

import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
  from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
  __UpperCAmelCase    				=     True
  from torch.cuda.amp import autocast

__UpperCAmelCase    				=     logging.getLogger(__name__)







def 	_snake_case		(    SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None				)       ->      Optional[int]:

     """simple docstring"""







     return field(default_factory=lambda: default		,							metadata=SCREAMING_SNAKE_CASE				)





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					str      =						field(
     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
 _lowercase     :					Optional[str]      =						field(
     default=A  ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}  ,		)
 _lowercase     :					Optional[bool]      =						field(
     default=A  ,		metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
 _lowercase     :					Optional[float]      =						field(
     default=0.1  ,		metadata={'''help''': '''The dropout ratio for the attention probabilities.'''} )
 _lowercase     :					Optional[float]      =						field(
     default=0.1  ,		metadata={'''help''': '''The dropout ratio for activations inside the fully connected layer.'''} )
 _lowercase     :					Optional[float]      =						field(
     default=0.1  ,		metadata={
         '''help''': '''The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.'''
     }  ,		)
 _lowercase     :					Optional[float]      =						field(
     default=0.1  ,		metadata={'''help''': '''The dropout probabilitiy for all 1D convolutional layers in feature extractor.'''}  ,		)
 _lowercase     :					Optional[float]      =						field(
     default=0.05  ,		metadata={
         '''help''': (
             '''Propability of each feature vector along the time axis to be chosen as the start of the vector'''
             '''span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature'''
             '''vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``.'''
         )
     }  ,		)
 _lowercase     :					Optional[float]      =						field(default=0.0  ,		metadata={'''help''': '''The LayerDrop probability.'''} )





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					Optional[str]      =						field(
     default=A  ,		metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
 _lowercase     :					Optional[str]      =						field(
     default='''train+validation'''  ,		metadata={
         '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
     }  ,		)
 _lowercase     :					bool      =						field(
     default=A  ,		metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
 _lowercase     :					Optional[int]      =						field(
     default=A  ,		metadata={'''help''': '''The number of processes to use for the preprocessing.'''}  ,		)
 _lowercase     :					Optional[int]      =						field(
     default=A  ,		metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     }  ,		)
 _lowercase     :					Optional[int]      =						field(
     default=A  ,		metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of validation examples to this '''
             '''value if set.'''
         )
     }  ,		)
 _lowercase     :					List[str]      =						list_field(
     default=[''',''', '''?''', '''.''', '''!''', '''-''', ''';''', ''':''', '''""''', '''%''', '''\'''', '''"''', '''�''']  ,		metadata={'''help''': '''A list of characters to remove from the transcripts.'''}  ,		)





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					WavaVecaProcessor
 _lowercase     :					Union[bool, str]      =						True
 _lowercase     :					Optional[int]      =						None
 _lowercase     :					Optional[int]      =						None
 _lowercase     :					Optional[int]      =						None
 _lowercase     :					Optional[int]      =						None

 def __call__(		self :	int     ,						A_ :	List[Dict[str, Union[List[int], torch.Tensor]]]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    [{"input_values": feature["input_values"]} for feature in features]
      _lowerCAmelCase   :					Tuple        =    [{"input_ids": feature["labels"]} for feature in features]

      _lowerCAmelCase   :					Tuple        =    self.processor.pad(
          A_     ,						padding=self.padding     ,						max_length=self.max_length     ,						pad_to_multiple_of=self.pad_to_multiple_of     ,						return_tensors="pt"     ,						)
      _lowerCAmelCase   :					List[str]        =    self.processor.pad(
          labels=A_     ,						padding=self.padding     ,						max_length=self.max_length_labels     ,						pad_to_multiple_of=self.pad_to_multiple_of_labels     ,						return_tensors="pt"     ,						)

      # replace padding with -100 to ignore loss correctly
      _lowerCAmelCase   :					Tuple        =    labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1       )     ,						-1_0_0       )

      _lowerCAmelCase   :					List[str]        =    labels

      return batch





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	str     ,						A_ :	nn.Module     ,						A_ :	Dict[str, Union[torch.Tensor, Any]]       ):






