"""simple docstring"""




import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	int:

						'''simple docstring'''



						if isinstance(__lowercase ,      torch.Tensor						):
												return image
						elif isinstance(__lowercase ,      PIL.Image.Image						):
												_snake_case						:					List[str]    =   [image]

						if isinstance(image[0] ,      PIL.Image.Image						):
												_snake_case						:					Optional[Any]    =   [np.array(i.resize((w, h) ,      resample=PIL_INTERPOLATION["lanczos"]						)						)[None, :] for i in image]
												_snake_case						:					List[Any]    =   np.concatenate(__lowercase ,      axis=0						)
												_snake_case						:					Optional[int]    =   np.array(__lowercase						).astype(np.floataa						) / 255.0
												_snake_case						:					Dict    =   image.transpose(0 ,      3 ,      1 ,      2						)
												_snake_case						:					Union[str, Any]    =   2.0 * image - 1.0
												_snake_case						:					List[Any]    =   torch.from_numpy(__lowercase						)
						elif isinstance(image[0] ,      torch.Tensor						):
												_snake_case						:					str    =   torch.cat(__lowercase ,      dim=0						)
						return image
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase=0.9995						)	->	Optional[Any]:

						'''simple docstring'''



						if not isinstance(__lowercase ,      np.ndarray						):
												_snake_case						:					str    =   True
												_snake_case						:					Union[str, Any]    =   va.device
												_snake_case						:					Union[str, Any]    =   va.cpu().numpy()
												_snake_case						:					Union[str, Any]    =   va.cpu().numpy()

						_snake_case						:					Union[str, Any]    =   np.sum(va * va / (np.linalg.norm(__lowercase						) * np.linalg.norm(__lowercase						))						)
						if np.abs(__lowercase						) > DOT_THRESHOLD:
												_snake_case						:					Tuple    =   (1 - t) * va + t * va
						else:
												_snake_case						:					Optional[Any]    =   np.arccos(__lowercase						)
												_snake_case						:					Any    =   np.sin(__lowercase						)
												_snake_case						:					List[str]    =   theta_a * t
												_snake_case						:					Union[str, Any]    =   np.sin(__lowercase						)
												_snake_case						:					List[Any]    =   np.sin(theta_a - theta_t						) / sin_theta_a
												_snake_case						:					Union[str, Any]    =   sin_theta_t / sin_theta_a
												_snake_case						:					List[Any]    =   sa * va + sa * va

						if inputs_are_torch:
												_snake_case						:					int    =   torch.from_numpy(__lowercase						).to(__lowercase						)

						return va
def  snake_case						(__lowercase ,      __lowercase						)	->	Union[str, Any]:

						'''simple docstring'''



						_snake_case						:					List[str]    =   F.normalize(__lowercase ,      dim=-1						)
						_snake_case						:					Dict    =   F.normalize(__lowercase ,      dim=-1						)
						return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)
def  snake_case						(__lowercase ,      __lowercase						)	->	Dict:

						'''simple docstring'''



						for param in model.parameters():
												_snake_case						:					Any    =   value



class        lowercase_   (					__lowerCAmelCase		):



							def __init__(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    ):
													super().__init__()
													self.register_modules(
													    vae=lowerCamelCase__				,    text_encoder=lowerCamelCase__				,    clip_model=lowerCamelCase__				,    tokenizer=lowerCamelCase__				,    unet=lowerCamelCase__				,    scheduler=lowerCamelCase__				,    feature_extractor=lowerCamelCase__				,    coca_model=lowerCamelCase__				,    coca_tokenizer=lowerCamelCase__				,    coca_transform=lowerCamelCase__				,    )
													_snake_case						:					List[str]    =   (
													    feature_extractor.size
													    if isinstance(feature_extractor.size				,    lowerCamelCase__    )
													    else feature_extractor.size['''shortest_edge''']
													)
													_snake_case						:					Optional[int]    =   transforms.Normalize(mean=feature_extractor.image_mean				,    std=feature_extractor.image_std    )
													set_requires_grad(self.text_encoder				,    lowerCamelCase__    )
													set_requires_grad(self.clip_model				,    lowerCamelCase__    )



