
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


UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)






def   lowerCamelCase__							(					a		,		a		,		a		,		a      )      ->   Tuple[int, int]:
   def constraint_to_multiple_of(a		,		a		,		a=0		,		a=None      ):
      _A:  List[Any]					=       round(val / multiple      ) * multiple

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

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

      return x

   _A:  str					=       (output_size, output_size) if isinstance(a		,		a      ) else output_size

   _A			,  _A:  List[str]					=       get_image_size(a      )
   _A			,  _A:  List[str]					=       output_size

   # determine new height and width
   _A:  List[Any]					=       output_height / input_height
   _A:  Any					=       output_width / input_width

   if keep_aspect_ratio:
      # scale as little as possible
      if abs(1 - scale_width      ) < abs(1 - scale_height      ):
         # fit width
         _A:  str					=       scale_width
      else:
         # fit height
         _A:  List[str]					=       scale_height

   _A:  Union[str, Any]					=       constraint_to_multiple_of(scale_height * input_height		,		multiple=a      )
   _A:  Optional[int]					=       constraint_to_multiple_of(scale_width * input_width		,		multiple=a      )

   return (new_height, new_width)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Tuple 				=    ['''pixel_values''']


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							Dict[str, int] = None			,			lowerCAmelCase_     :							PILImageResampling = PILImageResampling.BILINEAR			,			lowerCAmelCase_     :							bool = False			,			lowerCAmelCase_     :							int = 1			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							Union[int, float] = 1 / 2_5_5			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							Optional[Union[float, List[float]]] = None			,			lowerCAmelCase_     :							Optional[Union[float, List[float]]] = None			,			**lowerCAmelCase_     :							Dict			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )
      _A:  Tuple					=       size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
      _A:  Dict					=       get_size_dict(lowerCAmelCase_     )
      _A:  Dict					=       do_resize
      _A:  Dict					=       size
      _A:  Union[str, Any]					=       keep_aspect_ratio
      _A:  int					=       ensure_multiple_of
      _A:  Union[str, Any]					=       resample
      _A:  Dict					=       do_rescale
      _A:  Optional[int]					=       rescale_factor
      _A:  List[str]					=       do_normalize
      _A:  List[str]					=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _A:  str					=       image_std if image_std is not None else IMAGENET_STANDARD_STD


   def  __magic_name__				(					self     :							Dict			,			lowerCAmelCase_     :							np.ndarray			,			lowerCAmelCase_     :							Dict[str, int]			,			lowerCAmelCase_     :							bool = False			,			lowerCAmelCase_     :							int = 1			,			lowerCAmelCase_     :							PILImageResampling = PILImageResampling.BICUBIC			,			lowerCAmelCase_     :							Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_     :							int			,			):

      """simple docstring"""
      _A:  Dict					=       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()}"""     )
      _A:  List[str]					=       get_resize_output_image_size(
          lowerCAmelCase_			,			output_size=(size['''height'''], size['''width'''])			,			keep_aspect_ratio=lowerCAmelCase_			,			multiple=lowerCAmelCase_			,			)
      return resize(lowerCAmelCase_			,			size=lowerCAmelCase_			,			resample=lowerCAmelCase_			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							np.ndarray			,			lowerCAmelCase_     :							Union[int, float]			,			lowerCAmelCase_     :							Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_     :							str			,			):

      """simple docstring"""
      return rescale(lowerCAmelCase_			,			scale=lowerCAmelCase_			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							np.ndarray			,			lowerCAmelCase_     :							Union[float, List[float]]			,			lowerCAmelCase_     :							Union[float, List[float]]			,			lowerCAmelCase_     :							Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_     :							List[Any]			,			):

      """simple docstring"""
      return normalize(lowerCAmelCase_			,			mean=lowerCAmelCase_			,			std=lowerCAmelCase_			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							ImageInput			,			lowerCAmelCase_     :							bool = None			,			lowerCAmelCase_     :							int = None			,			lowerCAmelCase_     :							bool = None			,			lowerCAmelCase_     :							int = None			,			lowerCAmelCase_     :							PILImageResampling = None			,			lowerCAmelCase_     :							bool = None			,			lowerCAmelCase_     :							float = 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_     :							int			,			):

