





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
 from ... import PreTrainedTokenizerBase, TensorType

__A							:			List[str]         =			logging.get_logger(__name__)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'vision-encoder-decoder'
     __magic_name__   =			True




     def __init__(    self				,     **snake_case_							):
         super().__init__(**snake_case_							)
         if "encoder" not in kwargs or "decoder" not in kwargs:
             raise ValueError(
                 F"A configuraton of type {self.model_type} cannot be instantiated because "
                 F"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"							)

         _A     =							kwargs.pop('encoder'							)
         _A     =							encoder_config.pop('model_type'							)
         _A     =							kwargs.pop('decoder'							)
         _A     =							decoder_config.pop('model_type'							)

         _A     =							AutoConfig.for_model(snake_case_				,     **snake_case_							)
         _A     =							AutoConfig.for_model(snake_case_				,     **snake_case_							)
         _A     =							True




     @classmethod
     def        lowerCAmelCase__		(    cls				,     snake_case_				,     snake_case_				,     **snake_case_							):
         logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'							)
         _A     =							True
         _A     =							True

         return cls(encoder=encoder_config.to_dict()				,     decoder=decoder_config.to_dict()				,     **snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							copy.deepcopy(self.__dict__							)
         _A     =							self.encoder.to_dict()
         _A     =							self.decoder.to_dict()
         _A     =							self.__class__.model_type
         return output







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			version.parse('1.11'	)




     @property
     def        lowerCAmelCase__		(    self							):
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]							)




     @property
     def        lowerCAmelCase__		(    self							):
         return 1E-4




     @property
     def        lowerCAmelCase__		(    self							):
         return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}							)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         _A     =							OrderedDict()
         _A     =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
         _A     =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
         _A     =							{0: 'batch', 1: 'encoder_sequence'}

         return common_inputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = -1				,     snake_case_ = -1				,     snake_case_ = False				,     snake_case_ = None				,     ):
         import torch

         _A     =							OrderedDict()

         _A     =							super().generate_dummy_inputs(
             snake_case_				,     batch_size=snake_case_				,     seq_length=snake_case_				,     is_pair=snake_case_				,     framework=snake_case_							)

         _A,			_A     =							dummy_input['input_ids'].shape
         _A     =							(batch, encoder_sequence, self._config.encoder_hidden_size)
         _A     =							dummy_input.pop('input_ids'							)
         _A     =							dummy_input.pop('attention_mask'							)
         _A     =							torch.zeros(snake_case_							)

         return common_inputs







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         pass




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         return VisionEncoderDecoderEncoderOnnxConfig(snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = "default"							):
         _A     =							encoder_config.hidden_size
         return VisionEncoderDecoderDecoderOnnxConfig(snake_case_				,     snake_case_							)












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(       _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """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


__A							:			Union[str, Any]         =			logging.getLogger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




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

    _A     =							ModelCheckpoint(
        dirpath=_SCREAMING_SNAKE_CASE							,     filename=_SCREAMING_SNAKE_CASE							,     monitor=F"val_{metric}"							,     mode='max'							,     save_top_k=1							,     every_n_epochs=1							,     )

    return checkpoint_callback





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




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







class      lowerCamelCase(						pl.Callback	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							{F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
         pl_module.logger.log_metrics(snake_case_							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_=True							):
         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=snake_case_							)
             generations_file.parent.mkdir(exist_ok=snake_case_							)
         with open(snake_case_				,     'a+'							) as writer:
             for key in sorted(snake_case_							):
                 if key in ["log", "progress_bar", "preds"]:
                     continue
                 _A     =							metrics[key]
                 if isinstance(snake_case_				,     torch.Tensor							):
                     _A     =							val.item()
                 _A     =							F"{key}: {val:.6f}\n"
                 writer.write(snake_case_							)

         if not save_generations:
             return

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




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

         _A     =							count_trainable_parameters(snake_case_							)
         # 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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         return self._write_logs(snake_case_				,     snake_case_				,     'test'							)




     @rank_zero_only
     def        lowerCAmelCase__		(    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")





