


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 							UpperCamelCase__ :

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






										'''simple docstring'''






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

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






										'''simple docstring'''






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

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

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

					def 	UpperCAmelCase__							(  self							: Optional[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 	UpperCAmelCase__							(  self							: Optional[Any]				,       UpperCamelCase__							: Tuple				,       UpperCamelCase__							: Dict				,       UpperCamelCase__							: int							):






										'''simple docstring'''






										lowercase_					=      TFRegNetModel(config=__lowerCAmelCase							)
										lowercase_					=      model(__lowerCAmelCase				,       training=__lowerCAmelCase							)
										# 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 	UpperCAmelCase__							(  self							: List[str]				,       UpperCamelCase__							: Optional[Any]				,       UpperCamelCase__							: List[Any]				,       UpperCamelCase__							: int							):






										'''simple docstring'''






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







					def 	UpperCAmelCase__							(  self							: Any							):






										'''simple docstring'''






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







@require_tf
class 							UpperCamelCase__ (						SCREAMING_SNAKE_CASE__				,					SCREAMING_SNAKE_CASE__				,					unittest.TestCase ):
					__SCREAMING_SNAKE_CASE							:	Dict            = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
					__SCREAMING_SNAKE_CASE							:	Optional[int]            = (
					    {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					__SCREAMING_SNAKE_CASE							:	str            = False
					__SCREAMING_SNAKE_CASE							:	str            = False
					__SCREAMING_SNAKE_CASE							:	int            = False
					__SCREAMING_SNAKE_CASE							:	str            = False
					__SCREAMING_SNAKE_CASE							:	Tuple            = False

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






										'''simple docstring'''






										lowercase_					=      TFRegNetModelTester(self							)
										lowercase_					=      ConfigTester(self				,       config_class=__lowerCAmelCase				,       has_text_modality=__lowerCAmelCase							)

					def 	UpperCAmelCase__							(  self							: Optional[Any]							):






										'''simple docstring'''






										return

					@unittest.skip(reason="""RegNet does not use inputs_embeds"""							)
					def 	UpperCAmelCase__							(  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 	UpperCAmelCase__							(  self							: Optional[Any]							):






										'''simple docstring'''






										super().test_keras_fit()

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






										'''simple docstring'''






										pass

					def 	UpperCAmelCase__							(  self							: List[str]							):






										'''simple docstring'''






										lowercase_					=      self.model_tester.prepare_config_and_inputs_for_common()

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

															lowercase_					=      ["""pixel_values"""]
															self.assertListEqual(arg_names[:1]				,       __lowerCAmelCase							)

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






										'''simple docstring'''






										lowercase_					=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowerCAmelCase							)

					def 	UpperCAmelCase__							(  self							: Dict							):






										'''simple docstring'''






										def check_hidden_states_output(UpperCamelCase__							: int				,       UpperCamelCase__							: Dict				,       UpperCamelCase__							: Dict							):
															lowercase_					=      model_class(__lowerCAmelCase							)
															lowercase_					=      model(**self._prepare_for_class(__lowerCAmelCase				,       __lowerCAmelCase							)				,       training=__lowerCAmelCase							)

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

															lowercase_					=      self.model_tester.num_stages
															self.assertEqual(len(__lowerCAmelCase							)				,       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]				,       )

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

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

																				check_hidden_states_output(__lowerCAmelCase				,       __lowerCAmelCase				,       __lowerCAmelCase							)

					def 	UpperCAmelCase__							(  self							: Dict							):






										'''simple docstring'''






										lowercase_					=      self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(UpperCamelCase__							: Optional[int]				,       UpperCamelCase__							: Optional[Any]				,       UpperCamelCase__							: List[str]				,       UpperCamelCase__							: Any={}							):
															lowercase_					=      model(__lowerCAmelCase				,       return_dict=__lowerCAmelCase				,       **__lowerCAmelCase							)
															lowercase_					=      model(__lowerCAmelCase				,       return_dict=__lowerCAmelCase				,       **__lowerCAmelCase							).to_tuple()

