


import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


snake_case							:       Optional[int]     			=      logging.get_logger(__name__)

enable_full_determinism()







class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Union[str, Any]          =			UNetaDModel
					UpperCAmelCase__					:    List[Any]          =			'''sample'''
					@property
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												a__             =     4
												a__             =     3
												a__             =     (32, 32)

												a__             =     floats_tensor((batch_size, num_channels) + sizes      ).to(__snake_case      )
												a__             =     torch.tensor([10]      ).to(__snake_case      )

												return {"sample": noise, "timestep": time_step}
					@property
					def  lowerCamelCase__(      self   :str      )					->    str:
												return (3, 32, 32)
					@property
					def  lowerCamelCase__(      self   :Tuple      )					->    Optional[int]:
												return (3, 32, 32)



					def  lowerCamelCase__(      self   :Dict      )					->    Any:
												a__             =     {
												    'block_out_channels': (32, 64),
												    'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
												    'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
												    'attention_head_dim': 3,
												    'out_channels': 3,
												    'in_channels': 3,
												    'layers_per_block': 2,
												    'sample_size': 32,
												}
												a__             =     self.dummy_input
												return init_dict, inputs_dict







class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Tuple          =			UNetaDModel
					UpperCAmelCase__					:    Union[str, Any]          =			'''sample'''
					@property
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												a__             =     4
												a__             =     4
												a__             =     (32, 32)

												a__             =     floats_tensor((batch_size, num_channels) + sizes      ).to(__snake_case      )
												a__             =     torch.tensor([10]      ).to(__snake_case      )

												return {"sample": noise, "timestep": time_step}
					@property
					def  lowerCamelCase__(      self   :int      )					->    Optional[int]:
												return (4, 32, 32)
					@property
					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[Any]:
												return (4, 32, 32)
					def  lowerCamelCase__(      self   :str      )					->    Any:
												a__             =     {
												    'sample_size': 32,
												    'in_channels': 4,
												    'out_channels': 4,
												    'layers_per_block': 2,
												    'block_out_channels': (32, 64),
												    'attention_head_dim': 32,
												    'down_block_types': ('DownBlock2D', 'DownBlock2D'),
												    'up_block_types': ('UpBlock2D', 'UpBlock2D'),
												}
												a__             =     self.dummy_input
												return init_dict, inputs_dict
					def  lowerCamelCase__(      self   :Tuple      )					->    List[str]:
												a__      ,					a__             =     UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'			,output_loading_info=__snake_case      )

												self.assertIsNotNone(__snake_case      )
												self.assertEqual(len(loading_info['missing_keys']      )			,0      )

												model.to(__snake_case      )
												a__             =     model(**self.dummy_input      ).sample

												assert image is not None, "Make sure output is not None"
					@unittest.skipIf(torch_device != 'cuda'			,'This test is supposed to run on GPU'      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Any:
												a__      ,					a__             =     UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'			,output_loading_info=__snake_case      )
												model.to(__snake_case      )
												a__             =     model(**self.dummy_input      ).sample

												assert image is not None, "Make sure output is not None"
					@unittest.skipIf(torch_device != 'cuda'			,'This test is supposed to run on GPU'      )
					def  lowerCamelCase__(      self   :Tuple      )					->    List[Any]:
												# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
												a__      ,					a__             =     UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'			,output_loading_info=__snake_case      )
												model_accelerate.to(__snake_case      )
												model_accelerate.eval()

												a__             =     torch.randn(
												    1			,model_accelerate.config.in_channels			,model_accelerate.config.sample_size			,model_accelerate.config.sample_size			,generator=torch.manual_seed(0      )			,)
												a__             =     noise.to(__snake_case      )
												a__             =     torch.tensor([10] * noise.shape[0]      ).to(__snake_case      )

												a__             =     model_accelerate(__snake_case			,__snake_case      )['sample']

												# two models don't need to stay in the device at the same time
												del model_accelerate
												torch.cuda.empty_cache()
												gc.collect()

												a__      ,					a__             =     UNetaDModel.from_pretrained(
												    'fusing/unet-ldm-dummy-update'			,output_loading_info=__snake_case			,low_cpu_mem_usage=__snake_case      )
												model_normal_load.to(__snake_case      )
												model_normal_load.eval()
												a__             =     model_normal_load(__snake_case			,__snake_case      )['sample']

												assert torch_all_close(__snake_case			,__snake_case			,rtol=1E-3      )



					def  lowerCamelCase__(      self   :List[Any]      )					->    Optional[int]:
												a__             =     UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'      )
												model.eval()
												model.to(__snake_case      )

												a__             =     torch.randn(
												    1			,model.config.in_channels			,model.config.sample_size			,model.config.sample_size			,generator=torch.manual_seed(0      )			,)
												a__             =     noise.to(__snake_case      )
												a__             =     torch.tensor([10] * noise.shape[0]      ).to(__snake_case      )

												with torch.no_grad():
																			a__             =     model(__snake_case			,__snake_case      ).sample

												a__             =     output[0, -1, -3:, -3:].flatten().cpu()
												# fmt: off
												a__             =     torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]      )
												# fmt: on

												self.assertTrue(torch_all_close(__snake_case			,__snake_case			,rtol=1E-3      )      )







