



'''simple docstring'''

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_  )      ->				str:
		if isinstance(lowerCAmelCase_						,      torch.Tensor  ):
				return image
		elif isinstance(lowerCAmelCase_						,      PIL.Image.Image  ):
				_a					:				Optional[int]				=							[image]

		if isinstance(image[0]						,      PIL.Image.Image  ):
				_a					:				Tuple				=							[np.array(i.resize((w, h)						,      resample=PIL_INTERPOLATION['lanczos']  )  )[None, :] for i in image]
				_a					:				Union[str, Any]				=							np.concatenate(lowerCAmelCase_						,      axis=0  )
				_a					:				List[str]				=							np.array(lowerCAmelCase_  ).astype(np.floataa  ) / 255.0
				_a					:				Union[str, Any]				=							image.transpose(0						,      3						,      1						,      2  )
				_a					:				Union[str, Any]				=							2.0 * image - 1.0
				_a					:				Dict				=							torch.from_numpy(lowerCAmelCase_  )
		elif isinstance(image[0]						,      torch.Tensor  ):
				_a					:				Optional[Any]				=							torch.cat(lowerCAmelCase_						,      dim=0  )
		return image



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=0.9_995  )      ->				Optional[int]:
		if not isinstance(lowerCAmelCase_						,      np.ndarray  ):
				_a					:				Optional[Any]				=							True
				_a					:				Union[str, Any]				=							va.device
				_a					:				Any				=							va.cpu().numpy()
				_a					:				Optional[Any]				=							va.cpu().numpy()

		_a					:				Dict				=							np.sum(va * va / (np.linalg.norm(lowerCAmelCase_  ) * np.linalg.norm(lowerCAmelCase_  ))  )
		if np.abs(lowerCAmelCase_  ) > DOT_THRESHOLD:
				_a					:				Any				=							(1 - t) * va + t * va
		else:
				_a					:				Tuple				=							np.arccos(lowerCAmelCase_  )
				_a					:				Optional[Any]				=							np.sin(lowerCAmelCase_  )
				_a					:				int				=							theta_a * t
				_a					:				List[Any]				=							np.sin(lowerCAmelCase_  )
				_a					:				Any				=							np.sin(theta_a - theta_t  ) / sin_theta_a
				_a					:				Union[str, Any]				=							sin_theta_t / sin_theta_a
				_a					:				List[str]				=							sa * va + sa * va

		if inputs_are_torch:
				_a					:				Any				=							torch.from_numpy(lowerCAmelCase_  ).to(lowerCAmelCase_  )

		return va



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Tuple:
		_a					:				Dict				=							F.normalize(lowerCAmelCase_						,      dim=-1  )
		_a					:				Union[str, Any]				=							F.normalize(lowerCAmelCase_						,      dim=-1  )
		return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Union[str, Any]:
		for param in model.parameters():
				_a					:				Optional[Any]				=							value

class      __magic_name__						(   _UpperCamelCase       ):







							def __init__(							self :	Tuple     ,_UpperCAmelCase :	AutoencoderKL     ,_UpperCAmelCase :	CLIPTextModel     ,_UpperCAmelCase :	CLIPModel     ,_UpperCAmelCase :	CLIPTokenizer     ,_UpperCAmelCase :	UNetaDConditionModel     ,_UpperCAmelCase :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]     ,_UpperCAmelCase :	CLIPFeatureExtractor     ,_UpperCAmelCase :	List[str]=None     ,_UpperCAmelCase :	str=None     ,_UpperCAmelCase :	List[Any]=None     ,):
									super().__init__()
									self.register_modules(
									    vae=_UpperCAmelCase     ,text_encoder=_UpperCAmelCase     ,clip_model=_UpperCAmelCase     ,tokenizer=_UpperCAmelCase     ,unet=_UpperCAmelCase     ,scheduler=_UpperCAmelCase     ,feature_extractor=_UpperCAmelCase     ,coca_model=_UpperCAmelCase     ,coca_tokenizer=_UpperCAmelCase     ,coca_transform=_UpperCAmelCase     ,)
									_a					:				List[Any]				=							(
									    feature_extractor.size
									    if isinstance(feature_extractor.size     ,_UpperCAmelCase		)
									    else feature_extractor.size['shortest_edge']
									)
									_a					:				str				=							transforms.Normalize(mean=feature_extractor.image_mean     ,std=feature_extractor.image_std		)
									set_requires_grad(self.text_encoder     ,_UpperCAmelCase		)
									set_requires_grad(self.clip_model     ,_UpperCAmelCase		)







