





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFDeiTForImageClassification,
        TFDeiTForImageClassificationWithTeacher,
        TFDeiTForMaskedImageModeling,
        TFDeiTModel,
    )
    from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor





class 			A__   :




       """simple docstring"""



       def __init__( self ,  lowercase ,  lowercase=13 ,  lowercase=30 ,  lowercase=2 ,  lowercase=3 ,  lowercase=True ,  lowercase=True ,  lowercase=32 ,  lowercase=2 ,  lowercase=4 ,  lowercase=37 ,  lowercase="gelu" ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=10 ,  lowercase=0.02 ,  lowercase=3 ,  lowercase=None ,  lowercase=2 ,  )      ->						Tuple:







              '''simple docstring'''

              a__						:     List[str]			=    parent
              a__						:     Optional[int]			=    batch_size
              a__						:     Dict			=    image_size
              a__						:     Any			=    patch_size
              a__						:     int			=    num_channels
              a__						:     str			=    is_training
              a__						:     List[str]			=    use_labels
              a__						:     List[Any]			=    hidden_size
              a__						:     Optional[int]			=    num_hidden_layers
              a__						:     List[str]			=    num_attention_heads
              a__						:     int			=    intermediate_size
              a__						:     Tuple			=    hidden_act
              a__						:     int			=    hidden_dropout_prob
              a__						:     Dict			=    attention_probs_dropout_prob
              a__						:     Optional[int]			=    type_sequence_label_size
              a__						:     Union[str, Any]			=    initializer_range
              a__						:     Any			=    scope
              a__						:     Optional[int]			=    encoder_stride

              # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
              a__						:     Union[str, Any]			=    (image_size // patch_size) ** 2
              a__						:     Dict			=    num_patches + 2

       def 							__lowercase		( self)      ->						Tuple:







              '''simple docstring'''

              a__						:     int			=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

              a__						:     int			=    None
              if self.use_labels:
                     a__						:     int			=    ids_tensor([self.batch_size] ,  self.type_sequence_label_size)

              a__						:     Dict			=    self.get_config()

              return config, pixel_values, labels

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







              '''simple docstring'''

              return DeiTConfig(
                  image_size=self.image_size ,  patch_size=self.patch_size ,  num_channels=self.num_channels ,  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 ,  is_decoder=lowercase ,  initializer_range=self.initializer_range ,  encoder_stride=self.encoder_stride ,  )

       def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						List[str]:







              '''simple docstring'''

              a__						:     Tuple			=    TFDeiTModel(config=lowercase)
              a__						:     int			=    model(lowercase)
              self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size))

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







              '''simple docstring'''

              a__						:     int			=    TFDeiTForMaskedImageModeling(config=lowercase)
              a__						:     Union[str, Any]			=    model(lowercase)
              self.parent.assertEqual(
                  result.reconstruction.shape ,  (self.batch_size, self.num_channels, self.image_size, self.image_size))

              # test greyscale images
              a__						:     List[str]			=    1
              a__						:     List[str]			=    TFDeiTForMaskedImageModeling(lowercase)

              a__						:     Tuple			=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
              a__						:     Tuple			=    model(lowercase)
              self.parent.assertEqual(result.reconstruction.shape ,  (self.batch_size, 1, self.image_size, self.image_size))

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







              '''simple docstring'''

              a__						:     List[str]			=    self.type_sequence_label_size
              a__						:     Optional[Any]			=    TFDeiTForImageClassification(lowercase)
              a__						:     Optional[Any]			=    model(lowercase ,  labels=lowercase)
              self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size))

              # test greyscale images
              a__						:     str			=    1
              a__						:     Optional[int]			=    TFDeiTForImageClassification(lowercase)

              a__						:     int			=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
              a__						:     Optional[int]			=    model(lowercase ,  labels=lowercase)
              self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size))

