



'''simple docstring'''




from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

a   :			Union[str, Any]					  =    logging.get_logger(__name__)

@add_end_docstrings(__magic_name__			)
class 			UpperCamelCase_ (							__magic_name__			):





	def __init__(  self		,		**A    )			->			List[str]:
								super().__init__(**A    )

								requires_backends(self		,		"""vision"""    )
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == """tf"""
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING    )





	def __call__(  self		,		A		,		**A    )			->			Optional[Any]:
								return super().__call__(A		,		**A    )





	def  _lowercase(  self		,		**A    )			->			Optional[Any]:
								UpperCAmelCase     :		List[Any]							     =		{}
								if "candidate_labels" in kwargs:
															UpperCAmelCase     :		Dict							     =		kwargs["""candidate_labels"""]
								if "hypothesis_template" in kwargs:
															UpperCAmelCase     :		Optional[Any]							     =		kwargs["""hypothesis_template"""]

								return preprocess_params, {}, {}





	def  _lowercase(  self		,		A		,		A=None		,		A="This is a photo of {}."    )			->			Optional[Any]:
								UpperCAmelCase     :		int							     =		load_image(A    )
								UpperCAmelCase     :		List[str]							     =		self.image_processor(images=[image]		,		return_tensors=self.framework    )
								UpperCAmelCase     :		List[str]							     =		candidate_labels
								UpperCAmelCase     :		Tuple							     =		[hypothesis_template.format(A    ) for x in candidate_labels]
								UpperCAmelCase     :		Union[str, Any]							     =		self.tokenizer(A		,		return_tensors=self.framework		,		padding=A    )
								UpperCAmelCase     :		Union[str, Any]							     =		[text_inputs]
								return inputs





	def  _lowercase(  self		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		List[Any]							     =		model_inputs.pop("""candidate_labels"""    )
								UpperCAmelCase     :		Optional[Any]							     =		model_inputs.pop("""text_inputs"""    )
								if isinstance(text_inputs[0]		,		A    ):
															UpperCAmelCase     :		Optional[Any]							     =		text_inputs[0]
								else:
															# Batching case.
															UpperCAmelCase     :		Any							     =		text_inputs[0][0]

								UpperCAmelCase     :		Dict							     =		self.model(**A		,		**A    )

								UpperCAmelCase     :		List[Any]							     =		{
								    """candidate_labels""": candidate_labels,
								    """logits""": outputs.logits_per_image,
								}
								return model_outputs






	def  _lowercase(  self		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		int							     =		model_outputs.pop("""candidate_labels"""    )
								UpperCAmelCase     :		int							     =		model_outputs["""logits"""][0]
								if self.framework == "pt":
															UpperCAmelCase     :		Optional[int]							     =		logits.softmax(dim=-1    ).squeeze(-1    )
															UpperCAmelCase     :		Any							     =		probs.tolist()
															if not isinstance(A		,		A    ):
																						UpperCAmelCase     :		Any							     =		[scores]
								elif self.framework == "tf":
															UpperCAmelCase     :		List[str]							     =		stable_softmax(A		,		axis=-1    )
															UpperCAmelCase     :		Union[str, Any]							     =		probs.numpy().tolist()
								else:
															raise ValueError(f'''Unsupported framework: {self.framework}'''    )

								UpperCAmelCase     :		Any							     =		[
								    {"""score""": score, """label""": candidate_label}
								    for score, candidate_label in sorted(zip(A		,		A    )		,		key=lambda A    : -x[0]    )
								]
								return result






'''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


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

class 			UpperCamelCase_ (							__magic_name__			):





	def __init__(  self		,		A		,		A		,		A		,		A		,		A		,		A		,		A		,		)			->			Optional[Any]:
								super().__init__()
								self.register_modules(
								    vae=A		,		text_encoder=A		,		tokenizer=A		,		unet=A		,		scheduler=A		,		safety_checker=A		,		feature_extractor=A		,		)





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





	def  _lowercase(  self    )			->			Dict:
								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		,		)			->			List[Any]:

								if isinstance(A		,		A    ):
															UpperCAmelCase     :		List[str]							     =		1
								elif isinstance(A		,		A    ):
															UpperCAmelCase     :		Dict							     =		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
								UpperCAmelCase     :		List[str]							     =		self.tokenizer(
								    A		,		padding="""max_length"""		,		max_length=self.tokenizer.model_max_length		,		return_tensors="""pt"""		,		)
								UpperCAmelCase     :		List[Any]							     =		text_inputs.input_ids

