
import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				if isinstance(lowerCamelCase__	, torch.Tensor    ):
								return image
				elif isinstance(lowerCamelCase__	, PIL.Image.Image    ):
								lowerCamelCase_						=			[image]

				if isinstance(image[0]	, PIL.Image.Image    ):
								lowerCamelCase_						=			[np.array(i.resize((w, h)	, resample=PIL_INTERPOLATION["lanczos"]    )    )[None, :] for i in image]
								lowerCamelCase_						=			np.concatenate(lowerCamelCase__	, axis=0    )
								lowerCamelCase_						=			np.array(lowerCamelCase__    ).astype(np.floataa    ) / 2_55.0
								lowerCamelCase_						=			image.transpose(0	, 3	, 1	, 2    )
								lowerCamelCase_						=			2.0 * image - 1.0
								lowerCamelCase_						=			torch.from_numpy(lowerCamelCase__    )
				elif isinstance(image[0]	, torch.Tensor    ):
								lowerCamelCase_						=			torch.cat(lowerCamelCase__	, dim=0    )
				return image





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=0.99_95    ):
				if not isinstance(lowerCamelCase__	, np.ndarray    ):
								lowerCamelCase_						=			True
								lowerCamelCase_						=			va.device
								lowerCamelCase_						=			va.cpu().numpy()
								lowerCamelCase_						=			va.cpu().numpy()

				lowerCamelCase_						=			np.sum(va * va / (np.linalg.norm(lowerCamelCase__    ) * np.linalg.norm(lowerCamelCase__    ))    )
				if np.abs(lowerCamelCase__    ) > DOT_THRESHOLD:
								lowerCamelCase_						=			(1 - t) * va + t * va
				else:
								lowerCamelCase_						=			np.arccos(lowerCamelCase__    )
								lowerCamelCase_						=			np.sin(lowerCamelCase__    )
								lowerCamelCase_						=			theta_a * t
								lowerCamelCase_						=			np.sin(lowerCamelCase__    )
								lowerCamelCase_						=			np.sin(theta_a - theta_t    ) / sin_theta_a
								lowerCamelCase_						=			sin_theta_t / sin_theta_a
								lowerCamelCase_						=			sa * va + sa * va

				if inputs_are_torch:
								lowerCamelCase_						=			torch.from_numpy(lowerCamelCase__    ).to(lowerCamelCase__    )

				return va





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			F.normalize(lowerCamelCase__	, dim=-1    )
				lowerCamelCase_						=			F.normalize(lowerCamelCase__	, dim=-1    )
				return (x - y).norm(dim=-1    ).div(2    ).arcsin().pow(2    ).mul(2    )





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				for param in model.parameters():
								lowerCamelCase_						=			value





class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase=None						,  lowercase=None						,  lowercase=None						,  )				->       Union[str, Any]:
					super().__init__()
					self.register_modules(
					    vae=lowercase						,  text_encoder=lowercase						,  clip_model=lowercase						,  tokenizer=lowercase						,  unet=lowercase						,  scheduler=lowercase						,  feature_extractor=lowercase						,  coca_model=lowercase						,  coca_tokenizer=lowercase						,  coca_transform=lowercase						,  )
					lowerCamelCase_						=			(
					    feature_extractor.size
					    if isinstance(feature_extractor.size						,  lowercase )
					    else feature_extractor.size["shortest_edge"]
					)
					lowerCamelCase_						=			transforms.Normalize(mean=feature_extractor.image_mean						,  std=feature_extractor.image_std )
					set_requires_grad(self.text_encoder						,  lowercase )
					set_requires_grad(self.clip_model						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase = "auto" )				->       Union[str, Any]:
					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(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					self.enable_attention_slicing(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					set_requires_grad(self.vae						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					set_requires_grad(self.vae						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					set_requires_grad(self.unet						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					set_requires_grad(self.unet						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase )				->       List[Any]:
					# get the original timestep using init_timestep
					lowerCamelCase_						=			min(int(num_inference_steps * strength )						,  lowercase )

					lowerCamelCase_						=			max(num_inference_steps - init_timestep						,  0 )
					lowerCamelCase_						=			self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase=None )				->       Any:
					if not isinstance(lowercase						,  torch.Tensor ):
									raise ValueError(f'`image` has to be of type `torch.Tensor` but is {type(lowercase )}' )

					lowerCamelCase_						=			image.to(device=lowercase						,  dtype=lowercase )

					if isinstance(lowercase						,  lowercase ):
									lowerCamelCase_						=			[
									    self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowercase )
									]
									lowerCamelCase_						=			torch.cat(lowercase						,  dim=0 )
					else:
									lowerCamelCase_						=			self.vae.encode(lowercase ).latent_dist.sample(lowercase )

					# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
					lowerCamelCase_						=			0.1_8_2_1_5 * init_latents
					lowerCamelCase_						=			init_latents.repeat_interleave(lowercase						,  dim=0 )

					lowerCamelCase_						=			randn_tensor(init_latents.shape						,  generator=lowercase						,  device=lowercase						,  dtype=lowercase )

