





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
 import PIL







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


     __magic_name__   =			['pixel_values']




     def __init__(    self				,     snake_case_ = True				,     snake_case_ = None				,     snake_case_ = PILImageResampling.BICUBIC				,     snake_case_ = True				,     snake_case_ = None				,     snake_case_ = True				,     snake_case_ = 1 / 255				,     snake_case_ = True				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = True				,     **snake_case_				,     ):
         super().__init__(**snake_case_							)
         _A     =							size if size is not None else {'shortest_edge': 224}
         _A     =							get_size_dict(snake_case_				,     default_to_square=snake_case_							)
         _A     =							crop_size if crop_size is not None else {'height': 224, 'width': 224}
         _A     =							get_size_dict(snake_case_				,     default_to_square=snake_case_				,     param_name='crop_size'							)

         _A     =							do_resize
         _A     =							size
         _A     =							resample
         _A     =							do_center_crop
         _A     =							crop_size
         _A     =							do_rescale
         _A     =							rescale_factor
         _A     =							do_normalize
         _A     =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         _A     =							image_std if image_std is not None else OPENAI_CLIP_STD
         _A     =							do_convert_rgb




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = PILImageResampling.BICUBIC				,     snake_case_ = None				,     **snake_case_				,     ):
         _A     =							get_size_dict(snake_case_				,     default_to_square=snake_case_							)
         if "shortest_edge" not in size:
             raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"							)
         _A     =							get_resize_output_image_size(snake_case_				,     size=size['shortest_edge']				,     default_to_square=snake_case_							)
         return resize(snake_case_				,     size=snake_case_				,     resample=snake_case_				,     data_format=snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = None				,     **snake_case_				,     ):
         _A     =							get_size_dict(snake_case_							)
         if "height" not in size or "width" not in size:
             raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"							)
         return center_crop(snake_case_				,     size=(size['height'], size['width'])				,     data_format=snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = None				,     **snake_case_				,     ):
         return rescale(snake_case_				,     scale=snake_case_				,     data_format=snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_ = None				,     **snake_case_				,     ):
         return normalize(snake_case_				,     mean=snake_case_				,     std=snake_case_				,     data_format=snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = ChannelDimension.FIRST				,     **snake_case_				,     ):
         _A     =							do_resize if do_resize is not None else self.do_resize
         _A     =							size if size is not None else self.size
         _A     =							get_size_dict(snake_case_				,     param_name='size'				,     default_to_square=snake_case_							)
         _A     =							resample if resample is not None else self.resample
         _A     =							do_center_crop if do_center_crop is not None else self.do_center_crop
         _A     =							crop_size if crop_size is not None else self.crop_size
         _A     =							get_size_dict(snake_case_				,     param_name='crop_size'				,     default_to_square=snake_case_							)
         _A     =							do_rescale if do_rescale is not None else self.do_rescale
         _A     =							rescale_factor if rescale_factor is not None else self.rescale_factor
         _A     =							do_normalize if do_normalize is not None else self.do_normalize
         _A     =							image_mean if image_mean is not None else self.image_mean
         _A     =							image_std if image_std is not None else self.image_std
         _A     =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         _A     =							make_list_of_images(snake_case_							)

         if not valid_images(snake_case_							):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'							)

         if do_resize and size is None:
             raise ValueError('Size must be specified if do_resize is True.'							)

         if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'							)

         if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'							)

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'							)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
             _A     =							[convert_to_rgb(snake_case_							) for image in images]

         # All transformations expect numpy arrays.
         _A     =							[to_numpy_array(snake_case_							) for image in images]

         if do_resize:
             _A     =							[self.resize(image=snake_case_				,     size=snake_case_				,     resample=snake_case_							) for image in images]

         if do_center_crop:
             _A     =							[self.center_crop(image=snake_case_				,     size=snake_case_							) for image in images]

         if do_rescale:
             _A     =							[self.rescale(image=snake_case_				,     scale=snake_case_							) for image in images]

         if do_normalize:
             _A     =							[self.normalize(image=snake_case_				,     mean=snake_case_				,     std=snake_case_							) for image in images]

         _A     =							[to_channel_dimension_format(snake_case_				,     snake_case_							) for image in images]

         _A     =							{'pixel_values': images}
         return BatchFeature(data=snake_case_				,     tensor_type=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")





