
from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
							import torch
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		str            =				["image_processor", "tokenizer"]
							lowerCAmelCase__						:		Optional[Any]            =				"OneFormerImageProcessor"
							lowerCAmelCase__						:		str            =				("CLIPTokenizer", "CLIPTokenizerFast")





							def __init__(					self    :  Optional[int]	,					_UpperCAmelCase    :  List[Any]=None	,					_UpperCAmelCase    :  int=None	,					_UpperCAmelCase    :  int = 77	,					_UpperCAmelCase    :  int = 77	,					**_UpperCAmelCase    :  Any							)		->						int:



											"""simple docstring"""


											if image_processor is None:
															raise ValueError('You need to specify an `image_processor`.'							)
											if tokenizer is None:
															raise ValueError('You need to specify a `tokenizer`.'							)

											__lowercase								=   max_seq_length
											__lowercase								=   task_seq_length

											super().__init__(_UpperCAmelCase	,					_UpperCAmelCase							)





							def      a__					(					self    :  Any	,					_UpperCAmelCase    :  Dict=None	,					_UpperCAmelCase    :  Dict=77							)		->						Any:



											"""simple docstring"""


											if text_list is None:
															raise ValueError('tokens cannot be None.'							)

											__lowercase								=   self.tokenizer(_UpperCAmelCase	,					padding='max_length'	,					max_length=_UpperCAmelCase	,					truncation=_UpperCAmelCase							)

											__lowercase						,   __lowercase								=   tokens['attention_mask'], tokens['input_ids']

											__lowercase								=   []
											for attn_mask, input_id in zip(_UpperCAmelCase	,					_UpperCAmelCase							):
															__lowercase								=   torch.tensor(_UpperCAmelCase							) * torch.tensor(_UpperCAmelCase							)
															token_inputs.append(token.unsqueeze(0							)							)

											__lowercase								=   torch.cat(_UpperCAmelCase	,					dim=0							)
											return token_inputs





							def __call__(					self    :  List[Any]	,					_UpperCAmelCase    :  Dict=None	,					_UpperCAmelCase    :  Optional[Any]=None	,					_UpperCAmelCase    :  Optional[Any]=None	,					**_UpperCAmelCase    :  int							)		->						List[Any]:



											"""simple docstring"""


											if task_inputs is None:
															raise ValueError('You have to specify the task_input. Found None.'							)
											elif images is None:
															raise ValueError('You have to specify the image. Found None.'							)

											if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs							):
															raise ValueError('task_inputs must be semantic, instance, or panoptic.'							)

											__lowercase								=   self.image_processor(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					**_UpperCAmelCase							)

											if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
															__lowercase								=   [task_inputs]

											if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							) and all(isinstance(_UpperCAmelCase	,					_UpperCAmelCase							) for task_input in task_inputs							):
															__lowercase								=   []
															for task in task_inputs:
																			__lowercase								=   f"""the task is {task}"""
																			task_token_inputs.append(_UpperCAmelCase							)
															__lowercase								=   self._preprocess_text(_UpperCAmelCase	,					max_length=self.task_seq_length							)
											else:
															raise TypeError('Task Inputs should be a string or a list of strings.'							)

											if hasattr(_UpperCAmelCase	,					'text_inputs'							):
															__lowercase								=   encoded_inputs.text_inputs

															__lowercase								=   []
															for texts in texts_list:
																			__lowercase								=   self._preprocess_text(_UpperCAmelCase	,					max_length=self.max_seq_length							)
																			text_inputs.append(text_input_list.unsqueeze(0							)							)

															__lowercase								=   torch.cat(_UpperCAmelCase	,					dim=0							)

											return encoded_inputs





							def      a__					(					self    :  int	,					_UpperCAmelCase    :  str=None	,					_UpperCAmelCase    :  Dict=None	,					_UpperCAmelCase    :  Union[str, Any]=None	,					**_UpperCAmelCase    :  Optional[Any]							)		->						Union[str, Any]:



											"""simple docstring"""


											if task_inputs is None:
															raise ValueError('You have to specify the task_input. Found None.'							)
											elif images is None:
															raise ValueError('You have to specify the image. Found None.'							)

											if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs							):
															raise ValueError('task_inputs must be semantic, instance, or panoptic.'							)

