"""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

_a      :		int           =   logging.get_logger(__name__)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any			,_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       Any	)       ->      Union[str, Any]:
  return [
      int(1000 * (box[0] / width)	),
      int(1000 * (box[1] / height)	),
      int(1000 * (box[2] / width)	),
      int(1000 * (box[3] / height)	),
  ]





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       np.ndarray			,_lowerCamelCase						:       Optional[str]			,_lowerCamelCase						:       Optional[str] = None	)       ->      Optional[Any]:
  _lowerCAmelCase						:       Dict  			=     tesseract_config if tesseract_config is not None else """"""

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

  # filter empty words and corresponding coordinates
  _lowerCAmelCase						:       List[str]  			=     [idx for idx, word in enumerate(_lowerCamelCase	) if not word.strip()]
  _lowerCAmelCase						:       List[Any]  			=     [word for idx, word in enumerate(_lowerCamelCase	) if idx not in irrelevant_indices]
  _lowerCAmelCase						:       Tuple  			=     [coord for idx, coord in enumerate(_lowerCamelCase	) if idx not in irrelevant_indices]
  _lowerCAmelCase						:       Dict  			=     [coord for idx, coord in enumerate(_lowerCamelCase	) if idx not in irrelevant_indices]
  _lowerCAmelCase						:       Dict  			=     [coord for idx, coord in enumerate(_lowerCamelCase	) if idx not in irrelevant_indices]
  _lowerCAmelCase						:       Optional[Any]  			=     [coord for idx, coord in enumerate(_lowerCamelCase	) if idx not in irrelevant_indices]

  # turn coordinates into (left, top, left+width, top+height) format
  _lowerCAmelCase						:       List[str]  			=     []
  for x, y, w, h in zip(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	):
    _lowerCAmelCase						:       Optional[Any]  			=     [x, y, x + w, y + h]
    actual_boxes.append(_lowerCamelCase	)

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

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

  return words, normalized_boxes






class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[int]							=		["pixel_values"]






     def __init__(		self				,     a__ = True				,     a__ = None				,     a__ = PILImageResampling.BILINEAR				,     a__ = True				,     a__ = None				,     a__ = ""				,     **a__				,     ):
       super().__init__(**a__  )
       _lowerCAmelCase						:       Tuple  			=     size if size is not None else {"""height""": 224, """width""": 224}
       _lowerCAmelCase						:       Dict  			=     get_size_dict(a__  )

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






     def     __A  (		self				,     a__				,     a__				,     a__ = PILImageResampling.BILINEAR				,     a__ = None				,     **a__				,     ):
       _lowerCAmelCase						:       Dict  			=     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						:       Optional[int]  			=     (size["""height"""], size["""width"""])
       return resize(a__				,     size=a__				,     resample=a__				,     data_format=a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = None				,     a__ = ChannelDimension.FIRST				,     **a__				,     ):
       _lowerCAmelCase						:       str  			=     do_resize if do_resize is not None else self.do_resize
       _lowerCAmelCase						:       str  			=     size if size is not None else self.size
       _lowerCAmelCase						:       Optional[Any]  			=     get_size_dict(a__  )
       _lowerCAmelCase						:       List[Any]  			=     resample if resample is not None else self.resample
       _lowerCAmelCase						:       Any  			=     apply_ocr if apply_ocr is not None else self.apply_ocr
       _lowerCAmelCase						:       Dict  			=     ocr_lang if ocr_lang is not None else self.ocr_lang
       _lowerCAmelCase						:       int  			=     tesseract_config if tesseract_config is not None else self.tesseract_config

       _lowerCAmelCase						:       str  			=     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						:       Optional[int]  			=     [to_numpy_array(a__  ) for image in images]

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

       if do_resize:
         _lowerCAmelCase						:       List[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						:       Union[str, Any]  			=     [flip_channel_order(a__  ) for image in images]
       _lowerCAmelCase						:       Tuple  			=     [to_channel_dimension_format(a__				,     a__  ) for image in images]

       _lowerCAmelCase						:       Any  			=     BatchFeature(data={"""pixel_values""": images}				,     tensor_type=a__  )

       if apply_ocr:
         _lowerCAmelCase						:       Optional[Any]  			=     words_batch
         _lowerCAmelCase						:       Optional[int]  			=     boxes_batch
       return data





"""simple docstring"""




