


"""simple docstring"""






import os

import pytest
from attr import dataclass


_lowerCamelCase     =  'us-east-1'  # defaults region


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      str
				_lowerCAmelCase      :      int      							=					"arn:aws:iam::558105141721:role/sagemaker_execution_role"
				_lowerCAmelCase      :      Optional[Any]      							=					{
				    "task_name": "mnli",
				    "per_device_train_batch_size": 16,
				    "per_device_eval_batch_size": 16,
				    "do_train": True,
				    "do_eval": True,
				    "do_predict": True,
				    "output_dir": "/opt/ml/model",
				    "overwrite_output_dir": True,
				    "max_steps": 500,
				    "save_steps": 5_500,
				}
				_lowerCAmelCase      :      List[str]      							=					{**hyperparameters, "max_steps": 1_000}



				@property
				def   lowerCAmelCase__		(						self     ):
								if self.framework == "pytorch":
												return [
												    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
												    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
												    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
												]
								else:
												return [
												    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
												    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
												    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
												]



				@property
				def   lowerCAmelCase__		(						self     ):
								return f'''{self.framework}-transfromers-test'''



				@property
				def   lowerCAmelCase__		(						self     ):
								return f'''./tests/sagemaker/scripts/{self.framework}'''






				@property
				def   lowerCAmelCase__		(						self     ):
								if self.framework == "pytorch":
												return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
								else:
												return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"


@pytest.fixture(scope="class"  )
def __lowercase     (      lowerCamelCase_  :      Dict  ):
				SCREAMING_SNAKE_CASE__     							=				SageMakerTestEnvironment(framework=request.cls.framework  )




"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_lowerCamelCase     =  0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_lowerCamelCase     =  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				WATERMARK_BITS
								SCREAMING_SNAKE_CASE__     							=				WatermarkEncoder()

								self.encoder.set_watermark("bits"       ,		self.watermark     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# can't encode images that are smaller than 256
								if images.shape[-1] < 256:
												return images

								SCREAMING_SNAKE_CASE__     							=				(255 * (images / 2 + 0.5)).cpu().permute(0       ,		2       ,		3       ,		1     ).float().numpy()

								SCREAMING_SNAKE_CASE__     							=				[self.encoder.encode(UpperCAmelCase__       ,		"dwtDct"     ) for image in images]

								SCREAMING_SNAKE_CASE__     							=				torch.from_numpy(np.array(UpperCAmelCase__     )     ).permute(0       ,		3       ,		1       ,		2     )

								SCREAMING_SNAKE_CASE__     							=				torch.clamp(2 * (images / 255 - 0.5)       ,		min=-1.0       ,		max=1.0     )
								return images