											__lowercase								=   self.image_processor.encode_inputs(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					**_UpperCAmelCase							)

											if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
															__lowercase								=   [task_inputs]

											if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							) and all(isinstance(_UpperCAmelCase	,					_UpperCAmelCase							) for task_input in task_inputs							):
															__lowercase								=   []
															for task in task_inputs:
																			__lowercase								=   f"""the task is {task}"""
																			task_token_inputs.append(_UpperCAmelCase							)
															__lowercase								=   self._preprocess_text(_UpperCAmelCase	,					max_length=self.task_seq_length							)
											else:
															raise TypeError('Task Inputs should be a string or a list of strings.'							)

											if hasattr(_UpperCAmelCase	,					'text_inputs'							):
															__lowercase								=   encoded_inputs.text_inputs

															__lowercase								=   []
															for texts in texts_list:
																			__lowercase								=   self._preprocess_text(_UpperCAmelCase	,					max_length=self.max_seq_length							)
																			text_inputs.append(text_input_list.unsqueeze(0							)							)

															__lowercase								=   torch.cat(_UpperCAmelCase	,					dim=0							)

											return encoded_inputs





							def      a__					(					self    :  Any	,					*_UpperCAmelCase    :  Optional[int]	,					**_UpperCAmelCase    :  Tuple							)		->						Dict:



											"""simple docstring"""


											return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase	,					**_UpperCAmelCase							)





							def      a__					(					self    :  Any	,					*_UpperCAmelCase    :  Any	,					**_UpperCAmelCase    :  Optional[int]							)		->						str:



											"""simple docstring"""


											return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase	,					**_UpperCAmelCase							)





							def      a__					(					self    :  List[Any]	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  Optional[int]							)		->						int:



											"""simple docstring"""


											return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase	,					**_UpperCAmelCase							)







import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]     )	->		str:
				__lowercase								=   filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad       ,				model.parameters()     )
				__lowercase								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
				return params


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
class       A__  (		pl.Callback							):





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   {f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
											pl_module.logger.log_metrics(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  int=True							)		->						None:



											"""simple docstring"""


											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
											__lowercase								=   trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}							)
											# Log results
											__lowercase								=   Path(pl_module.hparams.output_dir							)
											if type_path == "test":
															__lowercase								=   od / 'test_results.txt'
															__lowercase								=   od / 'test_generations.txt'
											else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															__lowercase								=   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
															__lowercase								=   od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
															results_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
															generations_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
											with open(_UpperCAmelCase	,					'a+'							) as writer:
															for key in sorted(_UpperCAmelCase							):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__lowercase								=   metrics[key]
																			if isinstance(_UpperCAmelCase	,					torch.Tensor							):
																							__lowercase								=   val.item()
																			__lowercase								=   f"""{key}: {val:.6f}\n"""
																			writer.write(_UpperCAmelCase							)

											if not save_generations:
															return

											if "preds" in metrics:
															__lowercase								=   '\n'.join(metrics['preds']							)
															generations_file.open('w+'							).write(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


											try:
															__lowercase								=   pl_module.model.model.num_parameters()
											except AttributeError:
															__lowercase								=   pl_module.model.num_parameters()

											__lowercase								=   count_trainable_parameters(_UpperCAmelCase							)
											# mp stands for million parameters
											trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}							)





							@rank_zero_only
							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule							)		->						Optional[Any]:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											return self._write_logs(_UpperCAmelCase	,					_UpperCAmelCase	,					'test'							)





							@rank_zero_only
							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  Tuple							)		->						str:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: List[str]       ,				SCREAMING_SNAKE_CASE			: Union[str, Any]=1       ,				SCREAMING_SNAKE_CASE			: str=False     )	->		List[str]:
				if metric == "rouge2":
								__lowercase								=   '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								__lowercase								=   '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "loss":
								__lowercase								=   '{val_avg_loss:.4f}-{step_count}'
				else:
								raise NotImplementedError(
								    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
								    ' this function.'     )

				__lowercase								=   ModelCheckpoint(
				    dirpath=SCREAMING_SNAKE_CASE       ,				filename=SCREAMING_SNAKE_CASE       ,				monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				save_top_k=SCREAMING_SNAKE_CASE       ,				)
				return checkpoint_callback




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any       ,				SCREAMING_SNAKE_CASE			: int     )	->		Union[str, Any]:
				return EarlyStopping(
				    monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				patience=SCREAMING_SNAKE_CASE       ,				verbose=SCREAMING_SNAKE_CASE       ,				)





