

from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerModel,
  )
  from transformers.models.roformer.modeling_tf_roformer import (
      TFRoFormerSelfAttention,
      TFRoFormerSinusoidalPositionalEmbedding,
  )





class A__    :



 """simple docstring"""







 def __init__(		self :	Union[str, Any]     ,						A_ :	Union[str, Any]     ,						A_ :	Optional[Any]=1_3     ,						A_ :	Dict=7     ,						A_ :	Optional[Any]=True     ,						A_ :	Tuple=True     ,						A_ :	int=True     ,						A_ :	Union[str, Any]=True     ,						A_ :	List[str]=9_9     ,						A_ :	Optional[int]=3_2     ,						A_ :	Any=2     ,						A_ :	Tuple=4     ,						A_ :	Dict=3_7     ,						A_ :	Optional[Any]="gelu"     ,						A_ :	Optional[Any]=0.1     ,						A_ :	Dict=0.1     ,						A_ :	str=5_1_2     ,						A_ :	str=1_6     ,						A_ :	str=2     ,						A_ :	List[str]=0.02     ,						A_ :	Union[str, Any]=3     ,						A_ :	Optional[int]=4     ,						A_ :	Tuple=None     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    parent
      _lowerCAmelCase   :					List[str]        =    1_3
      _lowerCAmelCase   :					Union[str, Any]        =    7
      _lowerCAmelCase   :					str        =    True
      _lowerCAmelCase   :					Any        =    True
      _lowerCAmelCase   :					Optional[int]        =    True
      _lowerCAmelCase   :					List[str]        =    True
      _lowerCAmelCase   :					Dict        =    9_9
      _lowerCAmelCase   :					Optional[int]        =    3_2
      _lowerCAmelCase   :					Dict        =    2
      _lowerCAmelCase   :					List[str]        =    4
      _lowerCAmelCase   :					List[str]        =    3_7
      _lowerCAmelCase   :					int        =    "gelu"
      _lowerCAmelCase   :					List[str]        =    0.1
      _lowerCAmelCase   :					Optional[Any]        =    0.1
      _lowerCAmelCase   :					List[str]        =    5_1_2
      _lowerCAmelCase   :					Any        =    1_6
      _lowerCAmelCase   :					int        =    2
      _lowerCAmelCase   :					Dict        =    0.02
      _lowerCAmelCase   :					Dict        =    3
      _lowerCAmelCase   :					Optional[Any]        =    4
      _lowerCAmelCase   :					Optional[Any]        =    None

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size       )

      _lowerCAmelCase   :					Optional[int]        =    None
      if self.use_input_mask:
           _lowerCAmelCase   :					Tuple        =    random_attention_mask([self.batch_size, self.seq_length]       )

      _lowerCAmelCase   :					List[Any]        =    None
      if self.use_token_type_ids:
           _lowerCAmelCase   :					Tuple        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.type_vocab_size       )

      _lowerCAmelCase   :					Union[str, Any]        =    None
      _lowerCAmelCase   :					Union[str, Any]        =    None
      _lowerCAmelCase   :					Union[str, Any]        =    None
      if self.use_labels:
           _lowerCAmelCase   :					Union[str, Any]        =    ids_tensor([self.batch_size]     ,						self.type_sequence_label_size       )
           _lowerCAmelCase   :					Tuple        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels       )
           _lowerCAmelCase   :					int        =    ids_tensor([self.batch_size]     ,						self.num_choices       )

      _lowerCAmelCase   :					str        =    RoFormerConfig(
          vocab_size=self.vocab_size     ,						hidden_size=self.hidden_size     ,						num_hidden_layers=self.num_hidden_layers     ,						num_attention_heads=self.num_attention_heads     ,						intermediate_size=self.intermediate_size     ,						hidden_act=self.hidden_act     ,						hidden_dropout_prob=self.hidden_dropout_prob     ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,						max_position_embeddings=self.max_position_embeddings     ,						type_vocab_size=self.type_vocab_size     ,						initializer_range=self.initializer_range     ,						return_dict=A_     ,						)

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

 def    __magic_name__       (		self :	Dict     ,						A_ :	str     ,						A_ :	Any     ,						A_ :	List[str]     ,						A_ :	str     ,						A_ :	List[str]     ,						A_ :	Dict     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    TFRoFormerModel(config=A_       )
      _lowerCAmelCase   :					Tuple        =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

