
from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
 from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.config_class(**self.inputs_dict   )
      self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'embed_dim'   )   )
      self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'num_heads'   )   )




class       _lowerCAmelCase  :

 """simple docstring"""

 def __init__(						self			:			Any					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			List[str]=13					, SCREAMING_SNAKE_CASE__			:			Dict=64					, SCREAMING_SNAKE_CASE__			:			Tuple=3					, SCREAMING_SNAKE_CASE__			:			Tuple=[16, 48, 96]					, SCREAMING_SNAKE_CASE__			:			Any=[1, 3, 6]					, SCREAMING_SNAKE_CASE__			:			List[Any]=[1, 2, 10]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=[7, 3, 3]					, SCREAMING_SNAKE_CASE__			:			Any=[4, 2, 2]					, SCREAMING_SNAKE_CASE__			:			Any=[2, 1, 1]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=[2, 2, 2]					, SCREAMING_SNAKE_CASE__			:			str=[False, False, True]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=[0.0, 0.0, 0.0]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0.02					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=1e-12					, SCREAMING_SNAKE_CASE__			:			Tuple=True					, SCREAMING_SNAKE_CASE__			:			int=True					, SCREAMING_SNAKE_CASE__			:			int=2					, ):






      """simple docstring"""



      UpperCamelCase						    =					parent
      UpperCamelCase						    =					batch_size
      UpperCamelCase						    =					image_size
      UpperCamelCase						    =					patch_sizes
      UpperCamelCase						    =					patch_stride
      UpperCamelCase						    =					patch_padding
      UpperCamelCase						    =					is_training
      UpperCamelCase						    =					use_labels
      UpperCamelCase						    =					num_labels
      UpperCamelCase						    =					num_channels
      UpperCamelCase						    =					embed_dim
      UpperCamelCase						    =					num_heads
      UpperCamelCase						    =					stride_kv
      UpperCamelCase						    =					depth
      UpperCamelCase						    =					cls_token
      UpperCamelCase						    =					attention_drop_rate
      UpperCamelCase						    =					initializer_range
      UpperCamelCase						    =					layer_norm_eps

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






      """simple docstring"""



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

      UpperCamelCase						    =					None
      if self.use_labels:
           # create a random int32 tensor of given shape
           UpperCamelCase						    =					ids_tensor([self.batch_size]					, self.num_labels   )

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

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      return CvtConfig(
          image_size=self.image_size					, num_labels=self.num_labels					, num_channels=self.num_channels					, embed_dim=self.embed_dim					, num_heads=self.num_heads					, patch_sizes=self.patch_sizes					, patch_padding=self.patch_padding					, patch_stride=self.patch_stride					, stride_kv=self.stride_kv					, depth=self.depth					, cls_token=self.cls_token					, attention_drop_rate=self.attention_drop_rate					, initializer_range=self.initializer_range					, )

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






      """simple docstring"""



      UpperCamelCase						    =					TFCvtModel(config=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, training=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					(self.image_size, self.image_size)
      UpperCamelCase    ,		UpperCamelCase						    =					image_size[0], image_size[1]
      for i in range(len(self.depth   )   ):
           UpperCamelCase						    =					floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1   )
           UpperCamelCase						    =					floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1   )
      self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.embed_dim[-1], height, width)   )

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






      """simple docstring"""



      UpperCamelCase						    =					self.num_labels
      UpperCamelCase						    =					TFCvtForImageClassification(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			:			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_				:					str       						=(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=(
     {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
     if is_tf_available()
     else {}
 )
 SCREAMING_SNAKE_CASE_				:					str       						=False
 SCREAMING_SNAKE_CASE_				:					List[str]       						=False
 SCREAMING_SNAKE_CASE_				:					Dict       						=False
 SCREAMING_SNAKE_CASE_				:					Dict       						=False
 SCREAMING_SNAKE_CASE_				:					Any       						=False

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					TFCvtModelTester(self   )
      UpperCamelCase						    =					TFCvtConfigTester(self					, config_class=SCREAMING_SNAKE_CASE__					, has_text_modality=SCREAMING_SNAKE_CASE__					, hidden_size=37   )

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      self.config_tester.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()

 @unittest.skip(reason='Cvt does not output attentions'   )
 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      pass

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






      """simple docstring"""



      pass

 @unittest.skip(reason='Cvt does not support input and output embeddings'   )
 def      __lowerCAmelCase  (						self			:			Any   ):






      """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.'					, )
 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      super().test_dataset_conversion()

 @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			:			Optional[int]   ):






      """simple docstring"""



      super().test_keras_fit()

 @unittest.skip(reason='Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'   )
 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					tf.keras.mixed_precision.Policy('mixed_float16'   )
      tf.keras.mixed_precision.set_global_policy(SCREAMING_SNAKE_CASE__   )
      super().test_keras_fit()
      tf.keras.mixed_precision.set_global_policy('float32'   )

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """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[int]   ):






      """simple docstring"""



      def check_hidden_states_output(SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Optional[int]   ):
           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )

           UpperCamelCase						    =					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )   )
           UpperCamelCase						    =					outputs.hidden_states

           UpperCamelCase						    =					len(self.model_tester.depth   )
           self.assertEqual(len(SCREAMING_SNAKE_CASE__   )					, SCREAMING_SNAKE_CASE__   )

           # verify the first hidden states (first block)
           self.assertListEqual(
               list(hidden_states[0].shape[-3:]   )					, [
                   self.model_tester.embed_dim[0],
                   self.model_tester.image_size // 4,
                   self.model_tester.image_size // 4,
               ]					, )

      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           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			:			str   ):






      """simple docstring"""



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






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






      """simple docstring"""



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




def  __lowerCamelCase					( )       ->      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_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )






 @slow
 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					TFCvtForImageClassification.from_pretrained(TF_CVT_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__   )

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

      UpperCamelCase						    =					tf.constant([0.9285, 0.9015, -0.3150]   )
      self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()					, 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) = }")


