._validate_operand(A							)
						return self.tuple < other.tuple
			def __hash__(	self							):
						return hash(_version_tuple_to_str(self.tuple							)							)
			@classmethod
			def    _lowerCAmelCase		(	cls				,   A							):
						_lowerCamelCase					:					Optional[Any]	       =    {f.name for f in dataclasses.fields(cls							)}
						return cls(**{k: v for k, v in dic.items() if k in field_names}							)

			def    _lowerCAmelCase		(	self							):
						return self.version_str






def     UpperCAmelCase_					(				__a   :			Any						):




			'''simple docstring'''



			_lowerCamelCase					:					Union[str, Any]	       =    _VERSION_REG.match(__a						)
			if not res:
						raise ValueError(f"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."						)
			return tuple(int(__a						) for v in [res.group('major'						), res.group('minor'						), res.group('patch'						)]						)



def     UpperCAmelCase_					(				__a   :			List[str]						):




			'''simple docstring'''



			return ".".join(str(__a						) for v in version_tuple						)





