




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 __UpperCamelCase			(       _A ,  _A ,  _A  ):


						if isinstance(_snake_case ,  torch.Tensor  ):
												return image
						elif isinstance(_snake_case ,  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(_snake_case ,  axis=0  )
												lowerCAmelCase_      			=  np.array(_snake_case  ).astype(np.floataa  ) / 2_5_5.0
												lowerCAmelCase_      			=  image.transpose(0 ,  3 ,  1 ,  2  )
												lowerCAmelCase_      			=  2.0 * image - 1.0
												lowerCAmelCase_      			=  torch.from_numpy(_snake_case  )
						elif isinstance(image[0] ,  torch.Tensor  ):
												lowerCAmelCase_      			=  torch.cat(_snake_case ,  dim=0  )
						return image





def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A=0.9_9_9_5  ):


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

						lowerCAmelCase_      			=  np.sum(va * va / (np.linalg.norm(_snake_case  ) * np.linalg.norm(_snake_case  ))  )
						if np.abs(_snake_case  ) > DOT_THRESHOLD:
												lowerCAmelCase_      			=  (1 - t) * va + t * va
						else:
												lowerCAmelCase_      			=  np.arccos(_snake_case  )
												lowerCAmelCase_      			=  np.sin(_snake_case  )
												lowerCAmelCase_      			=  theta_a * t
												lowerCAmelCase_      			=  np.sin(_snake_case  )
												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(_snake_case  ).to(_snake_case  )

						return va





def __UpperCamelCase			(       _A ,  _A  ):
						lowerCAmelCase_      			=  F.normalize(_snake_case ,  dim=-1  )
						lowerCAmelCase_      			=  F.normalize(_snake_case ,  dim=-1  )
						return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )





def __UpperCamelCase			(       _A ,  _A  ):


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






class  A		(  __UpperCAmelCase     ):



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__=None,	UpperCamelCase__=None,	UpperCamelCase__=None,	):







								"""simple docstring"""



								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,	)
								lowerCAmelCase_      			=  (
								    feature_extractor.size
								    if isinstance(feature_extractor.size,	_a   )
								    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,	_a   )
								set_requires_grad(self.clip_model,	_a   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__ = "auto"   ):







								"""simple docstring"""



								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(_a   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self.enable_attention_slicing(_a   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								set_requires_grad(self.vae,	_a   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								set_requires_grad(self.vae,	_a   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								set_requires_grad(self.unet,	_a   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								set_requires_grad(self.unet,	_a   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  min(int(num_inference_steps * strength   ),	_a   )

								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,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__=None   ):







								"""simple docstring"""



								if not isinstance(_a,	torch.Tensor   ):
														raise ValueError(f"`image` has to be of type `torch.Tensor` but is {type(_a   )}"   )

								lowerCAmelCase_      			=  image.to(device=_a,	dtype=_a   )

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

								# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
								lowerCAmelCase_      			=  0.18_215 * init_latents
								lowerCAmelCase_      			=  init_latents.repeat_interleave(_a,	dim=0   )

								lowerCAmelCase_      			=  randn_tensor(init_latents.shape,	generator=_a,	device=_a,	dtype=_a   )

								# get latents
								lowerCAmelCase_      			=  self.scheduler.add_noise(_a,	_a,	_a   )
								lowerCAmelCase_      			=  init_latents

								return latents



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.coca_transform(_a   ).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,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.feature_extractor.preprocess(_a   )
								lowerCAmelCase_      			=  torch.from_numpy(clip_image_input['''pixel_values'''][0]   ).unsqueeze(0   ).to(self.device   ).half()
								lowerCAmelCase_      			=  self.clip_model.get_image_features(_a   )
								lowerCAmelCase_      			=  image_embeddings_clip / image_embeddings_clip.norm(p=2,	dim=-1,	keepdim=_a   )
								lowerCAmelCase_      			=  image_embeddings_clip.repeat_interleave(_a,	dim=0   )
								return image_embeddings_clip



		@torch.enable_grad()
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  latents.detach().requires_grad_()

								lowerCAmelCase_      			=  self.scheduler.scale_model_input(_a,	_a   )

