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        lowerCAmelCase_			(					__A,  __A,  __A							)					->    Optional[int]:







      '''simple docstring'''


      return [
          int(1_000 * (box[0] / width)							),
          int(1_000 * (box[1] / height)							),
          int(1_000 * (box[2] / width)							),
          int(1_000 * (box[3] / height)							),
      ]




def        lowerCAmelCase_			(					__A,  __A,  __A							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  to_pil_image(__A							)
      UpperCAmelCase__		,				UpperCAmelCase__								=  pil_image.size
      UpperCAmelCase__								=  pytesseract.image_to_data(__A,  lang=__A,  output_type="dict",  config=__A							)
      UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

      # finally, normalize the bounding boxes
      UpperCAmelCase__								=  []
      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 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			int              =      ['pixel_values']


   def __init__(self					:						List[str]	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						PILImageResampling = PILImageResampling.BILINEAR	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						float = 1 / 2_5_5	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Union[float, Iterable[float]] = None	,  __UpperCAmelCase					:						Union[float, Iterable[float]] = None	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Optional[str] = None	,  __UpperCAmelCase					:						Optional[str] = ""	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    None:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase		)
         UpperCAmelCase__								=  size if size is not None else {"height": 2_2_4, "width": 2_2_4}
         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase		)

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


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						np.ndarray	,  __UpperCAmelCase					:						Dict[str, int]	,  __UpperCAmelCase					:						PILImageResampling = PILImageResampling.BILINEAR	,  __UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None	,  **__UpperCAmelCase					:						List[str]	,  )						->    np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase		)
         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()}"""		)
         UpperCAmelCase__								=  (size["height"], size["width"])
         return resize(__UpperCAmelCase	,  size=__UpperCAmelCase	,  resample=__UpperCAmelCase	,  data_format=__UpperCAmelCase	,  **__UpperCAmelCase		)


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						np.ndarray	,  __UpperCAmelCase					:						Union[int, float]	,  __UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None	,  **__UpperCAmelCase					:						Tuple	,  )						->    np.ndarray:
         """simple docstring"""



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


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						np.ndarray	,  __UpperCAmelCase					:						Union[float, Iterable[float]]	,  __UpperCAmelCase					:						Union[float, Iterable[float]]	,  __UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None	,  **__UpperCAmelCase					:						Any	,  )						->    np.ndarray:
         """simple docstring"""



         return normalize(__UpperCAmelCase	,  mean=__UpperCAmelCase	,  std=__UpperCAmelCase	,  data_format=__UpperCAmelCase	,  **__UpperCAmelCase		)





   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						ImageInput	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						Any=None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						float = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Union[float, Iterable[float]] = None	,  __UpperCAmelCase					:						Union[float, Iterable[float]] = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Optional[str] = None	,  __UpperCAmelCase					:						Optional[str] = None	,  __UpperCAmelCase					:						Optional[Union[str, TensorType]] = None	,  __UpperCAmelCase					:						ChannelDimension = ChannelDimension.FIRST	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    PIL.Image.Image:
         """simple docstring"""



         UpperCAmelCase__								=  do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase__								=  size if size is not None else self.size
         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase		)
         UpperCAmelCase__								=  resample if resample is not None else self.resample
         UpperCAmelCase__								=  do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase__								=  rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase__								=  do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase__								=  image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase__								=  image_std if image_std is not None else self.image_std
         UpperCAmelCase__								=  apply_ocr if apply_ocr is not None else self.apply_ocr
         UpperCAmelCase__								=  ocr_lang if ocr_lang is not None else self.ocr_lang
         UpperCAmelCase__								=  tesseract_config if tesseract_config is not None else self.tesseract_config

         UpperCAmelCase__								=  make_list_of_images(__UpperCAmelCase		)

         if not valid_images(__UpperCAmelCase		):
               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.
         UpperCAmelCase__								=  [to_numpy_array(__UpperCAmelCase		) for image in images]

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

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

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

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

         UpperCAmelCase__								=  [to_channel_dimension_format(__UpperCAmelCase	,  __UpperCAmelCase		) for image in images]

         UpperCAmelCase__								=  BatchFeature(data={"pixel_values": images}	,  tensor_type=__UpperCAmelCase		)

         if apply_ocr:
               UpperCAmelCase__								=  words_batch
               UpperCAmelCase__								=  boxes_batch
         return data




import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCamelCase__    							= logging.getLogger(__name__)




def        lowerCAmelCase_			(					)					->    Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  argparse.ArgumentParser(
          description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."							)
      parser.add_argument(
          "--dataset_name",  type=__A,  default="wikitext",  help="Name of the training. Explore datasets at: hf.co/datasets.",  )
      parser.add_argument(
          "--dataset_config",  type=__A,  default="wikitext-103-raw-v1",  help="Configuration name of the dataset."							)
      parser.add_argument(
          "--tokenizer_name_or_path",  type=__A,  default="sayakpaul/unigram-tokenizer-wikitext",  help="Tokenizer identifier. Can be a local filepath or a Hub identifier.",  )
      parser.add_argument(
          "--shard_size",  type=__A,  default=1_000,  help="Number of entries to go in a single shard.",  )
      parser.add_argument("--split",  type=__A,  default="train",  choices=["train", "test", "validation"]							)
      parser.add_argument(
          "--limit",  default=__A,  type=__A,  help="Limit the number of shards (used for debugging).",  )
      parser.add_argument(
          "--max_length",  type=__A,  default=512,  help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
          " sequence length that is a multiple of 8.",  )
      parser.add_argument(
          "--output_dir",  default="tf-tpu",  type=__A,  help="Output directory where the TFRecord shards will be saved. If the"
          " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
          " shards will be directly saved to a Google Cloud Storage bucket.",  )