      """simple docstring"""
      _A:  Optional[int]					=       do_resize if do_resize is not None else self.do_resize
      _A:  Dict					=       size if size is not None else self.size
      _A:  Optional[int]					=       get_size_dict(lowerCAmelCase_     )
      _A:  List[str]					=       keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
      _A:  Any					=       ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
      _A:  Optional[Any]					=       resample if resample is not None else self.resample
      _A:  Optional[int]					=       do_rescale if do_rescale is not None else self.do_rescale
      _A:  Dict					=       rescale_factor if rescale_factor is not None else self.rescale_factor
      _A:  Dict					=       do_normalize if do_normalize is not None else self.do_normalize
      _A:  List[str]					=       image_mean if image_mean is not None else self.image_mean
      _A:  Optional[int]					=       image_std if image_std is not None else self.image_std

      _A:  Any					=       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_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.
      _A:  List[str]					=       [to_numpy_array(lowerCAmelCase_     ) for image in images]

      if do_resize:
         _A:  List[Any]					=       [self.resize(image=lowerCAmelCase_			,			size=lowerCAmelCase_			,			resample=lowerCAmelCase_     ) for image in images]

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

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

      _A:  List[str]					=       [to_channel_dimension_format(lowerCAmelCase_			,			lowerCAmelCase_     ) for image in images]

      _A:  List[Any]					=       {'''pixel_values''': images}
      return BatchFeature(data=lowerCAmelCase_			,			tensor_type=lowerCAmelCase_     )


   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							List[Tuple] = None     ):

      """simple docstring"""
      _A:  Any					=       outputs.logits

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

         if is_torch_tensor(lowerCAmelCase_     ):
            _A:  Union[str, Any]					=       target_sizes.numpy()

         _A:  List[Any]					=       []

         for idx in range(len(lowerCAmelCase_     )     ):
            _A:  str					=       torch.nn.functional.interpolate(
                logits[idx].unsqueeze(dim=0     )			,			size=target_sizes[idx]			,			mode='''bilinear'''			,			align_corners=lowerCAmelCase_     )
            _A:  List[Any]					=       resized_logits[0].argmax(dim=0     )
            semantic_segmentation.append(lowerCAmelCase_     )
      else:
         _A:  List[Any]					=       logits.argmax(dim=1     )
         _A:  List[str]					=       [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   lowerCamelCase__							(					a      )      ->   int:
   _A:  int					=       filter(lambda a      : p.requires_grad		,		model.parameters()      )
   _A:  Dict					=       sum([np.prod(p.size()      ) for p in model_parameters]      )
   return params


UpperCAmelCase__		:     str     			=      logging.getLogger(__name__)






def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   if metric == "rouge2":
      _A:  Tuple					=       '''{val_avg_rouge2:.4f}-{step_count}'''
   elif metric == "bleu":
      _A:  List[str]					=       '''{val_avg_bleu:.4f}-{step_count}'''
   elif metric == "em":
      _A:  str					=       '''{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.'''      )

   _A:  List[str]					=       ModelCheckpoint(
       dirpath=a		,		filename=a		,		monitor=f"""val_{metric}"""		,		mode='''max'''		,		save_top_k=3		,		every_n_epochs=1		,		)
   return checkpoint_callback






def   lowerCamelCase__							(					a		,		a      )      ->   Optional[Any]:
   return EarlyStopping(
       monitor=f"""val_{metric}"""		,		mode='''min''' if '''loss''' in metric else '''max'''		,		patience=a		,		verbose=a		,		)






class UpperCAmelCase   (						pl.Callback   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       {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  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							pl.LightningModule			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Any=True     ):

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

      if not save_generations:
         return

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


   @rank_zero_only
   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      try:
         _A:  Union[str, Any]					=       pl_module.model.model.num_parameters()
      except AttributeError:
         _A:  Any					=       pl_module.model.num_parameters()

      _A:  Optional[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  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							pl.LightningModule     ):

      """simple docstring"""
      save_json(pl_module.metrics			,			pl_module.metrics_save_path     )
      return self._write_logs(lowerCAmelCase_			,			lowerCAmelCase_			,			'''test'''     )


   @rank_zero_only
   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							Optional[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")



