






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        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:List[Any]   =       DiTPipeline
	__snake_case	:Any   =       CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	__snake_case	:Tuple   =       PipelineTesterMixin.required_optional_params - {
	    'latents',
	    'num_images_per_prompt',
	    'callback',
	    'callback_steps',
	}
	__snake_case	:str   =       CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	__snake_case	:List[Any]   =       False







	def  _a   (  self      :							str			)		-> Any:
		"""simple docstring"""







		torch.manual_seed(0			)
		__lowercase             =  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=1000       ,				norm_type="""ada_norm_zero"""       ,				norm_elementwise_affine=_lowerCAmelCase       ,				)
		__lowercase             =  AutoencoderKL()
		__lowercase             =  DDIMScheduler()
		__lowercase             =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
		return components







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Tuple=0			)		-> Optional[Any]:
		"""simple docstring"""







		if str(_lowerCAmelCase			).startswith("""mps"""			):
			__lowercase             =  torch.manual_seed(_lowerCAmelCase			)
		else:
			__lowercase             =  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
		__lowercase             =  {
		    """class_labels""": [1],
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """output_type""": """numpy""",
		}
		return inputs







	def  _a   (  self      :							str			)		-> Any:
		"""simple docstring"""







		__lowercase             =  """cpu"""

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

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

		self.assertEqual(image.shape       ,				(1, 16, 16, 3)			)
		__lowercase             =  np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]			)
		__lowercase             =  np.abs(image_slice.flatten() - expected_slice			).max()
		self.assertLessEqual(_lowerCAmelCase       ,				1e-3			)







	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		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  _a   (  self      :							int			)		-> Dict:
		"""simple docstring"""







		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)


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







	def  _a   (  self      :							int			)		-> int:
		"""simple docstring"""







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







	def  _a   (  self      :							Optional[Any]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  torch.manual_seed(0			)

		__lowercase             =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""			)
		pipe.to("""cuda"""			)

		__lowercase             =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
		__lowercase             =  pipe.get_label_ids(_lowerCAmelCase			)

		__lowercase             =  pipe(_lowerCAmelCase       ,				generator=_lowerCAmelCase       ,				num_inference_steps=40       ,				output_type="""np"""			).images

		for word, image in zip(_lowerCAmelCase       ,				_lowerCAmelCase			):
			__lowercase             =  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  _a   (  self      :							List[str]			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""			)
		__lowercase             =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
		pipe.to("""cuda"""			)

		__lowercase             =  ["""vase""", """umbrella"""]
		__lowercase             =  pipe.get_label_ids(_lowerCAmelCase			)

		__lowercase             =  torch.manual_seed(0			)
		__lowercase             =  pipe(_lowerCAmelCase       ,				generator=_lowerCAmelCase       ,				num_inference_steps=25       ,				output_type="""np"""			).images

		for word, image in zip(_lowerCAmelCase       ,				_lowerCAmelCase			):
			__lowercase             =  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









from __future__ import annotations

from scipy.special import comb  # type: ignore


class        __UpperCamelCase    :







	def __init__(  self      :							int       ,				_lowerCAmelCase      :							list[tuple[float, float]]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  list_of_points
		# Degree determines the flexibility of the curve.
		# Degree = 1 will produce a straight line.
		__lowercase             =  len(_lowerCAmelCase			) - 1







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							float			)		-> list[float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."
		__lowercase             =  []
		for i in range(len(self.list_of_points			)			):
			# basis function for each i
			output_values.append(
			    comb(self.degree       ,				_lowerCAmelCase			) * ((1 - t) ** (self.degree - i)) * (t**i)			)
		# the basis must sum up to 1 for it to produce a valid Bezier curve.
		assert round(sum(_lowerCAmelCase			)       ,				5			) == 1
		return output_values







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							float			)		-> tuple[float, float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."

		__lowercase             =  self.basis_function(_lowerCAmelCase			)
		__lowercase             =  0.0
		__lowercase             =  0.0
		for i in range(len(self.list_of_points			)			):
			# For all points, sum up the product of i-th basis function and i-th point.
			x += basis_function[i] * self.list_of_points[i][0]
			y += basis_function[i] * self.list_of_points[i][1]
		return (x, y)







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							float = 0.01			)		-> Union[str, Any]:
		"""simple docstring"""







		from matplotlib import pyplot as plt  # type: ignore

		__lowercase             =  []  # x coordinates of points to plot
		__lowercase             =  []  # y coordinates of points to plot

		__lowercase             =  0.0
		while t <= 1:
			__lowercase             =  self.bezier_curve_function(_lowerCAmelCase			)
			to_plot_x.append(value[0]			)
			to_plot_y.append(value[1]			)
			t += step_size

		__lowercase             =  [i[0] for i in self.list_of_points]
		__lowercase             =  [i[1] for i in self.list_of_points]

		plt.plot(
		    _lowerCAmelCase       ,				_lowerCAmelCase       ,				color="""blue"""       ,				label="""Curve of Degree """ + str(self.degree			)       ,				)
		plt.scatter(_lowerCAmelCase       ,				_lowerCAmelCase       ,				color="""red"""       ,				label="""Control Points"""			)
		plt.legend()
		plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
						BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
						BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

