
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__							(				__lowercase     ,						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    _lowerCamelCase				(					self		:Optional[int]						)		-> Optional[int]:
								return 3_2







	@property
	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Optional[Any]:
								return 3_2







	@property
	def    _lowerCamelCase				(					self		:Tuple						)		-> Union[str, Any]:
								return self.time_input_dim * 4







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







	@property
	def    _lowerCamelCase				(					self		:List[str]						)		-> int:
								torch.manual_seed(0						)
								__UpperCamelCase	:   Optional[int]			   =					CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size     ,   image_size=6_4     ,   projection_dim=self.text_embedder_hidden_size     ,   intermediate_size=3_7     ,   num_attention_heads=4     ,   num_channels=3     ,   num_hidden_layers=5     ,   patch_size=1     ,   )

								__UpperCamelCase	:   Union[str, Any]			   =					CLIPVisionModel(a						)
								return model







	@property
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> List[str]:
								__UpperCamelCase	:   Optional[Any]			   =					CLIPImageProcessor(
								    crop_size=2_2_4     ,   do_center_crop=a     ,   do_normalize=a     ,   do_resize=a     ,   image_mean=[0.48145466, 0.4578275, 0.40821073]     ,   image_std=[0.26862954, 0.26130258, 0.27577711]     ,   resample=3     ,   size=2_2_4     ,   )

								return image_processor







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

								__UpperCamelCase	:   Dict			   =					{
								    "num_attention_heads": 2,
								    "attention_head_dim": 1_6,
								    "embedding_dim": self.time_input_dim,
								    "num_embeddings": 3_2,
								    "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	:   int			   =					PriorTransformer(**a						)
								return model







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

								__UpperCamelCase	:   Dict			   =					{
								    "param_shapes": (
								        (self.renderer_dim, 9_3),
								        (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": 1_2,
								    "background": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								__UpperCamelCase	:   Dict			   =					ShapERenderer(**a						)
								return model







	def    _lowerCamelCase				(					self		:str						)		-> Union[str, Any]:
								__UpperCamelCase	:   int			   =					self.dummy_prior
								__UpperCamelCase	:   Optional[int]			   =					self.dummy_image_encoder
								__UpperCamelCase	:   Dict			   =					self.dummy_image_processor
								__UpperCamelCase	:   List[Any]			   =					self.dummy_renderer

								__UpperCamelCase	:   List[str]			   =					HeunDiscreteScheduler(
								    beta_schedule="exp"     ,   num_train_timesteps=1_0_2_4     ,   prediction_type="sample"     ,   use_karras_sigmas=a     ,   clip_sample=a     ,   clip_sample_range=1.0     ,   )
								__UpperCamelCase	:   Optional[int]			   =					{
								    "prior": prior,
								    "image_encoder": image_encoder,
								    "image_processor": image_processor,
								    "renderer": renderer,
								    "scheduler": scheduler,
								}

								return components







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:Tuple     ,   a		:Dict=0						)		-> Tuple:
								__UpperCamelCase	:   int			   =					floats_tensor((1, 3, 6_4, 6_4)     ,   rng=random.Random(a						)						).to(a						)

								if str(a						).startswith("mps"						):
															__UpperCamelCase	:   str			   =					torch.manual_seed(a						)
								else:
															__UpperCamelCase	:   List[Any]			   =					torch.Generator(device=a						).manual_seed(a						)
								__UpperCamelCase	:   int			   =					{
								    "image": input_image,
								    "generator": generator,
								    "num_inference_steps": 1,
								    "frame_size": 3_2,
								    "output_type": "np",
								}
								return inputs







	def    _lowerCamelCase				(					self		:int						)		-> Any:
								__UpperCamelCase	:   Optional[int]			   =					"cpu"

								__UpperCamelCase	:   int			   =					self.get_dummy_components()

								__UpperCamelCase	:   List[Any]			   =					self.pipeline_class(**a						)
								__UpperCamelCase	:   List[str]			   =					pipe.to(a						)

								pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   Union[str, Any]			   =					pipe(**self.get_dummy_inputs(a						)						)
								__UpperCamelCase	:   List[str]			   =					output.images[0]
								__UpperCamelCase	:   List[Any]			   =					image[0, -3:, -3:, -1]

								assert image.shape == (2_0, 3_2, 3_2, 3)

								__UpperCamelCase	:   int			   =					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    _lowerCamelCase				(					self		:str						)		-> Optional[Any]:
								# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
								self._test_inference_batch_consistent(batch_sizes=[1, 2]						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> str:
								__UpperCamelCase	:   Tuple			   =					torch_device == "cpu"
								__UpperCamelCase	:   Optional[Any]			   =					True
								self._test_inference_batch_single_identical(
								    batch_size=2     ,   test_max_difference=a     ,   relax_max_difference=a     ,   )







	def    _lowerCamelCase				(					self		:Any						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					self.get_dummy_components()
								__UpperCamelCase	:   str			   =					self.pipeline_class(**a						)
								__UpperCamelCase	:   List[Any]			   =					pipe.to(a						)
								pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   List[str]			   =					1
								__UpperCamelCase	:   Dict			   =					2

								__UpperCamelCase	:   Union[str, Any]			   =					self.get_dummy_inputs(a						)

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

								__UpperCamelCase	:   List[str]			   =					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):




	'''simple docstring'''




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







	def    _lowerCamelCase				(					self		:Dict						)		-> List[str]:
								__UpperCamelCase	:   Tuple			   =					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"						)
								__UpperCamelCase	:   Any			   =					load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/shap_e/test_shap_e_img2img_out.npy"						)
								__UpperCamelCase	:   List[str]			   =					ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"						)
								__UpperCamelCase	:   Union[str, Any]			   =					pipe.to(a						)
								pipe.set_progress_bar_config(disable=a						)

								__UpperCamelCase	:   List[Any]			   =					torch.Generator(device=a						).manual_seed(0						)

								__UpperCamelCase	:   List[Any]			   =					pipe(
								    a     ,   generator=a     ,   guidance_scale=3.0     ,   num_inference_steps=6_4     ,   frame_size=6_4     ,   output_type="np"     ,   ).images[0]

								assert images.shape == (2_0, 6_4, 6_4, 3)

								assert_mean_pixel_difference(a     ,   a						)

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     ComputeEnvironment.AMAZON_SAGEMAKER
	_A								=     True
	_A								=     'ml.p3.2xlarge'
	_A								=     'accelerate_sagemaker_execution_role'
	_A								=     'hf-sm'
	_A								=     'us-east-1'
	_A								=     1
	_A								=     'accelerate-sagemaker-1'
	_A								=     '1.6'
	_A								=     '4.4'
	_A								=     'train.py'
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Any						)		-> Optional[int]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase	:   Optional[int]			   =					_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args						)
								assert isinstance(converted_args["model_name_or_path"]     ,   a						)
								assert isinstance(converted_args["do_train"]     ,   a						)
								assert isinstance(converted_args["epochs"]     ,   a						)
								assert isinstance(converted_args["learning_rate"]     ,   a						)
								assert isinstance(converted_args["max_steps"]     ,   a						)

								with pytest.raises(a						):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args						)