


import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


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


    def        A   (  self      :							Optional[int]):
           _A					:     int     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _A					:     Optional[int]     = self.get_config()

           return config, pixel_values


    def        A   (  self      :							Tuple):
           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				,							image_size=self.image_size				,							)


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							List[str]):
           _A					:     Optional[int]     = FlaxRegNetModel(config=SCREAMING_SNAKE_CASE)
           _A					:     int     = model(SCREAMING_SNAKE_CASE)

           # Output shape (b, c, h, w)
           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      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           _A					:     List[Any]     = self.num_labels
           _A					:     Tuple     = FlaxRegNetForImageClassification(config=SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels))


    def        A   (  self      :							Union[str, Any]):
           _A					:     List[Any]     = self.prepare_config_and_inputs()
           _A					:     Optional[Any]     = config_and_inputs
           _A					:     Optional[int]     = {'pixel_values': pixel_values}
           return config, inputs_dict




@require_flax
class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

    a													=   False
    a													=   False
    a													=   False


    def        A   (  self      :							Union[str, Any]):
           _A					:     Optional[Any]     = FlaxRegNetModelTester(self)
           _A					:     Dict     = ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE				,							has_text_modality=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any):
           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      :							Union[str, Any]):
           return


    def        A   (  self      :							str):
           _A					:     Any     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           _A					:     List[Any]     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE)


    @unittest.skip(reason='RegNet does not use inputs_embeds')
    def        A   (  self      :							List[str]):
           pass


    @unittest.skip(reason='RegNet does not support input and output embeddings')
    def        A   (  self      :							Optional[Any]):
           pass


    def        A   (  self      :							Any):
           _A					:     List[str]     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A					:     Dict     = model_class(SCREAMING_SNAKE_CASE)
                  _A					:     str     = inspect.signature(model.__call__)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  _A					:     List[str]     = [*signature.parameters.keys()]

                  _A					:     Optional[Any]     = ['pixel_values']
                  self.assertListEqual(arg_names[:1]				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[Any]):
           def check_hidden_states_output(SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
                  _A					:     int     = model_class(SCREAMING_SNAKE_CASE)

                  _A					:     Optional[int]     = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE))

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

                  _A					:     Dict     = self.model_tester.num_stages
                  self.assertEqual(len(SCREAMING_SNAKE_CASE)				,							expected_num_stages + 1)

           _A					:     str     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A					:     Dict     = True
                  check_hidden_states_output(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

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

                  check_hidden_states_output(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any):
           _A					:     str     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__):
                         _A					:     List[str]     = self._prepare_for_class(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                         _A					:     Union[str, Any]     = model_class(SCREAMING_SNAKE_CASE)

                         @jax.jit
                         def model_jitted(SCREAMING_SNAKE_CASE      :							List[str]				,							**SCREAMING_SNAKE_CASE      :							Union[str, Any]):
                                return model(pixel_values=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)

                         with self.subTest('JIT Enabled'):
                                _A					:     Union[str, Any]     = model_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

                         with self.subTest('JIT Disabled'):
                                with jax.disable_jit():
                                       _A					:     List[Any]     = model_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

                         self.assertEqual(len(SCREAMING_SNAKE_CASE)				,							len(SCREAMING_SNAKE_CASE))
                         for jitted_output, output in zip(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                                self.assertEqual(jitted_output.shape				,							output.shape)






def 				lowerCAmelCase__	(    ):
       _A					:     Dict     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
       return image


@require_flax
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    @cached_property
    def        A   (  self      :							Any):
           return AutoImageProcessor.from_pretrained('facebook/regnet-y-040') if is_vision_available() else None


    @slow
    def        A   (  self      :							List[str]):
           _A					:     List[str]     = FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040')

           _A					:     Optional[int]     = self.default_image_processor
           _A					:     Any     = prepare_img()
           _A					:     Dict     = image_processor(images=SCREAMING_SNAKE_CASE				,							return_tensors='np')

           _A					:     List[Any]     = model(**SCREAMING_SNAKE_CASE)

           # verify the logits
           _A					:     List[Any]     = (1, 1000)
           self.assertEqual(outputs.logits.shape				,							SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = jnp.array([-0.4180, -1.5051, -3.4836])

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










'''simple docstring'''







from __future__ import annotations


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple=None):
           _A					:     Any     = data
           _A					:     Optional[Any]     = None


    def __repr__(  self      :							List[str]):
           _A					:     List[Any]     = []
           _A					:     Any     = self
           while temp:
                  string_rep.append(F'{temp.data}')
                  _A					:     List[Any]     = temp.next
           return "->".join(SCREAMING_SNAKE_CASE)






def 				lowerCAmelCase__	(    lowerCamelCase					:      list       ):
       if not elements_list:
              raise Exception('The Elements List is empty'       )

       _A					:     Union[str, Any]     = Node(elements_list[0]       )
       for i in range(1  ,len(lowerCamelCase       )       ):
              _A					:     Dict     = Node(elements_list[i]       )
              _A					:     int     = current.next
       return head






def 				lowerCAmelCase__	(    lowerCamelCase					:      Node       ):
       if head_node is not None and isinstance(lowerCamelCase  ,lowerCamelCase       ):
              print_reverse(head_node.next       )
              print(head_node.data       )






def 				lowerCAmelCase__	(    ):
       from doctest import testmod

       testmod()

       _A					:     List[str]     = make_linked_list([14, 52, 14, 12, 43]       )
       print('Linked List:'       )
       print(lowerCamelCase       )
       print('Elements in Reverse:'       )
       print_reverse(lowerCamelCase       )


if __name__ == "__main__":
   main()






