

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

_SCREAMING_SNAKE_CASE  :						str         =    logging.get_logger(__name__)
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     return [
         int(10_00 * (box[0] / width)       ),
         int(10_00 * (box[1] / height)       ),
         int(10_00 * (box[2] / width)       ),
         int(10_00 * (box[3] / height)       ),
     ]
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					to_pil_image(_A       )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					pil_image.size
     SCREAMING_SNAKE_CASE__					=					pytesseract.image_to_data(_A   ,	lang=_A   ,	output_type='''dict'''   ,	config=_A       )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

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

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

     return words, normalized_boxes







class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      ["pixel_values"]






  def __init__(							self		:	List[Any]			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	Dict[str, int] = None			,     __lowerCamelCase		:	PILImageResampling = PILImageResampling.BILINEAR			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	float = 1 / 255			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	Union[float, Iterable[float]] = None			,     __lowerCamelCase		:	Union[float, Iterable[float]] = None			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	Optional[str] = None			,     __lowerCamelCase		:	Optional[str] = ""			,     **__lowerCamelCase		:	str			,     )	->							None:
       super().__init__(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					size if size is not None else {'''height''': 224, '''width''': 224}
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					do_resize
       SCREAMING_SNAKE_CASE__					=					size
       SCREAMING_SNAKE_CASE__					=					resample
       SCREAMING_SNAKE_CASE__					=					do_rescale
       SCREAMING_SNAKE_CASE__					=					rescale_value
       SCREAMING_SNAKE_CASE__					=					do_normalize
       SCREAMING_SNAKE_CASE__					=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE__					=					image_std if image_std is not None else IMAGENET_STANDARD_STD
       SCREAMING_SNAKE_CASE__					=					apply_ocr
       SCREAMING_SNAKE_CASE__					=					ocr_lang
       SCREAMING_SNAKE_CASE__					=					tesseract_config






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Dict[str, int]			,     __lowerCamelCase		:	PILImageResampling = PILImageResampling.BILINEAR			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Any			,     )	->							np.ndarray:
       SCREAMING_SNAKE_CASE__					=					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()}'''      )
       SCREAMING_SNAKE_CASE__					=					(size['''height'''], size['''width'''])
       return resize(__lowerCamelCase			,     size=__lowerCamelCase			,     resample=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Union[int, float]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	int			,     )	->							np.ndarray:
       return rescale(__lowerCamelCase			,     scale=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Union[float, Iterable[float]]			,     __lowerCamelCase		:	Union[float, Iterable[float]]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Optional[Any]			,     )	->							np.ndarray:
       return normalize(__lowerCamelCase			,     mean=__lowerCamelCase			,     std=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )







  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	ImageInput			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	Dict[str, int] = None			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	float = None			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	Union[float, Iterable[float]] = None			,     __lowerCamelCase		:	Union[float, Iterable[float]] = None			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	Optional[str] = None			,     __lowerCamelCase		:	Optional[str] = None			,     __lowerCamelCase		:	Optional[Union[str, TensorType]] = None			,     __lowerCamelCase		:	ChannelDimension = ChannelDimension.FIRST			,     **__lowerCamelCase		:	Any			,     )	->							PIL.Image.Image:
       SCREAMING_SNAKE_CASE__					=					do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__					=					size if size is not None else self.size
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase      )
       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_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__					=					image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__					=					image_std if image_std is not None else self.image_std
       SCREAMING_SNAKE_CASE__					=					apply_ocr if apply_ocr is not None else self.apply_ocr
       SCREAMING_SNAKE_CASE__					=					ocr_lang if ocr_lang is not None else self.ocr_lang
       SCREAMING_SNAKE_CASE__					=					tesseract_config if tesseract_config is not None else self.tesseract_config

       SCREAMING_SNAKE_CASE__					=					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:
            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.
       SCREAMING_SNAKE_CASE__					=					[to_numpy_array(__lowerCamelCase      ) for image in images]