      _lowerCAmelCase   :					int        =    [input_ids, input_mask]
      _lowerCAmelCase   :					Tuple        =    model(A_       )

      _lowerCAmelCase   :					Dict        =    model(A_       )

      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)       )

 def    __magic_name__       (		self :	str     ,						A_ :	Any     ,						A_ :	Dict     ,						A_ :	Tuple     ,						A_ :	Union[str, Any]     ,						A_ :	Union[str, Any]     ,						A_ :	int     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    True
      _lowerCAmelCase   :					Any        =    TFRoFormerForCausalLM(config=A_       )
      _lowerCAmelCase   :					int        =    {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      _lowerCAmelCase   :					Union[str, Any]        =    model(A_       )["logits"]
      self.parent.assertListEqual(
          list(prediction_scores.numpy().shape       )     ,						[self.batch_size, self.seq_length, self.vocab_size]       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Union[str, Any]     ,						A_ :	Dict     ,						A_ :	List[Any]     ,						A_ :	List[str]     ,						A_ :	Optional[Any]     ,						A_ :	Optional[Any]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    TFRoFormerForMaskedLM(config=A_       )
      _lowerCAmelCase   :					List[str]        =    {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      _lowerCAmelCase   :					List[str]        =    model(A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)       )

 def    __magic_name__       (		self :	List[str]     ,						A_ :	str     ,						A_ :	Optional[Any]     ,						A_ :	Union[str, Any]     ,						A_ :	Dict     ,						A_ :	int     ,						A_ :	Union[str, Any]     ,						A_ :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.num_labels
      _lowerCAmelCase   :					str        =    TFRoFormerForSequenceClassification(config=A_       )
      _lowerCAmelCase   :					List[str]        =    {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

      _lowerCAmelCase   :					str        =    model(A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[str]     ,						A_ :	Dict     ,						A_ :	Dict     ,						A_ :	Dict     ,						A_ :	List[str]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.num_choices
      _lowerCAmelCase   :					List[Any]        =    TFRoFormerForMultipleChoice(config=A_       )
      _lowerCAmelCase   :					Dict        =    tf.tile(tf.expand_dims(A_     ,						1       )     ,						(1, self.num_choices, 1)       )
      _lowerCAmelCase   :					Dict        =    tf.tile(tf.expand_dims(A_     ,						1       )     ,						(1, self.num_choices, 1)       )
      _lowerCAmelCase   :					str        =    tf.tile(tf.expand_dims(A_     ,						1       )     ,						(1, self.num_choices, 1)       )
      _lowerCAmelCase   :					int        =    {
          "input_ids": multiple_choice_inputs_ids,
          "attention_mask": multiple_choice_input_mask,
          "token_type_ids": multiple_choice_token_type_ids,
      }
      _lowerCAmelCase   :					Optional[Any]        =    model(A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_choices)       )

 def    __magic_name__       (		self :	Dict     ,						A_ :	Union[str, Any]     ,						A_ :	List[str]     ,						A_ :	List[Any]     ,						A_ :	Union[str, Any]     ,						A_ :	str     ,						A_ :	Any     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.num_labels
      _lowerCAmelCase   :					Optional[int]        =    TFRoFormerForTokenClassification(config=A_       )
      _lowerCAmelCase   :					int        =    {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      _lowerCAmelCase   :					str        =    model(A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.num_labels)       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	Union[str, Any]     ,						A_ :	List[str]     ,						A_ :	str     ,						A_ :	List[str]     ,						A_ :	Dict     ,						A_ :	str     ,						A_ :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    TFRoFormerForQuestionAnswering(config=A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

      _lowerCAmelCase   :					Union[str, Any]        =    model(A_       )
      self.parent.assertEqual(result.start_logits.shape     ,						(self.batch_size, self.seq_length)       )
      self.parent.assertEqual(result.end_logits.shape     ,						(self.batch_size, self.seq_length)       )


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.prepare_config_and_inputs()
      (
          (
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			
      )   :					List[Any]        =    config_and_inputs
      _lowerCAmelCase   :					str        =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict





