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 							a				(     unittest.TestCase			):







 """simple docstring"""






 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						,							)    ->   List[str]:
        _A	  =    parent
        _A	  =    batch_size
        _A	  =    image_size
        _A	  =    num_channels
        _A	  =    embeddings_size
        _A	  =    hidden_sizes
        _A	  =    depths
        _A	  =    is_training
        _A	  =    use_labels
        _A	  =    hidden_act
        _A	  =    num_labels
        _A	  =    scope
        _A	  =    len(__UpperCAmelCase				)



 def 	UpperCAmelCase				(   self				)    ->   str:
        _A	  =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        _A	  =    self.get_config()

        return config, pixel_values



 def 	UpperCAmelCase				(   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 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_				)    ->   int:
        _A	  =    FlaxRegNetModel(config=__UpperCAmelCase				)
        _A	  =    model(__UpperCAmelCase				)

        # 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 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_				)    ->   List[Any]:
        _A	  =    self.num_labels
        _A	  =    FlaxRegNetForImageClassification(config=__UpperCAmelCase				)
        _A	  =    model(__UpperCAmelCase				)
        self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_labels)				)



 def 	UpperCAmelCase				(   self				)    ->   Any:
        _A	  =    self.prepare_config_and_inputs()
        _A  ,    _A	  =    config_and_inputs
        _A	  =    {"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_flax
class 							a				(     A__			,     unittest.TestCase			):







 """simple docstring"""






 lowerCamelCase      :str            =						(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 lowerCamelCase      :Optional[int]            =						False
 lowerCamelCase      :Optional[int]            =						False
 lowerCamelCase      :Tuple            =						False



 def 	UpperCAmelCase				(   self				)    ->   None:
        _A	  =    FlaxRegNetModelTester(self				)
        _A	  =    ConfigTester(self						,							config_class=__UpperCAmelCase						,							has_text_modality=__UpperCAmelCase				)



 def 	UpperCAmelCase				(   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 	UpperCAmelCase				(   self				)    ->   Dict:
        return



 def 	UpperCAmelCase				(   self				)    ->   Union[str, Any]:
        _A	  =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCAmelCase				)



 def 	UpperCAmelCase				(   self				)    ->   List[Any]:
        _A	  =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)



 @unittest.skip(reason="""RegNet does not use inputs_embeds"""				)
 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        pass



 @unittest.skip(reason="""RegNet does not support input and output embeddings"""				)
 def 	UpperCAmelCase				(   self				)    ->   List[str]:
        pass



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

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

               _A	  =    ["""pixel_values"""]
               self.assertListEqual(arg_names[:1]						,							__UpperCAmelCase				)



 def 	UpperCAmelCase				(   self				)    ->   Union[str, Any]:
        def check_hidden_states_output(lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_				):
               _A	  =    model_class(__UpperCAmelCase				)

               _A	  =    model(**self._prepare_for_class(__UpperCAmelCase						,							__UpperCAmelCase				)				)

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

               _A	  =    self.model_tester.num_stages
               self.assertEqual(len(__UpperCAmelCase				)						,							expected_num_stages + 1				)

        _A  ,    _A	  =    self.model_tester.prepare_config_and_inputs_for_common()

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

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

               check_hidden_states_output(__UpperCAmelCase						,							__UpperCAmelCase						,							__UpperCAmelCase				)



 def 	UpperCAmelCase				(   self				)    ->   List[str]:
        _A  ,    _A	  =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__				):
                      _A	  =    self._prepare_for_class(__UpperCAmelCase						,							__UpperCAmelCase				)
                      _A	  =    model_class(__UpperCAmelCase				)

                      @jax.jit
                      def model_jitted(lowerCAmelCase_						,							**lowerCAmelCase_				):
                             return model(pixel_values=__UpperCAmelCase						,							**__UpperCAmelCase				)

                      with self.subTest("""JIT Enabled"""				):
                             _A	  =    model_jitted(**__UpperCAmelCase				).to_tuple()

                      with self.subTest("""JIT Disabled"""				):
                             with jax.disable_jit():
                                    _A	  =    model_jitted(**__UpperCAmelCase				).to_tuple()

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






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







@require_flax
class 							a				(     unittest.TestCase			):







 """simple docstring"""






 @cached_property
 def 	UpperCAmelCase				(   self				)    ->   Dict:
        return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""				) if is_vision_available() else None



 @slow
 def 	UpperCAmelCase				(   self				)    ->   int:
        _A	  =    FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""				)

        _A	  =    self.default_image_processor
        _A	  =    prepare_img()
        _A	  =    image_processor(images=__UpperCAmelCase						,							return_tensors="""np"""				)

        _A	  =    model(**__UpperCAmelCase				)

        # verify the logits
        _A	  =    (1, 10_00)
        self.assertEqual(outputs.logits.shape						,							__UpperCAmelCase				)

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

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







"""simple docstring"""




from typing import List

from .keymap import KEYMAP, get_character

def 						A							(  snake_case__     ):




			'''simple docstring'''


			def decorator(snake_case__     ):
						SCREAMING_SNAKE_CASE__										=   getattr(snake_case__		,					"""handle_key"""		,					[]     )
						handle += [key]
						setattr(snake_case__		,					"""handle_key"""		,					snake_case__     )
						return func

			return decorator

def 						A							(  *snake_case__     ):




			'''simple docstring'''


			def decorator(snake_case__     ):
						SCREAMING_SNAKE_CASE__										=   getattr(snake_case__		,					"""handle_key"""		,					[]     )
						handle += keys
						setattr(snake_case__		,					"""handle_key"""		,					snake_case__     )
						return func

			return decorator





class  lowerCamelCase    (A__      ):

			def __new__(       cls					:    Dict     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Optional[Any]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   super().__new__(cls     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)
						if not hasattr(__UpperCAmelCase     ,					"""key_handler"""							):
									setattr(__UpperCAmelCase     ,					"""key_handler"""     ,					{}							)
						setattr(__UpperCAmelCase     ,					"""handle_input"""     ,					KeyHandler.handle_input							)

						for value in attrs.values():
									SCREAMING_SNAKE_CASE__										=   getattr(__UpperCAmelCase     ,					"""handle_key"""     ,					[]							)
									for key in handled_keys:
												SCREAMING_SNAKE_CASE__										=   value
						return new_cls





			@staticmethod
			def        SCREAMING_SNAKE_CASE					(       cls					:    Tuple							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   get_character()
						if char != KEYMAP["undefined"]:
									SCREAMING_SNAKE_CASE__										=   ord(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   cls.key_handler.get(__UpperCAmelCase							)
						if handler:
									SCREAMING_SNAKE_CASE__										=   char
									return handler(cls							)
						else:
									return None

def 						A							(  cls     ):




			'''simple docstring'''


			return KeyHandler(cls.__name__		,					cls.__bases__		,					cls.__dict__.copy()     )


