



'''simple docstring'''





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     UpperCAmelCase_       :
    """simple docstring"""







    def __init__(    self					:     int ,     snake_case_					:     Tuple ,     snake_case_					:     List[str]=3 ,     snake_case_					:     Tuple=32 ,     snake_case_					:     List[Any]=3 ,     snake_case_					:     List[str]=10 ,     snake_case_					:     List[str]=[10, 20, 30, 40] ,     snake_case_					:     Tuple=[1, 1, 2, 1] ,     snake_case_					:     Tuple=True ,     snake_case_					:     str=True ,     snake_case_					:     int="relu" ,     snake_case_					:     List[Any]=3 ,     snake_case_					:     str=None ,     ):
          snake_case__  :    List[Any]    						=      parent
          snake_case__  :    List[Any]    						=      batch_size
          snake_case__  :    int    						=      image_size
          snake_case__  :    List[Any]    						=      num_channels
          snake_case__  :    Optional[Any]    						=      embeddings_size
          snake_case__  :    Optional[int]    						=      hidden_sizes
          snake_case__  :    Tuple    						=      depths
          snake_case__  :    Any    						=      is_training
          snake_case__  :    Optional[int]    						=      use_labels
          snake_case__  :    Optional[int]    						=      hidden_act
          snake_case__  :    Optional[int]    						=      num_labels
          snake_case__  :    int    						=      scope
          snake_case__  :    Tuple    						=      len(snake_case_						)




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    Union[str, Any]    						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

          snake_case__  :    Union[str, Any]    						=      None
          if self.use_labels:
                snake_case__  :    Optional[Any]    						=      ids_tensor([self.batch_size] ,     self.num_labels						)

          snake_case__  :    List[str]    						=      self.get_config()

          return config, pixel_values, labels




    def lowerCamelCase       (    self					:     int						):
          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					:     Tuple ,     snake_case_					:     Tuple ,     snake_case_					:     List[Any] ,     snake_case_					:     Optional[int]						):
          snake_case__  :    Optional[Any]    						=      TFResNetModel(config=snake_case_						)
          snake_case__  :    int    						=      model(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[str] ,     snake_case_					:     List[str] ,     snake_case_					:     str ,     snake_case_					:     Union[str, Any]						):
          snake_case__  :    str    						=      self.num_labels
          snake_case__  :    Optional[int]    						=      TFResNetForImageClassification(snake_case_						)
          snake_case__  :    Tuple    						=      model(snake_case_ ,     labels=snake_case_						)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_labels)						)




    def lowerCamelCase       (    self					:     Tuple						):
          snake_case__  :    List[Any]    						=      self.prepare_config_and_inputs()
          snake_case__						,				snake_case__						,				snake_case__  :    str    						=      config_and_inputs
          snake_case__  :    int    						=      {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_tf
class     UpperCAmelCase_       (       _a		,		_a		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
    lowercase					=		(
        {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
        if is_tf_available()
        else {}
    )

    lowercase					=		False
    lowercase					=		False
    lowercase					=		False
    lowercase					=		False
    lowercase					=		False




    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    Tuple    						=      TFResNetModelTester(self						)
          snake_case__  :    List[str]    						=      ConfigTester(self ,     config_class=snake_case_ ,     has_text_modality=snake_case_						)




    def lowerCamelCase       (    self					:     Dict						):
          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					:     str						):
          return




    @unittest.skip(reason="""ResNet does not use inputs_embeds"""						)
    def lowerCamelCase       (    self					:     int						):
          pass




    @unittest.skip(reason="""ResNet does not support input and output embeddings"""						)
    def lowerCamelCase       (    self					:     List[Any]						):
          pass




