

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

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







							@property
							def 				__lowercase				(			self       )    ->    List[str]:
												torch.manual_seed(0       )
												_a	:    List[Any]         =		UNetaDModel(
												    block_out_channels=(3_2, 6_4)       ,							layers_per_block=2       ,							sample_size=3_2       ,							in_channels=3       ,							out_channels=3       ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,							)
												return model




							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    Any         =		self.dummy_uncond_unet
												_a	:    Union[str, Any]         =		KarrasVeScheduler()

												_a	:    Union[str, Any]         =		KarrasVePipeline(unet=lowerCamelCase__       ,							scheduler=lowerCamelCase__       )
												pipe.to(lowerCamelCase__       )
												pipe.set_progress_bar_config(disable=lowerCamelCase__       )

												_a	:    Optional[Any]         =		torch.manual_seed(0       )
												_a	:    Any         =		pipe(num_inference_steps=2       ,							generator=lowerCamelCase__       ,							output_type='''numpy'''       ).images

												_a	:    List[str]         =		torch.manual_seed(0       )
												_a	:    str         =		pipe(num_inference_steps=2       ,							generator=lowerCamelCase__       ,							output_type='''numpy'''       ,							return_dict=lowerCamelCase__       )[0]

												_a	:    Any         =		image[0, -3:, -3:, -1]
												_a	:    Tuple         =		image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 3_2, 3_2, 3)
												_a	:    Dict         =		np.array([0.0, 1.0, 0.0, 0.0, 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 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    Optional[Any]         =		'''google/ncsnpp-celebahq-256'''
												_a	:    Tuple         =		UNetaDModel.from_pretrained(lowerCamelCase__       )
												_a	:    Dict         =		KarrasVeScheduler()

												_a	:    Union[str, Any]         =		KarrasVePipeline(unet=lowerCamelCase__       ,							scheduler=lowerCamelCase__       )
												pipe.to(lowerCamelCase__       )
												pipe.set_progress_bar_config(disable=lowerCamelCase__       )

												_a	:    Dict         =		torch.manual_seed(0       )
												_a	:    str         =		pipe(num_inference_steps=2_0       ,							generator=lowerCamelCase__       ,							output_type='''numpy'''       ).images

												_a	:    Any         =		image[0, -3:, -3:, -1]
												assert image.shape == (1, 2_5_6, 2_5_6, 3)
												_a	:    List[str]         =		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

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




"""simple docstring"""






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__  :  Optional[Any]						     =	logging.getLogger()



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







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							logging.StreamHandler(sys.stdout			)
									logger.addHandler(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							get_gpu_count()

									if n_gpu > 1:
													pass
													# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
													# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
													# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
													# cmd = [sys.executable] + distributed_args + args
													# execute_subprocess_async(cmd, env=self.get_env())
													# XXX: test the results - need to save them first into .json file
									else:
													args.insert(0    ,'run_glue_deebert.py'			)
													with patch.object(lowerCamelCase__    ,'argv'    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							run_glue_deebert.main()
																	for value in result.values():
																					self.assertGreaterEqual(lowerCamelCase__    ,0.6_6_6			)







					@slow
					@require_torch_non_multi_gpu
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)