							def 	UpperCamelCase	(						self				,    lowercase_ = "auto"    ):






													if slice_size == "auto":
																			# half the attention head size is usually a good trade-off between
																			# speed and memory
																			_snake_case						:					Union[str, Any]    =   self.unet.config.attention_head_dim // 2
													self.unet.set_attention_slice(lowerCamelCase__    )



							def 	UpperCamelCase	(						self    ):
													self.enable_attention_slicing(lowerCamelCase__    )



							def 	UpperCamelCase	(						self    ):
													set_requires_grad(self.vae				,    lowerCamelCase__    )



							def 	UpperCamelCase	(						self    ):
													set_requires_grad(self.vae				,    lowerCamelCase__    )



							def 	UpperCamelCase	(						self    ):
													set_requires_grad(self.unet				,    lowerCamelCase__    )



							def 	UpperCamelCase	(						self    ):
													set_requires_grad(self.unet				,    lowerCamelCase__    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Any    =   min(int(num_inference_steps * strength    )				,    lowerCamelCase__    )

													_snake_case						:					Any    =   max(num_inference_steps - init_timestep				,    0    )
													_snake_case						:					Tuple    =   self.scheduler.timesteps[t_start:]

													return timesteps, num_inference_steps - t_start



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_=None    ):






													if not isinstance(lowerCamelCase__				,    torch.Tensor    ):
																			raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(lowerCamelCase__    )}"""    )

													_snake_case						:					Any    =   image.to(device=lowerCamelCase__				,    dtype=lowerCamelCase__    )

													if isinstance(lowerCamelCase__				,    lowerCamelCase__    ):
																			_snake_case						:					Union[str, Any]    =   [
																			    self.vae.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(lowerCamelCase__    )
																			]
																			_snake_case						:					str    =   torch.cat(lowerCamelCase__				,    dim=0    )
													else:
																			_snake_case						:					Optional[Any]    =   self.vae.encode(lowerCamelCase__    ).latent_dist.sample(lowerCamelCase__    )

													# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
													_snake_case						:					int    =   0.18_215 * init_latents
													_snake_case						:					Dict    =   init_latents.repeat_interleave(lowerCamelCase__				,    dim=0    )

													_snake_case						:					Optional[int]    =   randn_tensor(init_latents.shape				,    generator=lowerCamelCase__				,    device=lowerCamelCase__				,    dtype=lowerCamelCase__    )

													# get latents
													_snake_case						:					int    =   self.scheduler.add_noise(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__    )
													_snake_case						:					List[Any]    =   init_latents

													return latents



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					List[Any]    =   self.coca_transform(lowerCamelCase__    ).unsqueeze(0    )
													with torch.no_grad(), torch.cuda.amp.autocast():
																			_snake_case						:					Dict    =   self.coca_model.generate(transformed_image.to(device=self.device				,    dtype=self.coca_model.dtype    )    )
													_snake_case						:					List[str]    =   self.coca_tokenizer.decode(generated[0].cpu().numpy()    )
													return generated.split("<end_of_text>"    )[0].replace("<start_of_text>"				,    ""    ).rstrip(" .,"    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Union[str, Any]    =   self.feature_extractor.preprocess(lowerCamelCase__    )
													_snake_case						:					List[str]    =   torch.from_numpy(clip_image_input["pixel_values"][0]    ).unsqueeze(0    ).to(self.device    ).half()
													_snake_case						:					Optional[int]    =   self.clip_model.get_image_features(lowerCamelCase__    )
													_snake_case						:					Union[str, Any]    =   image_embeddings_clip / image_embeddings_clip.norm(p=2				,    dim=-1				,    keepdim=lowerCamelCase__    )
													_snake_case						:					str    =   image_embeddings_clip.repeat_interleave(lowerCamelCase__				,    dim=0    )
													return image_embeddings_clip



							@torch.enable_grad()
							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    ):
													_snake_case						:					Union[str, Any]    =   latents.detach().requires_grad_()

													_snake_case						:					int    =   self.scheduler.scale_model_input(lowerCamelCase__				,    lowerCamelCase__    )

