
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       _lowerCAmelCase  :

 """simple docstring"""

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






      """simple docstring"""



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

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

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



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

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

      UpperCamelCase						    =					self.get_config()

      return config, pixel_values, labels

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """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=SCREAMING_SNAKE_CASE__					, initializer_range=self.initializer_range					, encoder_stride=self.encoder_stride					, )

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			List[Any]   ):






      """simple docstring"""



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

      # test greyscale images
      UpperCamelCase						    =					1
      UpperCamelCase						    =					TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE__   )

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

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



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

      # test greyscale images
      UpperCamelCase						    =					1
      UpperCamelCase						    =					TFDeiTForImageClassification(SCREAMING_SNAKE_CASE__   )

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






 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.prepare_config_and_inputs()
      UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					config_and_inputs
      UpperCamelCase						    =					{'pixel_values': pixel_values}
      return config, inputs_dict




@require_tf
class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=(
     (
         TFDeiTModel,
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=(
     {
         "feature-extraction": TFDeiTModel,
         "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
     }
     if is_tf_available()
     else {}
 )

 SCREAMING_SNAKE_CASE_				:					Dict       						=False
 SCREAMING_SNAKE_CASE_				:					Tuple       						=False
 SCREAMING_SNAKE_CASE_				:					List[str]       						=False
 SCREAMING_SNAKE_CASE_				:					int       						=False

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      UpperCamelCase						    =					TFDeiTModelTester(self   )
      UpperCamelCase						    =					ConfigTester(self					, config_class=SCREAMING_SNAKE_CASE__					, has_text_modality=SCREAMING_SNAKE_CASE__					, hidden_size=37   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      self.config_tester.run_common_tests()

 @unittest.skip(reason='DeiT does not use inputs_embeds'   )
 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

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

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

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

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

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Tuple=False   ):






      """simple docstring"""



      UpperCamelCase						    =					super()._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, return_labels=SCREAMING_SNAKE_CASE__   )

      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      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



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




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







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

 """simple docstring"""

 @cached_property
 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



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






 @slow
 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



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

      UpperCamelCase						    =					self.default_image_processor
      UpperCamelCase						    =					prepare_img()
      UpperCamelCase						    =					image_processor(images=SCREAMING_SNAKE_CASE__					, return_tensors='tf'   )

      # forward pass
      UpperCamelCase						    =					model(**SCREAMING_SNAKE_CASE__   )

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

      UpperCamelCase						    =					tf.constant([-1.0266, 0.1912, -1.2861]   )

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




def  __lowerCamelCase					( _lowercase			)       ->      list:
     for i in range(len(_lowercase			) - 1						,     0						,     -1			):
          UpperCamelCase						    =					False

          for j in range(_lowercase						,     0						,     -1			):
               if unsorted[j] < unsorted[j - 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j - 1], unsorted[j]
                    UpperCamelCase						    =					True

          for j in range(_lowercase			):
               if unsorted[j] > unsorted[j + 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j + 1], unsorted[j]
                    UpperCamelCase						    =					True

          if not swapped:
               break
     return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _snake_case      = input('''Enter numbers separated by a comma:\n''').strip()
 _snake_case      = [int(item) for item in user_input.split(''',''')]
 print(F"{cocktail_shaker_sort(unsorted) = }")


