





'''simple docstring'''






import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 		lowerCamelCase  (      _A      ,   unittest.TestCase       ):
	snake_case_      =   DiTPipeline
	snake_case_      =   CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	snake_case_      =   PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	snake_case_      =   CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	snake_case_      =   False



	def   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    Optional[Any]						=   TransformeraDModel(
			    sample_size=16		,      num_layers=2		,      patch_size=4		,      attention_head_dim=8		,      num_attention_heads=2		,      in_channels=4		,      out_channels=8		,      attention_bias=_lowerCAmelCase		,      activation_fn="gelu-approximate"		,      num_embeds_ada_norm=1_000		,      norm_type="ada_norm_zero"		,      norm_elementwise_affine=_lowerCAmelCase		,      )
			lowerCAmelCase     :    Any						=   AutoencoderKL()
			lowerCAmelCase     :    Optional[Any]						=   DDIMScheduler()
			lowerCAmelCase     :    int						=   {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
			return components



	def   _lowerCamelCase    (   self		,      a_		,      a_=0	):
			if str(_lowerCAmelCase	).startswith("mps"	):
					lowerCAmelCase     :    Tuple						=   torch.manual_seed(_lowerCAmelCase	)
			else:
					lowerCAmelCase     :    Tuple						=   torch.Generator(device=_lowerCAmelCase	).manual_seed(_lowerCAmelCase	)
			lowerCAmelCase     :    Optional[Any]						=   {
			    "class_labels": [1],
			    "generator": generator,
			    "num_inference_steps": 2,
			    "output_type": "numpy",
			}
			return inputs



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   "cpu"

			lowerCAmelCase     :    Dict						=   self.get_dummy_components()
			lowerCAmelCase     :    Tuple						=   self.pipeline_class(**_lowerCAmelCase	)
			pipe.to(_lowerCAmelCase	)
			pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

			lowerCAmelCase     :    List[Any]						=   self.get_dummy_inputs(_lowerCAmelCase	)
			lowerCAmelCase     :    Any						=   pipe(**_lowerCAmelCase	).images
			lowerCAmelCase     :    List[Any]						=   image[0, -3:, -3:, -1]

			self.assertEqual(image.shape		,      (1, 16, 16, 3)	)
			lowerCAmelCase     :    Tuple						=   np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]	)
			lowerCAmelCase     :    List[Any]						=   np.abs(image_slice.flatten() - expected_slice	).max()
			self.assertLessEqual(_lowerCAmelCase		,      1e-3	)



	def   _lowerCamelCase    (   self	):
			self._test_inference_batch_single_identical(relax_max_difference=_lowerCAmelCase		,      expected_max_diff=1e-3	)





	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()		,      reason="XFormers attention is only available with CUDA and `xformers` installed"		,      )
	def   _lowerCamelCase    (   self	):
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)






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



	def   _lowerCamelCase    (   self	):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   torch.manual_seed(0	)

			lowerCAmelCase     :    Union[str, Any]						=   DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"	)
			pipe.to("cuda"	)

			lowerCAmelCase     :    str						=   ["vase", "umbrella", "white shark", "white wolf"]
			lowerCAmelCase     :    int						=   pipe.get_label_ids(_lowerCAmelCase	)

			lowerCAmelCase     :    Optional[int]						=   pipe(_lowerCAmelCase		,      generator=_lowerCAmelCase		,      num_inference_steps=40		,      output_type="np"	).images

			for word, image in zip(_lowerCAmelCase		,      _lowerCAmelCase	):
					lowerCAmelCase     :    Union[str, Any]						=   load_numpy(
					    F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''	)
					assert np.abs((expected_image - image).max()	) < 1e-2





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"	)
			lowerCAmelCase     :    Any						=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
			pipe.to("cuda"	)

			lowerCAmelCase     :    Optional[int]						=   ["vase", "umbrella"]
			lowerCAmelCase     :    List[str]						=   pipe.get_label_ids(_lowerCAmelCase	)

			lowerCAmelCase     :    Optional[Any]						=   torch.manual_seed(0	)
			lowerCAmelCase     :    Optional[Any]						=   pipe(_lowerCAmelCase		,      generator=_lowerCAmelCase		,      num_inference_steps=25		,      output_type="np"	).images

			for word, image in zip(_lowerCAmelCase		,      _lowerCAmelCase	):
					lowerCAmelCase     :    int						=   load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    F'''/dit/{word}_512.npy'''	)

					assert np.abs((expected_image - image).max()	) < 1e-1










'''simple docstring'''






from __future__ import annotations






def      __A						(							a_						:  float      ,a_						:  float      ,a_						:  float      ,):
		if (stress, tangential_force, area).count(0	) != 1:
				raise ValueError("You cannot supply more or less than 2 values"	)
		elif stress < 0:
				raise ValueError("Stress cannot be negative"	)
		elif tangential_force < 0:
				raise ValueError("Tangential Force cannot be negative"	)
		elif area < 0:
				raise ValueError("Area cannot be negative"	)
		elif stress == 0:
				return (
				    "stress",
				    tangential_force / area,
				)
		elif tangential_force == 0:
				return (
				    "tangential_force",
				    stress * area,
				)
		else:
				return (
				    "area",
				    tangential_force / stress,
				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



