
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
						import torch

if is_vision_available():
						import PIL


__a         =   logging.get_logger(__name__)




def 							a		(				snake_case__:   np.ndarray		,  snake_case__:   Union[int, Iterable[int]]		,  snake_case__:   bool		,  snake_case__:   int   ):


				'''simple docstring'''
				def constraint_to_multiple_of(snake_case__:   Any		,  snake_case__:   str		,  snake_case__:   Union[str, Any]=0		,  snake_case__:   str=None   ):
								lowercase_	    =   round(val / multiple   ) * multiple

								if max_val is not None and x > max_val:
												lowercase_	    =   math.floor(val / multiple   ) * multiple

								if x < min_val:
												lowercase_	    =   math.ceil(val / multiple   ) * multiple

								return x

				lowercase_	    =   (output_size, output_size) if isinstance(snake_case__		,  snake_case__   ) else output_size

				lowercase_    ,      lowercase_	    =   get_image_size(snake_case__   )
				lowercase_    ,      lowercase_	    =   output_size

				# determine new height and width
				lowercase_	    =   output_height / input_height
				lowercase_	    =   output_width / input_width

				if keep_aspect_ratio:
								# scale as little as possible
								if abs(1 - scale_width   ) < abs(1 - scale_height   ):
												# fit width
												lowercase_	    =   scale_width
								else:
												# fit height
												lowercase_	    =   scale_height

				lowercase_	    =   constraint_to_multiple_of(scale_height * input_height		,  multiple=snake_case__   )
				lowercase_	    =   constraint_to_multiple_of(scale_width * input_width		,  multiple=snake_case__   )

				return (new_height, new_width)


class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Union[str, Any]       	=      ['pixel_values']
	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BILINEAR			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				SCREAMING_SNAKE_CASE_    :     int = 1			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Union[int, float] = 1 / 2_5_5			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				**SCREAMING_SNAKE_CASE_    :     int			,				)			->	None:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   do_resize
					lowercase_	    =   size
					lowercase_	    =   keep_aspect_ratio
					lowercase_	    =   ensure_multiple_of
					lowercase_	    =   resample
					lowercase_	    =   do_rescale
					lowercase_	    =   rescale_factor
					lowercase_	    =   do_normalize
					lowercase_	    =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					lowercase_	    =   image_std if image_std is not None else IMAGENET_STANDARD_STD
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int]			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				SCREAMING_SNAKE_CASE_    :     int = 1			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BICUBIC			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     Any			,				)			->	np.ndarray:
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)
					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()}'''			)
					lowercase_	    =   get_resize_output_image_size(
					    SCREAMING_SNAKE_CASE_			,				output_size=(size['''height'''], size['''width'''])			,				keep_aspect_ratio=SCREAMING_SNAKE_CASE_			,				multiple=SCREAMING_SNAKE_CASE_			,				)
					return resize(SCREAMING_SNAKE_CASE_			,				size=SCREAMING_SNAKE_CASE_			,				resample=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Union[int, float]			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			,				)			->	List[Any]:
					return rescale(SCREAMING_SNAKE_CASE_			,				scale=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Union[float, List[float]]			,				SCREAMING_SNAKE_CASE_    :     Union[float, List[float]]			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     List[str]			,				)			->	np.ndarray:
					return normalize(SCREAMING_SNAKE_CASE_			,				mean=SCREAMING_SNAKE_CASE_			,				std=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     ImageInput			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     int = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     int = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     float = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, TensorType]] = None			,				SCREAMING_SNAKE_CASE_    :     ChannelDimension = ChannelDimension.FIRST			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			,				)			->	PIL.Image.Image:
					lowercase_	    =   do_resize if do_resize is not None else self.do_resize
					lowercase_	    =   size if size is not None else self.size
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
					lowercase_	    =   ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
					lowercase_	    =   resample if resample is not None else self.resample
					lowercase_	    =   do_rescale if do_rescale is not None else self.do_rescale
					lowercase_	    =   rescale_factor if rescale_factor is not None else self.rescale_factor
					lowercase_	    =   do_normalize if do_normalize is not None else self.do_normalize
					lowercase_	    =   image_mean if image_mean is not None else self.image_mean
					lowercase_	    =   image_std if image_std is not None else self.image_std

					lowercase_	    =   make_list_of_images(SCREAMING_SNAKE_CASE_			)

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

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

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

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

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

					lowercase_	    =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) for image in images]

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




	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Tuple] = None			)			->	Dict:
					lowercase_	    =   outputs.logits

					# Resize logits and compute semantic segmentation maps
					if target_sizes is not None:
									if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
													raise ValueError(
													    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''			)

									if is_torch_tensor(SCREAMING_SNAKE_CASE_			):
													lowercase_	    =   target_sizes.numpy()

									lowercase_	    =   []

									for idx in range(len(SCREAMING_SNAKE_CASE_			)			):
													lowercase_	    =   torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0			)			,				size=target_sizes[idx]			,				mode='''bilinear'''			,				align_corners=SCREAMING_SNAKE_CASE_			)
													lowercase_	    =   resized_logits[0].argmax(dim=0			)
													semantic_segmentation.append(SCREAMING_SNAKE_CASE_			)
					else:
									lowercase_	    =   logits.argmax(dim=1			)
									lowercase_	    =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]			)]

					return semantic_segmentation






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		(				snake_case__:   Any   ):


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


__a         =   logging.getLogger(__name__)




def 							a		(				snake_case__:   int		,  snake_case__:   Any   ):


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

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

				return checkpoint_callback




def 							a		(				snake_case__:   str		,  snake_case__:   List[str]   ):


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


class 					lowercase__(							pl.Callback				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Optional[Any]:
					lowercase_	    =   {f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
					pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_			)
	@rank_zero_only
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     pl.Trainer			,				SCREAMING_SNAKE_CASE_    :     pl.LightningModule			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			)			->	None:
					logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''			)
					lowercase_	    =   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
					lowercase_	    =   Path(pl_module.hparams.output_dir			)
					if type_path == "test":
									lowercase_	    =   od / '''test_results.txt'''
									lowercase_	    =   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.
									lowercase_	    =   od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
									lowercase_	    =   od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
									results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
									generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
					with open(SCREAMING_SNAKE_CASE_			,				'''a+'''			) as writer:
									for key in sorted(SCREAMING_SNAKE_CASE_			):
													if key in ["log", "progress_bar", "preds"]:
																	continue
													lowercase_	    =   metrics[key]
													if isinstance(SCREAMING_SNAKE_CASE_			,				torch.Tensor			):
																	lowercase_	    =   val.item()
													lowercase_	    =   f'''{key}: {val:.6f}\n'''
													writer.write(SCREAMING_SNAKE_CASE_			)

					if not save_generations:
									return

					if "preds" in metrics:
									lowercase_	    =   '''\n'''.join(metrics['''preds''']			)
									generations_file.open('''w+'''			).write(SCREAMING_SNAKE_CASE_			)
	@rank_zero_only
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Union[str, Any]:
					try:
									lowercase_	    =   pl_module.model.model.num_parameters()
					except AttributeError:
									lowercase_	    =   pl_module.model.num_parameters()

					lowercase_	    =   count_trainable_parameters(SCREAMING_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 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     pl.Trainer			,				SCREAMING_SNAKE_CASE_    :     pl.LightningModule			)			->	Any:
					save_json(pl_module.metrics			,				pl_module.metrics_save_path			)
					return self._write_logs(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				'''test'''			)




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