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

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

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

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

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

       SCREAMING_SNAKE_CASE__					=					BatchFeature(data={'''pixel_values''': images}			,     tensor_type=__lowerCamelCase      )

       if apply_ocr:
            SCREAMING_SNAKE_CASE__					=					words_batch
            SCREAMING_SNAKE_CASE__					=					boxes_batch
       return data





import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm
       import torch_xla.debug.metrics as met






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	Tuple			,     *__lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	Optional[int]=None			,     **__lowerCamelCase		:	Optional[int]      )	->							List[str]:
       super().__init__(*__lowerCamelCase			,     **__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					eval_examples
       SCREAMING_SNAKE_CASE__					=					post_process_function






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	Dict=None			,     __lowerCamelCase		:	int=None			,     __lowerCamelCase		:	str=None			,     __lowerCamelCase		:	str = "eval"      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.eval_dataset if eval_dataset is None else eval_dataset
       SCREAMING_SNAKE_CASE__					=					self.get_eval_dataloader(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       SCREAMING_SNAKE_CASE__					=					time.time()
       try:
            SCREAMING_SNAKE_CASE__					=					eval_loop(
                __lowerCamelCase			,     description='''Evaluation'''			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCamelCase			,     metric_key_prefix=__lowerCamelCase			,     )
       finally:
            SCREAMING_SNAKE_CASE__					=					compute_metrics
       SCREAMING_SNAKE_CASE__					=					self.args.eval_batch_size * self.args.world_size
       if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
            start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               __lowerCamelCase			,     __lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size      )			,     )      )
       if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
            # Only the main node write the results by default
            SCREAMING_SNAKE_CASE__					=					self.post_process_function(__lowerCamelCase			,     __lowerCamelCase			,     output.predictions      )
            SCREAMING_SNAKE_CASE__					=					self.compute_metrics(__lowerCamelCase      )

            # Prefix all keys with metric_key_prefix + '_'
            for key in list(metrics.keys()      ):
                 if not key.startswith(f'''{metric_key_prefix}_'''      ):
                      SCREAMING_SNAKE_CASE__					=					metrics.pop(__lowerCamelCase      )
            metrics.update(output.metrics      )
       else:
            SCREAMING_SNAKE_CASE__					=					output.metrics

       if self.args.should_log:
            # Only the main node log the results by default
            self.log(__lowerCamelCase      )

       if self.args.tpu_metrics_debug or self.args.debug:
            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report()      )

       SCREAMING_SNAKE_CASE__					=					self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     __lowerCamelCase      )
       return metrics







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	str=None			,     __lowerCamelCase		:	str = "test"      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.get_test_dataloader(__lowerCamelCase      )

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       SCREAMING_SNAKE_CASE__					=					time.time()
       try:
            SCREAMING_SNAKE_CASE__					=					eval_loop(
                __lowerCamelCase			,     description='''Prediction'''			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCamelCase			,     metric_key_prefix=__lowerCamelCase			,     )
       finally:
            SCREAMING_SNAKE_CASE__					=					compute_metrics
       SCREAMING_SNAKE_CASE__					=					self.args.eval_batch_size * self.args.world_size
       if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
            start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               __lowerCamelCase			,     __lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size      )			,     )      )

       if self.post_process_function is None or self.compute_metrics is None:
            return output

       SCREAMING_SNAKE_CASE__					=					self.post_process_function(__lowerCamelCase			,     __lowerCamelCase			,     output.predictions			,     '''predict'''      )
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics(__lowerCamelCase      )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys()      ):
            if not key.startswith(f'''{metric_key_prefix}_'''      ):
                 SCREAMING_SNAKE_CASE__					=					metrics.pop(__lowerCamelCase      )
       metrics.update(output.metrics      )
       return PredictionOutput(predictions=predictions.predictions			,     label_ids=predictions.label_ids			,     metrics=__lowerCamelCase      )


