




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       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Any			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Optional[Any]=13			,				lowerCAmelCase  :			List[str]=30			,				lowerCAmelCase  :			Union[str, Any]=2			,				lowerCAmelCase  :			List[str]=3			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			Union[str, Any]=True			,				lowerCAmelCase  :			Union[str, Any]=32			,				lowerCAmelCase  :			Optional[Any]=2			,				lowerCAmelCase  :			Dict=4			,				lowerCAmelCase  :			Union[str, Any]=37			,				lowerCAmelCase  :			int="gelu"			,				lowerCAmelCase  :			Optional[Any]=0.1			,				lowerCAmelCase  :			Optional[Any]=0.1			,				lowerCAmelCase  :			Optional[int]=10			,				lowerCAmelCase  :			List[str]=0.02			,				lowerCAmelCase  :			Dict=3			,				lowerCAmelCase  :			int=None			,				lowerCAmelCase  :			Optional[Any]=2			,				)	->				Any:




         """simple docstring"""

         lowercase__     =  parent
         lowercase__     =  batch_size
         lowercase__     =  image_size
         lowercase__     =  patch_size
         lowercase__     =  num_channels
         lowercase__     =  is_training
         lowercase__     =  use_labels
         lowercase__     =  hidden_size
         lowercase__     =  num_hidden_layers
         lowercase__     =  num_attention_heads
         lowercase__     =  intermediate_size
         lowercase__     =  hidden_act
         lowercase__     =  hidden_dropout_prob
         lowercase__     =  attention_probs_dropout_prob
         lowercase__     =  type_sequence_label_size
         lowercase__     =  initializer_range
         lowercase__     =  scope
         lowercase__     =  encoder_stride

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


   def 			UpperCAmelCase		(						self  :			List[Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

         lowercase__     =  self.get_config()

         return config, pixel_values, labels


   def 			UpperCAmelCase		(						self  :			str)	->				str:




         """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 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			str)	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  TFDeiTModel(config=lowercase_)
         lowercase__     =  model(lowercase_)
         self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size))


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			List[str]			,				lowerCAmelCase  :			Any)	->				Any:




         """simple docstring"""

         lowercase__     =  TFDeiTForMaskedImageModeling(config=lowercase_)
         lowercase__     =  model(lowercase_)
         self.parent.assertEqual(
             result.reconstruction.shape			,				(self.batch_size, self.num_channels, self.image_size, self.image_size))

         # test greyscale images
         lowercase__     =  1
         lowercase__     =  TFDeiTForMaskedImageModeling(lowercase_)

         lowercase__     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
         lowercase__     =  model(lowercase_)
         self.parent.assertEqual(result.reconstruction.shape			,				(self.batch_size, 1, self.image_size, self.image_size))


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Any)	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.type_sequence_label_size
         lowercase__     =  TFDeiTForImageClassification(lowercase_)
         lowercase__     =  model(lowercase_			,				labels=lowercase_)
         self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.type_sequence_label_size))

         # test greyscale images
         lowercase__     =  1
         lowercase__     =  TFDeiTForImageClassification(lowercase_)

         lowercase__     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
         lowercase__     =  model(lowercase_			,				labels=lowercase_)
         self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.type_sequence_label_size))



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




         """simple docstring"""

         lowercase__     =  self.prepare_config_and_inputs()
         lowercase__     =  config_and_inputs
         lowercase__     =  {"pixel_values": pixel_values}
         return config, inputs_dict



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

   '''simple docstring'''




   A    :  Optional[int]       				=    (
       (
           TFDeiTModel,
           TFDeiTForImageClassification,
           TFDeiTForImageClassificationWithTeacher,
           TFDeiTForMaskedImageModeling,
       )
       if is_tf_available()
       else ()
   )
   A    :  Union[str, Any]       				=    (
       {
           "feature-extraction": TFDeiTModel,
           "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
       }
       if is_tf_available()
       else {}
   )

   A    :  Tuple       				=    False
   A    :  str       				=    False
   A    :  int       				=    False
   A    :  str       				=    False


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  TFDeiTModelTester(self)
         lowercase__     =  ConfigTester(self			,				config_class=lowercase_			,				has_text_modality=lowercase_			,				hidden_size=37)


   def 			UpperCAmelCase		(						self  :			Dict)	->				Optional[Any]:




         """simple docstring"""

         self.config_tester.run_common_tests()


   @unittest.skip(reason='DeiT does not use inputs_embeds')
   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				List[Any]:




         """simple docstring"""

         pass


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				int:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase__     =  model_class(lowercase_)
               self.assertIsInstance(model.get_input_embeddings()			,				(tf.keras.layers.Layer))
               lowercase__     =  model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowercase_			,				tf.keras.layers.Dense))


   def 			UpperCAmelCase		(						self  :			Tuple)	->				str:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase__     =  model_class(lowercase_)
               lowercase__     =  inspect.signature(model.call)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase__     =  [*signature.parameters.keys()]

               lowercase__     =  ["pixel_values"]
               self.assertListEqual(arg_names[:1]			,				lowercase_)


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




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase_)


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




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_)


   def 			UpperCAmelCase		(						self  :			Any)	->				Any:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowercase_)


   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			List[Any]=False)	->				Dict:




         """simple docstring"""

         lowercase__     =  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 			UpperCAmelCase		(						self  :			Optional[Any])	->				Any:




         """simple docstring"""

         for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__     =  TFDeiTModel.from_pretrained(lowercase_)
               self.assertIsNotNone(lowercase_)

def   _lowerCAmelCase   (						):
      lowercase__     =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
      return image



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

   '''simple docstring'''




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




         """simple docstring"""

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



   @slow
   def 			UpperCAmelCase		(						self  :			List[Any])	->				Tuple:




         """simple docstring"""

         lowercase__     =  TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224')

         lowercase__     =  self.default_image_processor
         lowercase__     =  prepare_img()
         lowercase__     =  image_processor(images=lowercase_			,				return_tensors='tf')

         # forward pass
         lowercase__     =  model(**lowercase_)

         # verify the logits
         lowercase__     =  tf.TensorShape((1, 10_00))
         self.assertEqual(outputs.logits.shape			,				lowercase_)

         lowercase__     =  tf.constant([-1.02_66, 0.19_12, -1.28_61])

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





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  prime_factors(A__	)
      if is_square_free(A__	):
            return -1 if len(A__	) % 2 else 1
      return 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()
