


import copy
import random

from transformers import CLIPTokenizer







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						def __init__(				self   ,						*__lowercase   ,						**__lowercase							)					->    Dict:
										super().__init__(*__lowercase   ,						**__lowercase							)
										lowerCAmelCase_ :      Union[str, Any]    				=				{}




						def 						lowercase_ (				self   ,						__lowercase   ,						*__lowercase   ,						**__lowercase							)					->    int:
										lowerCAmelCase_ :      Optional[Any]    				=				super().add_tokens(__lowercase   ,						*__lowercase   ,						**__lowercase							)
										if num_added_tokens == 0:
														raise ValueError(
														    f"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
														    ''' `placeholder_token` that is not already in the tokenizer.'''							)




						def 						lowercase_ (				self   ,						__lowercase   ,						*__lowercase   ,						__lowercase=1   ,						**__lowercase							)					->    Any:
										lowerCAmelCase_ :      Union[str, Any]    				=				[]
										if num_vec_per_token == 1:
														self.try_adding_tokens(__lowercase   ,						*__lowercase   ,						**__lowercase							)
														output.append(__lowercase							)
										else:
														lowerCAmelCase_ :      Any    				=				[]
														for i in range(__lowercase							):
																		lowerCAmelCase_ :      Union[str, Any]    				=				placeholder_token + f"""_{i}"""
																		self.try_adding_tokens(__lowercase   ,						*__lowercase   ,						**__lowercase							)
																		output.append(__lowercase							)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
										for token in self.token_map:
														if token in placeholder_token:
																		raise ValueError(
																		    f"""The tokenizer already has placeholder token {token} that can get confused with"""
																		    f""" {placeholder_token}keep placeholder tokens independent"""							)
										lowerCAmelCase_ :      Dict    				=				output




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase=False   ,						__lowercase=1.0							)					->    str:
										if isinstance(__lowercase   ,						__lowercase							):
														lowerCAmelCase_ :      str    				=				[]
														for i in range(len(__lowercase							)							):
																		output.append(self.replace_placeholder_tokens_in_text(text[i]   ,						vector_shuffle=__lowercase							)							)
														return output
										for placeholder_token in self.token_map:
														if placeholder_token in text:
																		lowerCAmelCase_ :      Optional[int]    				=				self.token_map[placeholder_token]
																		lowerCAmelCase_ :      int    				=				tokens[: 1 + int(len(__lowercase							) * prop_tokens_to_load							)]
																		if vector_shuffle:
																						lowerCAmelCase_ :      str    				=				copy.copy(__lowercase							)
																						random.shuffle(__lowercase							)
																		lowerCAmelCase_ :      Any    				=				text.replace(__lowercase   ,						''' '''.join(__lowercase							)							)
										return text




						def __call__(				self   ,						__lowercase   ,						*__lowercase   ,						__lowercase=False   ,						__lowercase=1.0   ,						**__lowercase							)					->    Any:
										return super().__call__(
										    self.replace_placeholder_tokens_in_text(
										        __lowercase   ,						vector_shuffle=__lowercase   ,						prop_tokens_to_load=__lowercase							)   ,						*__lowercase   ,						**__lowercase   ,						)




						def 						lowercase_ (				self   ,						__lowercase   ,						*__lowercase   ,						__lowercase=False   ,						__lowercase=1.0   ,						**__lowercase							)					->    Dict:
										return super().encode(
										    self.replace_placeholder_tokens_in_text(
										        __lowercase   ,						vector_shuffle=__lowercase   ,						prop_tokens_to_load=__lowercase							)   ,						*__lowercase   ,						**__lowercase   ,						)








# 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 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = True   ,						__lowercase = False							)					->    Tuple:
										lowerCAmelCase_ :      Optional[int]    				=				scheduler
										lowerCAmelCase_ :      Dict    				=				optimizers if isinstance(__lowercase   ,						(list, tuple)							) else [optimizers]
										lowerCAmelCase_ :      str    				=				split_batches
										lowerCAmelCase_ :      Any    				=				step_with_optimizer
										lowerCAmelCase_ :      Optional[Any]    				=				GradientState()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    Any:
										if not self.step_with_optimizer:
														# No link between scheduler and optimizer -> just step
														self.scheduler.step(*__lowercase   ,						**__lowercase							)
														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(*__lowercase   ,						**__lowercase							)
										else:
														# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
														# num_processes steps per training step
														lowerCAmelCase_ :      Optional[Any]    				=				AcceleratorState().num_processes
														for _ in range(__lowercase							):
																		# 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(*__lowercase   ,						**__lowercase							)
																		else:
																						self.scheduler.step(*__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										return self.scheduler.get_last_lr()




						def 						lowercase_ (				self							)					->    List[str]:
										return self.scheduler.state_dict()




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										self.scheduler.load_state_dict(__lowercase							)




						def 						lowercase_ (				self							)					->    Tuple:
										return self.scheduler.get_lr()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    int:
										return self.scheduler.print_lr(*__lowercase   ,						**__lowercase							)