





import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.generation import (
         FlaxForcedBOSTokenLogitsProcessor,
         FlaxForcedEOSTokenLogitsProcessor,
         FlaxLogitsProcessorList,
         FlaxMinLengthLogitsProcessor,
         FlaxTemperatureLogitsWarper,
         FlaxTopKLogitsWarper,
         FlaxTopPLogitsWarper,
     )

@require_flax
class   A							(						unittest.TestCase    ):



      def 						lowerCAmelCase__     (						self:    Dict		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    int							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =jnp.ones((batch_size, length)							) / length
         return scores



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =None
         UpperCAmelCase_           =20

         UpperCAmelCase_           =self._get_uniform_logits(batch_size=2		,	length=_lowerCAmelCase							)

         # tweak scores to not be uniform anymore
         UpperCAmelCase_           =scores.at[1, 5].set((1 / length) + 0.1							)  # peak, 1st batch
         UpperCAmelCase_           =scores.at[1, 10].set((1 / length) - 0.4							)  # valley, 1st batch

         # compute softmax
         UpperCAmelCase_           =jax.nn.softmax(_lowerCAmelCase		,	axis=-1							)

         UpperCAmelCase_           =FlaxTemperatureLogitsWarper(temperature=0.5							)
         UpperCAmelCase_           =FlaxTemperatureLogitsWarper(temperature=1.3							)

         UpperCAmelCase_           =jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase		,	scores.copy()		,	cur_len=_lowerCAmelCase							)		,	axis=-1							)
         UpperCAmelCase_           =jax.nn.softmax(temp_dist_warper_smoother(_lowerCAmelCase		,	scores.copy()		,	cur_len=_lowerCAmelCase							)		,	axis=-1							)

         # uniform distribution stays uniform
         self.assertTrue(jnp.allclose(probs[0, :]		,	warped_prob_sharp[0, :]		,	atol=1e-3							)							)
         self.assertTrue(jnp.allclose(probs[0, :]		,	warped_prob_smooth[0, :]		,	atol=1e-3							)							)

         # sharp peaks get higher, valleys get lower
         self.assertLess(probs[1, :].max()		,	warped_prob_sharp[1, :].max()							)
         self.assertGreater(probs[1, :].min()		,	warped_prob_sharp[1, :].min()							)

         # smooth peaks get lower, valleys get higher
         self.assertGreater(probs[1, :].max()		,	warped_prob_smooth[1, :].max()							)
         self.assertLess(probs[1, :].min()		,	warped_prob_smooth[1, :].min()							)



      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =None
         UpperCAmelCase_           =10
         UpperCAmelCase_           =2

         # create ramp distribution
         UpperCAmelCase_           =np.broadcast_to(np.arange(_lowerCAmelCase							)[None, :]		,	(batch_size, vocab_size)							).copy()
         UpperCAmelCase_           =ramp_logits[1:, : vocab_size // 2] + vocab_size

         UpperCAmelCase_           =FlaxTopKLogitsWarper(3							)

         UpperCAmelCase_           =top_k_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)

         # check that correct tokens are filtered
         self.assertListEqual(jnp.isinf(scores[0]							).tolist()		,	7 * [True] + 3 * [False]							)
         self.assertListEqual(jnp.isinf(scores[1]							).tolist()		,	2 * [True] + 3 * [False] + 5 * [True]							)

         # check special case
         UpperCAmelCase_           =5
         UpperCAmelCase_           =FlaxTopKLogitsWarper(top_k=1		,	filter_value=0.0		,	min_tokens_to_keep=3							)

         UpperCAmelCase_           =np.broadcast_to(np.arange(_lowerCAmelCase							)[None, :]		,	(batch_size, length)							).copy()
         UpperCAmelCase_           =top_k_warp_safety_check(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)

         # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
         self.assertListEqual((scores == 0.0).sum(axis=-1							).tolist()		,	[2, 2]							)



      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =None
         UpperCAmelCase_           =10
         UpperCAmelCase_           =2

         # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
         UpperCAmelCase_           =np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]							)							)

         UpperCAmelCase_           =FlaxTopPLogitsWarper(0.8							)
         UpperCAmelCase_           =np.exp(top_p_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)							)

         # dist should be filtered to keep min num values so that sum is >= top_p
         # exp (-inf) => 0
         UpperCAmelCase_           =np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]							)
         self.assertTrue(np.allclose(_lowerCAmelCase		,	_lowerCAmelCase		,	atol=1e-3							)							)