													# predict the noise residual
													_snake_case						:					Any    =   self.unet(lowerCamelCase__				,    lowerCamelCase__				,    encoder_hidden_states=lowerCamelCase__    ).sample

													if isinstance(self.scheduler				,    (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)    ):
																			_snake_case						:					List[str]    =   self.scheduler.alphas_cumprod[timestep]
																			_snake_case						:					Dict    =   1 - alpha_prod_t
																			# compute predicted original sample from predicted noise also called
																			# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
																			_snake_case						:					Optional[Any]    =   (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																			_snake_case						:					Dict    =   torch.sqrt(lowerCamelCase__    )
																			_snake_case						:					List[Any]    =   pred_original_sample * (fac) + latents * (1 - fac)
													elif isinstance(self.scheduler				,    lowerCamelCase__    ):
																			_snake_case						:					int    =   self.scheduler.sigmas[index]
																			_snake_case						:					Optional[Any]    =   latents - sigma * noise_pred
													else:
																			raise ValueError(f"""scheduler type {type(self.scheduler    )} not supported"""    )

													# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
													_snake_case						:					List[str]    =   1 / 0.18_215 * sample
													_snake_case						:					Any    =   self.vae.decode(lowerCamelCase__    ).sample
													_snake_case						:					str    =   (image / 2 + 0.5).clamp(0				,    1    )

													_snake_case						:					Optional[Any]    =   transforms.Resize(self.feature_extractor_size    )(lowerCamelCase__    )
													_snake_case						:					str    =   self.normalize(lowerCamelCase__    ).to(latents.dtype    )

													_snake_case						:					Tuple    =   self.clip_model.get_image_features(lowerCamelCase__    )
													_snake_case						:					Any    =   image_embeddings_clip / image_embeddings_clip.norm(p=2				,    dim=-1				,    keepdim=lowerCamelCase__    )

													_snake_case						:					Union[str, Any]    =   spherical_dist_loss(lowerCamelCase__				,    lowerCamelCase__    ).mean() * clip_guidance_scale

													_snake_case						:					Tuple    =   -torch.autograd.grad(lowerCamelCase__				,    lowerCamelCase__    )[0]

													if isinstance(self.scheduler				,    lowerCamelCase__    ):
																			_snake_case						:					Optional[int]    =   latents.detach() + grads * (sigma**2)
																			_snake_case						:					Union[str, Any]    =   noise_pred_original
													else:
																			_snake_case						:					List[str]    =   noise_pred_original - torch.sqrt(lowerCamelCase__    ) * grads
													return noise_pred, latents



							@torch.no_grad()
							def __call__(						self				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = 512				,    lowercase_ = 512				,    lowercase_ = 0.6				,    lowercase_ = 50				,    lowercase_ = 7.5				,    lowercase_ = 1				,    lowercase_ = 0.0				,    lowercase_ = 100				,    lowercase_ = None				,    lowercase_ = "pil"				,    lowercase_ = True				,    lowercase_ = 0.8				,    lowercase_ = 0.1				,    lowercase_ = 0.1				,    ):






													if isinstance(lowerCamelCase__				,    lowerCamelCase__    ) and len(lowerCamelCase__    ) != batch_size:
																			raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(lowerCamelCase__    )} generators."""    )

													if height % 8 != 0 or width % 8 != 0:
																			raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""    )

													if isinstance(lowerCamelCase__				,    torch.Generator    ) and batch_size > 1:
																			_snake_case						:					Union[str, Any]    =   [generator] + [None] * (batch_size - 1)

													_snake_case						:					Optional[int]    =   [
													    ('''model''', self.coca_model is None),
													    ('''tokenizer''', self.coca_tokenizer is None),
													    ('''transform''', self.coca_transform is None),
													]
													_snake_case						:					List[str]    =   [x[0] for x in coca_is_none if x[1]]
													_snake_case						:					Optional[Any]    =   ''', '''.join(lowerCamelCase__    )
													# generate prompts with coca model if prompt is None
													if content_prompt is None:
																			if len(lowerCamelCase__    ):
																									raise ValueError(
																									    f"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
																									    f"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""    )
																			_snake_case						:					str    =   self.get_image_description(lowerCamelCase__    )
													if style_prompt is None:
																			if len(lowerCamelCase__    ):
																									raise ValueError(
																									    f"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
																									    f""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""    )
																			_snake_case						:					str    =   self.get_image_description(lowerCamelCase__    )