					# get latents
					lowerCamelCase_						=			self.scheduler.add_noise(lowercase						,  lowercase						,  lowercase )
					lowerCamelCase_						=			init_latents

					return latents



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Tuple:
					lowerCamelCase_						=			self.coca_transform(lowercase ).unsqueeze(0 )
					with torch.no_grad(), torch.cuda.amp.autocast():
									lowerCamelCase_						=			self.coca_model.generate(transformed_image.to(device=self.device						,  dtype=self.coca_model.dtype ) )
					lowerCamelCase_						=			self.coca_tokenizer.decode(generated[0].cpu().numpy() )
					return generated.split("<end_of_text>" )[0].replace("<start_of_text>"						,  "" ).rstrip(" .," )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       str:
					lowerCamelCase_						=			self.feature_extractor.preprocess(lowercase )
					lowerCamelCase_						=			torch.from_numpy(clip_image_input["pixel_values"][0] ).unsqueeze(0 ).to(self.device ).half()
					lowerCamelCase_						=			self.clip_model.get_image_features(lowercase )
					lowerCamelCase_						=			image_embeddings_clip / image_embeddings_clip.norm(p=2						,  dim=-1						,  keepdim=lowercase )
					lowerCamelCase_						=			image_embeddings_clip.repeat_interleave(lowercase						,  dim=0 )
					return image_embeddings_clip



	@torch.enable_grad()
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       Optional[Any]:
					lowerCamelCase_						=			latents.detach().requires_grad_()

					lowerCamelCase_						=			self.scheduler.scale_model_input(lowercase						,  lowercase )

					# predict the noise residual
					lowerCamelCase_						=			self.unet(lowercase						,  lowercase						,  encoder_hidden_states=lowercase ).sample

					if isinstance(self.scheduler						,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
									lowerCamelCase_						=			self.scheduler.alphas_cumprod[timestep]
									lowerCamelCase_						=			1 - alpha_prod_t
									# compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									lowerCamelCase_						=			(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

									lowerCamelCase_						=			torch.sqrt(lowercase )
									lowerCamelCase_						=			pred_original_sample * (fac) + latents * (1 - fac)
					elif isinstance(self.scheduler						,  lowercase ):
									lowerCamelCase_						=			self.scheduler.sigmas[index]
									lowerCamelCase_						=			latents - sigma * noise_pred
					else:
									raise ValueError(f'scheduler type {type(self.scheduler )} not supported' )

					# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
					lowerCamelCase_						=			1 / 0.1_8_2_1_5 * sample
					lowerCamelCase_						=			self.vae.decode(lowercase ).sample
					lowerCamelCase_						=			(image / 2 + 0.5).clamp(0						,  1 )

					lowerCamelCase_						=			transforms.Resize(self.feature_extractor_size )(lowercase )
					lowerCamelCase_						=			self.normalize(lowercase ).to(latents.dtype )

					lowerCamelCase_						=			self.clip_model.get_image_features(lowercase )
					lowerCamelCase_						=			image_embeddings_clip / image_embeddings_clip.norm(p=2						,  dim=-1						,  keepdim=lowercase )

					lowerCamelCase_						=			spherical_dist_loss(lowercase						,  lowercase ).mean() * clip_guidance_scale

					lowerCamelCase_						=			-torch.autograd.grad(lowercase						,  lowercase )[0]

					if isinstance(self.scheduler						,  lowercase ):
									lowerCamelCase_						=			latents.detach() + grads * (sigma**2)
									lowerCamelCase_						=			noise_pred_original
					else:
									lowerCamelCase_						=			noise_pred_original - torch.sqrt(lowercase ) * grads
					return noise_pred, latents



	@torch.no_grad()
	def __call__(    self						,  lowercase						,  lowercase						,  lowercase = None						,  lowercase = None						,  lowercase = 512						,  lowercase = 512						,  lowercase = 0.6						,  lowercase = 50						,  lowercase = 7.5						,  lowercase = 1						,  lowercase = 0.0						,  lowercase = 100						,  lowercase = None						,  lowercase = "pil"						,  lowercase = True						,  lowercase = 0.8						,  lowercase = 0.1						,  lowercase = 0.1						,  )				->       int:
					if isinstance(lowercase						,  lowercase ) and len(lowercase ) != batch_size:
									raise ValueError(f'You have passed {batch_size} batch_size, but only {len(lowercase )} generators.' )

					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 isinstance(lowercase						,  torch.Generator ) and batch_size > 1:
									lowerCamelCase_						=			[generator] + [None] * (batch_size - 1)

					lowerCamelCase_						=			[
					    ("model", self.coca_model is None),
					    ("tokenizer", self.coca_tokenizer is None),
					    ("transform", self.coca_transform is None),
					]
					lowerCamelCase_						=			[x[0] for x in coca_is_none if x[1]]
					lowerCamelCase_						=			", ".join(lowercase )
					# generate prompts with coca model if prompt is None
					if content_prompt is None:
									if len(lowercase ):
													raise ValueError(
													    f'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
													    f'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
									lowerCamelCase_						=			self.get_image_description(lowercase )
					if style_prompt is None:
									if len(lowercase ):
													raise ValueError(
													    f'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
													    f' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
									lowerCamelCase_						=			self.get_image_description(lowercase )

