



'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
	import jax.numpy as jnp

if is_tf_available():
	import tensorflow as tf

if is_torch_available():
	import torch


class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : Dict      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= {
					    '''task_specific_params''': {
					        '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
					        '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
					        '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
					    }
					}
					SCREAMING_SNAKE_CASE  :						str      					= {
					    '''task_specific_params.summarization.length_penalty''': 1.0,
					    '''task_specific_params.summarization.max_length''': 128,
					    '''task_specific_params.summarization.min_length''': 12,
					    '''task_specific_params.summarization.num_beams''': 4,
					    '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
					    '''task_specific_params.summarization_cnn.max_length''': 142,
					    '''task_specific_params.summarization_cnn.min_length''': 56,
					    '''task_specific_params.summarization_cnn.num_beams''': 4,
					    '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
					    '''task_specific_params.summarization_xsum.max_length''': 62,
					    '''task_specific_params.summarization_xsum.min_length''': 11,
					    '''task_specific_params.summarization_xsum.num_beams''': 6,
					}

					self.assertEqual(flatten_dict(__lowerCamelCase      ),					__lowerCamelCase      )


	def 				lowercase__  (self : Dict      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.random.randn(3,					4      )
					self.assertTrue(np.allclose(transpose(__lowerCamelCase      ),					x.transpose()      )      )

					SCREAMING_SNAKE_CASE  :						str      					= np.random.randn(3,					4,					5      )
					self.assertTrue(np.allclose(transpose(__lowerCamelCase,					axes=(1, 2, 0)      ),					x.transpose((1, 2, 0)      )      )      )


	@require_torch
	def 				lowercase__  (self : List[str]      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= np.random.randn(3,					4      )
					SCREAMING_SNAKE_CASE  :						Dict      					= torch.tensor(__lowerCamelCase      )
					self.assertTrue(np.allclose(transpose(__lowerCamelCase      ),					transpose(__lowerCamelCase      ).numpy()      )      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.random.randn(3,					4,					5      )
					SCREAMING_SNAKE_CASE  :						Any      					= torch.tensor(__lowerCamelCase      )
					self.assertTrue(np.allclose(transpose(__lowerCamelCase,					axes=(1, 2, 0)      ),					transpose(__lowerCamelCase,					axes=(1, 2, 0)      ).numpy()      )      )


	@require_tf
	def 				lowercase__  (self : Optional[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.random.randn(3,					4      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= tf.constant(__lowerCamelCase      )
					self.assertTrue(np.allclose(transpose(__lowerCamelCase      ),					transpose(__lowerCamelCase      ).numpy()      )      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.random.randn(3,					4,					5      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= tf.constant(__lowerCamelCase      )
					self.assertTrue(np.allclose(transpose(__lowerCamelCase,					axes=(1, 2, 0)      ),					transpose(__lowerCamelCase,					axes=(1, 2, 0)      ).numpy()      )      )


	@require_flax
	def 				lowercase__  (self : Any      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= np.random.randn(3,					4      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= jnp.array(__lowerCamelCase      )
					self.assertTrue(np.allclose(transpose(__lowerCamelCase      ),					np.asarray(transpose(__lowerCamelCase      )      )      )      )

					SCREAMING_SNAKE_CASE  :						Any      					= np.random.randn(3,					4,					5      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= jnp.array(__lowerCamelCase      )
					self.assertTrue(np.allclose(transpose(__lowerCamelCase,					axes=(1, 2, 0)      ),					np.asarray(transpose(__lowerCamelCase,					axes=(1, 2, 0)      )      )      )      )


	def 				lowercase__  (self : Any      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= np.random.randn(3,					4      )
					self.assertTrue(np.allclose(reshape(__lowerCamelCase,					(4, 3)      ),					np.reshape(__lowerCamelCase,					(4, 3)      )      )      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= np.random.randn(3,					4,					5      )
					self.assertTrue(np.allclose(reshape(__lowerCamelCase,					(12, 5)      ),					np.reshape(__lowerCamelCase,					(12, 5)      )      )      )


	@require_torch
	def 				lowercase__  (self : Dict      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.random.randn(3,					4      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.tensor(__lowerCamelCase      )
					self.assertTrue(np.allclose(reshape(__lowerCamelCase,					(4, 3)      ),					reshape(__lowerCamelCase,					(4, 3)      ).numpy()      )      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= np.random.randn(3,					4,					5      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= torch.tensor(__lowerCamelCase      )
					self.assertTrue(np.allclose(reshape(__lowerCamelCase,					(12, 5)      ),					reshape(__lowerCamelCase,					(12, 5)      ).numpy()      )      )


	@require_tf
	def 				lowercase__  (self : Optional[Any]      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= np.random.randn(3,					4      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= tf.constant(__lowerCamelCase      )
					self.assertTrue(np.allclose(reshape(__lowerCamelCase,					(4, 3)      ),					reshape(__lowerCamelCase,					(4, 3)      ).numpy()      )      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= np.random.randn(3,					4,					5      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= tf.constant(__lowerCamelCase      )
					self.assertTrue(np.allclose(reshape(__lowerCamelCase,					(12, 5)      ),					reshape(__lowerCamelCase,					(12, 5)      ).numpy()      )      )


