



import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()


class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									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    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "cpu"
									UpperCamelCase_			= self.dummy_uncond_unet
									UpperCamelCase_			= DDPMScheduler()

									UpperCamelCase_			= DDPMPipeline(unet=snake_case__  ,			scheduler=snake_case__     )
									ddpm.to(snake_case__     )
									ddpm.set_progress_bar_config(disable=snake_case__     )

									UpperCamelCase_			= torch.Generator(device=snake_case__     ).manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"     ).images

									UpperCamelCase_			= torch.Generator(device=snake_case__     ).manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"  ,			return_dict=snake_case__     )[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(
									    [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]     )

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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.dummy_uncond_unet
									UpperCamelCase_			= DDPMScheduler(prediction_type="sample"     )

									UpperCamelCase_			= DDPMPipeline(unet=snake_case__  ,			scheduler=snake_case__     )
									ddpm.to(snake_case__     )
									ddpm.set_progress_bar_config(disable=snake_case__     )

									UpperCamelCase_			= torch.manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"     ).images

									UpperCamelCase_			= torch.manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"     )[0]

									UpperCamelCase_			= image[0, -3:, -3:, -1]
									UpperCamelCase_			= image_eps[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									UpperCamelCase_			= 1e-2 if torch_device != "mps" else 3e-2
									assert np.abs(image_slice.flatten() - image_eps_slice.flatten()     ).max() < tolerance




@slow
@require_torch_gpu
class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "google/ddpm-cifar10-32"

									UpperCamelCase_			= UNetaDModel.from_pretrained(snake_case__     )
									UpperCamelCase_			= DDPMScheduler.from_pretrained(snake_case__     )

									UpperCamelCase_			= DDPMPipeline(unet=snake_case__  ,			scheduler=snake_case__     )
									ddpm.to(snake_case__     )
									ddpm.set_progress_bar_config(disable=snake_case__     )

									UpperCamelCase_			= torch.manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			output_type="numpy"     ).images

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

									assert image.shape == (1, 32, 32, 3)
									UpperCamelCase_			= np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]     )
									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2

"""simple docstring"""







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      return True







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'	)
      __lowerCamelCase    		=					'Hugging Face大法好，谁用谁知道。'
      __lowerCamelCase    		=					'Hugging Face大法好,谁用谁知道。<unk>'
      __lowerCamelCase    		=					'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

      __lowerCamelCase    		=					tokenizer.tokenize(_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					tokens + [tokenizer.unk_token]

      __lowerCamelCase    		=					[13789, 13283, 1421, 8, 10, 1164, 13608, 16528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					tokenizer.decode(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)






