






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
  from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class    __lowercase   :







     '''simple docstring'''




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


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



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

         __A       =						None
         if self.use_labels:
             __A       =						ids_tensor([self.batch_size]			,						self.num_labels					)

         __A       =						self.get_config()

         return config, pixel_values, labels




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         return ResNetConfig(
             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			,						image_size=self.image_size			,						)




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						TFResNetModel(config=UpperCamelCase_					)
         __A       =						model(UpperCamelCase_					)
         # 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[str]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



         __A       =						self.num_labels
         __A       =						TFResNetForImageClassification(UpperCamelCase_					)
         __A       =						model(UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)					)





     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



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


@require_tf
class    __lowercase   (						lowercase_      ,   lowercase_      ,   unittest.TestCase	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE     =					(
         {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     SCREAMING_SNAKE_CASE     =					False
     SCREAMING_SNAKE_CASE     =					False
     SCREAMING_SNAKE_CASE     =					False
     SCREAMING_SNAKE_CASE     =					False
     SCREAMING_SNAKE_CASE     =					False




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						TFResNetModelTester(self					)
         __A       =						ConfigTester(self			,						config_class=UpperCamelCase_			,						has_text_modality=UpperCamelCase_					)




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


         """simple docstring"""



         self.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()




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         return




     @unittest.skip(reason="""ResNet does not use inputs_embeds"""					)
     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         pass




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


         """simple docstring"""



         pass




     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()

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

             __A       =						["""pixel_values"""]
             self.assertListEqual(arg_names[:1]			,						UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase_					)




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


         """simple docstring"""



         def check_hidden_states_output(UpperCamelCase_							:      int			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Dict					):
             __A       =						model_class(UpperCamelCase_					)
             __A       =						model(**self._prepare_for_class(UpperCamelCase_			,						UpperCamelCase_					)					)

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

             __A       =						self.model_tester.num_stages
             self.assertEqual(len(UpperCamelCase_					)			,						expected_num_stages + 1					)

             # ResNet'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 // 4, self.model_tester.image_size // 4]			,						)

         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()
         __A       =						["""basic""", """bottleneck"""]
         for model_class in self.all_model_classes:
             for layer_type in layers_type:
                 __A       =						layer_type
                 __A       =						True
                 check_hidden_states_output(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)

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

                 check_hidden_states_output(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)




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


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_					)





     @slow
     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A       =						TFResNetModel.from_pretrained(UpperCamelCase_					)
             self.assertIsNotNone(UpperCamelCase_					)



def       _SCREAMING_SNAKE_CASE    (			)    ->   Any:





    """simple docstring"""

    __A       =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
    return image






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







     '''simple docstring'''




     @cached_property
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



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





     @slow
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         __A       =						TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)

         __A       =						self.default_image_processor
         __A       =						prepare_img()
         __A       =						image_processor(images=UpperCamelCase_			,						return_tensors="""tf"""					)

         # forward pass
         __A       =						model(**UpperCamelCase_					)

         # verify the logits
         __A       =						tf.TensorShape((1, 1_000)					)
         self.assertEqual(outputs.logits.shape			,						UpperCamelCase_					)

         __A       =						tf.constant([-11.1069, -9.7877, -8.3777]					)

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












def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]     )    ->   Any:





    """simple docstring"""

    stooge(__lowercase      ,					0      ,					len(__lowercase     ) - 1     )
    return arr



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      Dict      ,					__lowercase :      Dict     )    ->   int:





    """simple docstring"""

    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        __A     ,						__A       =						arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        __A       =						(int)((h - i + 1) / 3     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )

        # Recursively sort last 2/3 elements
        stooge(__lowercase      ,					i + t      ,					(__lowercase)     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )


if __name__ == "__main__":
  __a   :							List[Any]						= input("Enter numbers separated by a comma:\n").strip()
  __a   :							List[str]						= [int(item) for item in user_input.split(",")]
  print(stooge_sort(unsorted))




