



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):
							__lowerCamelCase		:		Tuple      =['image_processor', 'tokenizer']
							__lowerCamelCase		:		int      ='AutoImageProcessor'
							__lowerCamelCase		:		List[str]      ='AutoTokenizer'





							def __init__(				self						:						Tuple ,			__lowercase						:						Optional[int]=None ,			__lowercase						:						int=None ,			**__lowercase						:						List[Any]		):

											'''simple docstring'''
											__a					       =						None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead.""" ,			__lowercase ,			)
															__a					       =						kwargs.pop("""feature_extractor"""		)

											__a					       =						image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
															raise ValueError("""You have to specify an image_processor."""		)
											if tokenizer is None:
															raise ValueError("""You have to specify a tokenizer."""		)

											super().__init__(__lowercase ,			__lowercase		)
											__a					       =						self.image_processor





							def __call__(				self						:						List[Any] ,			__lowercase						:						str=None ,			__lowercase						:						Union[str, Any]=None ,			__lowercase						:						List[Any]=None ,			**__lowercase						:						str		):

											'''simple docstring'''

											if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""		)

											if text is not None:
															__a					       =						self.tokenizer(__lowercase ,			return_tensors=__lowercase ,			**__lowercase		)

											if images is not None:
															__a					       =						self.image_processor(__lowercase ,			return_tensors=__lowercase ,			**__lowercase		)

											if text is not None and images is not None:
															__a					       =						image_features.pixel_values
															return encoding
											elif text is not None:
															return encoding
											else:
															return BatchEncoding(data=dict(**__lowercase		) ,			tensor_type=__lowercase		)





							def   UpperCamelCase_   (				self						:						Union[str, Any] ,			*__lowercase						:						List[str] ,			**__lowercase						:						str		):

											'''simple docstring'''
											return self.tokenizer.batch_decode(*__lowercase ,			**__lowercase		)





							def   UpperCamelCase_   (				self						:						int ,			*__lowercase						:						int ,			**__lowercase						:						Optional[int]		):

											'''simple docstring'''
											return self.tokenizer.decode(*__lowercase ,			**__lowercase		)





							@property
							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											__a					       =						self.tokenizer.model_input_names
											__a					       =						self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





							@property
							def   UpperCamelCase_   (				self						:						str		):

											'''simple docstring'''
											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,			__lowercase ,			)
											return self.image_processor_class







							@property
							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											warnings.warn(
											    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,			__lowercase ,			)
											return self.image_processor





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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Dict      ):
				"""simple docstring"""







				__a					       =						filter(lambda _SCREAMING_SNAKE_CASE      : p.requires_grad			,       model.parameters()      )
				__a					       =						sum([np.prod(p.size()      ) for p in model_parameters]      )
				return params


lowerCamelCase__     =	logging.getLogger(__name__)






class 				SCREAMING_SNAKE_CASE     (							pl.Callback   ):





							def   UpperCamelCase_   (				self						:						Dict ,			__lowercase						:						Dict ,			__lowercase						:						str		):

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





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule ,			__lowercase						:						str ,			__lowercase						:						Tuple=True		):

											'''simple docstring'''
											logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"		)
											__a					       =						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
											__a					       =						Path(pl_module.hparams.output_dir		)
											if type_path == "test":
															__a					       =						od / """test_results.txt"""
															__a					       =						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.
															__a					       =						od / F"{type_path}_results/{trainer.global_step:05d}.txt"
															__a					       =						od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
															results_file.parent.mkdir(exist_ok=__lowercase		)
															generations_file.parent.mkdir(exist_ok=__lowercase		)
											with open(__lowercase ,			"""a+"""		) as writer:
															for key in sorted(__lowercase		):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__a					       =						metrics[key]
																			if isinstance(__lowercase ,			torch.Tensor		):
																							__a					       =						val.item()
																			__a					       =						F"{key}: {val:.6f}\n"
																			writer.write(__lowercase		)

											if not save_generations:
															return

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





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											try:
															__a					       =						pl_module.model.model.num_parameters()
											except AttributeError:
															__a					       =						pl_module.model.num_parameters()

											__a					       =						count_trainable_parameters(__lowercase		)
											# 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   UpperCamelCase_   (				self						:						Union[str, Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule		):

											'''simple docstring'''
											save_json(pl_module.metrics ,			pl_module.metrics_save_path		)
											return self._write_logs(__lowercase ,			__lowercase ,			"""test"""		)







							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						Dict		):

											'''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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[int]			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     Tuple=1			,       _SCREAMING_SNAKE_CASE    :     List[str]=False      ):
				"""simple docstring"""







				if metric == "rouge2":
								__a					       =						"""{val_avg_rouge2:.4f}-{step_count}"""
				elif metric == "bleu":
								__a					       =						"""{val_avg_bleu:.4f}-{step_count}"""
				elif metric == "loss":
								__a					       =						"""{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."""      )

				__a					       =						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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				return EarlyStopping(
				    monitor=f"val_{metric}"			,       mode="""min""" if """loss""" in metric else """max"""			,       patience=_SCREAMING_SNAKE_CASE			,       verbose=_SCREAMING_SNAKE_CASE			,       )
