
import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


def 				A	( lowercase			)       ->  Dict:
		'''simple docstring'''

		UpperCamelCase     ,  UpperCamelCase          =					image.size
		UpperCamelCase     ,  UpperCamelCase          =					(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		UpperCamelCase          =					image.resize((w, h)   ,	resample=PIL_INTERPOLATION['lanczos']			)
		UpperCamelCase          =					np.array(lowercase			).astype(np.floataa			) / 255.0
		UpperCamelCase          =					image[None].transpose(0   ,	3   ,	1   ,	2			)
		UpperCamelCase          =					torch.from_numpy(lowercase			)
		return 2.0 * image - 1.0


class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_    ,				)  -> Dict:
				"""simple docstring"""
				super().__init__()
				self.register_modules(vqvae=A_    ,				unet=A_    ,				scheduler=A_	)



		@torch.no_grad()
		def __call__(			self    ,				A_ = None    ,				A_ = 1    ,				A_ = 100    ,				A_ = 0.0    ,				A_ = None    ,				A_ = "pil"    ,				A_ = True    ,				)  -> Union[Tuple, ImagePipelineOutput]:
				"""simple docstring"""
				if isinstance(A_    ,				PIL.Image.Image	):
						UpperCamelCase          =					1
				elif isinstance(A_    ,				torch.Tensor	):
						UpperCamelCase          =					image.shape[0]
				else:
						raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(A_	)}'''	)

				if isinstance(A_    ,				PIL.Image.Image	):
						UpperCamelCase          =					preprocess(A_	)

				UpperCamelCase     ,  UpperCamelCase          =					image.shape[-2:]

				# in_channels should be 6: 3 for latents, 3 for low resolution image
				UpperCamelCase          =					(batch_size, self.unet.config.in_channels // 2, height, width)
				UpperCamelCase          =					next(self.unet.parameters()	).dtype

				UpperCamelCase          =					randn_tensor(A_    ,				generator=A_    ,				device=self.device    ,				dtype=A_	)

				UpperCamelCase          =					image.to(device=self.device    ,				dtype=A_	)

				# set timesteps and move to the correct device
				self.scheduler.set_timesteps(A_    ,				device=self.device	)
				UpperCamelCase          =					self.scheduler.timesteps

				# scale the initial noise by the standard deviation required by the scheduler
				UpperCamelCase          =					latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				UpperCamelCase          =					'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
				UpperCamelCase          =					{}
				if accepts_eta:
						UpperCamelCase          =					eta

				for t in self.progress_bar(A_	):
						# concat latents and low resolution image in the channel dimension.
						UpperCamelCase          =					torch.cat([latents, image]    ,				dim=1	)
						UpperCamelCase          =					self.scheduler.scale_model_input(A_    ,				A_	)
						# predict the noise residual
						UpperCamelCase          =					self.unet(A_    ,				A_	).sample
						# compute the previous noisy sample x_t -> x_t-1
						UpperCamelCase          =					self.scheduler.step(A_    ,				A_    ,				A_    ,				**A_	).prev_sample

				# decode the image latents with the VQVAE
				UpperCamelCase          =					self.vqvae.decode(A_	).sample
				UpperCamelCase          =					torch.clamp(A_    ,				-1.0    ,				1.0	)
				UpperCamelCase          =					image / 2 + 0.5
				UpperCamelCase          =					image.cpu().permute(0    ,				2    ,				3    ,				1	).numpy()

				if output_type == "pil":
						UpperCamelCase          =					self.numpy_to_pil(A_	)

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=A_	)


import os
import sys
import unittest


_UpperCAmelCase :			str              =	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
_UpperCAmelCase :			Tuple              =	os.path.join(git_repo_path, "src", "diffusers")


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					find_backend('    if not is_torch_available():'	)
				self.assertEqual(A_    ,				'torch'	)

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

				UpperCamelCase          =					find_backend('    if not (is_torch_available() and is_transformers_available()):'	)
				self.assertEqual(A_    ,				'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")

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



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					read_init()
				# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
				self.assertIn('torch'    ,				A_	)
				self.assertIn('torch_and_transformers'    ,				A_	)
				self.assertIn('flax_and_transformers'    ,				A_	)
				self.assertIn('torch_and_transformers_and_onnx'    ,				A_	)

				# 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 __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					create_dummy_object('CONSTANT'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				'\nCONSTANT = None\n'	)

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

				UpperCamelCase          =					'\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'
				UpperCamelCase          =					create_dummy_object('FakeClass'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'# 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'
				UpperCamelCase          =					create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}	)
				self.assertEqual(dummy_files['torch']    ,				A_	)
