



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

def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_ = None						):
						UpperCAmelCase             = tesseract_config if tesseract_config is not None else ''

						# apply OCR
						UpperCAmelCase             = to_pil_image(snake_case_						)
						UpperCAmelCase      ,    UpperCAmelCase             = pil_image.size
						UpperCAmelCase             = pytesseract.image_to_data(snake_case_		,	lang=snake_case_		,	output_type='dict'		,	config=snake_case_						)
						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(snake_case_						) if not word.strip()]
						UpperCAmelCase             = [word for idx, word in enumerate(snake_case_						) if idx not in irrelevant_indices]
						UpperCAmelCase             = [coord for idx, coord in enumerate(snake_case_						) if idx not in irrelevant_indices]
						UpperCAmelCase             = [coord for idx, coord in enumerate(snake_case_						) if idx not in irrelevant_indices]
						UpperCAmelCase             = [coord for idx, coord in enumerate(snake_case_						) if idx not in irrelevant_indices]
						UpperCAmelCase             = [coord for idx, coord in enumerate(snake_case_						) if idx not in irrelevant_indices]

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

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

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

						return words, normalized_boxes







class       A_			(     _UpperCAmelCase				):



						"""simple docstring"""
						__UpperCamelCase						=							['pixel_values']






						def __init__(					self :str   ,  lowercase_ :bool = True   ,  lowercase_ :Dict[str, int] = None   ,  lowercase_ :PILImageResampling = PILImageResampling.BILINEAR   ,  lowercase_ :bool = True   ,  lowercase_ :Optional[str] = None   ,  lowercase_ :Optional[str] = ""   ,  **lowercase_ :int   ,  )				->			List[Any]:
												super().__init__(**a__					)
												UpperCAmelCase             = size if size is not None else {'height': 2_24, 'width': 2_24}
												UpperCAmelCase             = get_size_dict(a__					)

												UpperCAmelCase             = do_resize
												UpperCAmelCase             = size
												UpperCAmelCase             = resample
												UpperCAmelCase             = apply_ocr
												UpperCAmelCase             = ocr_lang
												UpperCAmelCase             = tesseract_config






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






						def 	UpperCAmelCase__     (					self :Dict   ,  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_ :List[str]   ,  )				->			Union[str, Any]:
												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(a__					)
												UpperCAmelCase             = resample if resample is not None else self.resample
												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(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.
												UpperCAmelCase             = [to_numpy_array(a__					) for image in images]

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

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

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

												UpperCAmelCase             = BatchFeature(data={'pixel_values': images}   ,  tensor_type=a__					)

												if apply_ocr:
																		UpperCAmelCase             = words_batch
																		UpperCAmelCase             = boxes_batch
												return data












import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        LiltForQuestionAnswering,
        LiltForSequenceClassification,
        LiltForTokenClassification,
        LiltModel,
    )
    from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      Any				,			a__       :      Union[str, Any]				,			a__       :      int=13				,			a__       :      int=7				,			a__       :      Optional[Any]=True				,			a__       :      Optional[int]=True				,			a__       :      Any=True				,			a__       :      str=True				,			a__       :      List[Any]=99				,			a__       :      Any=24				,			a__       :      List[str]=2				,			a__       :      Optional[int]=6				,			a__       :      int=37				,			a__       :      List[str]="gelu"				,			a__       :      List[Any]=0.1				,			a__       :      Optional[int]=0.1				,			a__       :      Union[str, Any]=512				,			a__       :      List[str]=16				,			a__       :      Optional[int]=2				,			a__       :      Union[str, Any]=0.0_2				,			a__       :      str=3				,			a__       :      Optional[Any]=None				,			a__       :      Any=1000				,			):





         """simple docstring"""



         __snake_case							       =   parent
         __snake_case							       =   batch_size
         __snake_case							       =   seq_length
         __snake_case							       =   is_training
         __snake_case							       =   use_input_mask
         __snake_case							       =   use_token_type_ids
         __snake_case							       =   use_labels
         __snake_case							       =   vocab_size
         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   max_position_embeddings
         __snake_case							       =   type_vocab_size
         __snake_case							       =   type_sequence_label_size
         __snake_case							       =   initializer_range
         __snake_case							       =   num_labels
         __snake_case							       =   scope
         __snake_case							       =   range_bbox