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







							def 							__lowercase      (							self :	int		):
									self.enable_attention_slicing(_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[Any]		):
									set_requires_grad(self.vae     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any		):
									set_requires_grad(self.vae     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[Any]		):
									set_requires_grad(self.unet     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[Any]		):
									set_requires_grad(self.unet     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Dict		):
									# get the original timestep using init_timestep
									_a					:				Union[str, Any]				=							min(int(num_inference_steps * strength		)     ,_UpperCAmelCase		)

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

									return timesteps, num_inference_steps - t_start







							def 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Optional[int]=None		):
									if not isinstance(_UpperCAmelCase     ,torch.Tensor		):
											raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(_UpperCAmelCase		)}"""		)

									_a					:				Tuple				=							image.to(device=_UpperCAmelCase     ,dtype=_UpperCAmelCase		)

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

									# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
									_a					:				Dict				=							0.1_82_15 * init_latents
									_a					:				Optional[int]				=							init_latents.repeat_interleave(_UpperCAmelCase     ,dim=0		)

									_a					:				Optional[Any]				=							randn_tensor(init_latents.shape     ,generator=_UpperCAmelCase     ,device=_UpperCAmelCase     ,dtype=_UpperCAmelCase		)

									# get latents
									_a					:				Optional[Any]				=							self.scheduler.add_noise(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)
									_a					:				List[str]				=							init_latents

									return latents







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







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	List[str]		):
									_a					:				str				=							self.feature_extractor.preprocess(_UpperCAmelCase		)
									_a					:				Optional[Any]				=							torch.from_numpy(clip_image_input['pixel_values'][0]		).unsqueeze(0		).to(self.device		).half()
									_a					:				List[Any]				=							self.clip_model.get_image_features(_UpperCAmelCase		)
									_a					:				Any				=							image_embeddings_clip / image_embeddings_clip.norm(p=2     ,dim=-1     ,keepdim=_UpperCAmelCase		)
									_a					:				str				=							image_embeddings_clip.repeat_interleave(_UpperCAmelCase     ,dim=0		)
									return image_embeddings_clip







							@torch.enable_grad()
							def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Any     ,):
									_a					:				List[str]				=							latents.detach().requires_grad_()

									_a					:				Dict				=							self.scheduler.scale_model_input(_UpperCAmelCase     ,_UpperCAmelCase		)

									# predict the noise residual
									_a					:				int				=							self.unet(_UpperCAmelCase     ,_UpperCAmelCase     ,encoder_hidden_states=_UpperCAmelCase		).sample

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

											_a					:				Union[str, Any]				=							torch.sqrt(_UpperCAmelCase		)
											_a					:				int				=							pred_original_sample * (fac) + latents * (1 - fac)
									elif isinstance(self.scheduler     ,_UpperCAmelCase		):
											_a					:				List[str]				=							self.scheduler.sigmas[index]
											_a					:				Any				=							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					:				int				=							1 / 0.1_82_15 * sample
									_a					:				List[Any]				=							self.vae.decode(_UpperCAmelCase		).sample
									_a					:				Dict				=							(image / 2 + 0.5).clamp(0     ,1		)

