


"""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     _UpperCamelCase    (  lowerCamelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =4_2
							__UpperCAmelCase		:       str							 =4_2

							def __init__(	self       ,  __a       ,  __a      ):
										super().__init__()
										self.register_modules(unet=SCREAMING_SNAKE_CASE__       ,  scheduler=SCREAMING_SNAKE_CASE__      )



							@torch.no_grad()
							def __call__(	self       ,  __a = 1       ,  __a = 20_00       ,  __a = None       ,  __a = "pil"       ,  __a = True       ,  **__a       ,  ):
										__lowerCAmelCase							       =     self.unet.config.sample_size
										__lowerCAmelCase							       =     (batch_size, 3, img_size, img_size)

										__lowerCAmelCase							       =     self.unet

										__lowerCAmelCase							       =     randn_tensor(SCREAMING_SNAKE_CASE__       ,  generator=SCREAMING_SNAKE_CASE__      ) * self.scheduler.init_noise_sigma
										__lowerCAmelCase							       =     sample.to(self.device      )

										self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__      )
										self.scheduler.set_sigmas(SCREAMING_SNAKE_CASE__      )

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

													# correction step
													for _ in range(self.scheduler.config.correct_steps      ):
																__lowerCAmelCase							       =     self.unet(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__      ).sample
																__lowerCAmelCase							       =     self.scheduler.step_correct(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  generator=SCREAMING_SNAKE_CASE__      ).prev_sample

													# prediction step
													__lowerCAmelCase							       =     model(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__      ).sample
													__lowerCAmelCase							       =     self.scheduler.step_pred(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  generator=SCREAMING_SNAKE_CASE__      )

													__lowerCAmelCase							       =     output.prev_sample, output.prev_sample_mean

										__lowerCAmelCase							       =     sample_mean.clamp(0       ,  1      )
										__lowerCAmelCase							       =     sample.cpu().permute(0       ,  2       ,  3       ,  1      ).numpy()
										if output_type == "pil":
													__lowerCAmelCase							       =     self.numpy_to_pil(SCREAMING_SNAKE_CASE__      )

										if not return_dict:
													return (sample,)

										return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__      )








"""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		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	{
					    '''task_specific_params''': {
					        '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_28, '''min_length''': 12, '''num_beams''': 4},
					        '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_42, '''min_length''': 56, '''num_beams''': 4},
					        '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
					    }
					}
					_UpperCamelCase					:Union[str, Any]					     =	{
					    '''task_specific_params.summarization.length_penalty''': 1.0,
					    '''task_specific_params.summarization.max_length''': 1_28,
					    '''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''': 1_42,
					    '''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(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	np.random.randn(3					,				4   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__   )					,				x.transpose()   )   )

					_UpperCamelCase					:Tuple					     =	np.random.randn(3					,				4					,				5   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   )					,				x.transpose((1, 2, 0)   )   )   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:Any					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__   )					,				transpose(SCREAMING_SNAKE_CASE__   ).numpy()   )   )

					_UpperCamelCase					:int					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:Any					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   )					,				transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   ).numpy()   )   )
		@require_tf
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:List[str]					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__   )					,				transpose(SCREAMING_SNAKE_CASE__   ).numpy()   )   )

					_UpperCamelCase					:int					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:Dict					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   )					,				transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   ).numpy()   )   )
		@require_flax
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:Optional[Any]					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__   )					,				np.asarray(transpose(SCREAMING_SNAKE_CASE__   )   )   )   )

					_UpperCamelCase					:Dict					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:Dict					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   )					,				np.asarray(transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   )   )   )   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	np.random.randn(3					,				4   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )					,				np.reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )   )   )

					_UpperCamelCase					:Dict					     =	np.random.randn(3					,				4					,				5   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )					,				np.reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )   )   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:Optional[Any]					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )					,				reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   ).numpy()   )   )

					_UpperCamelCase					:str					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:str					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )					,				reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   ).numpy()   )   )
		@require_tf
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:Dict					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )					,				reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   ).numpy()   )   )

					_UpperCamelCase					:Union[str, Any]					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:Optional[int]					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )					,				reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   ).numpy()   )   )
		@require_flax
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:str					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )					,				np.asarray(reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )   )   )   )

					_UpperCamelCase					:Dict					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:Union[str, Any]					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )					,				np.asarray(reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )   )   )   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	np.random.randn(1					,				3					,				4   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__   )					,				np.squeeze(SCREAMING_SNAKE_CASE__   )   )   )

					_UpperCamelCase					:List[Any]					     =	np.random.randn(1					,				4					,				1					,				5   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )					,				np.squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )   )   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



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

					_UpperCamelCase					:List[str]					     =	np.random.randn(1					,				4					,				1					,				5   )
					_UpperCamelCase					:List[Any]					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )					,				squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   ).numpy()   )   )
		@require_tf
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	np.random.randn(1					,				3					,				4   )
					_UpperCamelCase					:str					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__   )					,				squeeze(SCREAMING_SNAKE_CASE__   ).numpy()   )   )

					_UpperCamelCase					:str					     =	np.random.randn(1					,				4					,				1					,				5   )
					_UpperCamelCase					:str					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )					,				squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   ).numpy()   )   )
		@require_flax
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	np.random.randn(1					,				3					,				4   )
					_UpperCamelCase					:Optional[Any]					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__   )					,				np.asarray(squeeze(SCREAMING_SNAKE_CASE__   )   )   )   )

					_UpperCamelCase					:Dict					     =	np.random.randn(1					,				4					,				1					,				5   )
					_UpperCamelCase					:int					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )					,				np.asarray(squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )   )   )   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	np.random.randn(3					,				4   )
					self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )					,				np.expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )   )   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:Union[str, Any]					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )					,				expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   ).numpy()   )   )
		@require_tf
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:int					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )					,				expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   ).numpy()   )   )





		@require_flax
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:str					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )					,				np.asarray(expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )   )   )   )

