



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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


UpperCAmelCase__     = logging.get_logger(__name__)







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  ['''pixel_values''']



			def __init__(		self     :						Tuple				,	A     :						bool = True				,	A     :						Dict[str, int] = None				,	A     :						float = None				,	A     :						PILImageResampling = PILImageResampling.BILINEAR				,	A     :						bool = True				,	A     :						Union[int, float] = 1 / 2_55				,	A     :						bool = True				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	**A     :						Optional[int]				,	)     ->					None:



						"""simple docstring"""






						super().__init__(**A)
						_UpperCAmelCase	 =     size if size is not None else {'shortest_edge': 3_84}
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)

						_UpperCAmelCase	 =     do_resize
						_UpperCAmelCase	 =     size
						# Default value set here for backwards compatibility where the value in config is None
						_UpperCAmelCase	 =     crop_pct if crop_pct is not None else 2_24 / 2_56
						_UpperCAmelCase	 =     resample
						_UpperCAmelCase	 =     do_rescale
						_UpperCAmelCase	 =     rescale_factor
						_UpperCAmelCase	 =     do_normalize
						_UpperCAmelCase	 =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_UpperCAmelCase	 =     image_std if image_std is not None else IMAGENET_STANDARD_STD



			def 					_lowerCamelCase (		self     :						str				,	A     :						np.ndarray				,	A     :						Dict[str, int]				,	A     :						float				,	A     :						PILImageResampling = PILImageResampling.BICUBIC				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Any				,	)     ->					np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						if "shortest_edge" not in size:
									raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}")
						_UpperCAmelCase	 =     size['shortest_edge']

						if shortest_edge < 3_84:
									# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
									_UpperCAmelCase	 =     int(shortest_edge / crop_pct)
									_UpperCAmelCase	 =     get_resize_output_image_size(A				,	size=A				,	default_to_square=A)
									_UpperCAmelCase	 =     resize(image=A				,	size=A				,	resample=A				,	data_format=A				,	**A)
									# then crop to (shortest_edge, shortest_edge)
									return center_crop(image=A				,	size=(shortest_edge, shortest_edge)				,	data_format=A				,	**A)
						else:
									# warping (no cropping) when evaluated at 384 or larger
									return resize(
									    A				,	size=(shortest_edge, shortest_edge)				,	resample=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						np.ndarray				,	A     :						Union[int, float]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						int				,	)     ->					Optional[Any]:



						"""simple docstring"""






						return rescale(A				,	scale=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						np.ndarray				,	A     :						Union[float, List[float]]				,	A     :						Union[float, List[float]]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Optional[int]				,	)     ->					np.ndarray:



						"""simple docstring"""






						return normalize(A				,	mean=A				,	std=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						ImageInput				,	A     :						bool = None				,	A     :						Dict[str, int] = None				,	A     :						float = None				,	A     :						PILImageResampling = None				,	A     :						bool = None				,	A     :						float = None				,	A     :						bool = None				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[str, TensorType]] = None				,	A     :						ChannelDimension = ChannelDimension.FIRST				,	**A     :						Optional[Any]				,	)     ->					PIL.Image.Image:



						"""simple docstring"""






						_UpperCAmelCase	 =     do_resize if do_resize is not None else self.do_resize
						_UpperCAmelCase	 =     crop_pct if crop_pct is not None else self.crop_pct
						_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	 =     size if size is not None else self.size
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)

						_UpperCAmelCase	 =     make_list_of_images(A)

						if not valid_images(A):
									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 or resample is None:
									raise ValueError('Size and resample must be specified if do_resize is True.')

						if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
									raise ValueError('crop_pct must be specified if size < 384.')

						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.')

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

						if do_resize:
									_UpperCAmelCase	 =     [self.resize(image=A				,	size=A				,	crop_pct=A				,	resample=A) for image in images]

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

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

						_UpperCAmelCase	 =     [to_channel_dimension_format(A				,	A) for image in images]

						_UpperCAmelCase	 =     {'pixel_values': images}
						return BatchFeature(data=A				,	tensor_type=A)





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 							A			(  _UpperCAmelCase       :     List[Any]					)       ->				int:







			'''simple docstring'''
			_UpperCAmelCase	 =     filter(lambda _UpperCAmelCase					: p.requires_grad							,     model.parameters()					)
			_UpperCAmelCase	 =     sum([np.prod(p.size()					) for p in model_parameters]					)
			return params


UpperCAmelCase__     = logging.getLogger(__name__)





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				Any:







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

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

			return checkpoint_callback





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     int					)       ->				str:







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







class     __lowerCAmelCase					(    pl.Callback		):



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						List[Any]				,	A     :						int)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     {F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
						pl_module.logger.log_metrics(A)



			@rank_zero_only
			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						pl.Trainer				,	A     :						pl.LightningModule				,	A     :						str				,	A     :						int=True)     ->					None:



						"""simple docstring"""






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

						if not save_generations:
									return

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



			@rank_zero_only
			def 					_lowerCamelCase (		self     :						str				,	A     :						Optional[int]				,	A     :						List[str])     ->					Optional[Any]:



						"""simple docstring"""






						try:
									_UpperCAmelCase	 =     pl_module.model.model.num_parameters()
						except AttributeError:
									_UpperCAmelCase	 =     pl_module.model.num_parameters()

						_UpperCAmelCase	 =     count_trainable_parameters(A)
						# 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     :						Dict				,	A     :						pl.Trainer				,	A     :						pl.LightningModule)     ->					int:



						"""simple docstring"""






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



			@rank_zero_only
			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						pl.Trainer				,	A     :						str)     ->					Dict:



						"""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")