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

																				recursive_check(__lowerCAmelCase				,       __lowerCAmelCase							)

										for model_class in self.all_model_classes:
															lowercase_					=      model_class(__lowerCAmelCase							)

															lowercase_					=      self._prepare_for_class(__lowerCAmelCase				,       __lowerCAmelCase							)
															lowercase_					=      self._prepare_for_class(__lowerCAmelCase				,       __lowerCAmelCase							)
															check_equivalence(__lowerCAmelCase				,       __lowerCAmelCase				,       __lowerCAmelCase							)

															lowercase_					=      self._prepare_for_class(__lowerCAmelCase				,       __lowerCAmelCase				,       return_labels=__lowerCAmelCase							)
															lowercase_					=      self._prepare_for_class(__lowerCAmelCase				,       __lowerCAmelCase				,       return_labels=__lowerCAmelCase							)
															check_equivalence(__lowerCAmelCase				,       __lowerCAmelCase				,       __lowerCAmelCase							)

															lowercase_					=      self._prepare_for_class(__lowerCAmelCase				,       __lowerCAmelCase							)
															lowercase_					=      self._prepare_for_class(__lowerCAmelCase				,       __lowerCAmelCase							)
															check_equivalence(__lowerCAmelCase				,       __lowerCAmelCase				,       __lowerCAmelCase				,       {"""output_hidden_states""": True}							)

															lowercase_					=      self._prepare_for_class(__lowerCAmelCase				,       __lowerCAmelCase				,       return_labels=__lowerCAmelCase							)
															lowercase_					=      self._prepare_for_class(__lowerCAmelCase				,       __lowerCAmelCase				,       return_labels=__lowerCAmelCase							)
															check_equivalence(__lowerCAmelCase				,       __lowerCAmelCase				,       __lowerCAmelCase				,       {"""output_hidden_states""": True}							)

					def 	UpperCAmelCase__							(  self							: List[str]							):






										'''simple docstring'''






										lowercase_					=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase							)







					@slow
					def 	UpperCAmelCase__							(  self							: Dict							):






										'''simple docstring'''






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



def       UpperCAmelCase_					(   ):
					lowercase_					=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
					return image







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

					@cached_property
					def 	UpperCAmelCase__							(  self							: Union[str, Any]							):






										'''simple docstring'''






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







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






										'''simple docstring'''






										lowercase_					=      TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

										lowercase_					=      self.default_image_processor
										lowercase_					=      prepare_img()
										lowercase_					=      image_processor(images=__lowerCAmelCase				,       return_tensors="""tf"""							)

										# forward pass
										lowercase_					=      model(**__lowerCAmelCase				,       training=__lowerCAmelCase							)

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

										lowercase_					=      tf.constant([-0.4_180, -1.5_051, -3.4_836]							)

										tf.debugging.assert_near(outputs.logits[0, :3]				,       __lowerCAmelCase				,       atol=1e-4							)










'''simple docstring'''

def 				snake_case       (  a_				:			list						)							->					list:





      """simple docstring"""

      UpperCamelCase_					: List[str]            =				False
      while is_sorted is False:  # Until all the indices are traversed keep looping
            UpperCamelCase_					: Tuple            =				True
            for i in range(0     , len(a_						) - 1     , 2						):  # iterating over all even indices
                  if input_list[i] > input_list[i + 1]:
                        UpperCamelCase_     ,      UpperCamelCase_					: Optional[Any]            =				input_list[i + 1], input_list[i]
                        # swapping if elements not in order
                        UpperCamelCase_					: Any            =				False

            for i in range(1     , len(a_						) - 1     , 2						):  # iterating over all odd indices
                  if input_list[i] > input_list[i + 1]:
                        UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				input_list[i + 1], input_list[i]
                        # swapping if elements not in order
                        UpperCamelCase_					: Dict            =				False
      return input_list


if __name__ == "__main__":
      print("Enter list to be sorted")
      UpperCamelCase      					=[int(x) for x in input().split()]
      # inputing elements of the list in one line
      UpperCamelCase      					=odd_even_sort(input_list)
      print("The sorted list is")
      print(sorted_list)






