


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


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)  # pylint: disable=invalid-name







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A		,      A		,      A		,      A		,      A		,      A		,      )  -> Union[str, Any]:







					'''simple docstring'''







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







		def 					__A   ( self		,      A = "auto"	)  -> Dict:







					'''simple docstring'''







					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 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					self.enable_attention_slicing(a_	)







		@torch.no_grad()
		def __call__( self		,      A		,      A = 5_12		,      A = 5_12		,      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		,      )  -> Union[str, Any]:







					'''simple docstring'''







					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    		=  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    		=  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 / 0.18215 * 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    		=  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_torch_available, is_vision_available


lowerCAmelCase   : Dict             =				{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : int             =				[
					    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """VanForImageClassification""",
					    """VanModel""",
					    """VanPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase   : Tuple             =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)