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

								if text_embeddings is None:
															UpperCAmelCase     :		Union[str, Any]							     =		self.text_encoder(text_input_ids.to(self.device    )    )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		text_embeddings.shape
								UpperCAmelCase     :		List[str]							     =		text_embeddings.repeat(1		,		A		,		1    )
								UpperCAmelCase     :		List[Any]							     =		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.
								UpperCAmelCase     :		Optional[int]							     =		guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
															UpperCAmelCase     :		List[str]
															if negative_prompt is None:
																						UpperCAmelCase     :		Any							     =		[""""""]
															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    ):
																						UpperCAmelCase     :		Optional[int]							     =		[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:
																						UpperCAmelCase     :		Any							     =		negative_prompt

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

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															UpperCAmelCase     :		int							     =		uncond_embeddings.shape[1]
															UpperCAmelCase     :		List[Any]							     =		uncond_embeddings.repeat(A		,		A		,		1    )
															UpperCAmelCase     :		List[str]							     =		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
															UpperCAmelCase     :		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`.
								UpperCAmelCase     :		Tuple							     =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								UpperCAmelCase     :		Optional[int]							     =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
								UpperCAmelCase     :		str							     =		text_embeddings.dtype
								if latents is None:
															if self.device.type == "mps":
																						# randn does not exist on mps
																						UpperCAmelCase     :		Dict							     =		torch.randn(
																						    A		,		generator=A		,		device="""cpu"""		,		dtype=A    ).to(self.device    )
																						UpperCAmelCase     :		int							     =		torch.randn(A		,		generator=A		,		device="""cpu"""		,		dtype=A    ).to(
																						    self.device    )
															else:
																						UpperCAmelCase     :		int							     =		torch.randn(
																						    A		,		generator=A		,		device=self.device		,		dtype=A    )
																						UpperCAmelCase     :		int							     =		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}'''    )
															UpperCAmelCase     :		Optional[Any]							     =		latents_reference.to(self.device    )
															UpperCAmelCase     :		Tuple							     =		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
								UpperCAmelCase     :		int							     =		(latents_shape[3] - latents_shape_reference[3]) // 2
								UpperCAmelCase     :		List[str]							     =		(latents_shape[2] - latents_shape_reference[2]) // 2
								UpperCAmelCase     :		Union[str, Any]							     =		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
								UpperCAmelCase     :		Union[str, Any]							     =		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
								UpperCAmelCase     :		Optional[int]							     =		0 if dx < 0 else dx
								UpperCAmelCase     :		List[str]							     =		0 if dy < 0 else dy
								UpperCAmelCase     :		Union[str, Any]							     =		max(-dx		,		0    )
								UpperCAmelCase     :		List[Any]							     =		max(-dy		,		0    )
								# import pdb
								# pdb.set_trace()
								UpperCAmelCase     :		str							     =		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
								UpperCAmelCase     :		Union[str, Any]							     =		self.scheduler.timesteps.to(self.device    )

								# scale the initial noise by the standard deviation required by the scheduler
								UpperCAmelCase     :		Optional[int]							     =		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]
								UpperCAmelCase     :		int							     =		"""eta""" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
								UpperCAmelCase     :		Optional[Any]							     =		{}
								if accepts_eta:
															UpperCAmelCase     :		List[str]							     =		eta

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

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

															# perform guidance
															if do_classifier_free_guidance:
																						UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		noise_pred.chunk(2    )
																						UpperCAmelCase     :		List[Any]							     =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# compute the previous noisy sample x_t -> x_t-1
															UpperCAmelCase     :		Dict							     =		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    )

								UpperCAmelCase     :		Union[str, Any]							     =		1 / 0.1_8_2_1_5 * latents
								UpperCAmelCase     :		Tuple							     =		self.vae.decode(A    ).sample

								UpperCAmelCase     :		Union[str, Any]							     =		(image / 2 + 0.5).clamp(0		,		1    )

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

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

								if output_type == "pil":
															UpperCAmelCase     :		int							     =		self.numpy_to_pil(A    )

								if not return_dict:
															return (image, has_nsfw_concept)

								return StableDiffusionPipelineOutput(images=A		,		nsfw_content_detected=A    )