	@require_flax
	def 				lowercase__  (self : List[str]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= np.random.randn(3,					4      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= jnp.array(__lowerCamelCase      )
					self.assertTrue(np.allclose(reshape(__lowerCamelCase,					(4, 3)      ),					np.asarray(reshape(__lowerCamelCase,					(4, 3)      )      )      )      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= np.random.randn(3,					4,					5      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= jnp.array(__lowerCamelCase      )
					self.assertTrue(np.allclose(reshape(__lowerCamelCase,					(12, 5)      ),					np.asarray(reshape(__lowerCamelCase,					(12, 5)      )      )      )      )


	def 				lowercase__  (self : Tuple      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.random.randn(1,					3,					4      )
					self.assertTrue(np.allclose(squeeze(__lowerCamelCase      ),					np.squeeze(__lowerCamelCase      )      )      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= np.random.randn(1,					4,					1,					5      )
					self.assertTrue(np.allclose(squeeze(__lowerCamelCase,					axis=2      ),					np.squeeze(__lowerCamelCase,					axis=2      )      )      )


	@require_torch
	def 				lowercase__  (self : List[str]      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= np.random.randn(1,					3,					4      )
					SCREAMING_SNAKE_CASE  :						Any      					= torch.tensor(__lowerCamelCase      )
					self.assertTrue(np.allclose(squeeze(__lowerCamelCase      ),					squeeze(__lowerCamelCase      ).numpy()      )      )

					SCREAMING_SNAKE_CASE  :						str      					= np.random.randn(1,					4,					1,					5      )
					SCREAMING_SNAKE_CASE  :						int      					= torch.tensor(__lowerCamelCase      )
					self.assertTrue(np.allclose(squeeze(__lowerCamelCase,					axis=2      ),					squeeze(__lowerCamelCase,					axis=2      ).numpy()      )      )


	@require_tf
	def 				lowercase__  (self : Tuple      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.random.randn(1,					3,					4      )
					SCREAMING_SNAKE_CASE  :						Dict      					= tf.constant(__lowerCamelCase      )
					self.assertTrue(np.allclose(squeeze(__lowerCamelCase      ),					squeeze(__lowerCamelCase      ).numpy()      )      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= np.random.randn(1,					4,					1,					5      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= tf.constant(__lowerCamelCase      )
					self.assertTrue(np.allclose(squeeze(__lowerCamelCase,					axis=2      ),					squeeze(__lowerCamelCase,					axis=2      ).numpy()      )      )


	@require_flax
	def 				lowercase__  (self : List[str]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= np.random.randn(1,					3,					4      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= jnp.array(__lowerCamelCase      )
					self.assertTrue(np.allclose(squeeze(__lowerCamelCase      ),					np.asarray(squeeze(__lowerCamelCase      )      )      )      )

					SCREAMING_SNAKE_CASE  :						Any      					= np.random.randn(1,					4,					1,					5      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= jnp.array(__lowerCamelCase      )
					self.assertTrue(np.allclose(squeeze(__lowerCamelCase,					axis=2      ),					np.asarray(squeeze(__lowerCamelCase,					axis=2      )      )      )      )


	def 				lowercase__  (self : List[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= np.random.randn(3,					4      )
					self.assertTrue(np.allclose(expand_dims(__lowerCamelCase,					axis=1      ),					np.expand_dims(__lowerCamelCase,					axis=1      )      )      )


	@require_torch
	def 				lowercase__  (self : Dict      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= np.random.randn(3,					4      )
					SCREAMING_SNAKE_CASE  :						Any      					= torch.tensor(__lowerCamelCase      )
					self.assertTrue(np.allclose(expand_dims(__lowerCamelCase,					axis=1      ),					expand_dims(__lowerCamelCase,					axis=1      ).numpy()      )      )


	@require_tf
	def 				lowercase__  (self : Dict      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= np.random.randn(3,					4      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= tf.constant(__lowerCamelCase      )
					self.assertTrue(np.allclose(expand_dims(__lowerCamelCase,					axis=1      ),					expand_dims(__lowerCamelCase,					axis=1      ).numpy()      )      )






	@require_flax
	def 				lowercase__  (self : Any      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= np.random.randn(3,					4      )
					SCREAMING_SNAKE_CASE  :						int      					= jnp.array(__lowerCamelCase      )
					self.assertTrue(np.allclose(expand_dims(__lowerCamelCase,					axis=1      ),					np.asarray(expand_dims(__lowerCamelCase,					axis=1      )      )      )      )









'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__     (				_lowercase	):
	__magic_name__		:    UNetaDModel
	__magic_name__		:    ScoreSdeVeScheduler


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDModel,					__UpperCAmelCase : ScoreSdeVeScheduler      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=__UpperCAmelCase,					scheduler=__UpperCAmelCase      )






	@torch.no_grad()
	def __call__(self : int,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : int = 2000,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					**__UpperCAmelCase : List[str],					)     ->			Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet.config.sample_size
					SCREAMING_SNAKE_CASE  :						Dict      					= (batch_size, 3, img_size, img_size)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.unet

					SCREAMING_SNAKE_CASE  :						str      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase      ) * self.scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						Any      					= sample.to(self.device      )

					self.scheduler.set_timesteps(__UpperCAmelCase      )
					self.scheduler.set_sigmas(__UpperCAmelCase      )

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
									SCREAMING_SNAKE_CASE  :						int      					= self.scheduler.sigmas[i] * torch.ones(shape[0],					device=self.device      )

									# correction step
									for _ in range(self.scheduler.config.correct_steps      ):
													SCREAMING_SNAKE_CASE  :						Any      					= self.unet(__UpperCAmelCase,					__UpperCAmelCase      ).sample
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step_correct(__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# prediction step
									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      ).sample
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step_pred(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.prev_sample, output.prev_sample_mean

					SCREAMING_SNAKE_CASE  :						Dict      					= sample_mean.clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						str      					= sample.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						int      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )




