



"""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,
    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


__A   :							List[Any]										=   logging.get_logger(__name__)
class 			_a (					lowerCAmelCase):
							"""simple docstring"""
							UpperCamelCase__			      =	["""pixel_values"""]



							def __init__(		self     :    str				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    Dict[str, int] = None				,   __UpperCamelCase     :    float = None				,   __UpperCamelCase     :    PILImageResampling = PILImageResampling.BILINEAR				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    Union[int, float] = 1 / 2_5_5				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    Optional[Union[float, List[float]]] = None				,   __UpperCamelCase     :    Optional[Union[float, List[float]]] = None				,   **__UpperCamelCase     :    Optional[int]				,   )->None:
											super().__init__(**_UpperCAmelCase   )
											_UpperCAmelCase					=    size if size is not None else {'''shortest_edge''': 3_8_4}
											_UpperCAmelCase					=    get_size_dict(_UpperCAmelCase				,   default_to_square=_UpperCAmelCase   )

											_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_2_4 / 2_5_6
											_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  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    np.ndarray				,   __UpperCamelCase     :    Dict[str, int]				,   __UpperCamelCase     :    float				,   __UpperCamelCase     :    PILImageResampling = PILImageResampling.BICUBIC				,   __UpperCamelCase     :    Optional[Union[str, ChannelDimension]] = None				,   **__UpperCamelCase     :    Optional[int]				,   )->np.ndarray:
											_UpperCAmelCase					=    get_size_dict(_UpperCAmelCase				,   default_to_square=_UpperCAmelCase   )
											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_8_4:
															# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
															_UpperCAmelCase					=    int(shortest_edge / crop_pct   )
															_UpperCAmelCase					=    get_resize_output_image_size(_UpperCAmelCase				,   size=_UpperCAmelCase				,   default_to_square=_UpperCAmelCase   )
															_UpperCAmelCase					=    resize(image=_UpperCAmelCase				,   size=_UpperCAmelCase				,   resample=_UpperCAmelCase				,   data_format=_UpperCAmelCase				,   **_UpperCAmelCase   )
															# then crop to (shortest_edge, shortest_edge)
															return center_crop(image=_UpperCAmelCase				,   size=(shortest_edge, shortest_edge)				,   data_format=_UpperCAmelCase				,   **_UpperCAmelCase   )
											else:
															# warping (no cropping) when evaluated at 384 or larger
															return resize(
															    _UpperCAmelCase				,   size=(shortest_edge, shortest_edge)				,   resample=_UpperCAmelCase				,   data_format=_UpperCAmelCase				,   **_UpperCAmelCase   )



							def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    np.ndarray				,   __UpperCamelCase     :    Union[int, float]				,   __UpperCamelCase     :    Optional[Union[str, ChannelDimension]] = None				,   **__UpperCamelCase     :    List[Any]				,   )->Any:
											return rescale(_UpperCAmelCase				,   scale=_UpperCAmelCase				,   data_format=_UpperCAmelCase				,   **_UpperCAmelCase   )



							def  lowercase__						(		self     :    Dict				,   __UpperCamelCase     :    np.ndarray				,   __UpperCamelCase     :    Union[float, List[float]]				,   __UpperCamelCase     :    Union[float, List[float]]				,   __UpperCamelCase     :    Optional[Union[str, ChannelDimension]] = None				,   **__UpperCamelCase     :    List[str]				,   )->np.ndarray:
											return normalize(_UpperCAmelCase				,   mean=_UpperCAmelCase				,   std=_UpperCAmelCase				,   data_format=_UpperCAmelCase				,   **_UpperCAmelCase   )



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

											_UpperCAmelCase					=    make_list_of_images(_UpperCAmelCase   )

											if not valid_images(_UpperCAmelCase   ):
															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_8_4 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(_UpperCAmelCase   ) for image in images]

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

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

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

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

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








"""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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



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


__A   :							Dict										=   logging.getLogger(__name__)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						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}'''
				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=_SCREAMING_SNAKE_CASE					,     filename=_SCREAMING_SNAKE_CASE					,     monitor=f'val_{metric}'					,     mode='''max'''					,     save_top_k=3					,     every_n_epochs=1					,     )
				return checkpoint_callback







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						List[str]				):






				'''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 			_a (					pl.Callback):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    str   )->Tuple:
							_UpperCAmelCase					=    {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
							pl_module.logger.log_metrics(__UpperCamelCase   )



			@rank_zero_only
			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    pl.LightningModule				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Tuple=True   )->None:
							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=__UpperCamelCase   )
											generations_file.parent.mkdir(exist_ok=__UpperCamelCase   )
							with open(__UpperCamelCase				,   '''a+'''   ) as writer:
											for key in sorted(__UpperCamelCase   ):
															if key in ["log", "progress_bar", "preds"]:
																			continue
															_UpperCAmelCase					=    metrics[key]
															if isinstance(__UpperCamelCase				,   torch.Tensor   ):
																			_UpperCAmelCase					=    val.item()
															_UpperCAmelCase					=    F'{key}: {val:.6f}\n'
															writer.write(__UpperCamelCase   )

							if not save_generations:
											return

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



			@rank_zero_only
			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Dict   )->Union[str, Any]:
							try:
											_UpperCAmelCase					=    pl_module.model.model.num_parameters()
							except AttributeError:
											_UpperCAmelCase					=    pl_module.model.num_parameters()

							_UpperCAmelCase					=    count_trainable_parameters(__UpperCamelCase   )
							# 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  lowercase__						(		self     :    str				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    pl.LightningModule   )->List[Any]:
							save_json(pl_module.metrics				,   pl_module.metrics_save_path   )
							return self._write_logs(__UpperCamelCase				,   __UpperCamelCase				,   '''test'''   )



			@rank_zero_only
			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    List[str]   )->Optional[Any]:
							save_json(pl_module.metrics				,   pl_module.metrics_save_path   )
							# Uncommenting this will save val generations
							# return self._write_logs(trainer, pl_module, "valid")



