"""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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Dict:






  if isinstance(_lowerCAmelCase		,     torch.Tensor   ):
    return image
  elif isinstance(_lowerCAmelCase		,     PIL.Image.Image   ):
    a__:     Optional[Any]										=	[image]

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=0.9_995   )	->Optional[int]:






  if not isinstance(_lowerCAmelCase		,     np.ndarray   ):
    a__:     Tuple										=	True
    a__:     Optional[int]										=	va.device
    a__:     Tuple										=	va.cpu().numpy()
    a__:     List[str]										=	va.cpu().numpy()

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

  if inputs_are_torch:
    a__:     List[Any]										=	torch.from_numpy(_lowerCAmelCase   ).to(_lowerCAmelCase   )

  return va







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  a__:     List[Any]										=	F.normalize(_lowerCAmelCase		,     dim=-1   )
  a__:     Optional[int]										=	F.normalize(_lowerCAmelCase		,     dim=-1   )
  return (x - y).norm(dim=-1   ).div(2   ).arcsin().pow(2   ).mul(2   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Dict:






  for param in model.parameters():
    a__:     Any										=	value




class    __snake_case       (     SCREAMING_SNAKE_CASE__				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			)	->			int:





        '''simple docstring'''





        super().__init__()
        self.register_modules(
            vae=lowerCamelCase__    ,			text_encoder=lowerCamelCase__    ,			clip_model=lowerCamelCase__    ,			tokenizer=lowerCamelCase__    ,			unet=lowerCamelCase__    ,			scheduler=lowerCamelCase__    ,			feature_extractor=lowerCamelCase__    ,			coca_model=lowerCamelCase__    ,			coca_tokenizer=lowerCamelCase__    ,			coca_transform=lowerCamelCase__    ,			)
        a__:     Union[str, Any]										=	(
            feature_extractor.size
            if isinstance(feature_extractor.size    ,			lowerCamelCase__)
            else feature_extractor.size["""shortest_edge"""]
        )
        a__:     Optional[Any]										=	transforms.Normalize(mean=feature_extractor.image_mean    ,			std=feature_extractor.image_std)
        set_requires_grad(self.text_encoder    ,			lowerCamelCase__)
        set_requires_grad(self.clip_model    ,			lowerCamelCase__)



      def   lowerCamelCase_   (						self    ,			lowercase = "auto")	->			int:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        self.enable_attention_slicing(lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        set_requires_grad(self.vae    ,			lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        set_requires_grad(self.vae    ,			lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        set_requires_grad(self.unet    ,			lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        set_requires_grad(self.unet    ,			lowerCamelCase__)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     Dict										=	min(int(num_inference_steps * strength)    ,			lowerCamelCase__)

        a__:     Union[str, Any]										=	max(num_inference_steps - init_timestep    ,			0)
        a__:     List[Any]										=	self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			Any:





        '''simple docstring'''





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

        a__:     List[Any]										=	image.to(device=lowerCamelCase__    ,			dtype=lowerCamelCase__)

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

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

        a__:     Union[str, Any]										=	randn_tensor(init_latents.shape    ,			generator=lowerCamelCase__    ,			device=lowerCamelCase__    ,			dtype=lowerCamelCase__)

        # get latents
        a__:     List[Any]										=	self.scheduler.add_noise(lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__)
        a__:     Dict										=	init_latents

        return latents



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     Union[str, Any]										=	self.coca_transform(lowerCamelCase__).unsqueeze(0)
        with torch.no_grad(), torch.cuda.amp.autocast():
          a__:     Union[str, Any]										=	self.coca_model.generate(transformed_image.to(device=self.device    ,			dtype=self.coca_model.dtype))
        a__:     Tuple										=	self.coca_tokenizer.decode(generated[0].cpu().numpy())
        return generated.split('<end_of_text>')[0].replace('<start_of_text>'    ,			'').rstrip(' .,')



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Any:





        '''simple docstring'''





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



      @torch.enable_grad()
      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			)	->			Dict:





        '''simple docstring'''





        a__:     int										=	latents.detach().requires_grad_()

        a__:     Any										=	self.scheduler.scale_model_input(lowerCamelCase__    ,			lowerCamelCase__)

