
from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor
class       _lowerCAmelCase  :

 """simple docstring"""

 def __init__(						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=3					, SCREAMING_SNAKE_CASE__			:			Tuple=32					, SCREAMING_SNAKE_CASE__			:			Dict=3					, SCREAMING_SNAKE_CASE__			:			Tuple=10					, SCREAMING_SNAKE_CASE__			:			int=[10, 20, 30, 40]					, SCREAMING_SNAKE_CASE__			:			Dict=[1, 1, 2, 1]					, SCREAMING_SNAKE_CASE__			:			Tuple=True					, SCREAMING_SNAKE_CASE__			:			Dict=True					, SCREAMING_SNAKE_CASE__			:			List[str]="relu"					, SCREAMING_SNAKE_CASE__			:			int=3					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=None					, ):






      """simple docstring"""



      UpperCamelCase						    =					parent
      UpperCamelCase						    =					batch_size
      UpperCamelCase						    =					image_size
      UpperCamelCase						    =					num_channels
      UpperCamelCase						    =					embeddings_size
      UpperCamelCase						    =					hidden_sizes
      UpperCamelCase						    =					depths
      UpperCamelCase						    =					is_training
      UpperCamelCase						    =					use_labels
      UpperCamelCase						    =					hidden_act
      UpperCamelCase						    =					num_labels
      UpperCamelCase						    =					scope
      UpperCamelCase						    =					len(SCREAMING_SNAKE_CASE__   )

 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.num_labels   )

      UpperCamelCase						    =					self.get_config()
      return config, pixel_values, labels

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      return RegNetConfig(
          num_channels=self.num_channels					, embeddings_size=self.embeddings_size					, hidden_sizes=self.hidden_sizes					, depths=self.depths					, hidden_act=self.hidden_act					, num_labels=self.num_labels					, )

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






      """simple docstring"""



      UpperCamelCase						    =					TFRegNetModel(config=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, training=SCREAMING_SNAKE_CASE__   )
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape					, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					, )

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






      """simple docstring"""



      UpperCamelCase						    =					self.num_labels
      UpperCamelCase						    =					TFRegNetForImageClassification(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, labels=SCREAMING_SNAKE_CASE__					, training=SCREAMING_SNAKE_CASE__   )
      self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels)   )






 def      __lowerCAmelCase  (						self			:			int   ):






      """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_				:					str       						=(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=(
     {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
     if is_tf_available()
     else {}
 )

 SCREAMING_SNAKE_CASE_				:					Any       						=False
 SCREAMING_SNAKE_CASE_				:					Any       						=False
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=False
 SCREAMING_SNAKE_CASE_				:					Any       						=False
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=False

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






      """simple docstring"""



      UpperCamelCase						    =					TFRegNetModelTester(self   )
      UpperCamelCase						    =					ConfigTester(self					, config_class=SCREAMING_SNAKE_CASE__					, has_text_modality=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      return

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






      """simple docstring"""



      pass

 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices('GPU'   )   ) == 0					, reason='TF does not support backprop for grouped convolutions on CPU.'					, )
 @slow
 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      super().test_keras_fit()

 @unittest.skip(reason='RegNet does not support input and output embeddings'   )
 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      pass

 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			:			Union[str, Any]   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      def check_hidden_states_output(SCREAMING_SNAKE_CASE__			:			Dict					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			List[str]   ):
           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )					, training=SCREAMING_SNAKE_CASE__   )

           UpperCamelCase						    =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           UpperCamelCase						    =					self.model_tester.num_stages
           self.assertEqual(len(SCREAMING_SNAKE_CASE__   )					, expected_num_stages + 1   )

           # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]   )					, [self.model_tester.image_size // 2, self.model_tester.image_size // 2]					, )

      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					['basic', 'bottleneck']
      for model_class in self.all_model_classes:
           for layer_type in layers_type:
                UpperCamelCase						    =					layer_type
                UpperCamelCase						    =					True
                check_hidden_states_output(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                UpperCamelCase						    =					True

                check_hidden_states_output(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      def check_equivalence(SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			List[str]={}   ):
           UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, return_dict=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, return_dict=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).to_tuple()

           def recursive_check(SCREAMING_SNAKE_CASE__			:			Dict					, SCREAMING_SNAKE_CASE__			:			str   ):
                if isinstance(SCREAMING_SNAKE_CASE__					, (List, Tuple)   ):
                     for tuple_iterable_value, dict_iterable_value in zip(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ):
                          recursive_check(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
                elif tuple_object is None:
                     return
                else:
                     self.assertTrue(
                         all(tf.equal(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )   )					, msg=(
                             'Tuple and dict output are not equal. Difference:'
                             F' {tf.math.reduce_max(tf.abs(tuple_object - dict_object   )   )}'
                         )					, )

                recursive_check(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )

           UpperCamelCase						    =					self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           check_equivalence(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

           UpperCamelCase						    =					self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, return_labels=SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, return_labels=SCREAMING_SNAKE_CASE__   )
           check_equivalence(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

           UpperCamelCase						    =					self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           check_equivalence(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, {'output_hidden_states': True}   )

           UpperCamelCase						    =					self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, return_labels=SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, return_labels=SCREAMING_SNAKE_CASE__   )
           check_equivalence(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, {'output_hidden_states': True}   )

 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__   )






 @slow
 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



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




def  __lowerCamelCase					( )       ->      List[str]:
     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			:			Optional[int]   ):






      """simple docstring"""



      return (
          AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
          if is_vision_available()
          else None
      )






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






      """simple docstring"""



      UpperCamelCase						    =					TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

      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__					, training=SCREAMING_SNAKE_CASE__   )

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

      UpperCamelCase						    =					tf.constant([-0.4180, -1.5051, -3.4836]   )

      tf.debugging.assert_near(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) = }")


