# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')



class     _A   :






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = False	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      scheduler
       UpperCamelCase__										=      optimizers if isinstance(SCREAMING_SNAKE_CASE_   ,				(list, tuple)	) else [optimizers]
       UpperCamelCase__										=      split_batches
       UpperCamelCase__										=      step_with_optimizer
       UpperCamelCase__										=      GradientState()






  def 		_a					(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       if not self.step_with_optimizer:
            # No link between scheduler and optimizer -> just step
            self.scheduler.step(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
            return

       # Otherwise, first make sure the optimizer was stepped.
       if not self.gradient_state.sync_gradients:
            if self.gradient_state.adjust_scheduler:
                 self.scheduler._step_count += 1
            return

       for opt in self.optimizers:
            if opt.step_was_skipped:
                 return
       if self.split_batches:
            # Split batches -> the training dataloader batch size is not changed so one step per training step
            self.scheduler.step(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
       else:
            # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
            # num_processes steps per training step
            UpperCamelCase__										=      AcceleratorState().num_processes
            for _ in range(SCREAMING_SNAKE_CASE_	):
                 # Special case when using OneCycle and `drop_last` was not used
                 if hasattr(self.scheduler   ,				'''total_steps'''	):
                      if self.scheduler._step_count <= self.scheduler.total_steps:
                           self.scheduler.step(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
                 else:
                      self.scheduler.step(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       return self.scheduler.get_last_lr()






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       return self.scheduler.state_dict()






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Union[str, Any]:





       '''simple docstring'''





       self.scheduler.load_state_dict(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       return self.scheduler.get_lr()






  def 		_a					(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       return self.scheduler.print_lr(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__magic_name__								=logging.get_logger(__name__)  # pylint: disable=invalid-name



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=768	)		->      Dict:





       '''simple docstring'''





       super().__init__(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      proj_size

       UpperCamelCase__										=      CLIPVisionModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      PaintByExampleMapper(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      nn.LayerNorm(config.hidden_size	)
       UpperCamelCase__										=      nn.Linear(config.hidden_size   ,				self.proj_size	)

       # uncondition for scaling
       UpperCamelCase__										=      nn.Parameter(torch.randn((1, 1, self.proj_size)	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model(pixel_values=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      clip_output.pooler_output
       UpperCamelCase__										=      self.mapper(latent_states[:, None]	)
       UpperCamelCase__										=      self.final_layer_norm(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.proj_out(SCREAMING_SNAKE_CASE_	)
       if return_uncond_vector:
            return latent_states, self.uncond_vector

       return latent_states



class     _A   (    nn.Module							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       super().__init__()
       UpperCamelCase__										=      (config.num_hidden_layers + 1) // 5
       UpperCamelCase__										=      config.hidden_size
       UpperCamelCase__										=      1
       UpperCamelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				activation_fn='''gelu'''   ,				attention_bias=SCREAMING_SNAKE_CASE_	)
               for _ in range(SCREAMING_SNAKE_CASE_	)
           ]	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       for block in self.blocks:
            UpperCamelCase__										=      block(SCREAMING_SNAKE_CASE_	)

       return hidden_states



