



'''simple docstring'''






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_rag import save_json


def 					snake_case_	(   _lowerCAmelCase    :		Any    ) ->  List[str]:
							UpperCAmelCase					:			Optional[int]									=						filter(lambda _lowerCAmelCase    : p.requires_grad  , model.parameters()    )
							UpperCAmelCase					:			Optional[Any]									=						sum([np.prod(p.size()    ) for p in model_parameters]    )
							return params


UpperCamelCase__:  Optional[Any]				      =     logging.getLogger(__name__)


def 					snake_case_	(   _lowerCAmelCase    :		str  , _lowerCAmelCase    :		str    ) ->  Optional[Any]:
							if metric == "rouge2":
														UpperCAmelCase					:			int									=						"{val_avg_rouge2:.4f}-{step_count}"
							elif metric == "bleu":
														UpperCAmelCase					:			Optional[Any]									=						"{val_avg_bleu:.4f}-{step_count}"
							elif metric == "em":
														UpperCAmelCase					:			Tuple									=						"{val_avg_em:.4f}-{step_count}"
							else:
														raise NotImplementedError(
														    f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
														    ''' function.'''    )

							UpperCAmelCase					:			Dict									=						ModelCheckpoint(
							    dirpath=A__  , filename=A__  , monitor=f"""val_{metric}"""  , mode='''max'''  , save_top_k=3  , every_n_epochs=1  , )
							return checkpoint_callback


def 					snake_case_	(   _lowerCAmelCase    :		List[Any]  , _lowerCAmelCase    :		Union[str, Any]    ) ->  Optional[Any]:
							return EarlyStopping(
							    monitor=f"""val_{metric}"""  , mode='''min''' if '''loss''' in metric else '''max'''  , patience=A__  , verbose=A__  , )




class 							SCREAMING_SNAKE_CASE(				pl.Callback     ):







	"""simple docstring"""






	def 	A      (		self     :	Tuple						,     __snake_case     :	Dict						,     __snake_case     :	Dict						)     ->						str:
								UpperCAmelCase					:			Dict									=						{F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
								pl_module.logger.log_metrics(lowerCAmelCase_						)







	@rank_zero_only
	def 	A      (		self     :	Dict						,     __snake_case     :	pl.Trainer						,     __snake_case     :	pl.LightningModule						,     __snake_case     :	str						,     __snake_case     :	Dict=True						)     ->						None:
								logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""						)
								UpperCAmelCase					:			Optional[Any]									=						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
								UpperCAmelCase					:			int									=						Path(pl_module.hparams.output_dir						)
								if type_path == "test":
															UpperCAmelCase					:			List[str]									=						od / "test_results.txt"
															UpperCAmelCase					:			Any									=						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.
															UpperCAmelCase					:			Union[str, Any]									=						od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
															UpperCAmelCase					:			List[str]									=						od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
															results_file.parent.mkdir(exist_ok=lowerCAmelCase_						)
															generations_file.parent.mkdir(exist_ok=lowerCAmelCase_						)
								with open(lowerCAmelCase_						,     '''a+'''						) as writer:
															for key in sorted(lowerCAmelCase_						):
																						if key in ["log", "progress_bar", "preds"]:
																													continue
																						UpperCAmelCase					:			Any									=						metrics[key]
																						if isinstance(lowerCAmelCase_						,     torch.Tensor						):
																													UpperCAmelCase					:			Optional[Any]									=						val.item()
																						UpperCAmelCase					:			str									=						F"""{key}: {val:.6f}\n"""
																						writer.write(lowerCAmelCase_						)

								if not save_generations:
															return

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







	@rank_zero_only
	def 	A      (		self     :	Tuple						,     __snake_case     :	Any						,     __snake_case     :	List[str]						)     ->						Dict:
								try:
															UpperCAmelCase					:			Optional[Any]									=						pl_module.model.model.num_parameters()
								except AttributeError:
															UpperCAmelCase					:			Optional[Any]									=						pl_module.model.num_parameters()

								UpperCAmelCase					:			Tuple									=						count_trainable_parameters(lowerCAmelCase_						)
								# 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     :	Tuple						,     __snake_case     :	pl.Trainer						,     __snake_case     :	pl.LightningModule						)     ->						Optional[int]:
								save_json(pl_module.metrics						,     pl_module.metrics_save_path						)
								return self._write_logs(lowerCAmelCase_						,     lowerCAmelCase_						,     '''test'''						)







	@rank_zero_only
	def 	A      (		self     :	int						,     __snake_case     :	pl.Trainer						,     __snake_case     :	List[Any]						)     ->						List[Any]:
								save_json(pl_module.metrics						,     pl_module.metrics_save_path						)
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")













"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class      UpperCamelCase_			(__A	):
	__magic_name__       =							['''image_processor''', '''tokenizer''']
	__magic_name__       =							'''FlavaImageProcessor'''
	__magic_name__       =							('''BertTokenizer''', '''BertTokenizerFast''')





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple=None      ,      lowerCAmelCase_   :					List[str]=None      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     List[Any]:
				UpperCAmelCase_	:					Any     	=   None
				if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."      ,      lowerCAmelCase_      ,      )
							UpperCAmelCase_	:					Tuple     	=   kwargs.pop("feature_extractor"		)

				UpperCAmelCase_	:					Optional[Any]     	=   image_processor if image_processor is not None else feature_extractor
				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`."		)

				super().__init__(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   self.image_processor





	def __call__( self   :					List[Any]      ,      lowerCAmelCase_   :					Optional[ImageInput] = None      ,      lowerCAmelCase_   :					Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Union[bool, str, PaddingStrategy] = False      ,      lowerCAmelCase_   :					Union[bool, str, TruncationStrategy] = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Optional[Union[str, TensorType]] = None      ,      **lowerCAmelCase_   :					Optional[int]      ,      )		->     List[Any]:

				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:
							UpperCAmelCase_	:					Union[str, Any]     	=   self.tokenizer(
							    text=lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      stride=lowerCAmelCase_      ,      pad_to_multiple_of=lowerCAmelCase_      ,      return_token_type_ids=lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      return_overflowing_tokens=lowerCAmelCase_      ,      return_special_tokens_mask=lowerCAmelCase_      ,      return_offsets_mapping=lowerCAmelCase_      ,      return_length=lowerCAmelCase_      ,      verbose=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )
				if images is not None:
							UpperCAmelCase_	:					Optional[Any]     	=   self.image_processor(
							    lowerCAmelCase_      ,      return_image_mask=lowerCAmelCase_      ,      return_codebook_pixels=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				if text is not None and images is not None:
							encoding.update(lowerCAmelCase_		)
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowerCAmelCase_		)      ,      tensor_type=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      *lowerCAmelCase_   :					int      ,      **lowerCAmelCase_   :					List[str]		)		->     Any:
				return self.tokenizer.batch_decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      *lowerCAmelCase_   :					List[str]      ,      **lowerCAmelCase_   :					Optional[int]		)		->     List[Any]:
				return self.tokenizer.decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   self.tokenizer.model_input_names
				UpperCAmelCase_	:					Any     	=   self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[Any]:
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor_class





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor



