
"""simple docstring"""





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


UpperCamelCase_					:						Dict				= logging.get_logger(__name__)  # pylint: disable=invalid-name



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			def __init__(				self						:					Optional[int]       ,   _snake_case						:					WhisperForConditionalGeneration       ,   _snake_case						:					WhisperProcessor       ,   _snake_case						:					AutoencoderKL       ,   _snake_case						:					CLIPTextModel       ,   _snake_case						:					CLIPTokenizer       ,   _snake_case						:					UNetaDConditionModel       ,   _snake_case						:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,   _snake_case						:					StableDiffusionSafetyChecker       ,   _snake_case						:					CLIPImageProcessor       ,   )      -> Optional[int]:







								"""simple docstring"""
								super().__init__()

								if safety_checker is None:
													logger.warning(
													    F'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
													    " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
													    " results in services or applications open to the public. Both the diffusers team and Hugging Face"
													    " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
													    " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
													    " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."	)

								self.register_modules(
								    speech_model=_snake_case       ,   speech_processor=_snake_case       ,   vae=_snake_case       ,   text_encoder=_snake_case       ,   tokenizer=_snake_case       ,   unet=_snake_case       ,   scheduler=_snake_case       ,   feature_extractor=_snake_case       ,   )




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					Optional[Union[str, int]] = "auto"	)      -> str:







								"""simple docstring"""
								if slice_size == "auto":
													A_				 =						self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(_snake_case	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Tuple:







								"""simple docstring"""
								self.enable_attention_slicing(_snake_case	)






			@torch.no_grad()
			def __call__(				self						:					Dict       ,   _snake_case						:					Tuple       ,   _snake_case						:					List[str]=16_000       ,   _snake_case						:					int = 512       ,   _snake_case						:					int = 512       ,   _snake_case						:					int = 50       ,   _snake_case						:					float = 7.5       ,   _snake_case						:					Optional[Union[str, List[str]]] = None       ,   _snake_case						:					Optional[int] = 1       ,   _snake_case						:					float = 0.0       ,   _snake_case						:					Optional[torch.Generator] = None       ,   _snake_case						:					Optional[torch.FloatTensor] = None       ,   _snake_case						:					Optional[str] = "pil"       ,   _snake_case						:					bool = True       ,   _snake_case						:					Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,   _snake_case						:					int = 1       ,   **_snake_case						:					Optional[Any]       ,   )      -> Optional[int]:







								"""simple docstring"""
								A_				 =						self.speech_processor.feature_extractor(
								    _snake_case       ,   return_tensors="pt"       ,   sampling_rate=_snake_case	).input_features.to(self.device	)
								A_				 =						self.speech_model.generate(_snake_case       ,   max_length=480_000	)

								A_				 =						self.speech_processor.tokenizer.batch_decode(_snake_case       ,   skip_special_tokens=_snake_case       ,   normalize=_snake_case	)[
								    0
								]

								if isinstance(_snake_case       ,   _snake_case	):
													A_				 =						1
								elif isinstance(_snake_case       ,   _snake_case	):
													A_				 =						len(_snake_case	)
								else:
													raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(_snake_case	)}'	)

								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 (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(_snake_case       ,   _snake_case	) or callback_steps <= 0)
								):
													raise ValueError(
													    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
													    F' {type(_snake_case	)}.'	)

								# get prompt text embeddings
								A_				 =						self.tokenizer(
								    _snake_case       ,   padding="max_length"       ,   max_length=self.tokenizer.model_max_length       ,   return_tensors="pt"       ,   )
								A_				 =						text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													A_				 =						self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
													logger.warning(
													    "The following part of your input was truncated because CLIP can only handle sequences up to"
													    F' {self.tokenizer.model_max_length} tokens: {removed_text}'	)
													A_				 =						text_input_ids[:, : self.tokenizer.model_max_length]
								A_				 =						self.text_encoder(text_input_ids.to(self.device	)	)[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								A_ ,    A_ ,    A_				 =						text_embeddings.shape
								A_				 =						text_embeddings.repeat(1       ,   _snake_case       ,   1	)
								A_				 =						text_embeddings.view(bs_embed * num_images_per_prompt       ,   _snake_case       ,   -1	)