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

        if isinstance(self.scheduler    ,			(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
          a__:     Tuple										=	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__:     Union[str, Any]										=	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

          a__:     Tuple										=	torch.sqrt(lowerCamelCase__)
          a__:     Tuple										=	pred_original_sample * (fac) + latents * (1 - fac)
        elif isinstance(self.scheduler    ,			lowerCamelCase__):
          a__:     Optional[int]										=	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__:     List[str]										=	1 / 0.18215 * sample
        a__:     Dict										=	self.vae.decode(lowerCamelCase__).sample
        a__:     List[str]										=	(image / 2 + 0.5).clamp(0    ,			1)

        a__:     List[Any]										=	transforms.Resize(self.feature_extractor_size)(lowerCamelCase__)
        a__:     Optional[Any]										=	self.normalize(lowerCamelCase__).to(latents.dtype)

        a__:     int										=	self.clip_model.get_image_features(lowerCamelCase__)
        a__:     str										=	image_embeddings_clip / image_embeddings_clip.norm(p=2    ,			dim=-1    ,			keepdim=lowerCamelCase__)

        a__:     List[Any]										=	spherical_dist_loss(lowerCamelCase__    ,			lowerCamelCase__).mean() * clip_guidance_scale

        a__:     int										=	-torch.autograd.grad(lowerCamelCase__    ,			lowerCamelCase__)[0]

        if isinstance(self.scheduler    ,			lowerCamelCase__):
          a__:     int										=	latents.detach() + grads * (sigma**2)
          a__:     Tuple										=	noise_pred_original
        else:
          a__:     Optional[Any]										=	noise_pred_original - torch.sqrt(lowerCamelCase__) * grads
        return noise_pred, latents



      @torch.no_grad()
      def __call__(						self    ,			lowercase    ,			lowercase    ,			lowercase = None    ,			lowercase = None    ,			lowercase = 5_12    ,			lowercase = 5_12    ,			lowercase = 0.6    ,			lowercase = 50    ,			lowercase = 7.5    ,			lowercase = 1    ,			lowercase = 0.0    ,			lowercase = 1_00    ,			lowercase = None    ,			lowercase = "pil"    ,			lowercase = True    ,			lowercase = 0.8    ,			lowercase = 0.1    ,			lowercase = 0.1    ,			)	->			List[Any]:





        '''simple docstring'''





        if isinstance(lowerCamelCase__    ,			lowerCamelCase__) and len(lowerCamelCase__) != batch_size:
          raise ValueError(f'You have passed {batch_size} batch_size, but only {len(lowerCamelCase__)} 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(lowerCamelCase__    ,			torch.Generator) and batch_size > 1:
          a__:     Union[str, Any]										=	[generator] + [None] * (batch_size - 1)

        a__:     List[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__:     List[Any]										=	""", """.join(lowerCamelCase__)
        # generate prompts with coca model if prompt is None
        if content_prompt is None:
          if len(lowerCamelCase__):
            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__:     Any										=	self.get_image_description(lowerCamelCase__)
        if style_prompt is None:
          if len(lowerCamelCase__):
            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__:     Optional[Any]										=	self.get_image_description(lowerCamelCase__)

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

        a__:     Union[str, Any]										=	self.tokenizer(
            lowerCamelCase__    ,			padding='max_length'    ,			max_length=self.tokenizer.model_max_length    ,			truncation=lowerCamelCase__    ,			return_tensors='pt'    ,			)
        a__:     str										=	self.text_encoder(style_text_input.input_ids.to(self.device))[0]

        a__:     List[Any]										=	slerp(lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__)

        # duplicate text embeddings for each generation per prompt
        a__:     Optional[Any]										=	text_embeddings.repeat_interleave(lowerCamelCase__    ,			dim=0)

        # set timesteps
        a__:     Any										=	"""offset""" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
        a__:     Optional[int]										=	{}
        if accepts_offset:
          a__:     int										=	1

        self.scheduler.set_timesteps(lowerCamelCase__    ,			**lowerCamelCase__)
        # 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__:     int										=	self.get_timesteps(lowerCamelCase__    ,			lowerCamelCase__    ,			self.device)
        a__:     Union[str, Any]										=	timesteps[:1].repeat(lowerCamelCase__)

