
"""simple docstring"""

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      _UpperCAmelCase							(		unittest.TestCase							):




						def __init__(							self   ,       lowercase_   ,       lowercase_=3   ,       lowercase_=3_2   ,       lowercase_=3   ,       lowercase_=1_0   ,       lowercase_=[1_0, 2_0, 3_0, 4_0]   ,       lowercase_=[1, 1, 2, 1]   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_="relu"   ,       lowercase_=3   ,       lowercase_=None   ,       )						->					str:
										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     a_      (							self							)						->					Optional[int]:
										UpperCAmelCase									=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

										UpperCAmelCase									=					self.get_config()

										return config, pixel_values




						def     a_      (							self							)						->					Optional[Any]:
										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   ,       lowercase_   ,       lowercase_							)						->					List[Any]:
										UpperCAmelCase									=					FlaxRegNetModel(config=lowercase_							)
										UpperCAmelCase									=					model(lowercase_							)

										# Output shape (b, c, h, w)
										self.parent.assertEqual(
										    result.last_hidden_state.shape   ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   ,       )




						def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					Union[str, Any]:
										UpperCAmelCase									=					self.num_labels
										UpperCAmelCase									=					FlaxRegNetForImageClassification(config=lowercase_							)
										UpperCAmelCase									=					model(lowercase_							)
										self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)							)




						def     a_      (							self							)						->					List[Any]:
										UpperCAmelCase									=					self.prepare_config_and_inputs()
										UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs
										UpperCAmelCase									=					{'pixel_values': pixel_values}
										return config, inputs_dict

@require_flax
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
						__SCREAMING_SNAKE_CASE   :							Tuple							      =	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

						__SCREAMING_SNAKE_CASE   :							List[Any]							      =	False
						__SCREAMING_SNAKE_CASE   :							Optional[int]							      =	False
						__SCREAMING_SNAKE_CASE   :							Tuple							      =	False




						def     a_      (							self							)						->					None:
										UpperCAmelCase									=					FlaxRegNetModelTester(self							)
										UpperCAmelCase									=					ConfigTester(self   ,       config_class=lowercase_   ,       has_text_modality=lowercase_							)




						def     a_      (							self							)						->					List[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							)						->					Optional[Any]:
										return




						def     a_      (							self							)						->					Any:
										UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_							)




						def     a_      (							self							)						->					List[str]:
										UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowercase_							)




						@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[int]:
										pass




						def     a_      (							self							)						->					Dict:
										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     a_      (							self							)						->					int:
										def check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							):
														UpperCAmelCase									=					model_class(lowercase_							)

														UpperCAmelCase									=					model(**self._prepare_for_class(lowercase_   ,       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							)

										UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														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     a_      (							self							)						->					List[Any]:
										UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__							):
																		UpperCAmelCase									=					self._prepare_for_class(lowercase_   ,       lowercase_							)
																		UpperCAmelCase									=					model_class(lowercase_							)

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

																		with self.subTest('JIT Enabled'							):
																						UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

																		with self.subTest('JIT Disabled'							):
																						with jax.disable_jit():
																										UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

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




def     lowercase__						(					) ->      Tuple:






				"""simple docstring"""




				UpperCAmelCase									=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
				return image







@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):




						@cached_property
						def     a_      (							self							)						->					List[str]:
										return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'							) if is_vision_available() else None




						@slow
						def     a_      (							self							)						->					Union[str, Any]:
										UpperCAmelCase									=					FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'							)

										UpperCAmelCase									=					self.default_image_processor
										UpperCAmelCase									=					prepare_img()
										UpperCAmelCase									=					image_processor(images=lowercase_   ,       return_tensors='np'							)

										UpperCAmelCase									=					model(**lowercase_							)

										# verify the logits
										UpperCAmelCase									=					(1, 1_0_0_0)
										self.assertEqual(outputs.logits.shape   ,       lowercase_							)

										UpperCAmelCase									=					jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]							)

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





"""simple docstring"""

def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      int:






    """simple docstring"""




    _enforce_args(lowerCAmelCase ,				lowerCAmelCase )
    if n == 0:
        return 0
    UpperCAmelCase									=					float('-inf' )
    for i in range(1 ,				n + 1 ):
        UpperCAmelCase									=					max(
            lowerCAmelCase ,				prices[i - 1] + naive_cut_rod_recursive(n - i ,				lowerCAmelCase ) )

    return max_revue




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      Dict:






    """simple docstring"""




    _enforce_args(lowerCAmelCase ,				lowerCAmelCase )
    UpperCAmelCase									=					[float('-inf' ) for _ in range(n + 1 )]
    return _top_down_cut_rod_recursive(lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ,				lowerCAmelCase				:					list ) ->      int:






    """simple docstring"""




    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        UpperCAmelCase									=					float('-inf' )
        for i in range(1 ,				n + 1 ):
            UpperCAmelCase									=					max(
                lowerCAmelCase ,				prices[i - 1] + _top_down_cut_rod_recursive(n - i ,				lowerCAmelCase ,				lowerCAmelCase ) ,				)

        UpperCAmelCase									=					max_revenue

    return max_rev[n]




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      Optional[int]:






    """simple docstring"""




    _enforce_args(lowerCAmelCase ,				lowerCAmelCase )

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    UpperCAmelCase									=					[float('-inf' ) for _ in range(n + 1 )]
    UpperCAmelCase									=					0

    for i in range(1 ,				n + 1 ):
        UpperCAmelCase									=					max_rev[i]
        for j in range(1 ,				i + 1 ):
            UpperCAmelCase									=					max(lowerCAmelCase ,				prices[j - 1] + max_rev[i - j] )

        UpperCAmelCase									=					max_revenue_i

    return max_rev[n]




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      Union[str, Any]:






    """simple docstring"""




    if n < 0:
        UpperCAmelCase									=					F"n must be greater than or equal to 0. Got n = {n}"
        raise ValueError(lowerCAmelCase )

    if n > len(lowerCAmelCase ):
        UpperCAmelCase									=					(
            'Each integral piece of rod must have a corresponding price. '
            F"Got n = {n} but length of prices = {len(lowerCAmelCase )}"
        )
        raise ValueError(lowerCAmelCase )




def     lowercase__						(					) ->      Optional[int]:






    """simple docstring"""




    UpperCAmelCase									=					[6, 10, 12, 15, 20, 23]
    UpperCAmelCase									=					len(lowerCAmelCase )

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    UpperCAmelCase									=					36

    UpperCAmelCase									=					top_down_cut_rod(lowerCAmelCase ,				lowerCAmelCase )
    UpperCAmelCase									=					bottom_up_cut_rod(lowerCAmelCase ,				lowerCAmelCase )
    UpperCAmelCase									=					naive_cut_rod_recursive(lowerCAmelCase ,				lowerCAmelCase )

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
   main()