													# get prompt text embeddings for content and style
													_snake_case						:					Any    =   self.tokenizer(
													    lowerCamelCase__				,    padding="max_length"				,    max_length=self.tokenizer.model_max_length				,    truncation=lowerCamelCase__				,    return_tensors="pt"				,    )
													_snake_case						:					Tuple    =   self.text_encoder(content_text_input.input_ids.to(self.device    )    )[0]

													_snake_case						:					str    =   self.tokenizer(
													    lowerCamelCase__				,    padding="max_length"				,    max_length=self.tokenizer.model_max_length				,    truncation=lowerCamelCase__				,    return_tensors="pt"				,    )
													_snake_case						:					Optional[Any]    =   self.text_encoder(style_text_input.input_ids.to(self.device    )    )[0]

													_snake_case						:					Dict    =   slerp(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__    )

													# duplicate text embeddings for each generation per prompt
													_snake_case						:					List[str]    =   text_embeddings.repeat_interleave(lowerCamelCase__				,    dim=0    )

													# set timesteps
													_snake_case						:					Optional[Any]    =   '''offset''' in set(inspect.signature(self.scheduler.set_timesteps    ).parameters.keys()    )
													_snake_case						:					Tuple    =   {}
													if accepts_offset:
																			_snake_case						:					List[str]    =   1

													self.scheduler.set_timesteps(lowerCamelCase__				,    **lowerCamelCase__    )
													# Some schedulers like PNDM have timesteps as arrays
													# It's more optimized to move all timesteps to correct device beforehand
													self.scheduler.timesteps.to(self.device    )

													_snake_case						:					Tuple    =   self.get_timesteps(lowerCamelCase__				,    lowerCamelCase__				,    self.device    )
													_snake_case						:					str    =   timesteps[:1].repeat(lowerCamelCase__    )

													# Preprocess image
													_snake_case						:					Union[str, Any]    =   preprocess(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__    )
													_snake_case						:					int    =   self.prepare_latents(
													    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    text_embeddings.dtype				,    self.device				,    lowerCamelCase__    )

													_snake_case						:					Tuple    =   preprocess(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__    )
													_snake_case						:					Tuple    =   self.prepare_latents(
													    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    text_embeddings.dtype				,    self.device				,    lowerCamelCase__    )

													_snake_case						:					str    =   slerp(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__    )

													if clip_guidance_scale > 0:
																			_snake_case						:					int    =   self.get_clip_image_embeddings(lowerCamelCase__				,    lowerCamelCase__    )
																			_snake_case						:					Any    =   self.get_clip_image_embeddings(lowerCamelCase__				,    lowerCamelCase__    )
																			_snake_case						:					Dict    =   slerp(
																			    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__    )

													# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
													# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
													# corresponds to doing no classifier free guidance.
													_snake_case						:					Dict    =   guidance_scale > 1.0
													# get unconditional embeddings for classifier free guidance
													if do_classifier_free_guidance:
																			_snake_case						:					Tuple    =   content_text_input.input_ids.shape[-1]
																			_snake_case						:					Dict    =   self.tokenizer([""]				,    padding="max_length"				,    max_length=lowerCamelCase__				,    return_tensors="pt"    )
																			_snake_case						:					List[Any]    =   self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]
																			# duplicate unconditional embeddings for each generation per prompt
																			_snake_case						:					str    =   uncond_embeddings.repeat_interleave(lowerCamelCase__				,    dim=0    )

																			# For classifier free guidance, we need to do two forward passes.
																			# Here we concatenate the unconditional and text embeddings into a single batch
																			# to avoid doing two forward passes
																			_snake_case						:					Optional[int]    =   torch.cat([uncond_embeddings, text_embeddings]    )

