"""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  _A						:

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase=3		,       __lowerCAmelCase=32		,       __lowerCAmelCase=3		,       __lowerCAmelCase=10		,       __lowerCAmelCase=[10, 20, 30, 40]		,       __lowerCAmelCase=[1, 1, 2, 1]		,       __lowerCAmelCase=True		,       __lowerCAmelCase=True		,       __lowerCAmelCase="relu"		,       __lowerCAmelCase=3		,       __lowerCAmelCase=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 				A__       (						self						):







            """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 				A__       (						self						):







            """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 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	TFResNetModel(config=__lowerCAmelCase						)
            lowercase														=	model(__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 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



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







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.prepare_config_and_inputs()
            lowercase														=	config_and_inputs
            lowercase														=	{'''pixel_values''': pixel_values}
            return config, inputs_dict






@require_tf
class  _A						(       _A  ,    _A  ,    unittest.TestCase	):
      snake_case__			:   Union[str, Any]							  =	(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
      snake_case__			:   List[Any]							  =	(
          {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
          if is_tf_available()
          else {}
      )

      snake_case__			:   Optional[int]							  =	False
      snake_case__			:   Optional[int]							  =	False
      snake_case__			:   str							  =	False
      snake_case__			:   Optional[int]							  =	False
      snake_case__			:   Any							  =	False

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	TFResNetModelTester(self						)
            lowercase														=	ConfigTester(self		,       config_class=__lowerCAmelCase		,       has_text_modality=__lowerCAmelCase						)

      def 				A__       (						self						):







            """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 				A__       (						self						):







            """simple docstring"""



            return

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







            """simple docstring"""



            pass

      @unittest.skip(reason="""ResNet does not support input and output embeddings"""						)
      def 				A__       (						self						):







            """simple docstring"""



            pass

      def 				A__       (						self						):







            """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 				A__       (						self						):







            """simple docstring"""



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

      def 				A__       (						self						):







            """simple docstring"""



            def check_hidden_states_output(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):
                  lowercase														=	model_class(__lowerCAmelCase						)
                  lowercase														=	model(**self._prepare_for_class(__lowerCAmelCase		,       __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						)

                  # 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]		,       )

            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 				A__       (						self						):







            """simple docstring"""



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







      @slow
      def 				A__       (						self						):







            """simple docstring"""



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






def 			UpperCAmelCase__       (				) ->    Optional[int]:
      '''simple docstring'''


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






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

      @cached_property
      def 				A__       (						self						):







            """simple docstring"""



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







      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	TFResNetForImageClassification.from_pretrained(TF_RESNET_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						)

            # verify the logits
            lowercase														=	tf.TensorShape((1, 1000)						)
            self.assertEqual(outputs.logits.shape		,       __lowerCAmelCase						)

            lowercase														=	tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]						)

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











"""simple docstring"""



def 						lowercase    (				__snake_case				: Optional[int]	):
		lowercase_ : int							       =   0
		lowercase_ : Optional[Any]							       =   len(__snake_case	)

		for i in range(n - 1	):
				for j in range(i + 1		,					__snake_case	):
						if arr[i] > arr[j]:
								num_inversions += 1

		return num_inversions



def 						lowercase    (				__snake_case				: str	):
		if len(__snake_case	) <= 1:
				return arr, 0
		lowercase_ : Optional[Any]							       =   len(__snake_case	) // 2
		lowercase_ : List[Any]							       =   arr[0:mid]
		lowercase_ : Union[str, Any]							       =   arr[mid:]

		lowercase_       ,					lowercase_ : Tuple							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   _count_cross_inversions(__snake_case		,					__snake_case	)

		lowercase_ : List[Any]							       =   inversion_p + inversions_q + cross_inversions
		return c, num_inversions



def 						lowercase    (				__snake_case				: str		,					__snake_case				: Optional[int]	):
		lowercase_ : Optional[Any]							       =   []
		lowercase_ : Any							       =   0
		while i < len(__snake_case	) and j < len(__snake_case	):
				if p[i] > q[j]:
						# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
						# These are all inversions. The claim emerges from the
						# property that P is sorted.
						num_inversion += len(__snake_case	) - i
						r.append(q[j]	)
						j += 1
				else:
						r.append(p[i]	)
						i += 1

		if i < len(__snake_case	):
				r.extend(p[i:]	)
		else:
				r.extend(q[j:]	)

		return r, num_inversion



def 						lowercase    (				):
		lowercase_ : Union[str, Any]							       =   [1_0, 2, 1, 5, 5, 2, 1_1]

		# this arr has 8 inversions:
		# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

		lowercase_ : int							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = '''		,					__snake_case	)

		# testing an array with zero inversion (a sorted arr_1)

		arr_a.sort()
		lowercase_ : Dict							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)

		# an empty list should also have zero inversions
		lowercase_ : List[Any]							       =   []
		lowercase_ : Any							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : List[str]							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)


if __name__ == "__main__":
						main()



