





'''simple docstring'''




import os
import sys
import unittest


a__					:			List[Any]          =     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a__					:			Tuple          =     os.path.join(git_repo_path, '''src''', '''diffusers''')






class 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   find_backend('    if not is_torch_available():'		)
		self.assertEqual(UpperCamelCase_      ,       'torch'		)

		# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
		# self.assertEqual(backend_with_underscore, "tensorflow_text")

		snake_case__	       =   find_backend('    if not (is_torch_available() and is_transformers_available()):'		)
		self.assertEqual(UpperCamelCase_      ,       'torch_and_transformers'		)

		# double_backend_with_underscore = find_backend(
		#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
		# )
		# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

		snake_case__	       =   find_backend(
		    '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'		)
		self.assertEqual(UpperCamelCase_      ,       'torch_and_transformers_and_onnx'		)





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   read_init()
		# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
		self.assertIn('torch'      ,       UpperCamelCase_		)
		self.assertIn('torch_and_transformers'      ,       UpperCamelCase_		)
		self.assertIn('flax_and_transformers'      ,       UpperCamelCase_		)
		self.assertIn('torch_and_transformers_and_onnx'      ,       UpperCamelCase_		)

		# Likewise, we can't assert on the exact content of a key
		self.assertIn('UNet2DModel'      ,       objects['torch']		)
		self.assertIn('FlaxUNet2DConditionModel'      ,       objects['flax']		)
		self.assertIn('StableDiffusionPipeline'      ,       objects['torch_and_transformers']		)
		self.assertIn('FlaxStableDiffusionPipeline'      ,       objects['flax_and_transformers']		)
		self.assertIn('LMSDiscreteScheduler'      ,       objects['torch_and_scipy']		)
		self.assertIn('OnnxStableDiffusionPipeline'      ,       objects['torch_and_transformers_and_onnx']		)





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   create_dummy_object('CONSTANT'      ,       '\'torch\''		)
		self.assertEqual(UpperCamelCase_      ,       '\nCONSTANT = None\n'		)

		snake_case__	       =   create_dummy_object('function'      ,       '\'torch\''		)
		self.assertEqual(
		    UpperCamelCase_      ,       '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'		)

		snake_case__	       =   '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
		snake_case__	       =   create_dummy_object('FakeClass'      ,       '\'torch\''		)
		self.assertEqual(UpperCamelCase_      ,       UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n'
		snake_case__	       =   create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}		)
		self.assertEqual(dummy_files['torch']      ,       UpperCamelCase_		)










'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		super().__init__()

		# make sure scheduler can always be converted to DDIM
		snake_case__	       =   DDIMScheduler.from_config(scheduler.config		)

		self.register_modules(unet=UpperCamelCase_      ,       scheduler=UpperCamelCase_		)





	@torch.no_grad()
	def __call__(		self      ,       UpperCamelCase_ = 1      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = 0.0      ,       UpperCamelCase_ = 50      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "pil"      ,       UpperCamelCase_ = True      ,       )      ->		Union[ImagePipelineOutput, Tuple]:

		# Sample gaussian noise to begin loop
		if isinstance(self.unet.config.sample_size      ,       UpperCamelCase_		):
			snake_case__	       =   (
			    batch_size,
			    self.unet.config.in_channels,
			    self.unet.config.sample_size,
			    self.unet.config.sample_size,
			)
		else:
			snake_case__	       =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		) and len(UpperCamelCase_		) != batch_size:
			raise ValueError(
			    F'''You have passed a list of generators of length {len(UpperCamelCase_		)}, but requested an effective batch'''
			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

		snake_case__	       =   randn_tensor(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       device=self.device      ,       dtype=self.unet.dtype		)

		# set step values
		self.scheduler.set_timesteps(UpperCamelCase_		)

		for t in self.progress_bar(self.scheduler.timesteps		):
			# 1. predict noise model_output
			snake_case__	       =   self.unet(UpperCamelCase_      ,       UpperCamelCase_		).sample

			# 2. predict previous mean of image x_t-1 and add variance depending on eta
			# eta corresponds to η in paper and should be between [0, 1]
			# do x_t -> x_t-1
			snake_case__	       =   self.scheduler.step(
			    UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       eta=UpperCamelCase_      ,       use_clipped_model_output=UpperCamelCase_      ,       generator=UpperCamelCase_		).prev_sample

		snake_case__	       =   (image / 2 + 0.5).clamp(0      ,       1		)
		snake_case__	       =   image.cpu().permute(0      ,       2      ,       3      ,       1		).numpy()
		if output_type == "pil":
			snake_case__	       =   self.numpy_to_pil(UpperCamelCase_		)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=UpperCamelCase_		)