					# get prompt text embeddings for content and style
					lowerCamelCase_						=			self.tokenizer(
					    lowercase						,  padding="max_length"						,  max_length=self.tokenizer.model_max_length						,  truncation=lowercase						,  return_tensors="pt"						,  )
					lowerCamelCase_						=			self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]

					lowerCamelCase_						=			self.tokenizer(
					    lowercase						,  padding="max_length"						,  max_length=self.tokenizer.model_max_length						,  truncation=lowercase						,  return_tensors="pt"						,  )
					lowerCamelCase_						=			self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]

					lowerCamelCase_						=			slerp(lowercase						,  lowercase						,  lowercase )

					# duplicate text embeddings for each generation per prompt
					lowerCamelCase_						=			text_embeddings.repeat_interleave(lowercase						,  dim=0 )

					# set timesteps
					lowerCamelCase_						=			"offset" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
					lowerCamelCase_						=			{}
					if accepts_offset:
									lowerCamelCase_						=			1

					self.scheduler.set_timesteps(lowercase						,  **lowercase )
					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					self.scheduler.timesteps.to(self.device )

					lowerCamelCase_	,    lowerCamelCase_						=			self.get_timesteps(lowercase						,  lowercase						,  self.device )
					lowerCamelCase_						=			timesteps[:1].repeat(lowercase )

					# Preprocess image
					lowerCamelCase_						=			preprocess(lowercase						,  lowercase						,  lowercase )
					lowerCamelCase_						=			self.prepare_latents(
					    lowercase						,  lowercase						,  lowercase						,  text_embeddings.dtype						,  self.device						,  lowercase )

					lowerCamelCase_						=			preprocess(lowercase						,  lowercase						,  lowercase )
					lowerCamelCase_						=			self.prepare_latents(
					    lowercase						,  lowercase						,  lowercase						,  text_embeddings.dtype						,  self.device						,  lowercase )

					lowerCamelCase_						=			slerp(lowercase						,  lowercase						,  lowercase )

					if clip_guidance_scale > 0:
									lowerCamelCase_						=			self.get_clip_image_embeddings(lowercase						,  lowercase )
									lowerCamelCase_						=			self.get_clip_image_embeddings(lowercase						,  lowercase )
									lowerCamelCase_						=			slerp(
									    lowercase						,  lowercase						,  lowercase )

					# 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_						=			content_text_input.input_ids.shape[-1]
									lowerCamelCase_						=			self.tokenizer([""]						,  padding="max_length"						,  max_length=lowercase						,  return_tensors="pt" )
									lowerCamelCase_						=			self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
									# duplicate unconditional embeddings for each generation per prompt
									lowerCamelCase_						=			uncond_embeddings.repeat_interleave(lowercase						,  dim=0 )

									# 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, self.unet.config.in_channels, height // 8, width // 8)
					lowerCamelCase_						=			text_embeddings.dtype
					if latents is None:
									if self.device.type == "mps":
													# randn does not work reproducibly on mps
													lowerCamelCase_						=			torch.randn(lowercase						,  generator=lowercase						,  device="cpu"						,  dtype=lowercase ).to(
													    self.device )
									else:
													lowerCamelCase_						=			torch.randn(lowercase						,  generator=lowercase						,  device=self.device						,  dtype=lowercase )
					else:
									if latents.shape != latents_shape:
													raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
									lowerCamelCase_						=			latents.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

					# check if the scheduler accepts generator
					lowerCamelCase_						=			"generator" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
					if accepts_generator:
									lowerCamelCase_						=			generator

					with self.progress_bar(total=lowercase ):
									for i, t in enumerate(lowercase ):
													# 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(lowercase						,  lowercase )

													# predict the noise residual
													lowerCamelCase_						=			self.unet(lowercase						,  lowercase						,  encoder_hidden_states=lowercase ).sample

													# perform classifier free guidance
													if do_classifier_free_guidance:
																	lowerCamelCase_	,    lowerCamelCase_						=			noise_pred.chunk(2 )
																	lowerCamelCase_						=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# perform clip guidance
													if clip_guidance_scale > 0:
																	lowerCamelCase_						=			(
																	    text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
																	)
																	lowerCamelCase_	,    lowerCamelCase_						=			self.cond_fn(
																	    lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )

													# compute the previous noisy sample x_t -> x_t-1
													lowerCamelCase_						=			self.scheduler.step(lowercase						,  lowercase						,  lowercase						,  **lowercase ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
					lowerCamelCase_						=			1 / 0.1_8_2_1_5 * latents
					lowerCamelCase_						=			self.vae.decode(lowercase ).sample

					lowerCamelCase_						=			(image / 2 + 0.5).clamp(0						,  1 )
					lowerCamelCase_						=			image.cpu().permute(0						,  2						,  3						,  1 ).numpy()

					if output_type == "pil":
									lowerCamelCase_						=			self.numpy_to_pil(lowercase )

					if not return_dict:
									return (image, None)

					return StableDiffusionPipelineOutput(images=lowercase						,  nsfw_content_detected=lowercase )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