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size      )

         __snake_case							       =   ids_tensor([self.batch_size, self.seq_length, 4]				,			self.range_bbox      )
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]      ):
            for j in range(bbox.shape[1]      ):
               if bbox[i, j, 3] < bbox[i, j, 1]:
                  __snake_case							       =   bbox[i, j, 3]
                  __snake_case							       =   bbox[i, j, 1]
                  __snake_case							       =   t
               if bbox[i, j, 2] < bbox[i, j, 0]:
                  __snake_case							       =   bbox[i, j, 2]
                  __snake_case							       =   bbox[i, j, 0]
                  __snake_case							       =   t

         __snake_case							       =   None
         if self.use_input_mask:
            __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2      )

         __snake_case							       =   None
         if self.use_token_type_ids:
            __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size      )

         __snake_case							       =   None
         __snake_case							       =   None
         if self.use_labels:
            __snake_case							       =   ids_tensor([self.batch_size]				,			self.type_sequence_label_size      )
            __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels      )

         __snake_case							       =   self.get_config()

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


      def    a (self       :      List[str]      ):





         """simple docstring"""



         return LiltConfig(
             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				,			)


      def    a (self       :      List[Any]				,			a__       :      List[Any]				,			a__       :      Optional[Any]				,			a__       :      List[str]				,			a__       :      int				,			a__       :      Optional[int]				,			a__       :      str				,			a__       :      Optional[int]				,			):





         """simple docstring"""



         __snake_case							       =   LiltModel(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__      )
         __snake_case							       =   model(a__				,			bbox=a__				,			token_type_ids=a__      )
         __snake_case							       =   model(a__				,			bbox=a__      )

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


      def    a (self       :      Any				,			a__       :      Tuple				,			a__       :      Dict				,			a__       :      Optional[int]				,			a__       :      Dict				,			a__       :      Union[str, Any]				,			a__       :      str				,			a__       :      Tuple				,			):





         """simple docstring"""



         __snake_case							       =   self.num_labels
         __snake_case							       =   LiltForTokenClassification(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(
             a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__				,			labels=a__      )
         self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.num_labels)      )


      def    a (self       :      int				,			a__       :      Optional[Any]				,			a__       :      int				,			a__       :      int				,			a__       :      Optional[Any]				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      str				,			):





         """simple docstring"""



         __snake_case							       =   LiltForQuestionAnswering(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(
             a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__				,			start_positions=a__				,			end_positions=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    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	
         )							       =   config_and_inputs
         __snake_case							       =   {
             '''input_ids''': input_ids,
             '''bbox''': bbox,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict






@require_torch
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[Any]							=    (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      A_						:				Any							=    (
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      A_						:				Optional[int]							=    False
      A_						:				List[Any]							=    False


      def    a (self       :      Dict				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      Any      ):





         """simple docstring"""



         return True


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





         """simple docstring"""



         __snake_case							       =   LiltModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			hidden_size=37      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         self.config_tester.run_common_tests()


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
            __snake_case							       =   type
            self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__      )


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





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a__      )


      @slow
      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __snake_case							       =   LiltModel.from_pretrained(a__      )
            self.assertIsNotNone(a__      )






@require_torch
@slow
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''      ).to(a__      )

         __snake_case							       =   torch.tensor([[1, 2]]				,			device=a__      )
         __snake_case							       =   torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,			device=a__      )

         # forward pass
         with torch.no_grad():
            __snake_case							       =   model(input_ids=a__				,			bbox=a__      )

         __snake_case							       =   torch.Size([1, 2, 768]      )
         __snake_case							       =   torch.tensor(
             [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]				,			device=a__				,			)

         self.assertTrue(outputs.last_hidden_state.shape				,			a__      )
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]				,			a__				,			atol=1E-3      )      )