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__						,				snake_case__  :    List[Any]    						=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__  :    Dict    						=      model_class(snake_case_						)
                snake_case__  :    Optional[Any]    						=      inspect.signature(model.call						)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case__  :    Union[str, Any]    						=      [*signature.parameters.keys()]

                snake_case__  :    Optional[int]    						=      ["""pixel_values"""]
                self.assertListEqual(arg_names[:1] ,     snake_case_						)




    def lowerCamelCase       (    self					:     Union[str, Any]						):
          snake_case__  :    Optional[int]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_						)




    def lowerCamelCase       (    self					:     List[str]						):
          def check_hidden_states_output(snake_case_					:     Any ,     snake_case_					:     Any ,     snake_case_					:     List[str]						):
                snake_case__  :    List[Any]    						=      model_class(snake_case_						)
                snake_case__  :    Dict    						=      model(**self._prepare_for_class(snake_case_ ,     snake_case_						)						)

                snake_case__  :    str    						=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                snake_case__  :    List[Any]    						=      self.model_tester.num_stages
                self.assertEqual(len(snake_case_						) ,     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] ,     )

          snake_case__						,				snake_case__  :    Any    						=      self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__  :    List[Any]    						=      ["""basic""", """bottleneck"""]
          for model_class in self.all_model_classes:
                for layer_type in layers_type:
                      snake_case__  :    Dict    						=      layer_type
                      snake_case__  :    Optional[int]    						=      True
                      check_hidden_states_output(snake_case_ ,     snake_case_ ,     snake_case_						)

                      # check that output_hidden_states also work using config
                      del inputs_dict["output_hidden_states"]
                      snake_case__  :    List[Any]    						=      True

                      check_hidden_states_output(snake_case_ ,     snake_case_ ,     snake_case_						)




    def lowerCamelCase       (    self					:     Optional[Any]						):
          snake_case__  :    Tuple    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_						)




    @slow
    def lowerCamelCase       (    self					:     Optional[Any]						):
          for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__  :    str    						=      TFResNetModel.from_pretrained(snake_case_						)
                self.assertIsNotNone(snake_case_						)





def 						__snake_case(				)		-> Optional[int]:
      snake_case__  :    Optional[Any]    						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
      return image







@require_tf
@require_vision
class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    @cached_property
    def lowerCamelCase       (    self					:     List[Any]						):
          return (
              AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
              if is_vision_available()
              else None
          )




    @slow
    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    List[str]    						=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

          snake_case__  :    List[Any]    						=      self.default_image_processor
          snake_case__  :    List[Any]    						=      prepare_img()
          snake_case__  :    List[str]    						=      image_processor(images=snake_case_ ,     return_tensors="""tf"""						)

          # forward pass
          snake_case__  :    Optional[Any]    						=      model(**snake_case_						)

          # verify the logits
          snake_case__  :    Union[str, Any]    						=      tf.TensorShape((1, 1_000)						)
          self.assertEqual(outputs.logits.shape ,     snake_case_						)

          snake_case__  :    List[str]    						=      tf.constant([-11.1069, -9.7877, -8.3777]						)

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


"""simple docstring"""




from __future__ import annotations


def   __magic_name__	(      __snake_case				:       list[int]  ) ->				list[int]:
       if len(__snake_case  ) == 0:
              return array

       lowercase		,    lowercase    :				Tuple       		=							min(__snake_case  ), max(__snake_case  )

       # Compute the variables
       lowercase    :				Optional[Any]       		=							_max - _min + 1
       lowercase		,    lowercase    :				List[str]       		=							[0] * holes_range, [0] * holes_range

       # Make the sorting.
       for i in array:
              lowercase    :				Tuple       		=							i - _min
              lowercase    :				str       		=							i
              holes_repeat[index] += 1

       # Makes the array back by replacing the numbers.
       lowercase    :				Union[str, Any]       		=							0
       for i in range(__snake_case  ):
              while holes_repeat[i] > 0:
                     lowercase    :				Tuple       		=							holes[i]
                     index += 1
                     holes_repeat[i] -= 1

    # Returns the sorted array.
       return array


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _A			:       str   		=       input("""Enter numbers separated by comma:\n""")
   _A			:       Optional[Any]   		=       [int(x) for x in user_input.split(""",""")]
   print(pigeon_sort(unsorted))


