


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					( UpperCamelCase__:  int				,       UpperCamelCase__:  int				,       UpperCamelCase__:  Optional[Any]    )			-> List[Any]:





	"""simple docstring"""







	if isinstance(UpperCamelCase__				,       torch.Tensor    ):
		return image
	elif isinstance(UpperCamelCase__				,       PIL.Image.Image    ):
		A             =					[image]

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





def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Tuple=0.99_95    )			-> int:





	"""simple docstring"""







	if not isinstance(UpperCamelCase__				,       np.ndarray    ):
		A             =					True
		A             =					va.device
		A             =					va.cpu().numpy()
		A             =					va.cpu().numpy()

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

	if inputs_are_torch:
		A             =					torch.from_numpy(UpperCamelCase__    ).to(UpperCamelCase__    )

	return va





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  str    )			-> Optional[Any]:





	"""simple docstring"""







	A             =					F.normalize(UpperCamelCase__				,       dim=-1    )
	A             =					F.normalize(UpperCamelCase__				,       dim=-1    )
	return (x - y).norm(dim=-1    ).div(2    ).arcsin().pow(2    ).mul(2    )





def 	_lowerCAmelCase					( UpperCamelCase__:  Any				,       UpperCamelCase__:  List[Any]    )			-> Optional[Any]:





	"""simple docstring"""







	for param in model.parameters():
		A             =					value






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







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

				def   _UpperCAmelCase       (    self					, a__ = "auto" )						->					str:
					if slice_size == "auto":
						# half the attention head size is usually a good trade-off between
						# speed and memory
						A             =					self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(a__ )

				def   _UpperCAmelCase       (    self )						->					List[str]:
					self.enable_attention_slicing(a__ )

				def   _UpperCAmelCase       (    self )						->					List[str]:
					set_requires_grad(self.vae					, a__ )

				def   _UpperCAmelCase       (    self )						->					List[str]:
					set_requires_grad(self.vae					, a__ )

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					set_requires_grad(self.unet					, a__ )

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					set_requires_grad(self.unet					, a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					Dict:
					# get the original timestep using init_timestep
					A             =					min(int(num_inference_steps * strength )					, a__ )

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

					return timesteps, num_inference_steps - t_start

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

					A             =					image.to(device=a__					, dtype=a__ )

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

					# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
					A             =					0.1_82_15 * init_latents
					A             =					init_latents.repeat_interleave(a__					, dim=0 )

					A             =					randn_tensor(init_latents.shape					, generator=a__					, device=a__					, dtype=a__ )

					# get latents
					A             =					self.scheduler.add_noise(a__					, a__					, a__ )
					A             =					init_latents

					return latents

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

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

				@torch.enable_grad()
				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__					, a__					, a__					, a__					, )						->					Tuple:
					A             =					latents.detach().requires_grad_()

					A             =					self.scheduler.scale_model_input(a__					, a__ )

					# predict the noise residual
					A             =					self.unet(a__					, a__					, encoder_hidden_states=a__ ).sample

					if isinstance(self.scheduler					, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
						A             =					self.scheduler.alphas_cumprod[timestep]
						A             =					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
						A             =					(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

						A             =					torch.sqrt(a__ )
						A             =					pred_original_sample * (fac) + latents * (1 - fac)
					elif isinstance(self.scheduler					, a__ ):
						A             =					self.scheduler.sigmas[index]
						A             =					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
					A             =					1 / 0.1_82_15 * sample
					A             =					self.vae.decode(a__ ).sample
					A             =					(image / 2 + 0.5).clamp(0					, 1 )

					A             =					transforms.Resize(self.feature_extractor_size )(a__ )
					A             =					self.normalize(a__ ).to(latents.dtype )

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

					A             =					spherical_dist_loss(a__					, a__ ).mean() * clip_guidance_scale

					A             =					-torch.autograd.grad(a__					, a__ )[0]

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

				@torch.no_grad()
				def __call__(    self					, a__					, a__					, a__ = None					, a__ = None					, a__ = 512					, a__ = 512					, a__ = 0.6					, a__ = 50					, a__ = 7.5					, a__ = 1					, a__ = 0.0					, a__ = 100					, a__ = None					, a__ = "pil"					, a__ = True					, a__ = 0.8					, a__ = 0.1					, a__ = 0.1					, )						->					Tuple:
					if isinstance(a__					, a__ ) and len(a__ ) != batch_size:
						raise ValueError(f'You have passed {batch_size} batch_size, but only {len(a__ )} 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(a__					, torch.Generator ) and batch_size > 1:
						A             =					[generator] + [None] * (batch_size - 1)

					A             =					[
					    ("""model""", self.coca_model is None),
					    ("""tokenizer""", self.coca_tokenizer is None),
					    ("""transform""", self.coca_transform is None),
					]
					A             =					[x[0] for x in coca_is_none if x[1]]
					A             =					""", """.join(a__ )
					# generate prompts with coca model if prompt is None
					if content_prompt is None:
						if len(a__ ):
							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.' )
						A             =					self.get_image_description(a__ )
					if style_prompt is None:
						if len(a__ ):
							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.' )
						A             =					self.get_image_description(a__ )