								# predict the noise residual
								lowerCAmelCase_      			=  self.unet(_a,	_a,	encoder_hidden_states=_a   ).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(_a   )
														lowerCAmelCase_      			=  pred_original_sample * (fac) + latents * (1 - fac)
								elif isinstance(self.scheduler,	_a   ):
														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 / 0.18_215 * sample
								lowerCAmelCase_      			=  self.vae.decode(_a   ).sample
								lowerCAmelCase_      			=  (image / 2 + 0.5).clamp(0,	1   )

								lowerCAmelCase_      			=  transforms.Resize(self.feature_extractor_size   )(_a   )
								lowerCAmelCase_      			=  self.normalize(_a   ).to(latents.dtype   )

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

								lowerCAmelCase_      			=  spherical_dist_loss(_a,	_a   ).mean() * clip_guidance_scale

								lowerCAmelCase_      			=  -torch.autograd.grad(_a,	_a   )[0]

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







		@torch.no_grad()
		def __call__(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__ = 512,	UpperCamelCase__ = 512,	UpperCamelCase__ = 0.6,	UpperCamelCase__ = 50,	UpperCamelCase__ = 7.5,	UpperCamelCase__ = 1,	UpperCamelCase__ = 0.0,	UpperCamelCase__ = 100,	UpperCamelCase__ = None,	UpperCamelCase__ = "pil",	UpperCamelCase__ = True,	UpperCamelCase__ = 0.8,	UpperCamelCase__ = 0.1,	UpperCamelCase__ = 0.1,	):







								"""simple docstring"""



								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:
														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(_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."   )
														lowerCAmelCase_      			=  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."   )
														lowerCAmelCase_      			=  self.get_image_description(_a   )

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

								lowerCAmelCase_      			=  self.tokenizer(
								    _a,	padding='''max_length''',	max_length=self.tokenizer.model_max_length,	truncation=_a,	return_tensors='''pt''',	)
								lowerCAmelCase_      			=  self.text_encoder(style_text_input.input_ids.to(self.device   )   )[0]

								lowerCAmelCase_      			=  slerp(_a,	_a,	_a   )

								# duplicate text embeddings for each generation per prompt
								lowerCAmelCase_      			=  text_embeddings.repeat_interleave(_a,	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(_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   )

								lowerCAmelCase_      			=  self.get_timesteps(_a,	_a,	self.device   )
								lowerCAmelCase_      			=  timesteps[:1].repeat(_a   )

								# Preprocess image
								lowerCAmelCase_      			=  preprocess(_a,	_a,	_a   )
								lowerCAmelCase_      			=  self.prepare_latents(
								    _a,	_a,	_a,	text_embeddings.dtype,	self.device,	_a   )

								lowerCAmelCase_      			=  preprocess(_a,	_a,	_a   )
								lowerCAmelCase_      			=  self.prepare_latents(
								    _a,	_a,	_a,	text_embeddings.dtype,	self.device,	_a   )

								lowerCAmelCase_      			=  slerp(_a,	_a,	_a   )

								if clip_guidance_scale > 0:
														lowerCAmelCase_      			=  self.get_clip_image_embeddings(_a,	_a   )
														lowerCAmelCase_      			=  self.get_clip_image_embeddings(_a,	_a   )
														lowerCAmelCase_      			=  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.
								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=_a,	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(_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
														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(_a,	generator=_a,	device='''cpu''',	dtype=_a   ).to(
																				    self.device   )
														else:
																				lowerCAmelCase_      			=  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}"   )
														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=_a   ):
														for i, t in enumerate(_a   ):
																				# 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(_a,	_a   )

																				# predict the noise residual
																				lowerCAmelCase_      			=  self.unet(_a,	_a,	encoder_hidden_states=_a   ).sample

																				# perform classifier free guidance
																				if do_classifier_free_guidance:
																										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_      			=  self.cond_fn(
																										    _a,	_a,	_a,	_a,	_a,	_a,	_a,	)

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
								lowerCAmelCase_      			=  1 / 0.18_215 * latents
								lowerCAmelCase_      			=  self.vae.decode(_a   ).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(_a   )

								if not return_dict:
														return (image, None)

								return StableDiffusionPipelineOutput(images=_a,	nsfw_content_detected=_a   )






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

snake_case :		List[str]							      =  "facebook/wmt19-en-de"
snake_case :		Dict							      =  FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case :		List[str]							      =  FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

snake_case :		int							      =  FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
snake_case :		Optional[Any]							      =  tokenizer(["Making tiny model"], return_tensors="pt")
snake_case :		List[str]							      =  tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
snake_case :		Dict							      =  "tiny-wmt19-en-de"
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de
