


'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 				__lowerCAmelCase      (      lowerCamelCase_							, unittest.TestCase	):

      """simple docstring"""






      _UpperCAmelCase		:					Optional[Any]      	=RePaintPipeline
      _UpperCAmelCase		:					List[Any]      	=IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
      _UpperCAmelCase		:					List[Any]      	=PipelineTesterMixin.required_optional_params - {
          """latents""",
          """num_images_per_prompt""",
          """callback""",
          """callback_steps""",
      }
      _UpperCAmelCase		:					List[Any]      	=IMAGE_INPAINTING_BATCH_PARAMS
      _UpperCAmelCase		:					List[str]      	=False






      def 							_UpperCAmelCase					(     self       :			List[str]  ):
             torch.manual_seed(0  )
             torch.manual_seed(0  )
             A_ 						=		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")   ,     )
             A_ 						=		RePaintScheduler()
             A_ 						=		{"unet": unet, "scheduler": scheduler}
             return components






      def 							_UpperCAmelCase					(     self       :			int   ,     lowerCAmelCase       :			int   ,     lowerCAmelCase       :			List[str]=0  ):







             if str(lowerCAmelCase  ).startswith("mps"  ):
                    A_ 						=		torch.manual_seed(lowerCAmelCase  )
             else:
                    A_ 						=		torch.Generator(device=lowerCAmelCase  ).manual_seed(lowerCAmelCase  )
             A_ 						=		np.random.RandomState(lowerCAmelCase  ).standard_normal((1, 3, 32, 32)  )
             A_ 						=		torch.from_numpy(lowerCAmelCase  ).to(device=lowerCAmelCase   ,     dtype=torch.floataa  )
             A_ 						=		(image > 0).to(device=lowerCAmelCase   ,     dtype=torch.floataa  )
             A_ 						=		{
                 "image": image,
                 "mask_image": mask,
                 "generator": generator,
                 "num_inference_steps": 5,
                 "eta": 0.0,
                 "jump_length": 2,
                 "jump_n_sample": 2,
                 "output_type": "numpy",
             }
             return inputs






      def 							_UpperCAmelCase					(     self       :			List[Any]  ):
             A_ 						=		"cpu"  # ensure determinism for the device-dependent torch.Generator
             A_ 						=		self.get_dummy_components()
             A_ 						=		RePaintPipeline(**lowerCAmelCase  )
             A_ 						=		sd_pipe.to(lowerCAmelCase  )
             sd_pipe.set_progress_bar_config(disable=lowerCAmelCase  )

             A_ 						=		self.get_dummy_inputs(lowerCAmelCase  )
             A_ 						=		sd_pipe(**lowerCAmelCase  ).images
             A_ 						=		image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             A_ 						=		np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]  )

             assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3






      @skip_mps
      def 							_UpperCAmelCase					(     self       :			Dict  ):
             return super().test_save_load_local()






      @unittest.skip("non-deterministic pipeline"  )
      def 							_UpperCAmelCase					(     self       :			str  ):
             return super().test_inference_batch_single_identical()






      @skip_mps
      def 							_UpperCAmelCase					(     self       :			Optional[Any]  ):
             return super().test_dict_tuple_outputs_equivalent()






      @skip_mps
      def 							_UpperCAmelCase					(     self       :			List[Any]  ):
             return super().test_save_load_optional_components()






      @skip_mps
      def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
             return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class 				__lowerCAmelCase      (      unittest.TestCase	):

      """simple docstring"""






      def 							_UpperCAmelCase					(     self       :			str  ):
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()






      def 							_UpperCAmelCase					(     self       :			Any  ):
             A_ 						=		load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
                 "repaint/celeba_hq_256.png"  )
             A_ 						=		load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"  )
             A_ 						=		load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
                 "repaint/celeba_hq_256_result.npy"  )

             A_ 						=		"google/ddpm-ema-celebahq-256"
             A_ 						=		UNetaDModel.from_pretrained(lowerCAmelCase  )
             A_ 						=		RePaintScheduler.from_pretrained(lowerCAmelCase  )

             A_ 						=		RePaintPipeline(unet=lowerCAmelCase   ,     scheduler=lowerCAmelCase  ).to(lowerCAmelCase  )
             repaint.set_progress_bar_config(disable=lowerCAmelCase  )
             repaint.enable_attention_slicing()

             A_ 						=		torch.manual_seed(0  )
             A_ 						=		repaint(
                 lowerCAmelCase   ,     lowerCAmelCase   ,     num_inference_steps=2_50   ,     eta=0.0   ,     jump_length=10   ,     jump_n_sample=10   ,     generator=lowerCAmelCase   ,     output_type="np"   ,     )
             A_ 						=		output.images[0]

             assert image.shape == (2_56, 2_56, 3)
             assert np.abs(expected_image - image  ).mean() < 1e-2









'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE				:							str		       =		logging.getLogger()







def 			a_							(  ):
							A_ 						=		argparse.ArgumentParser()
							parser.add_argument("-f"   )
							A_ 						=		parser.parse_args()
							return args.f







class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		logging.StreamHandler(sys.stdout  )
													logger.addHandler(lowerCAmelCase  )

													A_ 						=		self.get_auto_remove_tmp_dir()
													A_ 						=		F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

													with patch.object(lowerCAmelCase   ,     "argv"   ,     lowerCAmelCase  ):
																				A_ 						=		run_glue_with_pabee.main()
																				for value in result.values():
																											self.assertGreaterEqual(lowerCAmelCase   ,     0.7_5  )





