


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 				UpperCamelCase_(      lowerCamelCase_ )    ->						Union[str, Any]:
    _lowercase       :							List[str]      		=		filter(lambda lowerCamelCase_ : p.requires_grad							, model.parameters() )
    _lowercase       :							Optional[int]      		=		sum([np.prod(p.size() ) for p in model_parameters] )
    return params


SCREAMING_SNAKE_CASE       :      Tuple												= logging.getLogger(__name__)



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Any:
    if metric == "rouge2":
        _lowercase       :							List[str]      		=		'{val_avg_rouge2:.4f}-{step_count}'
    elif metric == "bleu":
        _lowercase       :							Union[str, Any]      		=		'{val_avg_bleu:.4f}-{step_count}'
    elif metric == "em":
        _lowercase       :							Optional[Any]      		=		'{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.' )

    _lowercase       :							Any      		=		ModelCheckpoint(
        dirpath=lowerCamelCase_							, filename=lowerCamelCase_							, monitor=F'''val_{metric}'''							, mode='max'							, save_top_k=3							, every_n_epochs=1							, )
    return checkpoint_callback



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Dict:
    return EarlyStopping(
        monitor=F'''val_{metric}'''							, mode='min' if 'loss' in metric else 'max'							, patience=lowerCamelCase_							, verbose=lowerCamelCase_							, )






class _lowerCamelCase(						pl.Callback   ):
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							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 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=True)       ->   None:




           """simple docstring"""

           logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****''')
           _lowercase       :							str      		=		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       :							Optional[Any]      		=		Path(pl_module.hparams.output_dir)
           if type_path == "test":
               _lowercase       :							Optional[int]      		=		od / 'test_results.txt'
               _lowercase       :							Optional[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.
               _lowercase       :							Union[str, Any]      		=		od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
               _lowercase       :							int      		=		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
                   _lowercase       :							Optional[Any]      		=		metrics[key]
                   if isinstance(lowerCamelCase,      torch.Tensor):
                       _lowercase       :							Optional[int]      		=		val.item()
                   _lowercase       :							Union[str, Any]      		=		F'''{key}: {val:.6f}\n'''
                   writer.write(lowerCamelCase)

           if not save_generations:
               return

           if "preds" in metrics:
               _lowercase       :							Tuple      		=		'\n'.join(metrics['preds'])
               generations_file.open('w+').write(lowerCamelCase)
       @rank_zero_only
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           try:
               _lowercase       :							Optional[Any]      		=		pl_module.model.model.num_parameters()
           except AttributeError:
               _lowercase       :							List[Any]      		=		pl_module.model.num_parameters()

           _lowercase       :							Optional[int]      		=		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 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

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




       @rank_zero_only
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   Tuple:




           """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")





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase							:  Any   	=						TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase							:  Dict   	=						TaTokenizerFast

lowerCAmelCase							:  Optional[int]   	=						{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  int   	=						[
						    """MT5EncoderModel""",
						    """MT5ForConditionalGeneration""",
						    """MT5ForQuestionAnswering""",
						    """MT5Model""",
						    """MT5PreTrainedModel""",
						    """MT5Stack""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Tuple   	=						["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  List[str]   	=						["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase							:  Optional[Any]   	=						_LazyModule(
						    __name__,
						    globals()["""__file__"""],
						    _import_structure,
						    extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
						    module_spec=__spec__,
						)






