






"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					Union[str, Any] 			=					DDIMPipeline
	_A							:					Optional[int] 			=					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
	_A							:					Optional[Any] 			=					PipelineTesterMixin.required_optional_params - {
	    """num_images_per_prompt""",
	    """latents""",
	    """callback""",
	    """callback_steps""",
	}
	_A							:					Any 			=					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
	_A							:					List[str] 			=					False





	def  lowerCamelCase(self				):
		torch.manual_seed(0				)
		A_		:    Optional[Any]						     = UNetaDModel(
		    block_out_channels=(32, 64)  ,  layers_per_block=2  ,  sample_size=32  ,  in_channels=3  ,  out_channels=3  ,  down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")  ,  up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")  ,  )
		A_		:    Optional[Any]						     = DDIMScheduler()
		A_		:    int						     = {"""unet""": unet, """scheduler""": scheduler}
		return components





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=0				):
		if str(lowerCAmelCase_				).startswith("""mps"""				):
			A_		:    str						     = torch.manual_seed(lowerCAmelCase_				)
		else:
			A_		:    Dict						     = torch.Generator(device=lowerCAmelCase_				).manual_seed(lowerCAmelCase_				)
		A_		:    int						     = {
		    """batch_size""": 1,
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """output_type""": """numpy""",
		}
		return inputs





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = """cpu"""

		A_		:    Optional[int]						     = self.get_dummy_components()
		A_		:    List[str]						     = self.pipeline_class(**lowerCAmelCase_				)
		pipe.to(lowerCAmelCase_				)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_				)

		A_		:    Any						     = self.get_dummy_inputs(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = pipe(**lowerCAmelCase_				).images
		A_		:    Any						     = image[0, -3:, -3:, -1]

		self.assertEqual(image.shape  ,  (1, 32, 32, 3)				)
		A_		:    Dict						     = np.array(
		    [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4]				)
		A_		:    List[Any]						     = np.abs(image_slice.flatten() - expected_slice				).max()
		self.assertLessEqual(lowerCAmelCase_  ,  1e-3				)





	def  lowerCamelCase(self				):
		super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3				)





	def  lowerCamelCase(self				):
		super().test_save_load_local(expected_max_difference=3e-3				)





	def  lowerCamelCase(self				):
		super().test_save_load_optional_components(expected_max_difference=3e-3				)





	def  lowerCamelCase(self				):
		super().test_inference_batch_single_identical(expected_max_diff=3e-3				)






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





	"""simple docstring"""




	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = """google/ddpm-cifar10-32"""

		A_		:    Any						     = UNetaDModel.from_pretrained(lowerCAmelCase_				)
		A_		:    Optional[int]						     = DDIMScheduler()

		A_		:    str						     = DDIMPipeline(unet=lowerCAmelCase_  ,  scheduler=lowerCAmelCase_				)
		ddim.to(lowerCAmelCase_				)
		ddim.set_progress_bar_config(disable=lowerCAmelCase_				)

		A_		:    int						     = torch.manual_seed(0				)
		A_		:    int						     = ddim(generator=lowerCAmelCase_  ,  eta=0.0  ,  output_type="""numpy"""				).images

		A_		:    Optional[int]						     = image[0, -3:, -3:, -1]

		assert image.shape == (1, 32, 32, 3)
		A_		:    Dict						     = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]				)

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





	def  lowerCamelCase(self				):
		A_		:    List[Any]						     = """google/ddpm-ema-bedroom-256"""

		A_		:    str						     = UNetaDModel.from_pretrained(lowerCAmelCase_				)
		A_		:    str						     = DDIMScheduler.from_pretrained(lowerCAmelCase_				)

		A_		:    Any						     = DDIMPipeline(unet=lowerCAmelCase_  ,  scheduler=lowerCAmelCase_				)
		ddpm.to(lowerCAmelCase_				)
		ddpm.set_progress_bar_config(disable=lowerCAmelCase_				)

		A_		:    List[Any]						     = torch.manual_seed(0				)
		A_		:    Optional[Any]						     = ddpm(generator=lowerCAmelCase_  ,  output_type="""numpy"""				).images

		A_		:    Tuple						     = image[0, -3:, -3:, -1]

		assert image.shape == (1, 256, 256, 3)
		A_		:    Any						     = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]				)

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









"""simple docstring"""
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None				):
		A_		:    Dict						     = data
		A_		:    Dict						     = previous
		A_		:    int						     = next_node





	def __str__(self				):
		return f"""{self.data}"""





	def  lowerCamelCase(self				):
		return self.data





	def  lowerCamelCase(self				):
		return self.next





	def  lowerCamelCase(self				):
		return self.previous






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = head





	def __iter__(self				):
		return self





	def  lowerCamelCase(self				):
		if not self.current:
			raise StopIteration
		else:
			A_		:    str						     = self.current.get_data()
			A_		:    List[str]						     = self.current.get_next()
			return value






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self				):
		A_		:    str						     = None  # First node in list
		A_		:    Tuple						     = None  # Last node in list





	def __str__(self				):
		A_		:    int						     = self.head
		A_		:    str						     = []
		while current is not None:
			nodes.append(current.get_data()				)
			A_		:    Any						     = current.get_next()
		return " ".join(str(lowerCAmelCase_				) for node in nodes				)





	def __contains__(self  ,  lowerCAmelCase_				):
		A_		:    Union[str, Any]						     = self.head
		while current:
			if current.get_data() == value:
				return True
			A_		:    Union[str, Any]						     = current.get_next()
		return False





	def __iter__(self				):
		return LinkedListIterator(self.head				)





	def  lowerCamelCase(self				):
		if self.head:
			return self.head.get_data()
		return None





	def  lowerCamelCase(self				):
		if self.tail:
			return self.tail.get_data()
		return None





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if self.head is None:
			A_		:    str						     = node
			A_		:    Optional[int]						     = node
		else:
			self.insert_before_node(self.head  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if self.head is None:
			self.set_head(lowerCAmelCase_				)
		else:
			self.insert_after_node(self.tail  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = Node(lowerCAmelCase_				)
		if self.head is None:
			self.set_head(lowerCAmelCase_				)
		else:
			self.set_tail(lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = node
		A_		:    Union[str, Any]						     = node.previous

		if node.get_previous() is None:
			A_		:    Dict						     = node_to_insert
		else:
			A_		:    Optional[Any]						     = node_to_insert

		A_		:    Any						     = node_to_insert





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Dict						     = node
		A_		:    Optional[Any]						     = node.next

		if node.get_next() is None:
			A_		:    Optional[int]						     = node_to_insert
		else:
			A_		:    List[Any]						     = node_to_insert

		A_		:    Optional[Any]						     = node_to_insert





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = 1
		A_		:    Tuple						     = Node(lowerCAmelCase_				)
		A_		:    Dict						     = self.head
		while node:
			if current_position == position:
				self.insert_before_node(lowerCAmelCase_  ,  lowerCAmelCase_				)
				return
			current_position += 1
			A_		:    List[str]						     = node.next
		self.insert_after_node(self.tail  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    Dict						     = self.head
		while node:
			if node.get_data() == item:
				return node
			A_		:    Optional[int]						     = node.get_next()
		raise Exception("""Node not found"""				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if (node := self.get_node(lowerCAmelCase_				)) is not None:
			if node == self.head:
				A_		:    Tuple						     = self.head.get_next()

			if node == self.tail:
				A_		:    Dict						     = self.tail.get_previous()

			self.remove_node_pointers(lowerCAmelCase_				)





	@staticmethod
	def  lowerCamelCase(lowerCAmelCase_				):
		if node.get_next():
			A_		:    Any						     = node.previous

		if node.get_previous():
			A_		:    int						     = node.next

		A_		:    str						     = None
		A_		:    int						     = None





	def  lowerCamelCase(self				):
		return self.head is None






def       __UpperCamelCase						(		):
	pass


if __name__ == "__main__":
							import doctest

							doctest.testmod()