													# get the initial random noise unless the user supplied it

													# Unlike in other pipelines, latents need to be generated in the target device
													# for 1-to-1 results reproducibility with the CompVis implementation.
													# However this currently doesn't work in `mps`.
													_snake_case						:					Tuple    =   (batch_size, self.unet.config.in_channels, height // 8, width // 8)
													_snake_case						:					Tuple    =   text_embeddings.dtype
													if latents is None:
																			if self.device.type == "mps":
																									# randn does not work reproducibly on mps
																									_snake_case						:					Optional[int]    =   torch.randn(lowerCamelCase__				,    generator=lowerCamelCase__				,    device="cpu"				,    dtype=lowerCamelCase__    ).to(
																									    self.device    )
																			else:
																									_snake_case						:					Optional[Any]    =   torch.randn(lowerCamelCase__				,    generator=lowerCamelCase__				,    device=self.device				,    dtype=lowerCamelCase__    )
													else:
																			if latents.shape != latents_shape:
																									raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""    )
																			_snake_case						:					Dict    =   latents.to(self.device    )

													# scale the initial noise by the standard deviation required by the scheduler
													_snake_case						:					str    =   latents * self.scheduler.init_noise_sigma

													# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
													# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
													# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
													# and should be between [0, 1]
													_snake_case						:					Any    =   '''eta''' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
													_snake_case						:					Tuple    =   {}
													if accepts_eta:
																			_snake_case						:					Dict    =   eta

													# check if the scheduler accepts generator
													_snake_case						:					List[str]    =   '''generator''' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
													if accepts_generator:
																			_snake_case						:					List[Any]    =   generator

													with self.progress_bar(total=lowerCamelCase__    ):
																			for i, t in enumerate(lowerCamelCase__    ):
																									# expand the latents if we are doing classifier free guidance
																									_snake_case						:					Tuple    =   torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
																									_snake_case						:					Optional[int]    =   self.scheduler.scale_model_input(lowerCamelCase__				,    lowerCamelCase__    )

																									# predict the noise residual
																									_snake_case						:					List[str]    =   self.unet(lowerCamelCase__				,    lowerCamelCase__				,    encoder_hidden_states=lowerCamelCase__    ).sample

																									# perform classifier free guidance
																									if do_classifier_free_guidance:
																															_snake_case						:					int    =   noise_pred.chunk(2    )
																															_snake_case						:					int    =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																									# perform clip guidance
																									if clip_guidance_scale > 0:
																															_snake_case						:					Union[str, Any]    =   (
																															    text_embeddings.chunk(2    )[1] if do_classifier_free_guidance else text_embeddings
																															)
																															_snake_case						:					int    =   self.cond_fn(
																															    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    )

																									# compute the previous noisy sample x_t -> x_t-1
																									_snake_case						:					List[str]    =   self.scheduler.step(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    **lowerCamelCase__    ).prev_sample

																									# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
													_snake_case						:					Union[str, Any]    =   1 / 0.18_215 * latents
													_snake_case						:					Dict    =   self.vae.decode(lowerCamelCase__    ).sample

													_snake_case						:					int    =   (image / 2 + 0.5).clamp(0				,    1    )
													_snake_case						:					Union[str, Any]    =   image.cpu().permute(0				,    2				,    3				,    1    ).numpy()

													if output_type == "pil":
																			_snake_case						:					List[Any]    =   self.numpy_to_pil(lowerCamelCase__    )

													if not return_dict:
																			return (image, None)

													return StableDiffusionPipelineOutput(images=lowerCamelCase__				,    nsfw_content_detected=lowerCamelCase__    )
import os

import pytest
from attr import dataclass


__SCREAMING_SNAKE_CASE						:   int         =			'us-east-1'  # defaults region



@dataclass
class        lowercase_   :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		'arn:aws:iam::558105141721:role/sagemaker_execution_role'
							_lowerCamelCase						=		{
							    '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						=		{**hyperparameters, 'max_steps': 1_000}



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



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



							@property
							def 	UpperCamelCase	(						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  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					List[str]    =   SageMakerTestEnvironment(framework=request.cls.framework						)