									_a					:				Optional[Any]				=							transforms.Resize(self.feature_extractor_size		)(_UpperCAmelCase		)
									_a					:				Union[str, Any]				=							self.normalize(_UpperCAmelCase		).to(latents.dtype		)

									_a					:				Tuple				=							self.clip_model.get_image_features(_UpperCAmelCase		)
									_a					:				Dict				=							image_embeddings_clip / image_embeddings_clip.norm(p=2     ,dim=-1     ,keepdim=_UpperCAmelCase		)

									_a					:				Dict				=							spherical_dist_loss(_UpperCAmelCase     ,_UpperCAmelCase		).mean() * clip_guidance_scale

									_a					:				Optional[int]				=							-torch.autograd.grad(_UpperCAmelCase     ,_UpperCAmelCase		)[0]

									if isinstance(self.scheduler     ,_UpperCAmelCase		):
											_a					:				str				=							latents.detach() + grads * (sigma**2)
											_a					:				Union[str, Any]				=							noise_pred_original
									else:
											_a					:				Tuple				=							noise_pred_original - torch.sqrt(_UpperCAmelCase		) * grads
									return noise_pred, latents







							@torch.no_grad()
							def __call__(							self :	Union[str, Any]     ,_UpperCAmelCase :	Union[torch.FloatTensor, PIL.Image.Image]     ,_UpperCAmelCase :	Union[torch.FloatTensor, PIL.Image.Image]     ,_UpperCAmelCase :	Optional[str] = None     ,_UpperCAmelCase :	Optional[str] = None     ,_UpperCAmelCase :	Optional[int] = 512     ,_UpperCAmelCase :	Optional[int] = 512     ,_UpperCAmelCase :	float = 0.6     ,_UpperCAmelCase :	Optional[int] = 50     ,_UpperCAmelCase :	Optional[float] = 7.5     ,_UpperCAmelCase :	Optional[int] = 1     ,_UpperCAmelCase :	float = 0.0     ,_UpperCAmelCase :	Optional[float] = 100     ,_UpperCAmelCase :	Optional[torch.Generator] = None     ,_UpperCAmelCase :	Optional[str] = "pil"     ,_UpperCAmelCase :	bool = True     ,_UpperCAmelCase :	float = 0.8     ,_UpperCAmelCase :	float = 0.1     ,_UpperCAmelCase :	float = 0.1     ,):
									if isinstance(_UpperCAmelCase     ,_UpperCAmelCase		) and len(_UpperCAmelCase		) != batch_size:
											raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(_UpperCAmelCase		)} 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(_UpperCAmelCase     ,torch.Generator		) and batch_size > 1:
											_a					:				Optional[int]				=							[generator] + [None] * (batch_size - 1)

									_a					:				Optional[Any]				=							[
									    ('model', self.coca_model is None),
									    ('tokenizer', self.coca_tokenizer is None),
									    ('transform', self.coca_transform is None),
									]
									_a					:				List[Any]				=							[x[0] for x in coca_is_none if x[1]]
									_a					:				int				=							', '.join(_UpperCAmelCase		)
									# generate prompts with coca model if prompt is None
									if content_prompt is None:
											if len(_UpperCAmelCase		):
													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					:				Tuple				=							self.get_image_description(_UpperCAmelCase		)
									if style_prompt is None:
											if len(_UpperCAmelCase		):
													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					:				Union[str, Any]				=							self.get_image_description(_UpperCAmelCase		)

									# get prompt text embeddings for content and style
									_a					:				Optional[int]				=							self.tokenizer(
									    _UpperCAmelCase     ,padding='max_length'     ,max_length=self.tokenizer.model_max_length     ,truncation=_UpperCAmelCase     ,return_tensors='pt'     ,)
									_a					:				List[str]				=							self.text_encoder(content_text_input.input_ids.to(self.device		)		)[0]