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

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

					A             =					slerp(a__					, a__					, a__ )

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

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

					self.scheduler.set_timesteps(a__					, **a__ )
					# 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 )

					A       ,      A             =					self.get_timesteps(a__					, a__					, self.device )
					A             =					timesteps[:1].repeat(a__ )

					# Preprocess image
					A             =					preprocess(a__					, a__					, a__ )
					A             =					self.prepare_latents(
					    a__					, a__					, a__					, text_embeddings.dtype					, self.device					, a__ )

					A             =					preprocess(a__					, a__					, a__ )
					A             =					self.prepare_latents(
					    a__					, a__					, a__					, text_embeddings.dtype					, self.device					, a__ )

					A             =					slerp(a__					, a__					, a__ )

					if clip_guidance_scale > 0:
						A             =					self.get_clip_image_embeddings(a__					, a__ )
						A             =					self.get_clip_image_embeddings(a__					, a__ )
						A             =					slerp(
						    a__					, a__					, a__ )

					# 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.
					A             =					guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
						A             =					content_text_input.input_ids.shape[-1]
						A             =					self.tokenizer([""""""]					, padding="""max_length"""					, max_length=a__					, return_tensors="""pt""" )
						A             =					self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
						# duplicate unconditional embeddings for each generation per prompt
						A             =					uncond_embeddings.repeat_interleave(a__					, 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
						A             =					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`.
					A             =					(batch_size, self.unet.config.in_channels, height // 8, width // 8)
					A             =					text_embeddings.dtype
					if latents is None:
						if self.device.type == "mps":
							# randn does not work reproducibly on mps
							A             =					torch.randn(a__					, generator=a__					, device="""cpu"""					, dtype=a__ ).to(
							    self.device )
						else:
							A             =					torch.randn(a__					, generator=a__					, device=self.device					, dtype=a__ )
					else:
						if latents.shape != latents_shape:
							raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
						A             =					latents.to(self.device )

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

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

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

							# predict the noise residual
							A             =					self.unet(a__					, a__					, encoder_hidden_states=a__ ).sample

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

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

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
					A             =					1 / 0.1_82_15 * latents
					A             =					self.vae.decode(a__ ).sample

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

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

					if not return_dict:
						return (image, None)

					return StableDiffusionPipelineOutput(images=a__					, nsfw_content_detected=a__ )







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_albert import AlbertTokenizer
else:
				_lowercase							:   List[str]						=						None

_lowercase							:   List[str]						=						logging.get_logger(__name__)
_lowercase							:   List[Any]						=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase							:   str						=						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

_lowercase							:   List[str]						=						{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowercase							:   Dict						=						"▁"






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			VOCAB_FILES_NAMES
				lowerCAmelCase					=			PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase					=			AlbertTokenizer

				def __init__(    self					, a__=None					, a__=None					, a__=True					, a__=True					, a__=False					, a__="[CLS]"					, a__="[SEP]"					, a__="<unk>"					, a__="[SEP]"					, a__="<pad>"					, a__="[CLS]"					, a__="[MASK]"					, **a__					, )						->					Dict:
					# Mask token behave like a normal word, i.e. include the space before it and
					# is included in the raw text, there should be a match in a non-normalized sentence.
					A             =					(
					    AddedToken(a__					, lstrip=a__					, rstrip=a__					, normalized=a__ )
					    if isinstance(a__					, a__ )
					    else mask_token
					)

					super().__init__(
					    a__					, tokenizer_file=a__					, do_lower_case=a__					, remove_space=a__					, keep_accents=a__					, bos_token=a__					, eos_token=a__					, unk_token=a__					, sep_token=a__					, pad_token=a__					, cls_token=a__					, mask_token=a__					, **a__					, )

					A             =					do_lower_case
					A             =					remove_space
					A             =					keep_accents
					A             =					vocab_file
					A             =					False if not self.vocab_file else True

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[int]:
					A             =					[self.sep_token_id]
					A             =					[self.cls_token_id]
					if token_ids_a is None:
						return cls + token_ids_a + sep
					return cls + token_ids_a + sep + token_ids_a + sep

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[int]:
					A             =					[self.sep_token_id]
					A             =					[self.cls_token_id]

					if token_ids_a is None:
						return len(cls + token_ids_a + sep ) * [0]
					return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					Tuple[str]:
					if not self.can_save_slow_tokenizer:
						raise ValueError(
						    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
						    """tokenizer.""" )

					if not os.path.isdir(a__ ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
						return
					A             =					os.path.join(
					    a__					, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(a__ ):
						copyfile(self.vocab_file					, a__ )

					return (out_vocab_file,)



