

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 						__magic_name__					:






     def __init__(				self		,						_lowercase		,						_lowercase=3		,						_lowercase=32		,						_lowercase=3		,						_lowercase=10		,						_lowercase=[10, 20, 30, 40]		,						_lowercase=[1, 1, 2, 1]		,						_lowercase=True		,						_lowercase=True		,						_lowercase="relu"		,						_lowercase=3		,						_lowercase=None		,						)->     Optional[Any]:
         UpperCamelCase_			  =						parent
         UpperCamelCase_			  =						batch_size
         UpperCamelCase_			  =						image_size
         UpperCamelCase_			  =						num_channels
         UpperCamelCase_			  =						embeddings_size
         UpperCamelCase_			  =						hidden_sizes
         UpperCamelCase_			  =						depths
         UpperCamelCase_			  =						is_training
         UpperCamelCase_			  =						use_labels
         UpperCamelCase_			  =						hidden_act
         UpperCamelCase_			  =						num_labels
         UpperCamelCase_			  =						scope
         UpperCamelCase_			  =						len(_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         UpperCamelCase_			  =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

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






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         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		,						_lowercase		,						_lowercase		,						_lowercase	)->     List[str]:
         UpperCamelCase_			  =						TFRegNetModel(config=_lowercase	)
         UpperCamelCase_			  =						model(_lowercase		,						training=_lowercase	)
         # 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		,						_lowercase		,						_lowercase		,						_lowercase	)->     Dict:
         UpperCamelCase_			  =						self.num_labels
         UpperCamelCase_			  =						TFRegNetForImageClassification(_lowercase	)
         UpperCamelCase_			  =						model(_lowercase		,						labels=_lowercase		,						training=_lowercase	)
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         UpperCamelCase_			  =						self.prepare_config_and_inputs()
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						config_and_inputs
         UpperCamelCase_			  =						{"pixel_values": pixel_values}
         return config, inputs_dict






@require_tf
class 						__magic_name__					(							snake_case			,      snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:Dict										=      (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
     UpperCamelCase_	:str										=      (
         {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
         if is_tf_available()
         else {}
     )

     UpperCamelCase_	:Union[str, Any]										=      False
     UpperCamelCase_	:Dict										=      False
     UpperCamelCase_	:List[Any]										=      False
     UpperCamelCase_	:Optional[Any]										=      False
     UpperCamelCase_	:Optional[Any]										=      False






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						TFRegNetModelTester(self	)
         UpperCamelCase_			  =						ConfigTester(self		,						config_class=_lowercase		,						has_text_modality=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     int:
         return






     @unittest.skip(reason="RegNet does not use inputs_embeds"	)
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         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	)->     str:
         super().test_keras_fit()






     @unittest.skip(reason="RegNet does not support input and output embeddings"	)
     def 	UpperCAmelCase_				(				self	)->     Any:
         pass






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_  ,       UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)
             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]		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     str:
         def check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						model_class(_lowercase	)
             UpperCamelCase_			  =						model(**self._prepare_for_class(_lowercase		,						_lowercase	)		,						training=_lowercase	)

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

             UpperCamelCase_			  =						self.model_tester.num_stages
             self.assertEqual(len(_lowercase	)		,						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]		,						)

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

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

                 check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_  ,       UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs_for_common()

         def check_equivalence(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase={}	):
             UpperCamelCase_			  =						model(_lowercase		,						return_dict=_lowercase		,						**_lowercase	)
             UpperCamelCase_			  =						model(_lowercase		,						return_dict=_lowercase		,						**_lowercase	).to_tuple()

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

                 recursive_check(_lowercase		,						_lowercase	)

         for model_class in self.all_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						self._prepare_for_class(_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						self._prepare_for_class(_lowercase		,						_lowercase	)
             check_equivalence(_lowercase		,						_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						self._prepare_for_class(_lowercase		,						_lowercase		,						return_labels=_lowercase	)
             UpperCamelCase_			  =						self._prepare_for_class(_lowercase		,						_lowercase		,						return_labels=_lowercase	)
             check_equivalence(_lowercase		,						_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						self._prepare_for_class(_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						self._prepare_for_class(_lowercase		,						_lowercase	)
             check_equivalence(_lowercase		,						_lowercase		,						_lowercase		,						{"output_hidden_states": True}	)

