






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    @property
    def _a							(				self      :     List[Any]      )      ->							Tuple:
           torch.manual_seed(0      )
           __UpperCAmelCase     				=UNetaDModel(
               block_out_channels=(32, 64) ,     layers_per_block=2 ,     sample_size=32 ,     in_channels=3 ,     out_channels=3 ,     down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,     up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,     )
           return model



    def _a							(				self      :     Optional[int]      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=self.dummy_uncond_unet
           __UpperCAmelCase     				=PNDMScheduler()

           __UpperCAmelCase     				=PNDMPipeline(unet=__UpperCAmelCase ,     scheduler=__UpperCAmelCase      )
           pndm.to(__UpperCAmelCase      )
           pndm.set_progress_bar_config(disable=__UpperCAmelCase      )

           __UpperCAmelCase     				=torch.manual_seed(0      )
           __UpperCAmelCase     				=pndm(generator=__UpperCAmelCase ,     num_inference_steps=20 ,     output_type="""numpy"""      ).images

           __UpperCAmelCase     				=torch.manual_seed(0      )
           __UpperCAmelCase     				=pndm(generator=__UpperCAmelCase ,     num_inference_steps=20 ,     output_type="""numpy""" ,     return_dict=__UpperCAmelCase      )[0]

           __UpperCAmelCase     				=image[0, -3:, -3:, -1]
           __UpperCAmelCase     				=image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           __UpperCAmelCase     				=np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    def _a							(				self      :     Optional[Any]      )      ->							int:
           __UpperCAmelCase     				='google/ddpm-cifar10-32'

           __UpperCAmelCase     				=UNetaDModel.from_pretrained(__UpperCAmelCase      )
           __UpperCAmelCase     				=PNDMScheduler()

           __UpperCAmelCase     				=PNDMPipeline(unet=__UpperCAmelCase ,     scheduler=__UpperCAmelCase      )
           pndm.to(__UpperCAmelCase      )
           pndm.set_progress_bar_config(disable=__UpperCAmelCase      )
           __UpperCAmelCase     				=torch.manual_seed(0      )
           __UpperCAmelCase     				=pndm(generator=__UpperCAmelCase ,     output_type="""numpy"""      ).images

           __UpperCAmelCase     				=image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           __UpperCAmelCase     				=np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2











"""simple docstring"""





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


__A											=						"""sshleifer/bart-tiny-random"""
__A											=						"""patrickvonplaten/t5-tiny-random"""






@require_torch
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       @cached_property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return AutoConfig.from_pretrained(__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :List[str]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=1 )
         self.assertEqual(student.config.num_hidden_layers       ,					1 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :Optional[int]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :Optional[int]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=1 )
         self.assertEqual(student.config.encoder_layers       ,					1 )
         self.assertEqual(student.config.decoder_layers       ,					1 )






       def 	snake_case		(       self ):



         '''simple docstring'''

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