									_a					:				Optional[int]				=							self.tokenizer(
									    _UpperCAmelCase     ,padding='max_length'     ,max_length=self.tokenizer.model_max_length     ,truncation=_UpperCAmelCase     ,return_tensors='pt'     ,)
									_a					:				str				=							self.text_encoder(style_text_input.input_ids.to(self.device		)		)[0]

									_a					:				Any				=							slerp(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)

									# duplicate text embeddings for each generation per prompt
									_a					:				str				=							text_embeddings.repeat_interleave(_UpperCAmelCase     ,dim=0		)

									# set timesteps
									_a					:				int				=							'offset' in set(inspect.signature(self.scheduler.set_timesteps		).parameters.keys()		)
									_a					:				List[Any]				=							{}
									if accepts_offset:
											_a					:				Union[str, Any]				=							1

									self.scheduler.set_timesteps(_UpperCAmelCase     ,**_UpperCAmelCase		)
									# 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					:				List[Any]				=							self.get_timesteps(_UpperCAmelCase     ,_UpperCAmelCase     ,self.device		)
									_a					:				List[Any]				=							timesteps[:1].repeat(_UpperCAmelCase		)

									# Preprocess image
									_a					:				str				=							preprocess(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)
									_a					:				Union[str, Any]				=							self.prepare_latents(
									    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,text_embeddings.dtype     ,self.device     ,_UpperCAmelCase		)

									_a					:				str				=							preprocess(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)
									_a					:				Optional[int]				=							self.prepare_latents(
									    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,text_embeddings.dtype     ,self.device     ,_UpperCAmelCase		)

									_a					:				str				=							slerp(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)

									if clip_guidance_scale > 0:
											_a					:				Optional[Any]				=							self.get_clip_image_embeddings(_UpperCAmelCase     ,_UpperCAmelCase		)
											_a					:				Optional[Any]				=							self.get_clip_image_embeddings(_UpperCAmelCase     ,_UpperCAmelCase		)
											_a					:				List[str]				=							slerp(
											    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)

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

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

									# check if the scheduler accepts generator
									_a					:				Union[str, Any]				=							'generator' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
									if accepts_generator:
											_a					:				Union[str, Any]				=							generator

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

													# predict the noise residual
													_a					:				Union[str, Any]				=							self.unet(_UpperCAmelCase     ,_UpperCAmelCase     ,encoder_hidden_states=_UpperCAmelCase		).sample

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

													# perform clip guidance
													if clip_guidance_scale > 0:
															_a					:				Optional[Any]				=							(
															    text_embeddings.chunk(2		)[1] if do_classifier_free_guidance else text_embeddings
															)
															_a	, _a					:				int				=							self.cond_fn(
															    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,)

													# compute the previous noisy sample x_t -> x_t-1
													_a					:				Tuple				=							self.scheduler.step(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,**_UpperCAmelCase		).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
									_a					:				Tuple				=							1 / 0.1_82_15 * latents
									_a					:				Dict				=							self.vae.decode(_UpperCAmelCase		).sample

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

									if output_type == "pil":
											_a					:				str				=							self.numpy_to_pil(_UpperCAmelCase		)

									if not return_dict:
											return (image, None)

									return StableDiffusionPipelineOutput(images=_UpperCAmelCase     ,nsfw_content_detected=_UpperCAmelCase		)









'''simple docstring'''

import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase						      =       logging.get_logger(__name__)

set_seed(770)