         # check edge cases with negative and extreme logits
         UpperCAmelCase_           =np.broadcast_to(np.arange(_lowerCAmelCase							)[None, :]		,	(batch_size, vocab_size)							).copy() - (
             vocab_size // 2
         )

         # make ramp_logits more extreme
         UpperCAmelCase_           =ramp_logits[1] * 1_00.0

         # make sure at least 2 tokens are kept
         UpperCAmelCase_           =FlaxTopPLogitsWarper(0.9		,	min_tokens_to_keep=2		,	filter_value=0.0							)
         UpperCAmelCase_           =top_p_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)

         # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
         self.assertListEqual((filtered_dist != 0.0).sum(axis=-1							).tolist()		,	[3, 2]							)



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =20
         UpperCAmelCase_           =4
         UpperCAmelCase_           =0

         UpperCAmelCase_           =FlaxMinLengthLogitsProcessor(min_length=10		,	eos_token_id=_lowerCAmelCase							)

         # check that min length is applied at length 5
         UpperCAmelCase_           =ids_tensor((batch_size, 20)		,	vocab_size=20							)
         UpperCAmelCase_           =5
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =min_dist_processor(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()		,	4 * [-float("inf"							)]							)

         # check that min length is not applied anymore at length 15
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =15
         UpperCAmelCase_           =min_dist_processor(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         self.assertFalse(jnp.isinf(_lowerCAmelCase							).any()							)



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =20
         UpperCAmelCase_           =4
         UpperCAmelCase_           =0

         UpperCAmelCase_           =FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase							)

         # check that all scores are -inf except the bos_token_id score
         UpperCAmelCase_           =ids_tensor((batch_size, 1)		,	vocab_size=20							)
         UpperCAmelCase_           =1
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =logits_processor(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]							).all()							)
         self.assertListEqual(scores[:, bos_token_id].tolist()		,	4 * [0]							)  # score for bos_token_id shold be zero

         # check that bos_token_id is not forced if current length is greater than 1
         UpperCAmelCase_           =3
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =logits_processor(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         self.assertFalse(jnp.isinf(_lowerCAmelCase							).any()							)



      def 						lowerCAmelCase__     (						self:    str							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =20
         UpperCAmelCase_           =4
         UpperCAmelCase_           =0
         UpperCAmelCase_           =5

         UpperCAmelCase_           =FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase		,	eos_token_id=_lowerCAmelCase							)

         # check that all scores are -inf except the eos_token_id when max_length is reached
         UpperCAmelCase_           =ids_tensor((batch_size, 4)		,	vocab_size=20							)
         UpperCAmelCase_           =4
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =logits_processor(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]							).all()							)
         self.assertListEqual(scores[:, eos_token_id].tolist()		,	4 * [0]							)  # score for eos_token_id should be zero

         # check that eos_token_id is not forced if max_length is not reached
         UpperCAmelCase_           =3
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =logits_processor(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         self.assertFalse(jnp.isinf(_lowerCAmelCase							).any()							)



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =4
         UpperCAmelCase_           =10
         UpperCAmelCase_           =15
         UpperCAmelCase_           =2
         UpperCAmelCase_           =1
         UpperCAmelCase_           =15

         # dummy input_ids and scores
         UpperCAmelCase_           =ids_tensor((batch_size, sequence_length)		,	_lowerCAmelCase							)
         UpperCAmelCase_           =input_ids.copy()

         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =scores.copy()

         # instantiate all dist processors
         UpperCAmelCase_           =FlaxTemperatureLogitsWarper(temperature=0.5							)
         UpperCAmelCase_           =FlaxTopKLogitsWarper(3							)
         UpperCAmelCase_           =FlaxTopPLogitsWarper(0.8							)

         # instantiate all logits processors
         UpperCAmelCase_           =FlaxMinLengthLogitsProcessor(min_length=10		,	eos_token_id=_lowerCAmelCase							)
         UpperCAmelCase_           =FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase							)
         UpperCAmelCase_           =FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase		,	eos_token_id=_lowerCAmelCase							)

         UpperCAmelCase_           =10

         # no processor list
         UpperCAmelCase_           =temp_dist_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         UpperCAmelCase_           =top_k_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         UpperCAmelCase_           =top_p_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         UpperCAmelCase_           =min_dist_proc(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         UpperCAmelCase_           =bos_dist_proc(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         UpperCAmelCase_           =eos_dist_proc(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)

         # with processor list
         UpperCAmelCase_           =FlaxLogitsProcessorList(
             [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]							)
         UpperCAmelCase_           =processor(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)