      '''simple docstring'''




      model.train()
      _lowerCAmelCase   :					Optional[Any]        =    self._prepare_inputs(A_       )

      if self.use_amp:
           with autocast():
                _lowerCAmelCase   :					List[Any]        =    self.compute_loss(A_     ,						A_       )
      else:
           _lowerCAmelCase   :					List[str]        =    self.compute_loss(A_     ,						A_       )

      if self.args.n_gpu > 1:
           if model.module.config.ctc_loss_reduction == "mean":
                _lowerCAmelCase   :					int        =    loss.mean()
           elif model.module.config.ctc_loss_reduction == "sum":
                _lowerCAmelCase   :					Dict        =    loss.sum() / (inputs["labels"] >= 0).sum()
           else:
                raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''       )

      if self.args.gradient_accumulation_steps > 1:
           _lowerCAmelCase   :					List[str]        =    loss / self.args.gradient_accumulation_steps

      if self.use_amp:
           self.scaler.scale(A_       ).backward()
      elif self.use_apex:
           with amp.scale_loss(A_     ,						self.optimizer       ) as scaled_loss:
                scaled_loss.backward()
      elif self.deepspeed:
           self.deepspeed.backward(A_       )
      else:
           loss.backward()

      return loss.detach()







def 	_snake_case		(    )       ->      Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     if len(sys.argv				) == 2 and sys.argv[1].endswith(".json"				):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					str        =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Union[str, Any]        =    parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     _lowerCAmelCase   :					Tuple        =    None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          _lowerCAmelCase   :					str        =    get_last_checkpoint(training_args.output_dir				)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
               raise ValueError(
                   f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                   "Use --overwrite_output_dir to overcome."				)
          elif last_checkpoint is not None:
               logger.info(
                   f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                   "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."				)

    # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"		,							datefmt="%m/%d/%Y %H:%M:%S"		,							handlers=[logging.StreamHandler(sys.stdout				)]		,							)
     logger.setLevel(logging.INFO if is_main_process(training_args.local_rank				) else logging.WARN				)

     # Log on each process the small summary:
     logger.warning(
         f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
         + f'''distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'''				)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank				):
          transformers.utils.logging.set_verbosity_info()
     logger.info("Training/evaluation parameters %s"		,							SCREAMING_SNAKE_CASE				)

     # Set seed before initializing model.
     set_seed(training_args.seed				)

     # Get the datasets:
     _lowerCAmelCase   :					int        =    datasets.load_dataset(
         "common_voice"		,							data_args.dataset_config_name		,							split=data_args.train_split_name				)
     _lowerCAmelCase   :					Any        =    datasets.load_dataset("common_voice"		,							data_args.dataset_config_name		,							split="test"				)

     # Create and save tokenizer
     _lowerCAmelCase   :					Optional[Any]        =    f'''[{''.join(data_args.chars_to_ignore				)}]'''

     def remove_special_characters(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Tuple        =    re.sub(SCREAMING_SNAKE_CASE		,							""		,							batch["sentence"]				).lower() + " "
          return batch

     _lowerCAmelCase   :					int        =    train_dataset.map(SCREAMING_SNAKE_CASE		,							remove_columns=["sentence"]				)
     _lowerCAmelCase   :					Dict        =    eval_dataset.map(SCREAMING_SNAKE_CASE		,							remove_columns=["sentence"]				)

     def extract_all_chars(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Tuple        =    " ".join(batch["text"]				)
          _lowerCAmelCase   :					List[Any]        =    list(set(SCREAMING_SNAKE_CASE				)				)
          return {"vocab": [vocab], "all_text": [all_text]}

     _lowerCAmelCase   :					int        =    train_dataset.map(
         SCREAMING_SNAKE_CASE		,							batched=SCREAMING_SNAKE_CASE		,							batch_size=-1		,							keep_in_memory=SCREAMING_SNAKE_CASE		,							remove_columns=train_dataset.column_names		,							)
     _lowerCAmelCase   :					Any        =    train_dataset.map(
         SCREAMING_SNAKE_CASE		,							batched=SCREAMING_SNAKE_CASE		,							batch_size=-1		,							keep_in_memory=SCREAMING_SNAKE_CASE		,							remove_columns=eval_dataset.column_names		,							)

     _lowerCAmelCase   :					Union[str, Any]        =    list(set(vocab_train["vocab"][0]				) | set(vocab_test["vocab"][0]				)				)
     _lowerCAmelCase   :					List[str]        =    {v: k for k, v in enumerate(SCREAMING_SNAKE_CASE				)}
     _lowerCAmelCase   :					List[str]        =    vocab_dict[" "]
     del vocab_dict[" "]
     _lowerCAmelCase   :					Optional[Any]        =    len(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Any        =    len(SCREAMING_SNAKE_CASE				)

     with open("vocab.json"		,							"w"				) as vocab_file:
          json.dump(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     _lowerCAmelCase   :					List[str]        =    WavaVecaCTCTokenizer(
         "vocab.json"		,							unk_token="[UNK]"		,							pad_token="[PAD]"		,							word_delimiter_token="|"		,							)
     _lowerCAmelCase   :					int        =    WavaVecaFeatureExtractor(
         feature_size=1		,							sampling_rate=16000		,							padding_value=0.0		,							do_normalize=SCREAMING_SNAKE_CASE		,							return_attention_mask=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Tuple        =    WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE		,							tokenizer=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					List[Any]        =    WavaVecaForCTC.from_pretrained(
         model_args.model_name_or_path		,							cache_dir=model_args.cache_dir		,							activation_dropout=model_args.activation_dropout		,							attention_dropout=model_args.attention_dropout		,							hidden_dropout=model_args.hidden_dropout		,							feat_proj_dropout=model_args.feat_proj_dropout		,							mask_time_prob=model_args.mask_time_prob		,							gradient_checkpointing=training_args.gradient_checkpointing		,							layerdrop=model_args.layerdrop		,							ctc_loss_reduction="mean"		,							pad_token_id=processor.tokenizer.pad_token_id		,							vocab_size=len(processor.tokenizer				)		,							)

