


'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

lowerCamelCase__					=   logging.get_logger(__name__)






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		if isinstance(__lowerCAmelCase					,       (list, tuple)      ) and isinstance(videos[0]					,       (list, tuple)      ) and is_valid_image(videos[0][0]      ):
				return videos

		elif isinstance(__lowerCAmelCase					,       (list, tuple)      ) and is_valid_image(videos[0]      ):
				return [videos]

		elif is_valid_image(__lowerCAmelCase      ):
				return [[videos]]

		raise ValueError(F"""Could not make batched video from {videos}"""      )






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Tuple				      =   ["pixel_values"]




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



					'''simple docstring'''







					super().__init__(**lowerCamelCase__       )
					_UpperCAmelCase						:				str	    =					size if size is not None else {"shortest_edge": 2_56}
					_UpperCAmelCase						:				Tuple	    =					get_size_dict(lowerCamelCase__		,     default_to_square=lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
					_UpperCAmelCase						:				Dict	    =					get_size_dict(lowerCamelCase__		,     param_name="crop_size"       )

					_UpperCAmelCase						:				Tuple	    =					do_resize
					_UpperCAmelCase						:				Tuple	    =					size
					_UpperCAmelCase						:				List[Any]	    =					do_center_crop
					_UpperCAmelCase						:				Any	    =					crop_size
					_UpperCAmelCase						:				Dict	    =					resample
					_UpperCAmelCase						:				List[Any]	    =					do_rescale
					_UpperCAmelCase						:				Any	    =					rescale_factor
					_UpperCAmelCase						:				Dict	    =					offset
					_UpperCAmelCase						:				Any	    =					do_normalize
					_UpperCAmelCase						:				Optional[int]	    =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_UpperCAmelCase						:				int	    =					image_std if image_std is not None else IMAGENET_STANDARD_STD




			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   Dict[str, int]		,     lowerCamelCase__     :   PILImageResampling = PILImageResampling.BILINEAR		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None		,     **lowerCamelCase__     :   Optional[int]		,     )      ->np.ndarray:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					get_size_dict(lowerCamelCase__		,     default_to_square=lowerCamelCase__       )
					if "shortest_edge" in size:
							_UpperCAmelCase						:				Any	    =					get_resize_output_image_size(lowerCamelCase__		,     size["shortest_edge"]		,     default_to_square=lowerCamelCase__       )
					elif "height" in size and "width" in size:
							_UpperCAmelCase						:				List[str]	    =					(size["height"], size["width"])
					else:
							raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""       )
					return resize(lowerCamelCase__		,     size=lowerCamelCase__		,     resample=lowerCamelCase__		,     data_format=lowerCamelCase__		,     **lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   Dict[str, int]		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None		,     **lowerCamelCase__     :   Any		,     )      ->np.ndarray:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					get_size_dict(lowerCamelCase__       )
					if "height" not in size or "width" not in size:
							raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""       )
					return center_crop(lowerCamelCase__		,     size=(size["height"], size["width"])		,     data_format=lowerCamelCase__		,     **lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   Union[int, float]		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None		,     **lowerCamelCase__     :   Optional[Any]		,     )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					image.astype(np.floataa       )
					if offset:
							_UpperCAmelCase						:				Optional[int]	    =					image - (scale / 2)
					return rescale(lowerCamelCase__		,     scale=lowerCamelCase__		,     data_format=lowerCamelCase__		,     **lowerCamelCase__       )




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



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   ImageInput		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   Dict[str, int] = None		,     lowerCamelCase__     :   PILImageResampling = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   Dict[str, int] = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   float = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   Optional[Union[float, List[float]]] = None		,     lowerCamelCase__     :   Optional[Union[float, List[float]]] = None		,     lowerCamelCase__     :   Optional[ChannelDimension] = ChannelDimension.FIRST		,     )      ->np.ndarray:



					'''simple docstring'''







					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_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."       )

					if offset and not do_rescale:
							raise ValueError("For offset, do_rescale must also be set to True."       )

					# All transformations expect numpy arrays.
					_UpperCAmelCase						:				Any	    =					to_numpy_array(lowerCamelCase__       )

					if do_resize:
							_UpperCAmelCase						:				List[Any]	    =					self.resize(image=lowerCamelCase__		,     size=lowerCamelCase__		,     resample=lowerCamelCase__       )

					if do_center_crop:
							_UpperCAmelCase						:				Union[str, Any]	    =					self.center_crop(lowerCamelCase__		,     size=lowerCamelCase__       )

					if do_rescale:
							_UpperCAmelCase						:				Union[str, Any]	    =					self.rescale(image=lowerCamelCase__		,     scale=lowerCamelCase__		,     offset=lowerCamelCase__       )

					if do_normalize:
							_UpperCAmelCase						:				List[Any]	    =					self.normalize(image=lowerCamelCase__		,     mean=lowerCamelCase__		,     std=lowerCamelCase__       )

					_UpperCAmelCase						:				Optional[int]	    =					to_channel_dimension_format(lowerCamelCase__		,     lowerCamelCase__       )
					return image






			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   ImageInput		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   Dict[str, int] = None		,     lowerCamelCase__     :   PILImageResampling = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   Dict[str, int] = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   float = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   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[Any]		,     )      ->PIL.Image.Image:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					do_resize if do_resize is not None else self.do_resize
					_UpperCAmelCase						:				Optional[Any]	    =					resample if resample is not None else self.resample
					_UpperCAmelCase						:				int	    =					do_center_crop if do_center_crop is not None else self.do_center_crop
					_UpperCAmelCase						:				Dict	    =					do_rescale if do_rescale is not None else self.do_rescale
					_UpperCAmelCase						:				List[Any]	    =					rescale_factor if rescale_factor is not None else self.rescale_factor
					_UpperCAmelCase						:				Optional[Any]	    =					offset if offset is not None else self.offset
					_UpperCAmelCase						:				Any	    =					do_normalize if do_normalize is not None else self.do_normalize
					_UpperCAmelCase						:				str	    =					image_mean if image_mean is not None else self.image_mean
					_UpperCAmelCase						:				Union[str, Any]	    =					image_std if image_std is not None else self.image_std

					_UpperCAmelCase						:				List[Any]	    =					size if size is not None else self.size
					_UpperCAmelCase						:				List[Any]	    =					get_size_dict(lowerCamelCase__		,     default_to_square=lowerCamelCase__       )
					_UpperCAmelCase						:				Optional[Any]	    =					crop_size if crop_size is not None else self.crop_size
					_UpperCAmelCase						:				List[str]	    =					get_size_dict(lowerCamelCase__		,     param_name="crop_size"       )

					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."       )

					_UpperCAmelCase						:				str	    =					make_batched(lowerCamelCase__       )

					_UpperCAmelCase						:				Union[str, Any]	    =					[
					    [
					        self._preprocess_image(
					            image=lowerCamelCase__		,     do_resize=lowerCamelCase__		,     size=lowerCamelCase__		,     resample=lowerCamelCase__		,     do_center_crop=lowerCamelCase__		,     crop_size=lowerCamelCase__		,     do_rescale=lowerCamelCase__		,     rescale_factor=lowerCamelCase__		,     offset=lowerCamelCase__		,     do_normalize=lowerCamelCase__		,     image_mean=lowerCamelCase__		,     image_std=lowerCamelCase__		,     data_format=lowerCamelCase__		,     )
					        for img in video
					    ]
					    for video in videos
					]

					_UpperCAmelCase						:				Tuple	    =					{"pixel_values": videos}
					return BatchFeature(data=lowerCamelCase__		,     tensor_type=lowerCamelCase__       )







'''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_rag import save_json






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Dict	    =					filter(lambda __lowerCAmelCase      : p.requires_grad					,       model.parameters()      )
		_UpperCAmelCase						:				Optional[Any]	    =					sum([np.prod(p.size()      ) for p in model_parameters]      )
		return params


lowerCamelCase__					=   logging.getLogger(__name__)






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		if metric == "rouge2":
				_UpperCAmelCase						:				List[str]	    =					"{val_avg_rouge2:.4f}-{step_count}"
		elif metric == "bleu":
				_UpperCAmelCase						:				int	    =					"{val_avg_bleu:.4f}-{step_count}"
		elif metric == "em":
				_UpperCAmelCase						:				str	    =					"{val_avg_em:.4f}-{step_count}"
		elif metric == "loss":
				_UpperCAmelCase						:				List[Any]	    =					"{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						:				Any	    =					ModelCheckpoint(
		    dirpath=__lowerCAmelCase					,       filename=__lowerCAmelCase					,       monitor=F"""val_{metric}"""					,       mode="max"					,       save_top_k=1					,       every_n_epochs=1					,       )

		return checkpoint_callback






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






class 		lowerCAmelCase__	(	pl.Callback		):




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					{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     :   Optional[Any]		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   pl.LightningModule		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Union[str, Any]=True       )      ->None:



					'''simple docstring'''







					logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""       )
					_UpperCAmelCase						:				str	    =					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						:				Any	    =					Path(pl_module.hparams.output_dir       )
					if type_path == "test":
							_UpperCAmelCase						:				List[Any]	    =					od / "test_results.txt"
							_UpperCAmelCase						:				Union[str, Any]	    =					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						:				Optional[Any]	    =					od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
							_UpperCAmelCase						:				Dict	    =					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						:				Dict	    =					metrics[key]
									if isinstance(lowerCamelCase__		,     torch.Tensor       ):
											_UpperCAmelCase						:				Tuple	    =					val.item()
									_UpperCAmelCase						:				str	    =					F"""{key}: {val:.6f}\n"""
									writer.write(lowerCamelCase__       )

					if not save_generations:
							return

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




			@rank_zero_only
			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]       )      ->Any:



					'''simple docstring'''







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

					_UpperCAmelCase						:				int	    =					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     :   Dict		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   pl.LightningModule       )      ->int:



					'''simple docstring'''







					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__     :   Union[str, Any]       )      ->Union[str, Any]:



					'''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")



