

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    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






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


_UpperCAmelCase						:  Any									=			logging.getLogger(__name__)





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							if metric == "rouge2":
														snake_case_     							=       '{val_avg_rouge2:.4f}-{step_count}'
							elif metric == "bleu":
														snake_case_     							=       '{val_avg_bleu:.4f}-{step_count}'
							elif metric == "em":
														snake_case_     							=       '{val_avg_em:.4f}-{step_count}'
							elif metric == "loss":
														snake_case_     							=       '{val_avg_loss:.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.' )

							snake_case_     							=       ModelCheckpoint(
							    dirpath=__snake_case  ,				filename=__snake_case  ,				monitor=F'''val_{metric}'''  ,				mode='max'  ,				save_top_k=1  ,				every_n_epochs=1  ,				)

							return checkpoint_callback





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							return EarlyStopping(
							    monitor=F'''val_{metric}'''  ,				mode='min' if 'loss' in metric else 'max'  ,				patience=__snake_case  ,				verbose=__snake_case  ,				)







class 					lowercase							(			pl.Callback     ):




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_     							=       {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 ,  snake_case ,  snake_case ,  snake_case ,  snake_case=True			):
											logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''			)
											snake_case_     							=       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
											snake_case_     							=       Path(pl_module.hparams.output_dir			)
											if type_path == "test":
																		snake_case_     							=       od / 'test_results.txt'
																		snake_case_     							=       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.
																		snake_case_     							=       od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
																		snake_case_     							=       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
																									snake_case_     							=       metrics[key]
																									if isinstance(UpperCamelCase__ ,  torch.Tensor			):
																																snake_case_     							=       val.item()
																									snake_case_     							=       F'''{key}: {val:.6f}\n'''
																									writer.write(UpperCamelCase__			)

											if not save_generations:
																		return

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




				@rank_zero_only
				def        a				(   self ,  snake_case ,  snake_case			):
											try:
																		snake_case_     							=       pl_module.model.model.num_parameters()
											except AttributeError:
																		snake_case_     							=       pl_module.model.num_parameters()

											snake_case_     							=       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 ,  snake_case ,  snake_case			):
											save_json(pl_module.metrics ,  pl_module.metrics_save_path			)
											return self._write_logs(UpperCamelCase__ ,  UpperCamelCase__ ,  'test'			)




				@rank_zero_only
				def        a				(   self ,  snake_case ,  snake_case			):
											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'''






from typing import TYPE_CHECKING

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


_UpperCAmelCase    :     str         =       {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Tuple         =       ['''VisionEncoderDecoderModel''']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''TFVisionEncoderDecoderModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Any         =       ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
   from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
   import sys

   _UpperCAmelCase    :     List[str]         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



