




import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    int(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    t // 3_6_0_0, (t // 6_0) % 6_0, t % 6_0
							return F"{h}:{m:02d}:{s:02d}" if h != 0 else F"{m:02d}:{s:02d}"

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase=3_0_0     ):
							# docstyle-ignore
							return F"\n    <div>\n      {prefix}\n      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>\n      {label}\n    </div>\n    "

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    '''<table border="1" class="dataframe">\n'''
							html_code += """  <thead>\n <tr style="text-align: left;">\n"""
							for i in items[0]:
														html_code += F"      <th>{i}</th>\n"
							html_code += "    </tr>\n  </thead>\n  <tbody>\n"
							for line in items[1:]:
														html_code += "    <tr>\n"
														for elt in line:
																					SCREAMING_SNAKE_CASE_   	=    F"{elt:.6f}" if isinstance(_lowerCAmelCase  ,    _lowerCAmelCase     ) else str(_lowerCAmelCase     )
																					html_code += F"      <td>{elt}</td>\n"
														html_code += "    </tr>\n"
							html_code += "  </tbody>\n</table><p>"
							return html_code






class  __magic_name__						:
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					5
				SCREAMING_SNAKE_CASE__					:							int	      =					0.2


				def __init__(							self:   str		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Optional[str] = None		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   Optional["NotebookTrainingTracker"] = None		,					_lowerCamelCase:   int = 3_00		,					):
											SCREAMING_SNAKE_CASE_   	=    total
											SCREAMING_SNAKE_CASE_   	=    '''''' if prefix is None else prefix
											SCREAMING_SNAKE_CASE_   	=    leave
											SCREAMING_SNAKE_CASE_   	=    parent
											SCREAMING_SNAKE_CASE_   	=    width
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    None


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   bool = False		,					_lowerCamelCase:   str = None		):
											SCREAMING_SNAKE_CASE_   	=    value
											if comment is not None:
																		SCREAMING_SNAKE_CASE_   	=    comment
											if self.last_value is None:
																		SCREAMING_SNAKE_CASE_   	=    SCREAMING_SNAKE_CASE_   	=    time.time()
																		SCREAMING_SNAKE_CASE_   	=    SCREAMING_SNAKE_CASE_   	=    value
																		SCREAMING_SNAKE_CASE_   	=    SCREAMING_SNAKE_CASE_   	=    None
																		SCREAMING_SNAKE_CASE_   	=    self.warmup
																		SCREAMING_SNAKE_CASE_   	=    1
																		self.update_bar(_lowerCamelCase		)
											elif value <= self.last_value and not force_update:
																		return
											elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for		,					self.total		):
																		if self.first_calls > 0:
																									self.first_calls -= 1
																		SCREAMING_SNAKE_CASE_   	=    time.time()
																		SCREAMING_SNAKE_CASE_   	=    current_time - self.start_time
																		# We could have value = self.start_value if the update is called twixe with the same start value.
																		if value > self.start_value:
																									SCREAMING_SNAKE_CASE_   	=    self.elapsed_time / (value - self.start_value)
																		else:
																									SCREAMING_SNAKE_CASE_   	=    None
																		if value >= self.total:
																									SCREAMING_SNAKE_CASE_   	=    self.total
																									SCREAMING_SNAKE_CASE_   	=    None
																									if not self.leave:
																																self.close()
																		elif self.average_time_per_item is not None:
																									SCREAMING_SNAKE_CASE_   	=    self.average_time_per_item * (self.total - value)
																		self.update_bar(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    value
																		SCREAMING_SNAKE_CASE_   	=    current_time
																		if self.average_time_per_item is None:
																									SCREAMING_SNAKE_CASE_   	=    1
																		else:
																									SCREAMING_SNAKE_CASE_   	=    max(int(self.update_every / self.average_time_per_item		)		,					1		)


				def     _A							(							self:   int		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   List[str]=None		):
											SCREAMING_SNAKE_CASE_   	=    ''' ''' * (len(str(self.total		)		) - len(str(_lowerCamelCase		)		)) + str(_lowerCamelCase		)
											if self.elapsed_time is None:
																		SCREAMING_SNAKE_CASE_   	=    f"[{spaced_value}/{self.total} : < :"
											elif self.predicted_remaining is None:
																		SCREAMING_SNAKE_CASE_   	=    f"[{spaced_value}/{self.total} {format_time(self.elapsed_time		)}"
											else:
																		SCREAMING_SNAKE_CASE_   	=    (
																		    f"[{spaced_value}/{self.total} {format_time(self.elapsed_time		)} <"
																		    f" {format_time(self.predicted_remaining		)}"
																		)
																		self.label += f", {1/self.average_time_per_item:.2f} it/s"
											self.label += "]" if self.comment is None or len(self.comment		) == 0 else f", {self.comment}]"
											self.display()