         # scores should be equal
         self.assertTrue(jnp.allclose(_lowerCAmelCase		,	_lowerCAmelCase		,	atol=1e-3							)							)

         # input_ids should never be changed
         self.assertListEqual(input_ids.tolist()		,	input_ids_comp.tolist()							)






      def 						lowerCAmelCase__     (						self:    str							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =4
         UpperCAmelCase_           =10
         UpperCAmelCase_           =15
         UpperCAmelCase_           =2
         UpperCAmelCase_           =1
         UpperCAmelCase_           =15

         # dummy input_ids and scores
         UpperCAmelCase_           =ids_tensor((batch_size, sequence_length)		,	_lowerCAmelCase							)
         UpperCAmelCase_           =input_ids.copy()

         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =scores.copy()

         # instantiate all dist processors
         UpperCAmelCase_           =FlaxTemperatureLogitsWarper(temperature=0.5							)
         UpperCAmelCase_           =FlaxTopKLogitsWarper(3							)
         UpperCAmelCase_           =FlaxTopPLogitsWarper(0.8							)

         # instantiate all logits processors
         UpperCAmelCase_           =FlaxMinLengthLogitsProcessor(min_length=10		,	eos_token_id=_lowerCAmelCase							)
         UpperCAmelCase_           =FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase							)
         UpperCAmelCase_           =FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase		,	eos_token_id=_lowerCAmelCase							)

         UpperCAmelCase_           =10

         # no processor list
         def run_no_processor_list(_lowerCAmelCase:    int		,	_lowerCAmelCase:    Optional[Any]		,	_lowerCAmelCase:    Dict							):
            UpperCAmelCase_           =temp_dist_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
            UpperCAmelCase_           =top_k_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
            UpperCAmelCase_           =top_p_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
            UpperCAmelCase_           =min_dist_proc(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
            UpperCAmelCase_           =bos_dist_proc(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
            UpperCAmelCase_           =eos_dist_proc(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
            return scores

         # with processor list
         def run_processor_list(_lowerCAmelCase:    Optional[int]		,	_lowerCAmelCase:    Any		,	_lowerCAmelCase:    Optional[int]							):
            UpperCAmelCase_           =FlaxLogitsProcessorList(
                [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]							)
            UpperCAmelCase_           =processor(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
            return scores

         UpperCAmelCase_           =jax.jit(_lowerCAmelCase							)
         UpperCAmelCase_           =jax.jit(_lowerCAmelCase							)

         UpperCAmelCase_           =jitted_run_no_processor_list(_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =jitted_run_processor_list(_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase							)

         # scores should be equal
         self.assertTrue(jnp.allclose(_lowerCAmelCase		,	_lowerCAmelCase		,	atol=1e-3							)							)

         # input_ids should never be changed
         self.assertListEqual(input_ids.tolist()		,	input_ids_comp.tolist()							)





'''simple docstring'''


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

__snake_case						  =		logging.get_logger(__name__)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = None						)			->List[Any]:
  lowercase_     =       tesseract_config if tesseract_config is not None else """"""

  # apply OCR
  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    (		__a      ):


  """simple docstring"""

  A_    							=						['''pixel_values''']




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







    '''simple docstring'''


    super().__init__(**lowercase_      )
    lowercase_     =       size if size is not None else {"""height""": 224, """width""": 224}
    lowercase_     =       get_size_dict(lowercase_      )

    lowercase_     =       do_resize
    lowercase_     =       size
    lowercase_     =       resample
    lowercase_     =       apply_ocr
    lowercase_     =       ocr_lang
    lowercase_     =       tesseract_config




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







    '''simple docstring'''


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





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







    '''simple docstring'''


    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(lowercase_      )
    lowercase_     =       resample if resample is not None else self.resample
    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(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."""      )

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

    if apply_ocr:
      requires_backends(self   ,				"""pytesseract"""      )
      lowercase_     =       []
      lowercase_     =       []
      for image in images:
        lowercase_     ,	lowercase_     =       apply_tesseract(lowercase_   ,				lowercase_   ,				lowercase_      )
        words_batch.append(lowercase_      )
        boxes_batch.append(lowercase_      )

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

    # flip color channels from RGB to BGR (as Detectron2 requires this)
    lowercase_     =       [flip_channel_order(lowercase_      ) for image in images]
    lowercase_     =       [to_channel_dimension_format(lowercase_   ,				lowercase_      ) for image in images]

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

    if apply_ocr:
      lowercase_     =       words_batch
      lowercase_     =       boxes_batch
    return data