        # Preprocess image
        a__:     int										=	preprocess(lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__)
        a__:     Optional[Any]										=	self.prepare_latents(
            lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__    ,			text_embeddings.dtype    ,			self.device    ,			lowerCamelCase__)

        a__:     List[Any]										=	preprocess(lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__)
        a__:     List[str]										=	self.prepare_latents(
            lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__    ,			text_embeddings.dtype    ,			self.device    ,			lowerCamelCase__)

        a__:     str										=	slerp(lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__)

        if clip_guidance_scale > 0:
          a__:     str										=	self.get_clip_image_embeddings(lowerCamelCase__    ,			lowerCamelCase__)
          a__:     Dict										=	self.get_clip_image_embeddings(lowerCamelCase__    ,			lowerCamelCase__)
          a__:     List[str]										=	slerp(
              lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__)

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

        # check if the scheduler accepts generator
        a__:     List[str]										=	"""generator""" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
          a__:     List[str]										=	generator

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

            # predict the noise residual
            a__:     List[Any]										=	self.unet(lowerCamelCase__    ,			lowerCamelCase__    ,			encoder_hidden_states=lowerCamelCase__).sample

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

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

            # compute the previous noisy sample x_t -> x_t-1
            a__:     Union[str, Any]										=	self.scheduler.step(lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__    ,			**lowerCamelCase__).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
        a__:     List[str]										=	1 / 0.18215 * latents
        a__:     Optional[int]										=	self.vae.decode(lowerCamelCase__).sample

        a__:     Tuple										=	(image / 2 + 0.5).clamp(0    ,			1)
        a__:     Union[str, Any]										=	image.cpu().permute(0    ,			2    ,			3    ,			1).numpy()

        if output_type == "pil":
          a__:     Union[str, Any]										=	self.numpy_to_pil(lowerCamelCase__)

        if not return_dict:
          return (image, None)

        return StableDiffusionPipelineOutput(images=lowerCamelCase__    ,			nsfw_content_detected=lowerCamelCase__)







import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
     from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
     from tensorflow.keras.optimizers import Adam






class SCREAMING_SNAKE_CASE__     (		tf.keras.optimizers.schedules.LearningRateSchedule    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = 1.0,		lowerCamelCase__ = None,		):
        super().__init__()
        A :       Union[str, Any]          =   initial_learning_rate
        A :       List[Any]          =   warmup_steps
        A :       int          =   power
        A :       Optional[int]          =   decay_schedule_fn
        A :       int          =   name






    def __call__(       self,		lowerCamelCase__		):
        with tf.name_scope(self.name or """WarmUp"""		) as name:
            # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
            # learning rate will be `global_step/num_warmup_steps * init_lr`.
            A :       str          =   tf.cast(lowerCamelCase__,		tf.floataa		)
            A :       List[Any]          =   tf.cast(self.warmup_steps,		tf.floataa		)
            A :       Dict          =   global_step_float / warmup_steps_float
            A :       Union[str, Any]          =   self.initial_learning_rate * tf.math.pow(lowerCamelCase__,		self.power		)
            return tf.cond(
                global_step_float < warmup_steps_float,		lambda: warmup_learning_rate,		lambda: self.decay_schedule_fn(step - self.warmup_steps		),		name=lowerCamelCase__,		)






    def 			_lowerCAmelCase					(       self		):
        return {
            "initial_learning_rate": self.initial_learning_rate,
            "decay_schedule_fn": self.decay_schedule_fn,
            "warmup_steps": self.warmup_steps,
            "power": self.power,
            "name": self.name,
        }




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 0.9      ,     _lowerCAmelCase = 0.999      ,     _lowerCAmelCase = 1e-8      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 1.0      ,     _lowerCAmelCase = None      ,     )       ->  Union[str, Any]:



    """simple docstring"""






    A :       Optional[int]          =   tf.keras.optimizers.schedules.PolynomialDecay(
        initial_learning_rate=_lowerCAmelCase      ,     decay_steps=num_train_steps - num_warmup_steps      ,     end_learning_rate=init_lr * min_lr_ratio      ,     power=_lowerCAmelCase      ,     )
    if num_warmup_steps:
        A :       Dict          =   WarmUp(
            initial_learning_rate=_lowerCAmelCase      ,     decay_schedule_fn=_lowerCAmelCase      ,     warmup_steps=_lowerCAmelCase      ,     )
    if weight_decay_rate > 0.0:
        A :       str          =   AdamWeightDecay(
            learning_rate=_lowerCAmelCase      ,     weight_decay_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]      ,     include_in_weight_decay=_lowerCAmelCase      ,     )
    else:
        A :       Optional[int]          =   tf.keras.optimizers.Adam(
            learning_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     )
    # We return the optimizer and the LR scheduler in order to better track the
    # evolution of the LR independently of the optimizer.
    return optimizer, lr_schedule






