



"""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


__lowerCAmelCase							:       List[str]          =       logging.get_logger(__name__)  # pylint: disable=invalid-name



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     )			->    int:







								'''simple docstring'''

								super().__init__()
								self.register_modules(
								    vae=_lowercase				,     text_encoder=_lowercase				,     tokenizer=_lowercase				,     unet=_lowercase				,     scheduler=_lowercase				,     safety_checker=_lowercase				,     feature_extractor=_lowercase				,     )





		def  UpperCAmelCase__				(       self				,     _lowercase = "auto" )			->    List[str]:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self )			->    Optional[Any]:







								'''simple docstring'''

								self.enable_attention_slicing(_lowercase )





		@torch.no_grad()
		def __call__(       self				,     _lowercase				,     _lowercase = 5_1_2				,     _lowercase = 5_1_2				,     _lowercase = 5_0				,     _lowercase = 7.5				,     _lowercase = None				,     _lowercase = 1				,     _lowercase = 0.0				,     _lowercase = None				,     _lowercase = None				,     _lowercase = "pil"				,     _lowercase = True				,     _lowercase = None				,     _lowercase = 1				,     _lowercase = None				,     **_lowercase				,     )			->    Optional[Any]:







								'''simple docstring'''

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

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

								# get prompt text embeddings
								snake_case_ :			List[Any]            =      self.tokenizer(
								    _lowercase				,     padding="""max_length"""				,     max_length=self.tokenizer.model_max_length				,     return_tensors="""pt"""				,     )
								snake_case_ :			Dict            =      text_inputs.input_ids

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

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

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

														snake_case_ :			Tuple            =      text_input_ids.shape[-1]
														snake_case_ :			Tuple            =      self.tokenizer(
														    _lowercase				,     padding="""max_length"""				,     max_length=_lowercase				,     truncation=_lowercase				,     return_tensors="""pt"""				,     )
														snake_case_ :			Any            =      self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
														snake_case_ :			Any            =      uncond_embeddings.shape[1]
														snake_case_ :			List[Any]            =      uncond_embeddings.repeat(_lowercase				,     _lowercase				,     1 )
														snake_case_ :			Any            =      uncond_embeddings.view(batch_size * num_images_per_prompt				,     _lowercase				,     -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
														snake_case_ :			List[str]            =      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_ :			List[Any]            =      (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								snake_case_ :			Optional[int]            =      (batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
								snake_case_ :			List[Any]            =      text_embeddings.dtype
								if latents is None:
														if self.device.type == "mps":
																				# randn does not exist on mps
																				snake_case_ :			int            =      torch.randn(
																				    _lowercase				,     generator=_lowercase				,     device="""cpu"""				,     dtype=_lowercase ).to(self.device )
																				snake_case_ :			Any            =      torch.randn(_lowercase				,     generator=_lowercase				,     device="""cpu"""				,     dtype=_lowercase ).to(
																				    self.device )
														else:
																				snake_case_ :			Union[str, Any]            =      torch.randn(
																				    _lowercase				,     generator=_lowercase				,     device=self.device				,     dtype=_lowercase )
																				snake_case_ :			List[Any]            =      torch.randn(_lowercase				,     generator=_lowercase				,     device=self.device				,     dtype=_lowercase )
								else:
														if latents_reference.shape != latents_shape:
																				raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
														snake_case_ :			str            =      latents_reference.to(self.device )
														snake_case_ :			List[Any]            =      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
								snake_case_ :			int            =      (latents_shape[3] - latents_shape_reference[3]) // 2
								snake_case_ :			Tuple            =      (latents_shape[2] - latents_shape_reference[2]) // 2
								snake_case_ :			int            =      latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
								snake_case_ :			List[str]            =      latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
								snake_case_ :			Union[str, Any]            =      0 if dx < 0 else dx
								snake_case_ :			List[str]            =      0 if dy < 0 else dy
								snake_case_ :			Union[str, Any]            =      max(-dx				,     0 )
								snake_case_ :			Dict            =      max(-dy				,     0 )
								# import pdb
								# pdb.set_trace()
								snake_case_ :			Union[str, Any]            =      latents_reference[:, :, dy : dy + h, dx : dx + w]

								# set timesteps
								self.scheduler.set_timesteps(_lowercase )

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								snake_case_ :			Dict            =      self.scheduler.timesteps.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_ :			Optional[Any]            =      """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
								snake_case_ :			Optional[Any]            =      {}
								if accepts_eta:
														snake_case_ :			Tuple            =      eta

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

														# predict the noise residual
														snake_case_ :			List[Any]            =      self.unet(_lowercase				,     _lowercase				,     encoder_hidden_states=_lowercase ).sample

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

														# compute the previous noisy sample x_t -> x_t-1
														snake_case_ :			Tuple            =      self.scheduler.step(_lowercase				,     _lowercase				,     _lowercase				,     **_lowercase ).prev_sample

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

								snake_case_ :			Optional[Any]            =      1 / 0.1_8215 * latents
								snake_case_ :			Any            =      self.vae.decode(_lowercase ).sample

								snake_case_ :			Dict            =      (image / 2 + 0.5).clamp(0				,     1 )

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

								if self.safety_checker is not None:
														snake_case_ :			Any            =      self.feature_extractor(self.numpy_to_pil(_lowercase )				,     return_tensors="""pt""" ).to(
														    self.device )
														snake_case_      ,		snake_case_ :			str            =      self.safety_checker(
														    images=_lowercase				,     clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
								else:
														snake_case_ :			str            =      None

								if output_type == "pil":
														snake_case_ :			int            =      self.numpy_to_pil(_lowercase )

								if not return_dict:
														return (image, has_nsfw_concept)

								return StableDiffusionPipelineOutput(images=_lowercase				,     nsfw_content_detected=_lowercase )







'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


_lowercase  =			logging.get_logger(__name__)
class 						a_				(			UpperCAmelCase__						):




	def __init__(       self     :  Tuple       ,		*__lowerCAmelCase     :  List[str]       ,		**__lowerCAmelCase     :  List[str]      ):
							warnings.warn(
							    'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use VideoMAEImageProcessor instead.'       ,		__lowerCAmelCase       ,		)
							super().__init__(*__lowerCAmelCase       ,		**__lowerCAmelCase      )

