


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







class  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




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




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

										if isinstance(__lowercase      ,  __lowercase							):
														lowerCAmelCase_       :					List[str]			 = 1
										elif isinstance(__lowercase      ,  __lowercase							):
														lowerCAmelCase_       :					Dict			 = 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_       :					List[str]			 = self.tokenizer(
										    __lowercase      ,  padding='''max_length'''      ,  max_length=self.tokenizer.model_max_length      ,  return_tensors='''pt'''      ,  )
										lowerCAmelCase_       :					List[Any]			 = text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														lowerCAmelCase_       :					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}"""							)
														lowerCAmelCase_       :					Tuple			 = text_input_ids[:, : self.tokenizer.model_max_length]

										if text_embeddings is None:
														lowerCAmelCase_       :					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
										lowerCAmelCase_       :					Union[str, Any]			 = text_embeddings.shape
										lowerCAmelCase_       :					List[str]			 = text_embeddings.repeat(1      ,  __lowercase      ,  1							)
										lowerCAmelCase_       :					List[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.
										lowerCAmelCase_       :					Optional[int]			 = 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_       :					Any			 = [""""""]
														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_       :					Any			 = negative_prompt

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

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
														lowerCAmelCase_       :					int			 = uncond_embeddings.shape[1]
														lowerCAmelCase_       :					List[Any]			 = uncond_embeddings.repeat(__lowercase      ,  __lowercase      ,  1							)
														lowerCAmelCase_       :					List[str]			 = 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_       :					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`.
										lowerCAmelCase_       :					Tuple			 = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										lowerCAmelCase_       :					Optional[int]			 = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
										lowerCAmelCase_       :					str			 = text_embeddings.dtype
										if latents is None:
														if self.device.type == "mps":
																		# randn does not exist on mps
																		lowerCAmelCase_       :					Dict			 = torch.randn(
																		    __lowercase      ,  generator=__lowercase      ,  device='''cpu'''      ,  dtype=__lowercase							).to(self.device							)
																		lowerCAmelCase_       :					int			 = torch.randn(__lowercase      ,  generator=__lowercase      ,  device='''cpu'''      ,  dtype=__lowercase							).to(
																		    self.device							)
														else:
																		lowerCAmelCase_       :					int			 = torch.randn(
																		    __lowercase      ,  generator=__lowercase      ,  device=self.device      ,  dtype=__lowercase							)
																		lowerCAmelCase_       :					int			 = 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_       :					Optional[Any]			 = latents_reference.to(self.device							)
														lowerCAmelCase_       :					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
										lowerCAmelCase_       :					int			 = (latents_shape[3] - latents_shape_reference[3]) // 2
										lowerCAmelCase_       :					List[str]			 = (latents_shape[2] - latents_shape_reference[2]) // 2
										lowerCAmelCase_       :					Union[str, Any]			 = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
										lowerCAmelCase_       :					Union[str, Any]			 = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
										lowerCAmelCase_       :					Optional[int]			 = 0 if dx < 0 else dx
										lowerCAmelCase_       :					List[str]			 = 0 if dy < 0 else dy
										lowerCAmelCase_       :					Union[str, Any]			 = max(-dx      ,  0							)
										lowerCAmelCase_       :					List[Any]			 = max(-dy      ,  0							)
										# import pdb
										# pdb.set_trace()
										lowerCAmelCase_       :					str			 = 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_       :					Union[str, 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_       :					int			 = """eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
										lowerCAmelCase_       :					Optional[Any]			 = {}
										if accepts_eta:
														lowerCAmelCase_       :					List[str]			 = eta

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

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

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

														# compute the previous noisy sample x_t -> x_t-1
														lowerCAmelCase_       :					Dict			 = 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_       :					Union[str, Any]			 = 1 / 0.1_82_15 * latents
										lowerCAmelCase_       :					Tuple			 = self.vae.decode(__lowercase							).sample

										lowerCAmelCase_       :					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
										lowerCAmelCase_       :					Union[str, Any]			 = image.cpu().permute(0      ,  2      ,  3      ,  1							).float().numpy()

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

										if output_type == "pil":
														lowerCAmelCase_       :					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'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a   :			Optional[int]					  =    {
    """configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
    """processing_git""": ["""GitProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a   :			Tuple					  =    [
							    """GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GitForCausalLM""",
							    """GitModel""",
							    """GitPreTrainedModel""",
							    """GitVisionModel""",
							]

if TYPE_CHECKING:
							from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
							from .processing_git import GitProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_git import (
														    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GitForCausalLM,
														    GitModel,
														    GitPreTrainedModel,
														    GitVisionModel,
														)

else:
							import sys

							a   :			Tuple					  =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

