


"""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			(				A_       )->      int:
  '''simple docstring'''







  a						:		Any      							=			filter(lambda A_       : p.requires_grad  ,   model.parameters()       )
  a						:		Optional[int]      							=			sum([np.prod(p.size()       ) for p in model_parameters]       )
  return params


__lowercase    					=      logging.getLogger(__name__)


def      lowercase			(				A_  ,   A_       )->      Dict:
  '''simple docstring'''







  if metric == "rouge2":
    a						:		Union[str, Any]      							=			"{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    a						:		Any      							=			"{val_avg_bleu:.4f}-{step_count}"
  elif metric == "em":
    a						:		Optional[int]      							=			"{val_avg_em:.4f}-{step_count}"
  elif metric == "loss":
    a						:		Optional[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."       )

  a						:		Union[str, Any]      							=			ModelCheckpoint(
      dirpath=A_  ,   filename=A_  ,   monitor=F'''val_{metric}'''  ,   mode="max"  ,   save_top_k=1  ,   every_n_epochs=1  ,   )

  return checkpoint_callback


def      lowercase			(				A_  ,   A_       )->      List[str]:
  '''simple docstring'''







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







class 						_A  (  pl.Callback  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    int):
      a						:		Optional[Any]      							=			{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 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    pl.Trainer				, __UpperCAmelCase				:    pl.LightningModule				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Any=True):
      logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''')
      a						:		Dict      							=			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						:		Optional[Any]      							=			Path(pl_module.hparams.output_dir)
      if type_path == "test":
        a						:		str      							=			od / "test_results.txt"
        a						:		str      							=			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						:		str      							=			od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
        a						:		List[Any]      							=			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
          a						:		List[str]      							=			metrics[key]
          if isinstance(__UpperCAmelCase				, torch.Tensor):
            a						:		str      							=			val.item()
          a						:		Dict      							=			f'''{key}: {val:.6f}\n'''
          writer.write(__UpperCAmelCase)

      if not save_generations:
        return

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






    @rank_zero_only
    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    Dict				, __UpperCAmelCase				:    Dict):
      try:
        a						:		Optional[Any]      							=			pl_module.model.model.num_parameters()
      except AttributeError:
        a						:		Any      							=			pl_module.model.num_parameters()

      a						:		Dict      							=			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 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    pl.Trainer				, __UpperCAmelCase				:    pl.LightningModule):
      save_json(pl_module.metrics				, pl_module.metrics_save_path)
      return self._write_logs(__UpperCAmelCase				, __UpperCAmelCase				, "test")






    @rank_zero_only
    def 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    pl.Trainer				, __UpperCAmelCase				:    Union[str, 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")








from typing import Dict, Iterable, Optional, 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, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
	import PIL

# soft dependency
if is_pytesseract_available():
	import pytesseract

UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	return [
	    int(1000 * (box[0] / width)							),
	    int(1000 * (box[1] / height)							),
	    int(1000 * (box[2] / width)							),
	    int(1000 * (box[3] / height)							),
	]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    to_pil_image(__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase       =    pil_image.size
	lowercase       =    pytesseract.image_to_data(__SCREAMING_SNAKE_CASE ,							lang=__SCREAMING_SNAKE_CASE ,							output_type='dict' ,							config=__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    data['text'], data['left'], data['top'], data['width'], data['height']

	# filter empty words and corresponding coordinates
	lowercase       =    [idx for idx, word in enumerate(__SCREAMING_SNAKE_CASE							) if not word.strip()]
	lowercase       =    [word for idx, word in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]
	lowercase       =    [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE							) if idx not in irrelevant_indices]

	# turn coordinates into (left, top, left+width, top+height) format
	lowercase       =    []
	for x, y, w, h in zip(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
		lowercase       =    [x, y, x + w, y + h]
		actual_boxes.append(__SCREAMING_SNAKE_CASE							)

	# finally, normalize the bounding boxes
	lowercase       =    []
	for box in actual_boxes:
		normalized_boxes.append(normalize_box(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)							)

	assert len(__SCREAMING_SNAKE_CASE							) == len(__SCREAMING_SNAKE_CASE							), "Not as many words as there are bounding boxes"

	return words, normalized_boxes







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       ["""pixel_values"""]





	def __init__(       self		,    snake_case = True		,    snake_case = None		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = True		,    snake_case = 1 / 255		,    snake_case = True		,    snake_case = None		,    snake_case = None		,    snake_case = True		,    snake_case = None		,    snake_case = ""		,    **snake_case		,    ):
		super().__init__(**snake_case       )
		lowercase       =    size if size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(snake_case       )

		lowercase       =    do_resize
		lowercase       =    size
		lowercase       =    resample
		lowercase       =    do_rescale
		lowercase       =    rescale_value
		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
		lowercase       =    apply_ocr
		lowercase       =    ocr_lang
		lowercase       =    tesseract_config





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(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       =    (size['height'], size['width'])
		return resize(snake_case		,    size=snake_case		,    resample=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return rescale(snake_case		,    scale=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return normalize(snake_case		,    mean=snake_case		,    std=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None		,    snake_case = None		,    snake_case=None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = ChannelDimension.FIRST		,    **snake_case		,    ):
		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(snake_case       )
		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       =    apply_ocr if apply_ocr is not None else self.apply_ocr
		lowercase       =    ocr_lang if ocr_lang is not None else self.ocr_lang
		lowercase       =    tesseract_config if tesseract_config is not None else self.tesseract_config

		lowercase       =    make_list_of_images(snake_case       )

		if not valid_images(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:
			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_normalize and (image_mean is None or image_std is None):
			raise ValueError('If do_normalize is True, image_mean and image_std must be specified.'       )

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

		# Tesseract OCR to get words + normalized bounding boxes
		if apply_ocr:
			requires_backends(self		,    'pytesseract'       )
			lowercase       =    []
			lowercase       =    []
			for image in images:
				lowercase  ,					lowercase       =    apply_tesseract(snake_case		,    snake_case		,    snake_case       )
				words_batch.append(snake_case       )
				boxes_batch.append(snake_case       )

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

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

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

		lowercase       =    [to_channel_dimension_format(snake_case		,    snake_case       ) for image in images]

		lowercase       =    BatchFeature(data={'pixel_values': images}		,    tensor_type=snake_case       )

		if apply_ocr:
			lowercase       =    words_batch
			lowercase       =    boxes_batch
		return data