import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
         DebertaVaForMaskedLM,
         DebertaVaForMultipleChoice,
         DebertaVaForQuestionAnswering,
         DebertaVaForSequenceClassification,
         DebertaVaForTokenClassification,
         DebertaVaModel,
     )
     from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__=13				,     a__=7				,     a__=True				,     a__=True				,     a__=True				,     a__=True				,     a__=99				,     a__=32				,     a__=5				,     a__=4				,     a__=37				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=512				,     a__=16				,     a__=2				,     a__=0.0_2				,     a__=False				,     a__=True				,     a__="None"				,     a__=3				,     a__=4				,     a__=None				,     ):
       _lowerCAmelCase						:       Dict  			=     parent
       _lowerCAmelCase						:       str  			=     batch_size
       _lowerCAmelCase						:       List[Any]  			=     seq_length
       _lowerCAmelCase						:       Dict  			=     is_training
       _lowerCAmelCase						:       Dict  			=     use_input_mask
       _lowerCAmelCase						:       int  			=     use_token_type_ids
       _lowerCAmelCase						:       int  			=     use_labels
       _lowerCAmelCase						:       Optional[int]  			=     vocab_size
       _lowerCAmelCase						:       Tuple  			=     hidden_size
       _lowerCAmelCase						:       Tuple  			=     num_hidden_layers
       _lowerCAmelCase						:       Dict  			=     num_attention_heads
       _lowerCAmelCase						:       Union[str, Any]  			=     intermediate_size
       _lowerCAmelCase						:       str  			=     hidden_act
       _lowerCAmelCase						:       Any  			=     hidden_dropout_prob
       _lowerCAmelCase						:       List[str]  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       List[str]  			=     max_position_embeddings
       _lowerCAmelCase						:       List[str]  			=     type_vocab_size
       _lowerCAmelCase						:       Tuple  			=     type_sequence_label_size
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Union[str, Any]  			=     num_labels
       _lowerCAmelCase						:       Optional[Any]  			=     num_choices
       _lowerCAmelCase						:       Tuple  			=     relative_attention
       _lowerCAmelCase						:       Tuple  			=     position_biased_input
       _lowerCAmelCase						:       Dict  			=     pos_att_type
       _lowerCAmelCase						:       Any  			=     scope






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size  )

       _lowerCAmelCase						:       Optional[Any]  			=     None
       if self.use_input_mask:
         _lowerCAmelCase						:       str  			=     ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2  )

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

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

       _lowerCAmelCase						:       List[str]  			=     self.get_config()

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






     def     __A  (		self  ):
       return DebertaVaConfig(
           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				,     relative_attention=self.relative_attention				,     position_biased_input=self.position_biased_input				,     pos_att_type=self.pos_att_type				,     )






     def     __A  (		self				,     a__  ):
       self.parent.assertListEqual(list(result.loss.size()  )				,     []  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       List[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__  )[0]
       _lowerCAmelCase						:       List[Any]  			=     model(a__				,     token_type_ids=a__  )[0]
       _lowerCAmelCase						:       Any  			=     model(a__  )[0]

       self.parent.assertListEqual(list(sequence_output.size()  )				,     [self.batch_size, self.seq_length, self.hidden_size]  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     DebertaVaForMaskedLM(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__  )

       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       int  			=     self.num_labels
       _lowerCAmelCase						:       int  			=     DebertaVaForSequenceClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__  )
       self.parent.assertListEqual(list(result.logits.size()  )				,     [self.batch_size, self.num_labels]  )
       self.check_loss_output(a__  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[Any]  			=     self.num_labels
       _lowerCAmelCase						:       str  			=     DebertaVaForTokenClassification(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     model(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				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Any  			=     DebertaVaForQuestionAnswering(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Dict  			=     model(
           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				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaForMultipleChoice(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[int]  			=     input_ids.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       Optional[Any]  			=     token_type_ids.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       str  			=     input_mask.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       List[str]  			=     model(
           a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__				,     )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices)  )






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







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Union[str, Any]							=		(
         (
             DebertaVaModel,
             DebertaVaForMaskedLM,
             DebertaVaForSequenceClassification,
             DebertaVaForTokenClassification,
             DebertaVaForQuestionAnswering,
             DebertaVaForMultipleChoice,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:			str							=		(
         {
             "feature-extraction": DebertaVaModel,
             "fill-mask": DebertaVaForMaskedLM,
             "question-answering": DebertaVaForQuestionAnswering,
             "text-classification": DebertaVaForSequenceClassification,
             "token-classification": DebertaVaForTokenClassification,
             "zero-shot": DebertaVaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     _UpperCamelCase							:			Optional[Any]							=		True
     _UpperCamelCase							:			List[Any]							=		False
     _UpperCamelCase							:			List[Any]							=		False
     _UpperCamelCase							:			Dict							=		False
     _UpperCamelCase							:			Tuple							=		False






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     DebertaVaModelTester(self  )
       _lowerCAmelCase						:       Any  			=     ConfigTester(self				,     config_class=a__				,     hidden_size=37  )






     def     __A  (		self  ):
       self.config_tester.run_common_tests()






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_multiple_choice(*a__  )






     @slow
     def     __A  (		self  ):
       for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:       Tuple  			=     DebertaVaModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







@require_torch
@require_sentencepiece
@require_tokenizers
class 						__A							( unittest.TestCase		):






     @unittest.skip(reason="""Model not available yet"""  )
     def     __A  (		self  ):
       pass






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge"""  )

       _lowerCAmelCase						:       Dict  			=     torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  )
       _lowerCAmelCase						:       Any  			=     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
       with torch.no_grad():
         _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__  )[0]
       # compare the actual values for a slice.
       _lowerCAmelCase						:       str  			=     torch.tensor(
           [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]  )
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,     a__				,     atol=1e-4  )				,     F"{output[:, 1:4, 1:4]}"  )