__lowerCAmelCase						      =       {
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


__lowerCAmelCase						      =       {
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

__lowerCAmelCase						      =       os.path.dirname(os.path.abspath(__file__))
__lowerCAmelCase						      =       os.path.join(os.path.expanduser('''~'''), '''.cache''')
__lowerCAmelCase						      =       os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				Optional[int]:
		_a					:				int				=							model_type
		if use_small:
				key += "_small"
		return os.path.join(lowerCAmelCase_						,      REMOTE_MODEL_PATHS[key]['file_name']  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		os.makedirs(lowerCAmelCase_						,      exist_ok=lowerCAmelCase_  )
		hf_hub_download(repo_id=lowerCAmelCase_						,      filename=lowerCAmelCase_						,      local_dir=lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_="text"  )      ->				List[str]:
		if model_type == "text":
				_a					:				List[str]				=							BarkSemanticModel
				_a					:				Optional[Any]				=							BarkSemanticConfig
				_a					:				Any				=							BarkSemanticGenerationConfig
		elif model_type == "coarse":
				_a					:				Tuple				=							BarkCoarseModel
				_a					:				str				=							BarkCoarseConfig
				_a					:				str				=							BarkCoarseGenerationConfig
		elif model_type == "fine":
				_a					:				List[str]				=							BarkFineModel
				_a					:				Optional[Any]				=							BarkFineConfig
				_a					:				str				=							BarkFineGenerationConfig
		else:
				raise NotImplementedError()
		_a					:				Dict				=							f"""{model_type}_small""" if use_small else model_type
		_a					:				Union[str, Any]				=							REMOTE_MODEL_PATHS[model_key]
		if not os.path.exists(lowerCAmelCase_  ):
				logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""  )
				_download(model_info['repo_id']						,      model_info['file_name']  )
		_a					:				int				=							torch.load(lowerCAmelCase_						,      map_location=lowerCAmelCase_  )
		# this is a hack
		_a					:				List[Any]				=							checkpoint['model_args']
		if "input_vocab_size" not in model_args:
				_a					:				Dict				=							model_args['vocab_size']
				_a					:				Dict				=							model_args['vocab_size']
				del model_args["vocab_size"]

		# convert Bark model arguments to HF Bark model arguments
		_a					:				List[Any]				=							model_args.pop('n_head'  )
		_a					:				Any				=							model_args.pop('n_embd'  )
		_a					:				List[Any]				=							model_args.pop('n_layer'  )

		_a					:				Optional[int]				=							ConfigClass(**checkpoint['model_args']  )
		_a					:				List[str]				=							ModelClass(config=lowerCAmelCase_  )
		_a					:				Tuple				=							GenerationConfigClass()

		_a					:				Optional[Any]				=							model_generation_config
		_a					:				Optional[Any]				=							checkpoint['model']
		# fixup checkpoint
		_a					:				int				=							'_orig_mod.'
		for k, v in list(state_dict.items()  ):
				if k.startswith(lowerCAmelCase_  ):
						# replace part of the key with corresponding layer name in HF implementation
						_a					:				str				=							k[len(lowerCAmelCase_  ) :]
						for old_layer_name in new_layer_name_dict:
								_a					:				List[Any]				=							new_k.replace(lowerCAmelCase_						,      new_layer_name_dict[old_layer_name]  )

						_a					:				List[Any]				=							state_dict.pop(lowerCAmelCase_  )

		_a					:				List[Any]				=							set(state_dict.keys()  ) - set(model.state_dict().keys()  )
		_a					:				Tuple				=							{k for k in extra_keys if not k.endswith('.attn.bias'  )}
		_a					:				Tuple				=							set(model.state_dict().keys()  ) - set(state_dict.keys()  )
		_a					:				Optional[Any]				=							{k for k in missing_keys if not k.endswith('.attn.bias'  )}
		if len(lowerCAmelCase_  ) != 0:
				raise ValueError(f"""extra keys found: {extra_keys}"""  )
		if len(lowerCAmelCase_  ) != 0:
				raise ValueError(f"""missing keys: {missing_keys}"""  )
		model.load_state_dict(lowerCAmelCase_						,      strict=lowerCAmelCase_  )
		_a					:				Dict				=							model.num_parameters(exclude_embeddings=lowerCAmelCase_  )
		_a					:				Tuple				=							checkpoint['best_val_loss'].item()
		logger.info(f"""model loaded: {round(n_params/1E6						,      1  )}M params, {round(lowerCAmelCase_						,      3  )} loss"""  )
		model.eval()
		model.to(lowerCAmelCase_  )
		del checkpoint, state_dict

		return model



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_="text"  )      ->				List[Any]:
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		_a					:				Optional[int]				=							'cpu'  # do conversion on cpu