				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    html_progress_bar(self.value		,					self.total		,					self.prefix		,					self.label		,					self.width		)
											if self.parent is not None:
																		# If this is a child bar, the parent will take care of the display.
																		self.parent.display()
																		return
											if self.output is None:
																		SCREAMING_SNAKE_CASE_   	=    disp.display(disp.HTML(self.html_code		)		,					display_id=_lowerCamelCase		)
											else:
																		self.output.update(disp.HTML(self.html_code		)		)





				def     _A							(							self:   List[str]		):
											if self.parent is None and self.output is not None:
																		self.output.update(disp.HTML(''''''		)		)






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   Dict		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Optional[int]=None		):
											super().__init__(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    None if column_names is None else [column_names]
											SCREAMING_SNAKE_CASE_   	=    None


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    html_progress_bar(self.value		,					self.total		,					self.prefix		,					self.label		,					self.width		)
											if self.inner_table is not None:
																		self.html_code += text_to_html_table(self.inner_table		)
											if self.child_bar is not None:
																		self.html_code += self.child_bar.html_code
											if self.output is None:
																		SCREAMING_SNAKE_CASE_   	=    disp.display(disp.HTML(self.html_code		)		,					display_id=_lowerCamelCase		)
											else:
																		self.output.update(disp.HTML(self.html_code		)		)


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   Any		):
											if self.inner_table is None:
																		SCREAMING_SNAKE_CASE_   	=    [list(values.keys()		), list(values.values()		)]
											else:
																		SCREAMING_SNAKE_CASE_   	=    self.inner_table[0]
																		if len(self.inner_table		) == 1:
																									# We give a chance to update the column names at the first iteration
																									for key in values.keys():
																																if key not in columns:
																																							columns.append(_lowerCamelCase		)
																									SCREAMING_SNAKE_CASE_   	=    columns
																		self.inner_table.append([values[c] for c in columns]		)


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Union[str, Any]=None		,					_lowerCamelCase:   Any=3_00		):
											SCREAMING_SNAKE_CASE_   	=    NotebookProgressBar(_lowerCamelCase		,					prefix=_lowerCamelCase		,					parent=self		,					width=_lowerCamelCase		)
											return self.child_bar





				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    None
											self.display()






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    False


				def     _A							(							self:   int		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Optional[int]		,					**_lowerCamelCase:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
											SCREAMING_SNAKE_CASE_   	=    0
											SCREAMING_SNAKE_CASE_   	=    0
											SCREAMING_SNAKE_CASE_   	=    [self.first_column] + ['''Training Loss''']
											if args.evaluation_strategy != IntervalStrategy.NO:
																		column_names.append('''Validation Loss'''		)
											SCREAMING_SNAKE_CASE_   	=    NotebookTrainingTracker(state.max_steps		,					_lowerCamelCase		)


				def     _A							(							self:   str		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   str		,					**_lowerCamelCase:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    int(state.epoch		) if int(state.epoch		) == state.epoch else f"{state.epoch:.2f}"
											self.training_tracker.update(
											    state.global_step + 1		,					comment=f"Epoch {epoch}/{state.num_train_epochs}"		,					force_update=self._force_next_update		,					)
											SCREAMING_SNAKE_CASE_   	=    False


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Dict=None		,					**_lowerCamelCase:   Optional[Any]		):
											if not has_length(_lowerCamelCase		):
																		return
											if self.prediction_bar is None:
																		if self.training_tracker is not None:
																									SCREAMING_SNAKE_CASE_   	=    self.training_tracker.add_child(len(_lowerCamelCase		)		)
																		else:
																									SCREAMING_SNAKE_CASE_   	=    NotebookProgressBar(len(_lowerCamelCase		)		)
																		self.prediction_bar.update(1		)
											else:
																		self.prediction_bar.update(self.prediction_bar.value + 1		)


				def     _A							(							self:   int		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   int		,					**_lowerCamelCase:   Union[str, Any]		):
											if self.prediction_bar is not None:
																		self.prediction_bar.close()
											SCREAMING_SNAKE_CASE_   	=    None


