



import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A__  :  Optional[int]								=					logging.get_logger(__name__)  # pylint: disable=invalid-name


def 			UpperCamelCase(  __UpperCamelCase							:		Union[List, PIL.Image.Image, torch.Tensor] ):
		warnings.warn(
		    '''The preprocess method is deprecated and will be removed in a future version. Please'''
		    ''' use VaeImageProcessor.preprocess instead'''     ,__UpperCamelCase     ,)
		if isinstance(__UpperCamelCase     ,torch.Tensor ):
				return image
		elif isinstance(__UpperCamelCase     ,PIL.Image.Image ):
				lowerCAmelCase_			:     List[Any]									= [image]

		if isinstance(image[0]     ,PIL.Image.Image ):
				lowerCAmelCase_					,  lowerCAmelCase_			:     Union[str, Any]									= image[0].size
				lowerCAmelCase_					,  lowerCAmelCase_			:     List[Any]									= (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				lowerCAmelCase_			:     Tuple									= [np.array(i.resize((w, h)     ,resample=PIL_INTERPOLATION['''lanczos'''] ) )[None, :] for i in image]
				lowerCAmelCase_			:     List[str]									= np.concatenate(__UpperCamelCase     ,axis=0 )
				lowerCAmelCase_			:     str									= np.array(__UpperCamelCase ).astype(np.floataa ) / 2_5_5.0
				lowerCAmelCase_			:     str									= image.transpose(0     ,3     ,1     ,2 )
				lowerCAmelCase_			:     int									= 2.0 * image - 1.0
				lowerCAmelCase_			:     int									= torch.from_numpy(__UpperCamelCase )
		elif isinstance(image[0]     ,torch.Tensor ):
				lowerCAmelCase_			:     Dict									= torch.cat(__UpperCamelCase     ,dim=0 )
		return image


def 			UpperCamelCase(  __UpperCamelCase							:		Union[List, PIL.Image.Image, torch.Tensor] ):
		if isinstance(__UpperCamelCase     ,torch.Tensor ):
				return mask
		elif isinstance(__UpperCamelCase     ,PIL.Image.Image ):
				lowerCAmelCase_			:     int									= [mask]

		if isinstance(mask[0]     ,PIL.Image.Image ):
				lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[int]									= mask[0].size
				lowerCAmelCase_					,  lowerCAmelCase_			:     str									= (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				lowerCAmelCase_			:     int									= [np.array(m.convert('''L''' ).resize((w, h)     ,resample=PIL_INTERPOLATION['''nearest'''] ) )[None, :] for m in mask]
				lowerCAmelCase_			:     List[str]									= np.concatenate(__UpperCamelCase     ,axis=0 )
				lowerCAmelCase_			:     Union[str, Any]									= mask.astype(np.floataa ) / 2_5_5.0
				lowerCAmelCase_			:     int									= 0
				lowerCAmelCase_			:     Any									= 1
				lowerCAmelCase_			:     Any									= torch.from_numpy(__UpperCamelCase )
		elif isinstance(mask[0]     ,torch.Tensor ):
				lowerCAmelCase_			:     Optional[int]									= torch.cat(__UpperCamelCase     ,dim=0 )
		return mask





class 				__snake_case						(     UpperCamelCase_						):
							_a			      = 42
							_a			      = 42




							def __init__(       self     :       Optional[Any]				,				A_     :       Tuple				,				A_     :       Dict):
									super().__init__()
									self.register_modules(unet=A_				,				scheduler=A_)







							@torch.no_grad()
							def __call__(       self     :       Union[str, Any]				,				A_     :       Union[torch.Tensor, PIL.Image.Image]				,				A_     :       Union[torch.Tensor, PIL.Image.Image]				,				A_     :       int = 2_5_0				,				A_     :       float = 0.0				,				A_     :       int = 1_0				,				A_     :       int = 1_0				,				A_     :       Optional[Union[torch.Generator, List[torch.Generator]]] = None				,				A_     :       Optional[str] = "pil"				,				A_     :       bool = True				,				):
									lowerCAmelCase_			:     Union[str, Any]									= image

									lowerCAmelCase_			:     Optional[int]									= _preprocess_image(A_)
									lowerCAmelCase_			:     Tuple									= original_image.to(device=self.device				,				dtype=self.unet.dtype)
									lowerCAmelCase_			:     List[str]									= _preprocess_mask(A_)
									lowerCAmelCase_			:     Dict									= mask_image.to(device=self.device				,				dtype=self.unet.dtype)

									lowerCAmelCase_			:     Any									= original_image.shape[0]

									# sample gaussian noise to begin the loop
									if isinstance(A_				,				A_) and len(A_) != batch_size:
											raise ValueError(
											    F"""You have passed a list of generators of length {len(A_)}, but requested an effective batch"""
											    F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

									lowerCAmelCase_			:     Optional[int]									= original_image.shape
									lowerCAmelCase_			:     List[str]									= randn_tensor(A_				,				generator=A_				,				device=self.device				,				dtype=self.unet.dtype)

									# set step values
									self.scheduler.set_timesteps(A_				,				A_				,				A_				,				self.device)
									lowerCAmelCase_			:     Optional[Any]									= eta

									lowerCAmelCase_			:     int									= self.scheduler.timesteps[0] + 1
									lowerCAmelCase_			:     Dict									= generator[0] if isinstance(A_				,				A_) else generator
									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
											if t < t_last:
													# predict the noise residual
													lowerCAmelCase_			:     List[Any]									= self.unet(A_				,				A_).sample
													# compute previous image: x_t -> x_t-1
													lowerCAmelCase_			:     Union[str, Any]									= self.scheduler.step(A_				,				A_				,				A_				,				A_				,				A_				,				A_).prev_sample

											else:
													# compute the reverse: x_t-1 -> x_t
													lowerCAmelCase_			:     List[str]									= self.scheduler.undo_step(A_				,				A_				,				A_)
											lowerCAmelCase_			:     Dict									= t

									lowerCAmelCase_			:     Dict									= (image / 2 + 0.5).clamp(0				,				1)
									lowerCAmelCase_			:     Tuple									= image.cpu().permute(0				,				2				,				3				,				1).numpy()
									if output_type == "pil":
											lowerCAmelCase_			:     Dict									= self.numpy_to_pil(A_)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=A_)










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''': 650, '''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''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]						)
class 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       int):
									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=A_				,				)
									assert hasattr(self				,				'''env''')




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       str=1):
									# 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=A_				,				instance_type=self.instance_type				,				debugger_hook_config=A_				,				hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}				,				metric_definitions=self.env.metric_definitions				,				py_version='''py36'''				,				)




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Optional[Any]):
									TrainingJobAnalytics(A_).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







							def    UpperCAmelCase__			(       self     :       int):
									# create estimator
									lowerCAmelCase_			:     List[Any]									= self.create_estimator()

									# run training
									estimator.fit()

									# result dataframe
									lowerCAmelCase_			:     Union[str, Any]									= TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

									# extract kpis
									lowerCAmelCase_			:     Optional[Any]									= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
									lowerCAmelCase_			:     Optional[int]									= list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									lowerCAmelCase_			:     Dict									= (
									    Session().describe_training_job(estimator.latest_training_job.name).get('''TrainingTimeInSeconds'''				,				9_9_9_9_9_9)
									)

									# 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}				,				A_)