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 0.001,		lowerCamelCase__ = 0.9,		lowerCamelCase__ = 0.999,		lowerCamelCase__ = 1e-7,		lowerCamelCase__ = False,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = "AdamWeightDecay",		**lowerCamelCase__,		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)
        A :       int          =   weight_decay_rate
        A :       Any          =   include_in_weight_decay
        A :       Dict          =   exclude_from_weight_decay






    @classmethod
    def 			_lowerCAmelCase					(       cls,		lowerCamelCase__		):
        A :       Tuple          =   {"""WarmUp""": WarmUp}
        return super(lowerCamelCase__,		cls		).from_config(lowerCamelCase__,		custom_objects=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        super(lowerCamelCase__,		self		)._prepare_local(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        A :       List[str]          =   tf.constant(
            self.weight_decay_rate,		name="""adam_weight_decay_rate"""		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Any          =   self._do_use_weight_decay(var.name		)
        if do_decay:
            return var.assign_sub(
                learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["""weight_decay_rate"""],		use_locking=self._use_locking,		)
        return tf.no_op()






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None,		**lowerCamelCase__		):
        A     ,							A :       Dict          =   list(zip(*lowerCamelCase__		)		)
        return super(lowerCamelCase__,		self		).apply_gradients(zip(lowerCamelCase__,		lowerCamelCase__		),		name=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if apply_state is None:
            return self._decayed_lr_t[var_dtype], {}

        A :       Union[str, Any]          =   apply_state or {}
        A :       Optional[int]          =   apply_state.get((var_device, var_dtype)		)
        if coefficients is None:
            A :       Dict          =   self._fallback_apply_state(lowerCamelCase__,		lowerCamelCase__		)
            A :       List[Any]          =   coefficients

        return coefficients["lr_t"], {"apply_state": apply_state}






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       str          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Any          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_dense(lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       Tuple          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Optional[Any]          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_sparse(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   super().get_config()
        config.update({"""weight_decay_rate""": self.weight_decay_rate}		)
        return config






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if self.weight_decay_rate == 0:
            return False

        if self._include_in_weight_decay:
            for r in self._include_in_weight_decay:
                if re.search(lowerCamelCase__,		lowerCamelCase__		) is not None:
                    return True

        if self._exclude_from_weight_decay:
            for r in self._exclude_from_weight_decay:
                if re.search(lowerCamelCase__,		lowerCamelCase__		) is not None:
                    return False
        return True






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self		):
        A :       List[str]          =   []
        A :       List[str]          =   None






    @property
    def 			_lowerCAmelCase					(       self		):
        if self._accum_steps is None:
            A :       str          =   tf.Variable(
                tf.constant(0,		dtype=tf.intaa		),		trainable=lowerCamelCase__,		synchronization=tf.VariableSynchronization.ON_READ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,		)

        return self._accum_steps.value()






    @property
    def 			_lowerCAmelCase					(       self		):
        if not self._gradients:
            raise ValueError("""The accumulator should be called first to initialize the gradients"""		)
        return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






    def __call__(       self,		lowerCamelCase__		):
        if not self._gradients:
            A :       int          =   self.step  # Create the step variable.
            self._gradients.extend(
                [
                    tf.Variable(
                        tf.zeros_like(lowerCamelCase__		),		trainable=lowerCamelCase__,		synchronization=tf.VariableSynchronization.ON_READ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,		)
                    if gradient is not None
                    else gradient
                    for gradient in gradients
                ]		)
        if len(lowerCamelCase__		) != len(self._gradients		):
            raise ValueError(f'''Expected {len(self._gradients		)} gradients, but got {len(lowerCamelCase__		)}'''		)

        for accum_gradient, gradient in zip(self._gradients,		lowerCamelCase__		):
            if accum_gradient is not None and gradient is not None:
                accum_gradient.assign_add(lowerCamelCase__		)

        self._accum_steps.assign_add(1		)






    def 			_lowerCAmelCase					(       self		):
        if not self._gradients:
            return
        self._accum_steps.assign(0		)
        for gradient in self._gradients:
            if gradient is not None:
                gradient.assign(tf.zeros_like(lowerCamelCase__		)		)


