


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 							lowercase  :







     def __init__(						self :						List[Any]   ,   _lowercase :						List[str]   ,   _lowercase :						Dict=13   ,   _lowercase :						Any=30   ,   _lowercase :						Optional[int]=2   ,   _lowercase :						List[str]=3   ,   _lowercase :						Optional[Any]=True   ,   _lowercase :						Optional[Any]=True   ,   _lowercase :						Tuple=32   ,   _lowercase :						Any=2   ,   _lowercase :						int=4   ,   _lowercase :						str=37   ,   _lowercase :						str="gelu"   ,   _lowercase :						int=0.1   ,   _lowercase :						Tuple=0.1   ,   _lowercase :						int=10   ,   _lowercase :						Union[str, Any]=0.02   ,   _lowercase :						Any=3   ,   _lowercase :						str=None   ,   _lowercase :						int=2   ,   ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		parent
           SCREAMING_SNAKE_CASE__       :  Any						     =		batch_size
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		image_size
           SCREAMING_SNAKE_CASE__       :  str						     =		patch_size
           SCREAMING_SNAKE_CASE__       :  str						     =		num_channels
           SCREAMING_SNAKE_CASE__       :  Dict						     =		is_training
           SCREAMING_SNAKE_CASE__       :  str						     =		use_labels
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		hidden_size
           SCREAMING_SNAKE_CASE__       :  Any						     =		num_hidden_layers
           SCREAMING_SNAKE_CASE__       :  Dict						     =		num_attention_heads
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		intermediate_size
           SCREAMING_SNAKE_CASE__       :  int						     =		hidden_act
           SCREAMING_SNAKE_CASE__       :  str						     =		hidden_dropout_prob
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__       :  str						     =		type_sequence_label_size
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		initializer_range
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		scope
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		encoder_stride

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







     def 							lowercase__       (						self :						Union[str, Any]      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		ids_tensor([self.batch_size]   ,   self.type_sequence_label_size      )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.get_config()

           return config, pixel_values, labels







     def 							lowercase__       (						self :						str      ):
           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 :						List[Any]   ,   _lowercase :						Dict   ,   _lowercase :						Any   ,   _lowercase :						str      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		TFDeiTModel(config=_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		model(_lowercase      )
           self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)      )







     def 							lowercase__       (						self :						Optional[int]   ,   _lowercase :						Union[str, Any]   ,   _lowercase :						int   ,   _lowercase :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		TFDeiTForMaskedImageModeling(config=_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Any						     =		model(_lowercase      )
           self.parent.assertEqual(
               result.reconstruction.shape   ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

           # test greyscale images
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		1
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		TFDeiTForMaskedImageModeling(_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Any						     =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		model(_lowercase      )
           self.parent.assertEqual(result.reconstruction.shape   ,   (self.batch_size, 1, self.image_size, self.image_size)      )







     def 							lowercase__       (						self :						List[Any]   ,   _lowercase :						Any   ,   _lowercase :						List[Any]   ,   _lowercase :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.type_sequence_label_size
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		TFDeiTForImageClassification(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		model(_lowercase   ,   labels=_lowercase      )
           self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)      )

           # test greyscale images
           SCREAMING_SNAKE_CASE__       :  str						     =		1
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		TFDeiTForImageClassification(_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Any						     =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		model(_lowercase   ,   labels=_lowercase      )
           self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)      )







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




@require_tf
class 							lowercase  (						_UpperCAmelCase    ,       _UpperCAmelCase    ,       unittest.TestCase			):
     lowerCamelCase			:			Optional[Any]      = (
         (
             TFDeiTModel,
             TFDeiTForImageClassification,
             TFDeiTForImageClassificationWithTeacher,
             TFDeiTForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     lowerCamelCase			:			Optional[Any]      = (
         {
             '''feature-extraction''': TFDeiTModel,
             '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
         }
         if is_tf_available()
         else {}
     )

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







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		TFDeiTModelTester(self      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		ConfigTester(self   ,   config_class=_lowercase   ,   has_text_modality=_lowercase   ,   hidden_size=37      )







     def 							lowercase__       (						self :						Dict      ):
           self.config_tester.run_common_tests()







     @unittest.skip(reason='''DeiT does not use inputs_embeds'''      )
     def 							lowercase__       (						self :						Any      ):
           pass







     def 							lowercase__       (						self :						List[str]      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.model_tester.prepare_config_and_inputs_for_common()

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







     def 							lowercase__       (						self :						int      ):
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE__       :  int						     =		['''pixel_values''']
                 self.assertListEqual(arg_names[:1]   ,   _lowercase      )







     def 							lowercase__       (						self :						int      ):
           SCREAMING_SNAKE_CASE__       :  Any						     =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_lowercase      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*_lowercase      )







     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*_lowercase      )







     def 							lowercase__       (						self :						Tuple   ,   _lowercase :						Dict   ,   _lowercase :						Dict   ,   _lowercase :						List[Any]=False      ):
           SCREAMING_SNAKE_CASE__       :  int						     =		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 :						str      ):
           for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		TFDeiTModel.from_pretrained(_lowercase      )
                 self.assertIsNotNone(_lowercase      )




def    a   (							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  str						     =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      return image





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







     @cached_property
     def 							lowercase__       (						self :						int      ):
           return (
               DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      )
               if is_vision_available()
               else None
           )







     @slow
     def 							lowercase__       (						self :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      )

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.default_image_processor
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		prepare_img()
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		image_processor(images=_lowercase   ,   return_tensors='''tf'''      )

           # forward pass
           SCREAMING_SNAKE_CASE__       :  str						     =		model(**_lowercase      )

           # verify the logits
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		tf.TensorShape((1, 10_00)      )
           self.assertEqual(outputs.logits.shape   ,   _lowercase      )

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		tf.constant([-1.0266, 0.1912, -1.2861]      )

           self.assertTrue(np.allclose(outputs.logits[0, :3]   ,   _lowercase   ,   atol=1E-4      )      )












'''simple docstring'''

from numpy import exp, pi, sqrt


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0      )						->   int:

					'''simple docstring'''

					return 1 / sqrt(2 * pi * sigma**2      ) * exp(-((x - mu) ** 2) / (2 * sigma**2)      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

