"""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 import save_json



def     a_		(   lowerCamelCase			):
				UpperCAmelCase__			 =				filter(lambda lowerCamelCase			: p.requires_grad   ,     model.parameters()			)
				UpperCAmelCase__			 =				sum([np.prod(p.size()			) for p in model_parameters]			)
				return params


lowerCAmelCase__    :    Optional[Any]			=      logging.getLogger(__name__)
class 							snake_case			(					pl.Callback							):




					"""simple docstring"""

					def 						__lowerCAmelCase		(							self  :      List[str]       ,lowerCamelCase__  :      Tuple       ,lowerCamelCase__  :      List[Any]     ):
									UpperCAmelCase__			 =				{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 						__lowerCAmelCase		(							self  :      Any       ,lowerCamelCase__  :      pl.Trainer       ,lowerCamelCase__  :      pl.LightningModule       ,lowerCamelCase__  :      str       ,lowerCamelCase__  :      List[str]=True     ):
									logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''     )
									UpperCAmelCase__			 =				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__			 =				Path(pl_module.hparams.output_dir     )
									if type_path == "test":
													UpperCAmelCase__			 =				od / 'test_results.txt'
													UpperCAmelCase__			 =				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__			 =				od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
													UpperCAmelCase__			 =				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__			 =				metrics[key]
																	if isinstance(lowerCamelCase__       ,torch.Tensor     ):
																					UpperCAmelCase__			 =				val.item()
																	UpperCAmelCase__			 =				f'''{key}: {val:.6f}\n'''
																	writer.write(lowerCamelCase__     )

									if not save_generations:
													return

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







					@rank_zero_only
					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      Optional[int]       ,lowerCamelCase__  :      Dict     ):
									try:
													UpperCAmelCase__			 =				pl_module.model.model.num_parameters()
									except AttributeError:
													UpperCAmelCase__			 =				pl_module.model.num_parameters()

									UpperCAmelCase__			 =				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 						__lowerCAmelCase		(							self  :      Union[str, Any]       ,lowerCamelCase__  :      pl.Trainer       ,lowerCamelCase__  :      pl.LightningModule     ):
									save_json(pl_module.metrics       ,pl_module.metrics_save_path     )
									return self._write_logs(lowerCamelCase__       ,lowerCamelCase__       ,'test'     )







					@rank_zero_only
					def 						__lowerCAmelCase		(							self  :      Tuple       ,lowerCamelCase__  :      pl.Trainer       ,lowerCamelCase__  :      Optional[int]     ):
									save_json(pl_module.metrics       ,pl_module.metrics_save_path     )
									# Uncommenting this will save val generations
									# return self._write_logs(trainer, pl_module, "valid")






def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase=1   ,     lowerCamelCase=False			):
				if metric == "rouge2":
								UpperCAmelCase__			 =				'{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								UpperCAmelCase__			 =				'{val_avg_bleu:.4f}-{step_count}'
				elif metric == "loss":
								UpperCAmelCase__			 =				'{val_avg_loss:.4f}-{step_count}'
				else:
								raise NotImplementedError(
								    f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
								    ' this function.'			)

				UpperCAmelCase__			 =				ModelCheckpoint(
				    dirpath=lowerCamelCase   ,     filename=lowerCamelCase   ,     monitor=f'''val_{metric}'''   ,     mode='min' if 'loss' in metric else 'max'   ,     save_top_k=lowerCamelCase   ,     )
				return checkpoint_callback







def     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				return EarlyStopping(
				    monitor=f'''val_{metric}'''   ,     mode='min' if 'loss' in metric else 'max'   ,     patience=lowerCamelCase   ,     verbose=lowerCamelCase   ,     )




"""simple docstring"""






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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCAmelCase__    :    Tuple			=      logging.get_logger(__name__)
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					["pixel_values"]







					def __init__(							self  :      str       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Dict[str, int] = None       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Dict[str, int] = None       ,lowerCamelCase__  :      PILImageResampling = PILImageResampling.BICUBIC       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Union[int, float] = 1 / 255       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,**lowerCamelCase__  :      List[Any]       ,):
									super().__init__(**lowerCamelCase__     )
									UpperCAmelCase__			 =				crop_size if crop_size is not None else {'height': 256, 'width': 256}
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )
									UpperCAmelCase__			 =				size if size is not None else {'height': 224, 'width': 224}
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )

									UpperCAmelCase__			 =				do_center_crop
									UpperCAmelCase__			 =				crop_size
									UpperCAmelCase__			 =				do_resize
									UpperCAmelCase__			 =				size
									UpperCAmelCase__			 =				resample
									UpperCAmelCase__			 =				do_rescale
									UpperCAmelCase__			 =				rescale_factor
									UpperCAmelCase__			 =				do_normalize
									UpperCAmelCase__			 =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									UpperCAmelCase__			 =				image_std if image_std is not None else IMAGENET_DEFAULT_STD







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Dict[str, int]       ,lowerCamelCase__  :      Optional[int] = None       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      List[str]       ,):
									UpperCAmelCase__			 =				self.size if size is None else size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )

									UpperCAmelCase__ ,      UpperCAmelCase__			 =				get_image_size(lowerCamelCase__     )
									UpperCAmelCase__			 =				min(lowerCamelCase__       ,lowerCamelCase__     )
									UpperCAmelCase__			 =				(size['height'] / crop_size['height']) * min_dim
									UpperCAmelCase__			 =				(size['width'] / crop_size['width']) * min_dim
									return center_crop(lowerCamelCase__       ,size=(cropped_height, cropped_width)       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Any       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Dict[str, int]       ,lowerCamelCase__  :      PILImageResampling = PIL.Image.BICUBIC       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      Any       ,):
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''     )
									return resize(
									    lowerCamelCase__       ,size=(size['height'], size['width'])       ,resample=lowerCamelCase__       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Union[int, float]       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      int       ,):
									return rescale(lowerCamelCase__       ,scale=lowerCamelCase__       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      int       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Union[float, List[float]]       ,lowerCamelCase__  :      Union[float, List[float]]       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      List[Any]       ,):
									return normalize(lowerCamelCase__       ,mean=lowerCamelCase__       ,std=lowerCamelCase__       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Optional[Any]       ,lowerCamelCase__  :      ImageInput       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Dict[str, int]] = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Dict[str, int]] = None       ,lowerCamelCase__  :      PILImageResampling = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[float] = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[str, TensorType]] = None       ,lowerCamelCase__  :      ChannelDimension = ChannelDimension.FIRST       ,**lowerCamelCase__  :      Optional[int]       ,):
									UpperCAmelCase__			 =				do_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCAmelCase__			 =				crop_size if crop_size is not None else self.crop_size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )
									UpperCAmelCase__			 =				do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__			 =				size if size is not None else self.size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									UpperCAmelCase__			 =				resample if resample is not None else self.resample
									UpperCAmelCase__			 =				do_rescale if do_rescale is not None else self.do_rescale
									UpperCAmelCase__			 =				rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCAmelCase__			 =				do_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__			 =				image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__			 =				image_std if image_std is not None else self.image_std

									UpperCAmelCase__			 =				make_list_of_images(lowerCamelCase__     )

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

									if do_center_crop and crop_size is None:
													raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'     )

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

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

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

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

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

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

									UpperCAmelCase__			 =				[to_channel_dimension_format(lowerCamelCase__       ,lowerCamelCase__     ) for image in images]

									UpperCAmelCase__			 =				{'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__       ,tensor_type=lowerCamelCase__     )



