






import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


SCREAMING_SNAKE_CASE__		:							Dict 				=				"""sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__		:							List[str] 				=				"""patrickvonplaten/t5-tiny-random"""







@require_torch
class 				UpperCAmelCase_  (		unittest.TestCase		):




     @cached_property
     def   __UpperCAmelCase  (				self		):
          return AutoConfig.from_pretrained(_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      ,		*UpperCAmelCase__      :  Optional[int]				      =			create_student_by_copying_alternating_layers(_lowerCAmelCase	,						tempfile.mkdtemp()	,						e=1	,						d=1		)
          self.assertEqual(student.config.num_hidden_layers	,						1		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      ,		*UpperCAmelCase__      :  int				      =			create_student_by_copying_alternating_layers(_lowerCAmelCase	,						tempfile.mkdtemp()	,						e=1	,						d=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      ,		*UpperCAmelCase__      :  Any				      =			create_student_by_copying_alternating_layers(_lowerCAmelCase	,						tempfile.mkdtemp()	,						e=1	,						d=_lowerCAmelCase		)
          self.assertEqual(student.config.encoder_layers	,						1		)
          self.assertEqual(student.config.decoder_layers	,						self.teacher_config.encoder_layers		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      ,		*UpperCAmelCase__      :  List[Any]				      =			create_student_by_copying_alternating_layers(_lowerCAmelCase	,						tempfile.mkdtemp()	,						e=1	,						d=1		)
          self.assertEqual(student.config.encoder_layers	,						1		)
          self.assertEqual(student.config.decoder_layers	,						1		)




     def   __UpperCAmelCase  (				self		):
          with self.assertRaises(_lowerCAmelCase		):
               create_student_by_copying_alternating_layers(_lowerCAmelCase	,						tempfile.mkdtemp()	,						e=_lowerCAmelCase	,						d=_lowerCAmelCase		)












"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

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





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	UNetaDModel
 UpperCAmelCase       :	KarrasVeScheduler

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			UNetaDModel       ,				_UpperCAmelCase			:			KarrasVeScheduler						):
       super().__init__()
       self.register_modules(unet=_UpperCAmelCase       ,				scheduler=_UpperCAmelCase						)



 @torch.no_grad()
 def __call__(					self			:			Optional[int]       ,				_UpperCAmelCase			:			int = 1       ,				_UpperCAmelCase			:			int = 50       ,				_UpperCAmelCase			:			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,				_UpperCAmelCase			:			Optional[str] = "pil"       ,				_UpperCAmelCase			:			bool = True       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       _A     =  self.unet.config.sample_size
       _A     =  (batch_size, 3, img_size, img_size)

       _A     =  self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       _A     =  randn_tensor(_UpperCAmelCase       ,				generator=_UpperCAmelCase       ,				device=self.device						) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(_UpperCAmelCase						)

       for t in self.progress_bar(self.scheduler.timesteps						):
             # here sigma_t == t_i from the paper
             _A     =  self.scheduler.schedule[t]
             _A     =  self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             _A  ,       _A     =  self.scheduler.add_noise_to_input(_UpperCAmelCase       ,				_UpperCAmelCase       ,				generator=_UpperCAmelCase						)

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             _A     =  (sigma_hat / 2) * model((sample_hat + 1) / 2       ,				sigma_hat / 2						).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             _A     =  self.scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)

             if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   _A     =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,				sigma_prev / 2						).sample
                   _A     =  self.scheduler.step_correct(
                       _UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				step_output.prev_sample       ,				step_output['derivative']       ,				)
             _A     =  step_output.prev_sample

       _A     =  (sample / 2 + 0.5).clamp(0       ,				1						)
       _A     =  sample.cpu().permute(0       ,				2       ,				3       ,				1						).numpy()
       if output_type == "pil":
             _A     =  self.numpy_to_pil(_UpperCAmelCase						)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase						)