     if data_args.max_train_samples is not None:
          _lowerCAmelCase   :					List[str]        =    min(len(SCREAMING_SNAKE_CASE				)		,							data_args.max_train_samples				)
          _lowerCAmelCase   :					Optional[Any]        =    train_dataset.select(range(SCREAMING_SNAKE_CASE				)				)

     if data_args.max_val_samples is not None:
          _lowerCAmelCase   :					List[str]        =    eval_dataset.select(range(data_args.max_val_samples				)				)

     _lowerCAmelCase   :					Tuple        =    torchaudio.transforms.Resample(48000		,							16000				)

     # Preprocessing the datasets.
     # We need to read the aduio files as arrays and tokenize the targets.
     def speech_file_to_array_fn(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    torchaudio.load(batch["path"]				)
          _lowerCAmelCase   :					int        =    resampler(SCREAMING_SNAKE_CASE				).squeeze().numpy()
          _lowerCAmelCase   :					Optional[int]        =    16000
          _lowerCAmelCase   :					Union[str, Any]        =    batch["text"]
          return batch

     _lowerCAmelCase   :					Optional[Any]        =    train_dataset.map(
         SCREAMING_SNAKE_CASE		,							remove_columns=train_dataset.column_names		,							num_proc=data_args.preprocessing_num_workers		,							)
     _lowerCAmelCase   :					str        =    eval_dataset.map(
         SCREAMING_SNAKE_CASE		,							remove_columns=eval_dataset.column_names		,							num_proc=data_args.preprocessing_num_workers		,							)

     def prepare_dataset(SCREAMING_SNAKE_CASE				):
          # check that all files have the correct sampling rate
          assert (
              len(set(batch["sampling_rate"]				)				) == 1
          ), f'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

          _lowerCAmelCase   :					Any        =    processor(
              audio=batch["speech"]		,							text=batch["target_text"]		,							sampling_rate=batch["sampling_rate"][0]				)
          batch.update(SCREAMING_SNAKE_CASE				)
          return batch

     _lowerCAmelCase   :					Optional[Any]        =    train_dataset.map(
         SCREAMING_SNAKE_CASE		,							remove_columns=train_dataset.column_names		,							batch_size=training_args.per_device_train_batch_size		,							batched=SCREAMING_SNAKE_CASE		,							num_proc=data_args.preprocessing_num_workers		,							)
     _lowerCAmelCase   :					Optional[Any]        =    eval_dataset.map(
         SCREAMING_SNAKE_CASE		,							remove_columns=eval_dataset.column_names		,							batch_size=training_args.per_device_train_batch_size		,							batched=SCREAMING_SNAKE_CASE		,							num_proc=data_args.preprocessing_num_workers		,							)

     # Metric
     _lowerCAmelCase   :					Tuple        =    datasets.load_metric("wer"				)

     def compute_metrics(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Dict        =    pred.predictions
          _lowerCAmelCase   :					Tuple        =    np.argmax(SCREAMING_SNAKE_CASE		,							axis=-1				)

          _lowerCAmelCase   :					Dict        =    processor.tokenizer.pad_token_id

          _lowerCAmelCase   :					Tuple        =    processor.batch_decode(SCREAMING_SNAKE_CASE				)
          # we do not want to group tokens when computing the metrics
          _lowerCAmelCase   :					List[Any]        =    processor.batch_decode(pred.label_ids		,							group_tokens=SCREAMING_SNAKE_CASE				)

          _lowerCAmelCase   :					int        =    wer_metric.compute(predictions=SCREAMING_SNAKE_CASE		,							references=SCREAMING_SNAKE_CASE				)

          return {"wer": wer}

     if model_args.freeze_feature_extractor:
          model.freeze_feature_extractor()

     # Data collator
     _lowerCAmelCase   :					Optional[Any]        =    DataCollatorCTCWithPadding(processor=SCREAMING_SNAKE_CASE		,							padding=SCREAMING_SNAKE_CASE				)

     # Initialize our Trainer
     _lowerCAmelCase   :					Dict        =    CTCTrainer(
         model=SCREAMING_SNAKE_CASE		,							data_collator=SCREAMING_SNAKE_CASE		,							args=SCREAMING_SNAKE_CASE		,							compute_metrics=SCREAMING_SNAKE_CASE		,							train_dataset=train_dataset if training_args.do_train else None		,							eval_dataset=eval_dataset if training_args.do_eval else None		,							tokenizer=processor.feature_extractor		,							)