class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Tuple          =			UNetaDModel
					UpperCAmelCase__					:    Optional[int]          =			'''sample'''
					@property
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :Optional[int]=(32, 32)      )					->    Union[str, Any]:
												a__             =     4
												a__             =     3

												a__             =     floats_tensor((batch_size, num_channels) + sizes      ).to(__snake_case      )
												a__             =     torch.tensor(batch_size * [10]      ).to(dtype=torch.intaa			,device=__snake_case      )

												return {"sample": noise, "timestep": time_step}
					@property
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    int:
												return (3, 32, 32)
					@property
					def  lowerCamelCase__(      self   :Optional[int]      )					->    str:
												return (3, 32, 32)
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Optional[Any]:
												a__             =     {
												    'block_out_channels': [32, 64, 64, 64],
												    'in_channels': 3,
												    'layers_per_block': 1,
												    'out_channels': 3,
												    'time_embedding_type': 'fourier',
												    'norm_eps': 1E-6,
												    'mid_block_scale_factor': math.sqrt(2.0      ),
												    'norm_num_groups': None,
												    'down_block_types': [
												        'SkipDownBlock2D',
												        'AttnSkipDownBlock2D',
												        'SkipDownBlock2D',
												        'SkipDownBlock2D',
												    ],
												    'up_block_types': [
												        'SkipUpBlock2D',
												        'SkipUpBlock2D',
												        'AttnSkipUpBlock2D',
												        'SkipUpBlock2D',
												    ],
												}
												a__             =     self.dummy_input
												return init_dict, inputs_dict
					@slow
					def  lowerCamelCase__(      self   :int      )					->    List[str]:
												a__      ,					a__             =     UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'			,output_loading_info=__snake_case      )
												self.assertIsNotNone(__snake_case      )
												self.assertEqual(len(loading_info['missing_keys']      )			,0      )

												model.to(__snake_case      )
												a__             =     self.dummy_input
												a__             =     floats_tensor((4, 3) + (2_56, 2_56)      ).to(__snake_case      )
												a__             =     noise
												a__             =     model(**__snake_case      )

												assert image is not None, "Make sure output is not None"
					@slow
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Union[str, Any]:
												a__             =     UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'      )
												model.to(__snake_case      )

												a__             =     4
												a__             =     3
												a__             =     (2_56, 2_56)

												a__             =     torch.ones((batch_size, num_channels) + sizes      ).to(__snake_case      )
												a__             =     torch.tensor(batch_size * [1E-4]      ).to(__snake_case      )

												with torch.no_grad():
																			a__             =     model(__snake_case			,__snake_case      ).sample

												a__             =     output[0, -3:, -3:, -1].flatten().cpu()
												# fmt: off
												a__             =     torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]      )
												# fmt: on

												self.assertTrue(torch_all_close(__snake_case			,__snake_case			,rtol=1E-2      )      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												a__             =     UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'      )
												model.to(__snake_case      )

												a__             =     4
												a__             =     3
												a__             =     (32, 32)

												a__             =     torch.ones((batch_size, num_channels) + sizes      ).to(__snake_case      )
												a__             =     torch.tensor(batch_size * [1E-4]      ).to(__snake_case      )

												with torch.no_grad():
																			a__             =     model(__snake_case			,__snake_case      ).sample

												a__             =     output[0, -3:, -3:, -1].flatten().cpu()
												# fmt: off
												a__             =     torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]      )
												# fmt: on

												self.assertTrue(torch_all_close(__snake_case			,__snake_case			,rtol=1E-2      )      )



					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[Any]:
												# not required for this model
												pass








import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
						from sagemaker import Session, TrainingJobAnalytics
						from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''	,						'''False'''      )      ) is not True	,						reason='''Skipping test because should only be run when releasing minor transformers version'''	,						)
@pytest.mark.usefixtures('''sm_env'''      )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]      )
class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												if self.framework == "pytorch":
																			subprocess.run(
																			    F'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split()			,encoding='utf-8'			,check=__snake_case			,)
												assert hasattr(self			,'env'      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :Optional[Any]=1      )					->    int:
												# creates estimator
												return HuggingFace(
												    entry_point=self.script			,source_dir=self.env.test_path			,role=self.env.role			,image_uri=self.env.image_uri			,base_job_name=F'{self.env.base_job_name}-single'			,instance_count=__snake_case			,instance_type=self.instance_type			,debugger_hook_config=__snake_case			,hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}			,metric_definitions=self.env.metric_definitions			,py_version='py36'			,)
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :str      )					->    Optional[int]:
												TrainingJobAnalytics(__snake_case      ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'      )



					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												# create estimator
												a__             =     self.create_estimator()

												# run training
												estimator.fit()

												# result dataframe
												a__             =     TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

												# extract kpis
												a__             =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
												a__             =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												a__             =     (
												    Session().describe_training_job(estimator.latest_training_job.name      ).get('TrainingTimeInSeconds'			,99_99_99      )
												)

												# assert kpis
												assert train_runtime <= self.results["train_runtime"]
												assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy      )
												assert all(t <= self.results['eval_loss'] for t in eval_loss      )

												# dump tests result into json file to share in PR
												with open(F'{estimator.latest_training_job.name}.json'			,'w'      ) as outfile:
																			json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss}			,__snake_case      )




