'''simple docstring'''

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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowercase_     		= logging.get_logger(__name__)  # pylint: disable=invalid-name
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						) ->     int:
								super().__init__()
								self.register_modules(
								    vae=__A		,						text_encoder=__A		,						tokenizer=__A		,						unet=__A		,						scheduler=__A		,						safety_checker=__A		,						feature_extractor=__A		,						)






		def 		UpperCamelCase__       (   self		,						__A = "auto"				) ->     List[str]:
								if slice_size == "auto":
														# half the attention head size is usually a good trade-off between
														# speed and memory
														_lowerCAmelCase										=self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(__A				)






		def 		UpperCamelCase__       (   self				) ->     int:
								self.enable_attention_slicing(__A				)






		@torch.no_grad()
		def __call__(   self		,						__A		,						__A = 512		,						__A = 512		,						__A = 50		,						__A = 7.5		,						__A = None		,						__A = 1		,						__A = 0.0		,						__A = None		,						__A = None		,						__A = "pil"		,						__A = True		,						__A = None		,						__A = 1		,						__A = None		,						**__A		,						) ->     Tuple:

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

								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(__A		,						__A				) or callback_steps <= 0)
								):
														raise ValueError(
														    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
														    F''' {type(__A				)}.'''				)

								# get prompt text embeddings
								_lowerCAmelCase										=self.tokenizer(
								    __A		,						padding='max_length'		,						max_length=self.tokenizer.model_max_length		,						return_tensors='pt'		,						)
								_lowerCAmelCase										=text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														_lowerCAmelCase										=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}'''				)
														_lowerCAmelCase										=text_input_ids[:, : self.tokenizer.model_max_length]

								if text_embeddings is None:
														_lowerCAmelCase										=self.text_encoder(text_input_ids.to(self.device				)				)[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=text_embeddings.shape
								_lowerCAmelCase										=text_embeddings.repeat(1		,						__A		,						1				)
								_lowerCAmelCase										=text_embeddings.view(bs_embed * num_images_per_prompt		,						__A		,						-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.
								_lowerCAmelCase										=guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
														_lowerCAmelCase										=42
														if negative_prompt is None:
																				_lowerCAmelCase										=['']
														elif type(__A				) is not type(__A				):
																				raise TypeError(
																				    F'''`negative_prompt` should be the same type to `prompt`, but got {type(__A				)} !='''
																				    F''' {type(__A				)}.'''				)
														elif isinstance(__A		,						__A				):
																				_lowerCAmelCase										=[negative_prompt]
														elif batch_size != len(__A				):
																				raise ValueError(
																				    F'''`negative_prompt`: {negative_prompt} has batch size {len(__A				)}, but `prompt`:'''
																				    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																				    ' the batch size of `prompt`.'				)
														else:
																				_lowerCAmelCase										=negative_prompt

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

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
														_lowerCAmelCase										=uncond_embeddings.shape[1]
														_lowerCAmelCase										=uncond_embeddings.repeat(__A		,						__A		,						1				)
														_lowerCAmelCase										=uncond_embeddings.view(batch_size * num_images_per_prompt		,						__A		,						-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
														_lowerCAmelCase										=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`.
								_lowerCAmelCase										=(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								_lowerCAmelCase										=(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
								_lowerCAmelCase										=text_embeddings.dtype
								if latents is None:
														if self.device.type == "mps":
																				# randn does not exist on mps
																				_lowerCAmelCase										=torch.randn(
																				    __A		,						generator=__A		,						device='cpu'		,						dtype=__A				).to(self.device				)
																				_lowerCAmelCase										=torch.randn(__A		,						generator=__A		,						device='cpu'		,						dtype=__A				).to(
																				    self.device				)
														else:
																				_lowerCAmelCase										=torch.randn(
																				    __A		,						generator=__A		,						device=self.device		,						dtype=__A				)
																				_lowerCAmelCase										=torch.randn(__A		,						generator=__A		,						device=self.device		,						dtype=__A				)
								else:
														if latents_reference.shape != latents_shape:
																				raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''				)
														_lowerCAmelCase										=latents_reference.to(self.device				)
														_lowerCAmelCase										=latents.to(self.device				)

								# This is the key part of the pipeline where we
								# try to ensure that the generated images w/ the same seed
								# but different sizes actually result in similar images
								_lowerCAmelCase										=(latents_shape[3] - latents_shape_reference[3]) // 2
								_lowerCAmelCase										=(latents_shape[2] - latents_shape_reference[2]) // 2
								_lowerCAmelCase										=latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
								_lowerCAmelCase										=latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
								_lowerCAmelCase										=0 if dx < 0 else dx
								_lowerCAmelCase										=0 if dy < 0 else dy
								_lowerCAmelCase										=max(-dx		,						0				)
								_lowerCAmelCase										=max(-dy		,						0				)
								# import pdb
								# pdb.set_trace()
								_lowerCAmelCase										=latents_reference[:, :, dy : dy + h, dx : dx + w]

								# set timesteps
								self.scheduler.set_timesteps(__A				)

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

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

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

														# predict the noise residual
														_lowerCAmelCase										=self.unet(__A		,						__A		,						encoder_hidden_states=__A				).sample

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

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

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

								_lowerCAmelCase										=1 / 0.18_215 * latents
								_lowerCAmelCase										=self.vae.decode(__A				).sample

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

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

								if self.safety_checker is not None:
														_lowerCAmelCase										=self.feature_extractor(self.numpy_to_pil(__A				)		,						return_tensors='pt'				).to(
														    self.device				)
														_lowerCAmelCase						,				_lowerCAmelCase										=self.safety_checker(
														    images=__A		,						clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype				)				)
								else:
														_lowerCAmelCase										=None

								if output_type == "pil":
														_lowerCAmelCase										=self.numpy_to_pil(__A				)

								if not return_dict:
														return (image, has_nsfw_concept)

								return StableDiffusionPipelineOutput(images=__A		,						nsfw_content_detected=__A				)






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_     		= {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ViTMAEForPreTraining''',
							    '''ViTMAELayer''',
							    '''ViTMAEModel''',
							    '''ViTMAEPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''TFViTMAEForPreTraining''',
							    '''TFViTMAEModel''',
							    '''TFViTMAEPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_mae import (
														    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTMAEForPreTraining,
														    ViTMAELayer,
														    ViTMAEModel,
														    ViTMAEPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
							import sys

							lowercase_     		= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