             UpperCamelCase_			  =						self._prepare_for_class(_lowercase		,						_lowercase		,						return_labels=_lowercase	)
             UpperCamelCase_			  =						self._prepare_for_class(_lowercase		,						_lowercase		,						return_labels=_lowercase	)
             check_equivalence(_lowercase		,						_lowercase		,						_lowercase		,						{"output_hidden_states": True}	)






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowercase	)






     @slow
     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_			  =						TFRegNetModel.from_pretrained(_lowercase	)
             self.assertIsNotNone(_lowercase	)




def 			lowerCAmelCase(      )->						str:



    """simple docstring"""






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






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






     @cached_property
     def 	UpperCAmelCase_				(				self	)->     Dict:
         return (
             AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
             if is_vision_available()
             else None
         )






     @slow
     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

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

         # forward pass
         UpperCamelCase_			  =						model(**_lowercase		,						training=_lowercase	)

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

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

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



from typing import Any






class 						__magic_name__					:






     def __init__(				self		,						_lowercase	)->     List[str]:
         UpperCamelCase_			  =						data
         UpperCamelCase_			  =						None






     def __repr__(				self	)->     str:
         return F"Node({self.data})"






class 						__magic_name__					:






     def __init__(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						None






     def __iter__(				self	)->     Any:
         UpperCamelCase_			  =						self.head
         while node:
             yield node.data
             UpperCamelCase_			  =						node.next






     def __len__(				self	)->     int:
         return sum(1 for _ in self	)






     def __repr__(				self	)->     str:
         return "->".join([str(_lowercase	) for item in self]	)






     def __getitem__(				self		,						_lowercase	)->     Any:
         if not 0 <= index < len(self	):
             raise ValueError("list index out of range."	)
         for i, node in enumerate(self	):
             if i == index:
                 return node
         return None






     def __setitem__(				self		,						_lowercase		,						_lowercase	)->     None:
         if not 0 <= index < len(self	):
             raise ValueError("list index out of range."	)
         UpperCamelCase_			  =						self.head
         for _ in range(_lowercase	):
             UpperCamelCase_			  =						current.next
         UpperCamelCase_			  =						data






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     None:
         self.insert_nth(len(self	)		,						_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     None:
         self.insert_nth(0		,						_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     None:
         if not 0 <= index <= len(self	):
             raise IndexError("list index out of range"	)
         UpperCamelCase_			  =						Node(_lowercase	)
         if self.head is None:
             UpperCamelCase_			  =						new_node
         elif index == 0:
             UpperCamelCase_			  =						self.head  # link new_node to head
             UpperCamelCase_			  =						new_node
         else:
             UpperCamelCase_			  =						self.head
             for _ in range(index - 1	):
                 UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						new_node






     def 	UpperCAmelCase_				(				self	)->     None:  # print every node data
         print(self	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         return self.delete_nth(0	)






     def 	UpperCAmelCase_				(				self	)->     Any:  # delete from tail
         return self.delete_nth(len(self	) - 1	)






     def 	UpperCAmelCase_				(				self		,						_lowercase = 0	)->     Any:
         if not 0 <= index <= len(self	) - 1:  # test if index is valid
             raise IndexError("List index out of range."	)
         UpperCamelCase_			  =						self.head  # default first node
         if index == 0:
             UpperCamelCase_			  =						self.head.next
         else:
             UpperCamelCase_			  =						self.head
             for _ in range(index - 1	):
                 UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						temp.next.next
         return delete_node.data






     def 	UpperCAmelCase_				(				self	)->     bool:
         return self.head is None






     def 	UpperCAmelCase_				(				self	)->     None:
         UpperCamelCase_			  =						None
         UpperCamelCase_			  =						self.head

         while current:
             # Store the current node's next node.
             UpperCamelCase_			  =						current.next
             # Make the current node's next point backwards
             UpperCamelCase_			  =						prev
             # Make the previous node be the current node
             UpperCamelCase_			  =						current
             # Make the current node the next node (to progress iteration)
             UpperCamelCase_			  =						next_node
         # Return prev in order to put the head at the end
         UpperCamelCase_			  =						prev




def 			lowerCAmelCase(      )->						None:



    """simple docstring"""