		_a					:				Tuple				=							_get_ckpt_path(lowerCAmelCase_						,      use_small=lowerCAmelCase_  )
		_a					:				List[Any]				=							_load_model(lowerCAmelCase_						,      lowerCAmelCase_						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

		# load bark initial model
		_a					:				Any				=							_bark_load_model(lowerCAmelCase_						,      'cpu'						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

		if model_type == "text":
				_a					:				int				=							bark_model['model']

		if model.num_parameters(exclude_embeddings=lowerCAmelCase_  ) != bark_model.get_num_params():
				raise ValueError('initial and new models don\'t have the same number of parameters'  )

		# check if same output as the bark model
		_a					:				Any				=							5
		_a					:				List[str]				=							10

		if model_type in ["text", "coarse"]:
				_a					:				Dict				=							torch.randint(256						,      (batch_size, sequence_length)						,      dtype=torch.int  )
				_a					:				Dict				=							bark_model(lowerCAmelCase_  )[0]

				_a					:				Tuple				=							model(lowerCAmelCase_  )

				# take last logits
				_a					:				Optional[int]				=							output_new_model_total.logits[:, [-1], :]

		else:
				_a					:				List[str]				=							3
				_a					:				List[Any]				=							8
				_a					:				Tuple				=							torch.randint(256						,      (batch_size, sequence_length, n_codes_total)						,      dtype=torch.int  )

				_a					:				Union[str, Any]				=							model(lowerCAmelCase_						,      lowerCAmelCase_  )
				_a					:				int				=							bark_model(lowerCAmelCase_						,      lowerCAmelCase_  )

				_a					:				List[str]				=							output_new_model_total.logits

		# output difference should come from the difference of self-attention implementation design
		if output_new_model.shape != output_old_model.shape:
				raise ValueError('initial and new outputs don\'t have the same shape'  )
		if (output_new_model - output_old_model).abs().max().item() > 1E-3:
				raise ValueError('initial and new outputs are not equal'  )

		Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		model.save_pretrained(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )      ->				Any:
		_a					:				Any				=							os.path.join(lowerCAmelCase_						,      lowerCAmelCase_  )

		_a					:				int				=							BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				Any				=							BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				List[Any]				=							BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				List[str]				=							EncodecConfig.from_pretrained('facebook/encodec_24khz'  )

		_a					:				str				=							BarkSemanticModel.from_pretrained(lowerCAmelCase_  )
		_a					:				Dict				=							BarkCoarseModel.from_pretrained(lowerCAmelCase_  )
		_a					:				int				=							BarkFineModel.from_pretrained(lowerCAmelCase_  )
		_a					:				List[Any]				=							EncodecModel.from_pretrained('facebook/encodec_24khz'  )

		_a					:				Any				=							BarkConfig.from_sub_model_configs(
		    lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )

		_a					:				List[str]				=							BarkGenerationConfig.from_sub_model_configs(
		    semantic.generation_config						,      coarseAcoustic.generation_config						,      fineAcoustic.generation_config  )

		_a					:				Optional[Any]				=							BarkModel(lowerCAmelCase_  )

		_a					:				List[str]				=							semantic
		_a					:				Union[str, Any]				=							coarseAcoustic
		_a					:				Optional[int]				=							fineAcoustic
		_a					:				Optional[Any]				=							codec

		_a					:				List[Any]				=							bark_generation_config

		Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		bark.save_pretrained(lowerCAmelCase_						,      repo_id=lowerCAmelCase_						,      push_to_hub=lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase						      =       argparse.ArgumentParser()
							# Required parameters

							parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
							parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

							__lowerCAmelCase						      =       parser.parse_args()

							load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)