				def     _A							(							self:   Any		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Dict=None		,					**_lowerCamelCase:   str		):
											# Only for when there is no evaluation
											if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
																		SCREAMING_SNAKE_CASE_   	=    {'''Training Loss''': logs['''loss''']}
																		# First column is necessarily Step sine we're not in epoch eval strategy
																		SCREAMING_SNAKE_CASE_   	=    state.global_step
																		self.training_tracker.write_line(_lowerCamelCase		)


				def     _A							(							self:   str		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   Dict=None		,					**_lowerCamelCase:   List[Any]		):
											if self.training_tracker is not None:
																		SCREAMING_SNAKE_CASE_   	=    {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
																		for log in reversed(state.log_history		):
																									if "loss" in log:
																																SCREAMING_SNAKE_CASE_   	=    log['''loss''']
																																break

																		if self.first_column == "Epoch":
																									SCREAMING_SNAKE_CASE_   	=    int(state.epoch		)
																		else:
																									SCREAMING_SNAKE_CASE_   	=    state.global_step
																		SCREAMING_SNAKE_CASE_   	=    '''eval'''
																		for k in metrics:
																									if k.endswith('''_loss'''		):
																																SCREAMING_SNAKE_CASE_   	=    re.sub(R'''\_loss$'''		,					''''''		,					_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    metrics.pop('''total_flos'''		,					_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    metrics.pop('''epoch'''		,					_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    metrics.pop(f"{metric_key_prefix}_runtime"		,					_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    metrics.pop(f"{metric_key_prefix}_samples_per_second"		,					_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    metrics.pop(f"{metric_key_prefix}_steps_per_second"		,					_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    metrics.pop(f"{metric_key_prefix}_jit_compilation_time"		,					_lowerCamelCase		)
																		for k, v in metrics.items():
																									if k == f"{metric_key_prefix}_loss":
																																SCREAMING_SNAKE_CASE_   	=    v
																									else:
																																SCREAMING_SNAKE_CASE_   	=    k.split('''_'''		)
																																SCREAMING_SNAKE_CASE_   	=    ''' '''.join([part.capitalize() for part in splits[1:]]		)
																																SCREAMING_SNAKE_CASE_   	=    v
																		self.training_tracker.write_line(_lowerCamelCase		)
																		self.training_tracker.remove_child()
																		SCREAMING_SNAKE_CASE_   	=    None
																		# Evaluation takes a long time so we should force the next update.
																		SCREAMING_SNAKE_CASE_   	=    True





				def     _A							(							self:   int		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Any		,					**_lowerCamelCase:   List[str]		):
											self.training_tracker.update(
											    state.global_step		,					comment=f"Epoch {int(state.epoch		)}/{state.num_train_epochs}"		,					force_update=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    None








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  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					ShapEImgaImgPipeline
				SCREAMING_SNAKE_CASE__					:							Dict	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					[
				    "num_images_per_prompt",
				    "num_inference_steps",
				    "generator",
				    "latents",
				    "guidance_scale",
				    "frame_size",
				    "output_type",
				    "return_dict",
				]
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					False


				@property
				def     _A							(							self:   Optional[Any]		):
											return 32


				@property
				def     _A							(							self:   Optional[int]		):
											return 32


				@property
				def     _A							(							self:   List[Any]		):
											return self.time_input_dim * 4


				@property
				def     _A							(							self:   Any		):
											return 8


				@property
				def     _A							(							self:   int		):
											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(_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    CLIPImageProcessor(
											    crop_size=2_24		,					do_center_crop=_lowerCamelCase		,					do_normalize=_lowerCamelCase		,					do_resize=_lowerCamelCase		,					image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]		,					image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]		,					resample=3		,					size=2_24		,					)

											return image_processor


				@property
				def     _A							(							self:   Dict		):
											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(**_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											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(**_lowerCamelCase		)
											return model


				def     _A							(							self:   Optional[int]		):
											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=10_24		,					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     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[Any]=0		):
											SCREAMING_SNAKE_CASE_   	=    floats_tensor((1, 3, 64, 64)		,					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''': 32,
											    '''output_type''': '''np''',
											}
											return inputs


				def     _A							(							self:   Any		):
											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 == (20, 32, 32, 3)

											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											    ]		)

											assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2


				def     _A							(							self:   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     _A							(							self:   Tuple		):
											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     _A							(							self:   Tuple		):
											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  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   Any		):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def     _A							(							self:   Optional[int]		):
											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=64		,					frame_size=64		,					output_type='''np'''		,					).images[0]

											assert images.shape == (20, 64, 64, 3)

											assert_mean_pixel_difference(_lowerCamelCase		,					_lowerCamelCase		)