    UpperCamelCase_			  =						LinkedList()
    assert linked_list.is_empty() is True
    assert str(SCREAMING_SNAKE_CASE_			) == ""

    try:
        linked_list.delete_head()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    try:
        linked_list.delete_tail()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    for i in range(1_0			):
        assert len(SCREAMING_SNAKE_CASE_			) == i
        linked_list.insert_nth(SCREAMING_SNAKE_CASE_			,						i + 1			)
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(1			,						1_1			)			)

    linked_list.insert_head(0			)
    linked_list.insert_tail(1_1			)
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(0			,						1_2			)			)

    assert linked_list.delete_head() == 0
    assert linked_list.delete_nth(9			) == 1_0
    assert linked_list.delete_tail() == 1_1
    assert len(SCREAMING_SNAKE_CASE_			) == 9
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(1			,						1_0			)			)

    assert all(linked_list[i] == i + 1 for i in range(0			,						9			)			) is True

    for i in range(0			,						9			):
        UpperCamelCase_			  =						-i
    assert all(linked_list[i] == -i for i in range(0			,						9			)			) is True

    linked_list.reverse()
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(-8			,						1			)			)




def 			lowerCAmelCase(      )->						None:



    """simple docstring"""






    UpperCamelCase_			  =						[
        -9,
        1_0_0,
        Node(7_7_3_4_5_1_1_2			),
        "dlrow olleH",
        7,
        5_5_5_5,
        0,
        -192.5_5555,
        "Hello, world!",
        77.9,
        Node(1_0			),
        None,
        None,
        12.20,
    ]
    UpperCamelCase_			  =						LinkedList()

    for i in test_input:
        linked_list.insert_tail(SCREAMING_SNAKE_CASE_			)

    # Check if it's empty or not
    assert linked_list.is_empty() is False
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
        "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
    )

    # Delete the head
    UpperCamelCase_			  =						linked_list.delete_head()
    assert result == -9
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
        "Hello, world!->77.9->Node(10)->None->None->12.2"
    )

    # Delete the tail
    UpperCamelCase_			  =						linked_list.delete_tail()
    assert result == 12.2
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
        "Hello, world!->77.9->Node(10)->None->None"
    )

    # Delete a node in specific location in linked list
    UpperCamelCase_			  =						linked_list.delete_nth(1_0			)
    assert result is None
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
        "Hello, world!->77.9->Node(10)->None"
    )

    # Add a Node instance to its head
    linked_list.insert_head(Node("Hello again, world!"			)			)
    assert (
        str(SCREAMING_SNAKE_CASE_			)
        == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
        "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
    )

    # Add None to its tail
    linked_list.insert_tail(SCREAMING_SNAKE_CASE_			)
    assert (
        str(SCREAMING_SNAKE_CASE_			)
        == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
        "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
    )

    # Reverse the linked list
    linked_list.reverse()
    assert (
        str(SCREAMING_SNAKE_CASE_			)
        == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
        "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
    )




def 			lowerCAmelCase(      )->						List[Any]:



    """simple docstring"""






    from doctest import testmod

    testmod()

    UpperCamelCase_			  =						LinkedList()
    linked_list.insert_head(input("Inserting 1st at head "			).strip()			)
    linked_list.insert_head(input("Inserting 2nd at head "			).strip()			)
    print("\nPrint list:"			)
    linked_list.print_list()
    linked_list.insert_tail(input("\nInserting 1st at tail "			).strip()			)
    linked_list.insert_tail(input("Inserting 2nd at tail "			).strip()			)
    print("\nPrint list:"			)
    linked_list.print_list()
    print("\nDelete head"			)
    linked_list.delete_head()
    print("Delete tail"			)
    linked_list.delete_tail()
    print("\nPrint list:"			)
    linked_list.print_list()
    print("\nReverse linked list"			)
    linked_list.reverse()
    print("\nPrint list:"			)
    linked_list.print_list()
    print("\nString representation of linked list:"			)
    print(SCREAMING_SNAKE_CASE_			)
    print("\nReading/changing Node data using indexing:"			)
    print(f"Element at Position 1: {linked_list[1]}"			)
    UpperCamelCase_			  =						input("Enter New Value: "			).strip()
    print("New list:"			)
    print(SCREAMING_SNAKE_CASE_			)
    print(f"length of linked_list is : {len(SCREAMING_SNAKE_CASE_			)}"			)


if __name__ == "__main__":
 main()