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







              '''simple docstring'''

              a__						:     Optional[Any]			=    self.prepare_config_and_inputs()
              a__						:     str			=    config_and_inputs
              a__						:     Optional[int]			=    {'pixel_values': pixel_values}
              return config, inputs_dict





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




       """simple docstring"""



       __A       :    Optional[int]	  =  (
           (
               TFDeiTModel,
               TFDeiTForImageClassification,
               TFDeiTForImageClassificationWithTeacher,
               TFDeiTForMaskedImageModeling,
           )
           if is_tf_available()
           else ()
       )
       __A       :    Optional[int]	  =  (
           {
               '''feature-extraction''': TFDeiTModel,
               '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
           }
           if is_tf_available()
           else {}
       )

       __A       :    List[Any]	  =  False
       __A       :    Any	  =  False
       __A       :    List[Any]	  =  False
       __A       :    Union[str, Any]	  =  False

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







              '''simple docstring'''

              a__						:     Union[str, Any]			=    TFDeiTModelTester(self)
              a__						:     Any			=    ConfigTester(self ,  config_class=lowercase ,  has_text_modality=lowercase ,  hidden_size=37)

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







              '''simple docstring'''

              self.config_tester.run_common_tests()

       @unittest.skip(reason='DeiT does not use inputs_embeds')
       def 							__lowercase		( self)      ->						Any:







              '''simple docstring'''

              pass

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







              '''simple docstring'''

              a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     a__						:     Optional[int]			=    model_class(lowercase)
                     self.assertIsInstance(model.get_input_embeddings() ,  (tf.keras.layers.Layer))
                     a__						:     Union[str, Any]			=    model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(lowercase ,  tf.keras.layers.Dense))

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







              '''simple docstring'''

              a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     a__						:     str			=    model_class(lowercase)
                     a__						:     Tuple			=    inspect.signature(model.call)
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     a__						:     Union[str, Any]			=    [*signature.parameters.keys()]

                     a__						:     Tuple			=    ['pixel_values']
                     self.assertListEqual(arg_names[:1] ,  lowercase)

       def 							__lowercase		( self)      ->						Tuple:







              '''simple docstring'''

              a__						:     Dict			=    self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*lowercase)

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







              '''simple docstring'''

              a__						:     Any			=    self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_image_modeling(*lowercase)

       def 							__lowercase		( self)      ->						Any:







              '''simple docstring'''

              a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*lowercase)

       def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase=False)      ->						Dict:







              '''simple docstring'''

              a__						:     List[Any]			=    super()._prepare_for_class(lowercase ,  lowercase ,  return_labels=lowercase)

              if return_labels:
                     if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call).parameters:
                            del inputs_dict["labels"]

              return inputs_dict

       @slow
       def 							__lowercase		( self)      ->						Optional[Any]:







              '''simple docstring'''

              for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     a__						:     Union[str, Any]			=    TFDeiTModel.from_pretrained(lowercase)
                     self.assertIsNotNone(lowercase)



def A_     (		)  ->		Any:
       a__						:     List[Any]			=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
       return image





@require_tf
@require_vision
class 			A__   (							unittest.TestCase     ):




       """simple docstring"""



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







              '''simple docstring'''

              return (
                  DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
                  if is_vision_available()
                  else None
              )

       @slow
       def 							__lowercase		( self)      ->						str:







              '''simple docstring'''

              a__						:     int			=    TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224')

              a__						:     List[str]			=    self.default_image_processor
              a__						:     str			=    prepare_img()
              a__						:     str			=    image_processor(images=lowercase ,  return_tensors='tf')

              # forward pass
              a__						:     Tuple			=    model(**lowercase)

              # verify the logits
              a__						:     Optional[Any]			=    tf.TensorShape((1, 1000))
              self.assertEqual(outputs.logits.shape ,  lowercase)

              a__						:     Optional[int]			=    tf.constant([-1.02_66, 0.19_12, -1.28_61])

              self.assertTrue(np.allclose(outputs.logits[0, :3] ,  lowercase ,  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 ✅""")