@require_tf
class A__    ( A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					str      =						(
     (
         TFRoFormerModel,
         TFRoFormerForCausalLM,
         TFRoFormerForMaskedLM,
         TFRoFormerForQuestionAnswering,
         TFRoFormerForSequenceClassification,
         TFRoFormerForTokenClassification,
         TFRoFormerForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 _lowercase     :					Optional[Any]      =						(
     {
         '''feature-extraction''': TFRoFormerModel,
         '''fill-mask''': TFRoFormerForMaskedLM,
         '''question-answering''': TFRoFormerForQuestionAnswering,
         '''text-classification''': TFRoFormerForSequenceClassification,
         '''text-generation''': TFRoFormerForCausalLM,
         '''token-classification''': TFRoFormerForTokenClassification,
         '''zero-shot''': TFRoFormerForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 _lowercase     :					Optional[Any]      =						False
 _lowercase     :					List[str]      =						False

 def    __magic_name__       (		self :	Tuple     ,						A_ :	Dict     ,						A_ :	List[str]     ,						A_ :	List[Any]     ,						A_ :	Optional[int]     ,						A_ :	int       ):






      '''simple docstring'''




      if pipeline_test_casse_name == "TextGenerationPipelineTests":
           return True

      return False

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    TFRoFormerModelTester(self       )
      _lowerCAmelCase   :					Union[str, Any]        =    ConfigTester(self     ,						config_class=A_     ,						hidden_size=3_7       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_       )

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*A_       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_lm_head(*A_       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*A_       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*A_       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*A_       )


 @slow
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"       )
      self.assertIsNotNone(A_       )





@require_tf
class A__    ( unittest.TestCase ):



 """simple docstring"""







 @slow
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"       )
      _lowerCAmelCase   :					int        =    tf.constant([[0, 1, 2, 3, 4, 5]]       )
      _lowerCAmelCase   :					Union[str, Any]        =    model(A_       )[0]

      # TODO Replace vocab size
      _lowerCAmelCase   :					str        =    5_0_0_0_0

      _lowerCAmelCase   :					int        =    [1, 6, vocab_size]
      self.assertEqual(output.shape     ,						A_       )

      print(output[:, :3, :3]       )

      # TODO Replace values below with what was printed above.
      _lowerCAmelCase   :					str        =    tf.constant(
          [
              [
                  [-0.12053341, -1.0264901, 0.29221946],
                  [-1.5133783, 0.197433, 0.15190607],
                  [-5.0135403, -3.900256, -0.84038764],
              ]
          ]       )
      tf.debugging.assert_near(output[:, :3, :3]     ,						A_     ,						atol=1E-4       )





@require_tf
class A__    ( unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						1e-4

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    tf.constant([[4, 1_0]]       )
      _lowerCAmelCase   :					Optional[int]        =    TFRoFormerSinusoidalPositionalEmbedding(num_positions=6     ,						embedding_dim=6       )

      _lowerCAmelCase   :					int        =    emba(input_ids.shape       )
      _lowerCAmelCase   :					Tuple        =    tf.constant(
          [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]       )

      tf.debugging.assert_near(A_     ,						A_     ,						atol=self.tolerance       )


 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    tf.constant(
          [
              [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
              [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
              [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
          ]       )
      _lowerCAmelCase   :					int        =    TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2     ,						embedding_dim=5_1_2       )
      emba([2, 1_6, 5_1_2]       )
      _lowerCAmelCase   :					Tuple        =    emba.weight[:3, :5]

      tf.debugging.assert_near(A_     ,						A_     ,						atol=self.tolerance       )





@require_tf
class A__    ( unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						1e-4

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4     ,						dtype=tf.floataa       )     ,						shape=(2, 1_2, 1_6, 6_4)       ) / 1_0_0

      _lowerCAmelCase   :					Union[str, Any]        =    -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4     ,						dtype=tf.floataa       )     ,						shape=(2, 1_2, 1_6, 6_4)       ) / 1_0_0

      _lowerCAmelCase   :					List[Any]        =    TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2     ,						embedding_dim=6_4       )
      _lowerCAmelCase   :					List[str]        =    embed_positions([2, 1_6, 7_6_8]       )[None, None, :, :]

      _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    TFRoFormerSelfAttention.apply_rotary_position_embeddings(
          A_     ,						A_     ,						A_       )

      _lowerCAmelCase   :					List[Any]        =    tf.constant(
          [
              [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
              [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
              [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
              [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
              [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
              [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
          ]       )
      _lowerCAmelCase   :					int        =    tf.constant(
          [
              [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
              [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
              [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
              [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
              [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
              [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
          ]       )

      tf.debugging.assert_near(query_layer[0, 0, :6, :8]     ,						A_     ,						atol=self.tolerance       )
      tf.debugging.assert_near(key_layer[0, 0, :6, :8]     ,						A_     ,						atol=self.tolerance       )









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