      UpperCAmelCase__								=  parser.parse_args()
      return args




def        lowerCAmelCase_			(					__A							)					->    Optional[int]:







      '''simple docstring'''


      def fn(__A							):
            return tokenizer(examples["text"]							)

      return fn




def        lowerCAmelCase_			(					__A							)					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      for i in range(len(tokenized_data["input_ids"]							)							):
            UpperCAmelCase__								=  {
                "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]							)							),
                "attention_mask": tf.train.Feature(
                    intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]							)							),
            }
            UpperCAmelCase__								=  tf.train.Features(feature=__A							)
            UpperCAmelCase__								=  tf.train.Example(features=__A							)
            UpperCAmelCase__								=  example.SerializeToString()
            records.append(__A							)
      return records




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  datasets.load_dataset(args.dataset_name,  args.dataset_config,  split=args.split							)

      if args.limit is not None:
            UpperCAmelCase__								=  min(len(__A							),  args.limit							)
            UpperCAmelCase__								=  dataset.select(range(__A							)							)
            print(f"""Limiting the dataset to {args.limit} entries."""							)

      UpperCAmelCase__								=  AutoTokenizer.from_pretrained(args.tokenizer_name_or_path							)

      # Handle output directory creation.
      # For serializing into a Google Cloud Storage Bucket, one needs to first
      # create a bucket.
      if "gs" not in args.output_dir:
            if not os.path.exists(args.output_dir							):
                  os.makedirs(args.output_dir							)
            UpperCAmelCase__								=  os.path.join(args.output_dir,  args.split							)
            if not os.path.exists(__A							):
                  os.makedirs(__A							)
      else:
            UpperCAmelCase__								=  os.path.join(args.output_dir,  args.split							)

      # Tokenize the whole dataset at once.
      UpperCAmelCase__								=  tokenize_function(__A							)
      UpperCAmelCase__								=  dataset.map(__A,  batched=__A,  num_proc=4,  remove_columns=["text"]							)

      # We need to concatenate all our texts together, and then split the result
      # into chunks of a fixed size, which we will call block_size. To do this, we
      # will use the map method again, with the option batched=True. When we use batched=True,
      # the function we pass to map() will be passed multiple inputs at once, allowing us
      # to group them into more or fewer examples than we had in the input.
      # This allows us to create our new fixed-length samples. The advantage of this
      # method is that we don't lose a whole lot of content from the dataset compared to the
      # case where we simply tokenize with a pre-defined max_length.

      def group_texts(__A							):
            # Concatenate all texts.
            UpperCAmelCase__								=  {k: sum(examples[k],  []							) for k in examples.keys()}
            UpperCAmelCase__								=  len(concatenated_examples[list(examples.keys()							)[0]]							)
            # We drop the small remainder, though you could add padding instead if the model supports it
            # In this, as in all things, we advise you to follow your heart 🫀
            UpperCAmelCase__								=  (total_length // args.max_length) * args.max_length
            # Split by chunks of max_len.
            UpperCAmelCase__								=  {
                k: [t[i : i + args.max_length] for i in range(0,  __A,  args.max_length							)]
                for k, t in concatenated_examples.items()
            }
            return result

      UpperCAmelCase__								=  dataset_tokenized.map(__A,  batched=__A,  batch_size=1_000,  num_proc=4							)

      UpperCAmelCase__								=  0
      UpperCAmelCase__								=  0
      for shard in range(0,  len(__A							),  args.shard_size							):
            UpperCAmelCase__								=  grouped_dataset[shard : shard + args.shard_size]
            UpperCAmelCase__								=  len(dataset_snapshot["input_ids"]							)
            UpperCAmelCase__								=  os.path.join(__A,  f"""dataset-{shard_count}-{records_containing}.tfrecord"""							)
            UpperCAmelCase__								=  get_serialized_examples(__A							)

            with tf.io.TFRecordWriter(__A							) as out_file:
                  for i in range(len(__A							)							):
                        UpperCAmelCase__								=  serialized_examples[i]
                        out_file.write(__A							)
                  print("Wrote file {} containing {} records".format(__A,  __A							)							)

            shard_count += 1
            total_records += records_containing

      with open(f"""split-{args.split}-records-count.txt""",  "w"							) as f:
            print(f"""Total {args.split} records: {total_records}""",  file=__A							)


if __name__ == "__main__":
      UpperCamelCase__    							= parse_args()
      main(args)



