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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]     )							-> List[str]:







  """simple docstring"""





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


__lowerCamelCase      :			str							       =		logging.getLogger(__name__)



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	Union[str, Any]     )							-> Dict:







  """simple docstring"""





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

  SCREAMING_SNAKE_CASE__      							=				ModelCheckpoint(
      dirpath=__UpperCamelCase			,					filename=__UpperCamelCase			,					monitor=f"""val_{metric}"""			,					mode="""max"""			,					save_top_k=1			,					every_n_epochs=1			,					)

  return checkpoint_callback



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	List[Any]     )							-> Union[str, Any]:







  """simple docstring"""





  return EarlyStopping(
      monitor=f"""val_{metric}"""			,					mode="""min""" if """loss""" in metric else """max"""			,					patience=__UpperCamelCase			,					verbose=__UpperCamelCase			,					)







class __snake_case    (						pl.Callback		):




   def      __a						(   self      :    Optional[int]	,     _lowercase      :    Any	,     _lowercase      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				{f"""lr_group_{i}""": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups				)}
     pl_module.logger.log_metrics(_lowercase				)




   @rank_zero_only
   def      __a						(   self      :    List[Any]	,     _lowercase      :    pl.Trainer	,     _lowercase      :    pl.LightningModule	,     _lowercase      :    str	,     _lowercase      :    str=True				):



     """simple docstring"""






     logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""				)
     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				Path(pl_module.hparams.output_dir				)
     if type_path == "test":
       SCREAMING_SNAKE_CASE__      							=				od / """test_results.txt"""
       SCREAMING_SNAKE_CASE__      							=				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.
       SCREAMING_SNAKE_CASE__      							=				od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
       SCREAMING_SNAKE_CASE__      							=				od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
       results_file.parent.mkdir(exist_ok=_lowercase				)
       generations_file.parent.mkdir(exist_ok=_lowercase				)
     with open(_lowercase	,     """a+"""				) as writer:
       for key in sorted(_lowercase				):
         if key in ["log", "progress_bar", "preds"]:
           continue
         SCREAMING_SNAKE_CASE__      							=				metrics[key]
         if isinstance(_lowercase	,     torch.Tensor				):
           SCREAMING_SNAKE_CASE__      							=				val.item()
         SCREAMING_SNAKE_CASE__      							=				f"""{key}: {val:.6f}\n"""
         writer.write(_lowercase				)

     if not save_generations:
       return

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




   @rank_zero_only
   def      __a						(   self      :    Dict	,     _lowercase      :    Dict	,     _lowercase      :    str				):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				count_trainable_parameters(_lowercase				)
     # 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      __a						(   self      :    Optional[int]	,     _lowercase      :    pl.Trainer	,     _lowercase      :    pl.LightningModule				):



     """simple docstring"""






     save_json(pl_module.metrics	,     pl_module.metrics_save_path				)
     return self._write_logs(_lowercase	,     _lowercase	,     """test"""				)




   @rank_zero_only
   def      __a						(   self      :    Tuple	,     _lowercase      :    pl.Trainer	,     _lowercase      :    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")

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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


__lowerCamelCase      :			int							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




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



     """simple docstring"""






     super().__init__(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""shortest_edge""": 2_24}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop
     SCREAMING_SNAKE_CASE__      							=				crop_size
     SCREAMING_SNAKE_CASE__      							=				do_flip_channel_order




   def      __a						(   self      :    List[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    PILImageResampling = PIL.Image.BILINEAR	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    str	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     if "shortest_edge" not in size:
       raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
     SCREAMING_SNAKE_CASE__      							=				get_resize_output_image_size(_lowercase	,     size=size["""shortest_edge"""]	,     default_to_square=_lowercase				)
     return resize(_lowercase	,     size=_lowercase	,     resample=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    str	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase				)
     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()}"""				)
     return center_crop(_lowercase	,     size=(size["""height"""], size["""width"""])	,     data_format=_lowercase	,     **_lowercase				)




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



     """simple docstring"""






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




   def      __a						(   self      :    Tuple	,     _lowercase      :    np.ndarray	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None				):



     """simple docstring"""






     return flip_channel_order(_lowercase	,     data_format=_lowercase				)




   def      __a						(   self      :    List[str]	,     _lowercase      :    ImageInput	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = None	,     _lowercase      :    bool = None	,     _lowercase      :    float = None	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    ChannelDimension = ChannelDimension.FIRST	,     **_lowercase      :    int	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__      							=				resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__      							=				(
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

     SCREAMING_SNAKE_CASE__      							=				size if size is not None else self.size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				make_list_of_images(_lowercase				)

     if not valid_images(_lowercase				):
       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:
       raise ValueError("""Size 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_center_crop and crop_size is None:
       raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

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

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

     if do_center_crop:
       SCREAMING_SNAKE_CASE__      							=				[self.center_crop(image=_lowercase	,     size=_lowercase				) for image in images]

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

     # the pretrained checkpoints assume images are BGR, not RGB
     if do_flip_channel_order:
       SCREAMING_SNAKE_CASE__      							=				[self.flip_channel_order(image=_lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				[to_channel_dimension_format(_lowercase	,     _lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				{"""pixel_values""": images}
     return BatchFeature(data=_lowercase	,     tensor_type=_lowercase				)




   def      __a						(   self      :    List[Any]	,     _lowercase      :    Dict	,     _lowercase      :    List[Tuple] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				outputs.logits

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

       if is_torch_tensor(_lowercase				):
         SCREAMING_SNAKE_CASE__      							=				target_sizes.numpy()

       SCREAMING_SNAKE_CASE__      							=				[]

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

     return semantic_segmentation