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               _lowerCAmelCase   :					List[str]        =    last_checkpoint
          elif os.path.isdir(model_args.model_name_or_path				):
               _lowerCAmelCase   :					Union[str, Any]        =    model_args.model_name_or_path
          else:
               _lowerCAmelCase   :					Any        =    None

          # Save the feature_extractor and the tokenizer
          if is_main_process(training_args.local_rank				):
               processor.save_pretrained(training_args.output_dir				)

          _lowerCAmelCase   :					List[str]        =    trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE				)
          trainer.save_model()

          _lowerCAmelCase   :					List[Any]        =    train_result.metrics
          _lowerCAmelCase   :					Optional[Any]        =    (
              data_args.max_train_samples if data_args.max_train_samples is not None else len(SCREAMING_SNAKE_CASE				)
          )
          _lowerCAmelCase   :					Optional[int]        =    min(SCREAMING_SNAKE_CASE		,							len(SCREAMING_SNAKE_CASE				)				)

          trainer.log_metrics("train"		,							SCREAMING_SNAKE_CASE				)
          trainer.save_metrics("train"		,							SCREAMING_SNAKE_CASE				)
          trainer.save_state()

     # Evaluation
     _lowerCAmelCase   :					Optional[int]        =    {}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"				)
          _lowerCAmelCase   :					Tuple        =    trainer.evaluate()
          _lowerCAmelCase   :					List[Any]        =    data_args.max_val_samples if data_args.max_val_samples is not None else len(SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Optional[Any]        =    min(SCREAMING_SNAKE_CASE		,							len(SCREAMING_SNAKE_CASE				)				)

          trainer.log_metrics("eval"		,							SCREAMING_SNAKE_CASE				)
          trainer.save_metrics("eval"		,							SCREAMING_SNAKE_CASE				)

     return results


if __name__ == "__main__":
  main()









from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = None				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else ""

     # apply OCR
     _lowerCAmelCase   :					List[str]        =    to_pil_image(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    pil_image.size
     _lowerCAmelCase   :					Union[str, Any]        =    pytesseract.image_to_data(SCREAMING_SNAKE_CASE		,							lang=SCREAMING_SNAKE_CASE		,							output_type="dict"		,							config=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    data["text"], data["left"], data["top"], data["width"], data["height"]

     # filter empty words and corresponding coordinates
     _lowerCAmelCase   :					Dict        =    [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if not word.strip()]
     _lowerCAmelCase   :					Any        =    [word for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Dict        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[int]        =    [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
     _lowerCAmelCase   :					Union[str, Any]        =    []
     for x, y, w, h in zip(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Tuple        =    [x, y, x + w, y + h]
          actual_boxes.append(SCREAMING_SNAKE_CASE				)

     # finally, normalize the bounding boxes
     _lowerCAmelCase   :					Dict        =    []
     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__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	Tuple     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = ""     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					int        =    size if size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Any        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Optional[int]        =    apply_ocr
      _lowerCAmelCase   :					Dict        =    ocr_lang
      _lowerCAmelCase   :					Optional[int]        =    tesseract_config

 def    __magic_name__       (		self :	Dict     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_       )
      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()}'''       )
      _lowerCAmelCase   :					Dict        =    (size["height"], size["width"])
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )


 def    __magic_name__       (		self :	Dict     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[Any]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					Union[str, Any]        =    apply_ocr if apply_ocr is not None else self.apply_ocr
      _lowerCAmelCase   :					Tuple        =    ocr_lang if ocr_lang is not None else self.ocr_lang
      _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else self.tesseract_config

      _lowerCAmelCase   :					Union[str, Any]        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           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."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Dict        =    [to_numpy_array(A_       ) for image in images]

      if apply_ocr:
           requires_backends(self     ,						"pytesseract"       )
           _lowerCAmelCase   :					Any        =    []
           _lowerCAmelCase   :					List[Any]        =    []
           for image in images:
                _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    apply_tesseract(A_     ,						A_     ,						A_       )
                words_batch.append(A_       )
                boxes_batch.append(A_       )

      if do_resize:
           _lowerCAmelCase   :					Union[str, Any]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      # flip color channels from RGB to BGR (as Detectron2 requires this)
      _lowerCAmelCase   :					Optional[int]        =    [flip_channel_order(A_       ) for image in images]
      _lowerCAmelCase   :					List[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    BatchFeature(data={"pixel_values": images}     ,						tensor_type=A_       )

      if apply_ocr:
           _lowerCAmelCase   :					Tuple        =    words_batch
           _lowerCAmelCase   :					Any        =    boxes_batch
      return data






