





"""simple docstring"""


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 	__a   (  unittest.TestCase		):



     @property
     def 						snake_case_			(       self   ):
            torch.manual_seed(0   )
            _lowerCamelCase    	=					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 						snake_case_			(       self   ):
            _lowerCamelCase    	=					'cpu'
            _lowerCamelCase    	=					self.dummy_uncond_unet
            _lowerCamelCase    	=					DDPMScheduler()

            _lowerCamelCase    	=					DDPMPipeline(unet=a__					,     scheduler=a__   )
            ddpm.to(a__   )
            ddpm.set_progress_bar_config(disable=a__   )

            _lowerCamelCase    	=					torch.Generator(device=a__   ).manual_seed(0   )
            _lowerCamelCase    	=					ddpm(generator=a__					,     num_inference_steps=2					,     output_type='numpy'   ).images

            _lowerCamelCase    	=					torch.Generator(device=a__   ).manual_seed(0   )
            _lowerCamelCase    	=					ddpm(generator=a__					,     num_inference_steps=2					,     output_type='numpy'					,     return_dict=a__   )[0]

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

            assert image.shape == (1, 32, 32, 3)
            _lowerCamelCase    	=					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 						snake_case_			(       self   ):
            _lowerCamelCase    	=					self.dummy_uncond_unet
            _lowerCamelCase    	=					DDPMScheduler(prediction_type='sample'   )

            _lowerCamelCase    	=					DDPMPipeline(unet=a__					,     scheduler=a__   )
            ddpm.to(a__   )
            ddpm.set_progress_bar_config(disable=a__   )

            _lowerCamelCase    	=					torch.manual_seed(0   )
            _lowerCamelCase    	=					ddpm(generator=a__					,     num_inference_steps=2					,     output_type='numpy'   ).images

            _lowerCamelCase    	=					torch.manual_seed(0   )
            _lowerCamelCase    	=					ddpm(generator=a__					,     num_inference_steps=2					,     output_type='numpy'   )[0]

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

            assert image.shape == (1, 32, 32, 3)
            _lowerCamelCase    	=					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 	__a   (  unittest.TestCase		):



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					'google/ddpm-cifar10-32'

            _lowerCamelCase    	=					UNetaDModel.from_pretrained(a__   )
            _lowerCamelCase    	=					DDPMScheduler.from_pretrained(a__   )

            _lowerCamelCase    	=					DDPMPipeline(unet=a__					,     scheduler=a__   )
            ddpm.to(a__   )
            ddpm.set_progress_bar_config(disable=a__   )

            _lowerCamelCase    	=					torch.manual_seed(0   )
            _lowerCamelCase    	=					ddpm(generator=a__					,     output_type='numpy'   ).images

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

            assert image.shape == (1, 32, 32, 3)
            _lowerCamelCase    	=					np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]   )
            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2







# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 						a__							(   __snake_case		):



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls    ) ->    Optional[int]:
								return cls







			@slow
			@require_torch_gpu
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   self.get_auto_remove_tmp_dir()
								__a							=   f'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

								# we want this test to run even if there is only one GPU, but if there are more we use them all
								__a							=   get_gpu_count()
								__a							=   f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
								__a							=   [sys.executable] + distributed_args + args
								execute_subprocess_async(UpperCAmelCase      ,						env=self.get_env()    )

								__a							=   os.path.join(UpperCAmelCase      ,						'test_bleu.json'    )
								__a							=   load_json(UpperCAmelCase    )
								# print(metrics)
								self.assertGreaterEqual(metrics['bleu']      ,						2_5    )




