





from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
        TFDebertaVaForMaskedLM,
        TFDebertaVaForQuestionAnswering,
        TFDebertaVaForSequenceClassification,
        TFDebertaVaForTokenClassification,
        TFDebertaVaModel,
    )





class 			A__   :




       """simple docstring"""



       def __init__( self ,  lowercase ,  lowercase=13 ,  lowercase=7 ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=99 ,  lowercase=32 ,  lowercase=2 ,  lowercase=4 ,  lowercase=37 ,  lowercase="gelu" ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=512 ,  lowercase=16 ,  lowercase=2 ,  lowercase=0.02 ,  lowercase=False ,  lowercase=True ,  lowercase="None" ,  lowercase=3 ,  lowercase=4 ,  lowercase=None ,  )      ->						Optional[Any]:







              '''simple docstring'''

              a__						:     Optional[Any]			=    parent
              a__						:     Any			=    batch_size
              a__						:     str			=    seq_length
              a__						:     Any			=    is_training
              a__						:     str			=    use_input_mask
              a__						:     str			=    use_token_type_ids
              a__						:     List[str]			=    use_labels
              a__						:     Dict			=    vocab_size
              a__						:     List[str]			=    hidden_size
              a__						:     List[str]			=    num_hidden_layers
              a__						:     List[Any]			=    num_attention_heads
              a__						:     Dict			=    intermediate_size
              a__						:     Any			=    hidden_act
              a__						:     Dict			=    hidden_dropout_prob
              a__						:     Any			=    attention_probs_dropout_prob
              a__						:     Dict			=    max_position_embeddings
              a__						:     int			=    type_vocab_size
              a__						:     Optional[Any]			=    type_sequence_label_size
              a__						:     str			=    initializer_range
              a__						:     Optional[int]			=    num_labels
              a__						:     Optional[Any]			=    num_choices
              a__						:     List[str]			=    relative_attention
              a__						:     Union[str, Any]			=    position_biased_input
              a__						:     str			=    pos_att_type
              a__						:     Optional[int]			=    scope

       def 							__lowercase		( self)      ->						str:







              '''simple docstring'''

              a__						:     Any			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

              a__						:     List[Any]			=    None
              if self.use_input_mask:
                     a__						:     Dict			=    random_attention_mask([self.batch_size, self.seq_length])

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

              a__						:     int			=    None
              a__						:     Optional[int]			=    None
              a__						:     Dict			=    None
              if self.use_labels:
                     a__						:     List[Any]			=    ids_tensor([self.batch_size] ,  self.type_sequence_label_size)
                     a__						:     Dict			=    ids_tensor([self.batch_size, self.seq_length] ,  self.num_labels)

              a__						:     Dict			=    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 ,  relative_attention=self.relative_attention ,  position_biased_input=self.position_biased_input ,  initializer_range=self.initializer_range ,  return_dict=_A ,  )

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

       def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[Any]:







              '''simple docstring'''

              a__						:     Dict			=    TFDebertaVaModel(config=_A)
              a__						:     Dict			=    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

              a__						:     Dict			=    [input_ids, input_mask]
              a__						:     Dict			=    model(_A)

              a__						:     Tuple			=    model(_A)

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

       def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						str:







              '''simple docstring'''

              a__						:     List[str]			=    TFDebertaVaForMaskedLM(config=_A)
              a__						:     Any			=    {
                  'input_ids': input_ids,
                  'attention_mask': input_mask,
                  'token_type_ids': token_type_ids,
              }
              a__						:     Dict			=    model(_A)
              self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.vocab_size))

       def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[Any]:







              '''simple docstring'''

              a__						:     Union[str, Any]			=    self.num_labels
              a__						:     int			=    TFDebertaVaForSequenceClassification(config=_A)
              a__						:     Tuple			=    {
                  'input_ids': input_ids,
                  'attention_mask': input_mask,
                  'token_type_ids': token_type_ids,
              }

              a__						:     int			=    model(_A)
              self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels))

       def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[int]:







              '''simple docstring'''

              a__						:     List[str]			=    self.num_labels
              a__						:     Optional[Any]			=    TFDebertaVaForTokenClassification(config=_A)
              a__						:     List[str]			=    {
                  'input_ids': input_ids,
                  'attention_mask': input_mask,
                  'token_type_ids': token_type_ids,
              }
              a__						:     str			=    model(_A)
              self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.num_labels))

       def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Tuple:







              '''simple docstring'''

              a__						:     str			=    TFDebertaVaForQuestionAnswering(config=_A)
              a__						:     Tuple			=    {
                  'input_ids': input_ids,
                  'attention_mask': input_mask,
                  'token_type_ids': token_type_ids,
              }

              a__						:     Dict			=    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 							__lowercase		( self)      ->						str:







              '''simple docstring'''

              a__						:     Dict			=    self.prepare_config_and_inputs()
              (
                  (
                  a__							
              )			,							(
                  a__							
              )			,							(
                  a__							
              )			,							(
                  a__							
              )			,							(
                  a__							
              )			,							(
                  a__							
              )			,							(
                  a__							
              )			,														
              )						:     List[Any]			=    config_and_inputs
              a__						:     Optional[int]			=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
              return config, inputs_dict





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




       """simple docstring"""



       __A       :    List[Any]	  =  (
           (
               TFDebertaVaModel,
               TFDebertaVaForMaskedLM,
               TFDebertaVaForQuestionAnswering,
               TFDebertaVaForSequenceClassification,
               TFDebertaVaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       __A       :    Any	  =  (
           {
               """feature-extraction""": TFDebertaVaModel,
               """fill-mask""": TFDebertaVaForMaskedLM,
               """question-answering""": TFDebertaVaForQuestionAnswering,
               """text-classification""": TFDebertaVaForSequenceClassification,
               """token-classification""": TFDebertaVaForTokenClassification,
               """zero-shot""": TFDebertaVaForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       __A       :    int	  =  False
       __A       :    Any	  =  False

       def 							__lowercase		( self)      ->						Any:







              '''simple docstring'''

              a__						:     List[str]			=    TFDebertaVaModelTester(self)
              a__						:     Optional[Any]			=    ConfigTester(self ,  config_class=_A ,  hidden_size=37)

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







              '''simple docstring'''

              self.config_tester.run_common_tests()

       def 							__lowercase		( self)      ->						List[Any]:







              '''simple docstring'''

              a__						:     int			=    self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_A)

       def 							__lowercase		( self)      ->						str:







              '''simple docstring'''

              a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_lm(*_A)

       def 							__lowercase		( self)      ->						Any:







              '''simple docstring'''

              a__						:     List[str]			=    self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_question_answering(*_A)

       def 							__lowercase		( self)      ->						Any:







              '''simple docstring'''

              a__						:     Any			=    self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_sequence_classification(*_A)

       def 							__lowercase		( self)      ->						List[Any]:







              '''simple docstring'''

              a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_token_classification(*_A)

       @slow
       def 							__lowercase		( self)      ->						Any:







              '''simple docstring'''

              a__						:     Optional[Any]			=    TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
              self.assertIsNotNone(_A)





@require_tf
class 			A__   (							unittest.TestCase     ):




       """simple docstring"""



       @unittest.skip(reason='Model not available yet')
       def 							__lowercase		( self)      ->						int:







              '''simple docstring'''

              pass

       @slow
       def 							__lowercase		( self)      ->						Union[str, Any]:







              '''simple docstring'''

              a__						:     int			=    TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
              a__						:     Dict			=    tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]])
              a__						:     Tuple			=    tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
              a__						:     str			=    model(_A ,  attention_mask=_A)[0]

              a__						:     Optional[int]			=    tf.constant(
                  [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]])
              tf.debugging.assert_near(output[:, 1:4, 1:4] ,  _A ,  atol=1e-4)









import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowercase				:	Optional[Any]      						=  """"""
lowercase				:	int      						=  """"""
lowercase				:	List[Any]      						=  """"""
lowercase				:	Optional[int]      						=  1  # (0 is vertical, 1 is horizontal)



def A_     (		)  ->		None:
       a__			,							a__						:     str			=    get_dataset(A__		,				A__    )
       print('Processing...'    )
       a__			,							a__			,							a__						:     Tuple			=    update_image_and_anno(A__		,				A__		,				A__    )

       for index, image in enumerate(A__    ):
              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              a__						:     int			=    random_chars(32    )
              a__						:     Optional[Any]			=    paths[index].split(os.sep    )[-1].rsplit('.'		,				1    )[0]
              a__						:     Optional[int]			=    F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
              cva.imwrite(F'/{file_root}.jpg'		,				A__		,				[cva.IMWRITE_JPEG_QUALITY, 85]    )
              print(F'Success {index+1}/{len(A__    )} with {file_name}'    )
              a__						:     List[str]			=    []
              for anno in new_annos[index]:
                     a__						:     Union[str, Any]			=    F'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
                     annos_list.append(A__    )
              with open(F'/{file_root}.txt'		,				'w'    ) as outfile:
                     outfile.write('\n'.join(line for line in annos_list    )    )



def A_     (		A__		,				A__    )  ->		tuple[list, list]:
       a__						:     int			=    []
       a__						:     int			=    []
       for label_file in glob.glob(os.path.join(A__		,				'*.txt'    )    ):
              a__						:     Optional[Any]			=    label_file.split(os.sep    )[-1].rsplit('.'		,				1    )[0]
              with open(A__    ) as in_file:
                     a__						:     Tuple			=    in_file.readlines()
              a__						:     Dict			=    os.path.join(A__		,				F'{label_name}.jpg'    )

              a__						:     int			=    []
              for obj_list in obj_lists:
                     a__						:     Union[str, Any]			=    obj_list.rstrip('\n'    ).split(' '    )
                     boxes.append(
                         [
                             int(obj[0]    ),
                             float(obj[1]    ),
                             float(obj[2]    ),
                             float(obj[3]    ),
                             float(obj[4]    ),
                         ]    )
              if not boxes:
                     continue
              img_paths.append(A__    )
              labels.append(A__    )
       return img_paths, labels



def A_     (		A__		,				A__		,				A__ = 1    )  ->		tuple[list, list, list]:
       a__						:     Optional[int]			=    []
       a__						:     Any			=    []
       a__						:     Dict			=    []
       for idx in range(len(A__    )    ):
              a__						:     Optional[int]			=    []
              a__						:     Optional[Any]			=    img_list[idx]
              path_list.append(A__    )
              a__						:     Union[str, Any]			=    anno_list[idx]
              a__						:     List[str]			=    cva.imread(A__    )
              if flip_type == 1:
                     a__						:     List[str]			=    cva.flip(A__		,				A__    )
                     for bbox in img_annos:
                            a__						:     Optional[Any]			=    1 - bbox[1]
                            new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]    )
              elif flip_type == 0:
                     a__						:     Optional[Any]			=    cva.flip(A__		,				A__    )
                     for bbox in img_annos:
                            a__						:     Optional[int]			=    1 - bbox[2]
                            new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]    )
              new_annos_lists.append(A__    )
              new_imgs_list.append(A__    )
       return new_imgs_list, new_annos_lists, path_list



def A_     (		A__ = 32    )  ->		str:
       assert number_char > 1, "The number of character should greater than 1"
       a__						:     Optional[int]			=    ascii_lowercase + digits
       return "".join(random.choice(A__    ) for _ in range(A__    )    )


if __name__ == "__main__":
    main()
    print("""DONE ✅""")