								# 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.
								A_				 =						guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
													A_				 =						42
													if negative_prompt is None:
																		A_				 =						[""] * batch_size
													elif type(_snake_case	) is not type(_snake_case	):
																		raise TypeError(
																		    F'`negative_prompt` should be the same type to `prompt`, but got {type(_snake_case	)} !='
																		    F' {type(_snake_case	)}.'	)
													elif isinstance(_snake_case       ,   _snake_case	):
																		A_				 =						[negative_prompt]
													elif batch_size != len(_snake_case	):
																		raise ValueError(
																		    F'`negative_prompt`: {negative_prompt} has batch size {len(_snake_case	)}, but `prompt`:'
																		    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																		    " the batch size of `prompt`."	)
													else:
																		A_				 =						negative_prompt

													A_				 =						text_input_ids.shape[-1]
													A_				 =						self.tokenizer(
													    _snake_case       ,   padding="max_length"       ,   max_length=_snake_case       ,   truncation=_snake_case       ,   return_tensors="pt"       ,   )
													A_				 =						self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													A_				 =						uncond_embeddings.shape[1]
													A_				 =						uncond_embeddings.repeat(1       ,   _snake_case       ,   1	)
													A_				 =						uncond_embeddings.view(batch_size * num_images_per_prompt       ,   _snake_case       ,   -1	)

													# 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
													A_				 =						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`.
								A_				 =						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								A_				 =						text_embeddings.dtype
								if latents is None:
													if self.device.type == "mps":
																		# randn does not exist on mps
																		A_				 =						torch.randn(_snake_case       ,   generator=_snake_case       ,   device="cpu"       ,   dtype=_snake_case	).to(
																		    self.device	)
													else:
																		A_				 =						torch.randn(_snake_case       ,   generator=_snake_case       ,   device=self.device       ,   dtype=_snake_case	)
								else:
													if latents.shape != latents_shape:
																		raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'	)
													A_				 =						latents.to(self.device	)

								# set timesteps
								self.scheduler.set_timesteps(_snake_case	)

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

								# scale the initial noise by the standard deviation required by the scheduler
								A_				 =						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]
								A_				 =						"eta" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
								A_				 =						{}
								if accepts_eta:
													A_				 =						eta

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

													# predict the noise residual
													A_				 =						self.unet(_snake_case       ,   _snake_case       ,   encoder_hidden_states=_snake_case	).sample

													# perform guidance
													if do_classifier_free_guidance:
																		A_ ,    A_				 =						noise_pred.chunk(2	)
																		A_				 =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# compute the previous noisy sample x_t -> x_t-1
													A_				 =						self.scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample

													# call the callback, if provided
													if callback is not None and i % callback_steps == 0:
																		callback(_snake_case       ,   _snake_case       ,   _snake_case	)

								A_				 =						1 / 0.1_8_2_1_5 * latents
								A_				 =						self.vae.decode(_snake_case	).sample

								A_				 =						(image / 2 + 0.5).clamp(0       ,   1	)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								A_				 =						image.cpu().permute(0       ,   2       ,   3       ,   1	).float().numpy()

								if output_type == "pil":
													A_				 =						self.numpy_to_pil(_snake_case	)

								if not return_dict:
													return image

								return StableDiffusionPipelineOutput(images=_snake_case       ,   nsfw_content_detected=_snake_case	)






"""simple docstring"""





import os

import pytest
from attr import dataclass


UpperCamelCase_					:						str				= '''us-east-1'''  # defaults region



@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				42
			snake_case	      =				"arn:aws:iam::558105141721:role/sagemaker_execution_role"
			snake_case	      =				{
			    "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": 5_00,
			    "save_steps": 55_00,
			}
			snake_case	      =				{**hyperparameters, "max_steps": 10_00}




			@property
			def      lowerCamelCase__     (				self						:					int	)      -> str:







								"""simple docstring"""
								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						:					Any	)      -> str:







								"""simple docstring"""
								return F'{self.framework}-transfromers-test'




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> str:







								"""simple docstring"""
								return F'./tests/sagemaker/scripts/{self.framework}'






			@property
			def      lowerCamelCase__     (				self						:					Any	)      -> str:







								"""simple docstring"""
								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 							A_     (__a				):

					'''simple docstring'''





					A_				 =						SageMakerTestEnvironment(framework=request.cls.framework				)




