





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							=      logging.get_logger(__name__)  # pylint: disable=invalid-name
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :str						,    __lowercase  :AutoencoderKL						,    __lowercase  :CLIPTextModel						,    __lowercase  :CLIPTokenizer						,    __lowercase  :UNetaDConditionModel						,    __lowercase  :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,    __lowercase  :StableDiffusionSafetyChecker						,    __lowercase  :CLIPImageProcessor						,    ):
											super().__init__()
											self.register_modules(
											    vae=__lowercase						,    text_encoder=__lowercase						,    tokenizer=__lowercase						,    unet=__lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase						,    feature_extractor=__lowercase						,    )




				def 					__lowercase  (     self  :str						,    __lowercase  :Optional[Union[str, int]] = "auto"			):
											if slice_size == "auto":
																		# half the attention head size is usually a good trade-off between
																		# speed and memory
																		__lowerCamelCase      :      int         =self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(__lowercase			)




				def 					__lowercase  (     self  :Any			):
											self.enable_attention_slicing(__lowercase			)




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

											if isinstance(__lowercase						,    __lowercase			):
																		__lowerCamelCase      :      Dict         =1
											elif isinstance(__lowercase						,    __lowercase			):
																		__lowerCamelCase      :      Optional[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
											__lowerCamelCase      :      str         =self.tokenizer(
											    __lowercase						,    padding='''max_length'''						,    max_length=self.tokenizer.model_max_length						,    return_tensors='''pt'''						,    )
											__lowerCamelCase      :      Dict         =text_inputs.input_ids

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

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

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

																		__lowerCamelCase      :      Optional[int]         =text_input_ids.shape[-1]
																		__lowerCamelCase      :      List[Any]         =self.tokenizer(
																		    __lowercase						,    padding='''max_length'''						,    max_length=__lowercase						,    truncation=__lowercase						,    return_tensors='''pt'''						,    )
																		__lowerCamelCase      :      List[str]         =self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

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

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

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

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

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

																		# compute the previous noisy sample x_t -> x_t-1
																		__lowerCamelCase      :      List[str]         =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			)

											__lowerCamelCase      :      Any         =1 / 0.18215 * latents
											__lowerCamelCase      :      Optional[Any]         =self.vae.decode(__lowercase			).sample

											__lowerCamelCase      :      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
											__lowerCamelCase      :      Dict         =image.cpu().permute(0						,    2						,    3						,    1			).float().numpy()

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

											if output_type == "pil":
																		__lowerCamelCase      :      List[str]         =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 builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


_UpperCamelCase							=      False
try:
							_UpperCamelCase							=      _is_package_available('google.colab')
except ModuleNotFoundError:
							pass
@input.register
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :str = None						,    __lowercase  :list = []			):
											__lowerCamelCase      :      Any         =0
											__lowerCamelCase      :      List[str]         =choices
											__lowerCamelCase      :      int         =prompt
											if sys.platform == "win32":
																		__lowerCamelCase      :      Dict         ='''*'''
											else:
																		__lowerCamelCase      :      Union[str, Any]         ='''➔ '''




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Any						,    __lowercase  :str = ""			):
											if sys.platform != "win32":
																		writeColor(self.choices[index]						,    32						,    __lowercase			)
											else:
																		forceWrite(self.choices[index]						,    __lowercase			)




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int			):
											if index == self.position:
																		forceWrite(f' {self.arrow_char} '			)
																		self.write_choice(__lowercase			)
											else:
																		forceWrite(f'    {self.choices[index]}'			)
											reset_cursor()




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Direction						,    __lowercase  :int = 1			):
											__lowerCamelCase      :      List[str]         =self.position
											if direction == Direction.DOWN:
																		if self.position + 1 >= len(self.choices			):
																									return
																		self.position += num_spaces
											else:
																		if self.position - 1 < 0:
																									return
																		self.position -= num_spaces
											clear_line()
											self.print_choice(__lowercase			)
											move_cursor(__lowercase						,    direction.name			)
											self.print_choice(self.position			)




				@input.mark(KEYMAP['''up''']			)
				def 					__lowercase  (     self  :Union[str, Any]			):
											self.move_direction(Direction.UP			)




				@input.mark(KEYMAP['''down''']			)
				def 					__lowercase  (     self  :Union[str, Any]			):
											self.move_direction(Direction.DOWN			)




				@input.mark(KEYMAP['''newline''']			)
				def 					__lowercase  (     self  :Any			):
											move_cursor(len(self.choices			) - self.position						,    '''DOWN'''			)
											return self.position




				@input.mark(KEYMAP['''interrupt''']			)
				def 					__lowercase  (     self  :Any			):
											move_cursor(len(self.choices			) - self.position						,    '''DOWN'''			)
											raise KeyboardInterrupt




				@input.mark_multiple(*[KEYMAP[str(__lowercase			)] for number in range(10			)]			)
				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Tuple         =int(chr(self.current_selection			)			)
											__lowerCamelCase      :      Dict         =index - self.position
											if index == self.position:
																		return
											if index < len(self.choices			):
																		if self.position > index:
																									self.move_direction(Direction.UP						,    -movement			)
																		elif self.position < index:
																									self.move_direction(Direction.DOWN						,    __lowercase			)
																		else:
																									return
											else:
																		return




				def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :int = 0			):
											if self.prompt:
																		linebreak()
																		forceWrite(self.prompt						,    '''\n'''			)
																		if in_colab:
																									forceWrite('''Please input a choice index (starting from 0), and press enter'''						,    '''\n'''			)
																		else:
																									forceWrite('''Please select a choice using the arrow or number keys, and selecting with enter'''						,    '''\n'''			)
											__lowerCamelCase      :      Union[str, Any]         =default_choice
											for i in range(len(self.choices			)			):
																		self.print_choice(__lowercase			)
																		forceWrite('''\n'''			)
											move_cursor(len(self.choices			) - self.position						,    '''UP'''			)
											with cursor.hide():
																		while True:
																									if in_colab:
																																try:
																																							__lowerCamelCase      :      Union[str, Any]         =int(builtins.input()			)
																																except ValueError:
																																							__lowerCamelCase      :      Optional[Any]         =default_choice
																									else:
																																__lowerCamelCase      :      Dict         =self.handle_input()
																									if choice is not None:
																																reset_cursor()
																																for _ in range(len(self.choices			) + 1			):
																																							move_cursor(1						,    '''UP'''			)
																																							clear_line()
																																self.write_choice(__lowercase						,    '''\n'''			)
																																return choice




