
"""simple docstring"""







import inspect
import unittest

from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor



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




       def __init__( self       : Dict     ,   a_       : Any     ,   a_       : Optional[Any]=3     ,   a_       : int=32     ,   a_       : Optional[Any]=3     ,   a_       : Optional[int]=10     ,   a_       : List[Any]=[10, 20, 30, 40]     ,   a_       : List[str]=[1, 1, 2, 1]     ,   a_       : Optional[int]=True     ,   a_       : Union[str, Any]=True     ,   a_       : Tuple="relu"     ,   a_       : int=3     ,   a_       : Any=None     ,   )->					Optional[int]:



            """simple docstring"""

            UpperCAmelCase_						:    List[str]    					=  parent
            UpperCAmelCase_						:    List[Any]    					=  batch_size
            UpperCAmelCase_						:    Optional[int]    					=  image_size
            UpperCAmelCase_						:    List[str]    					=  num_channels
            UpperCAmelCase_						:    str    					=  embeddings_size
            UpperCAmelCase_						:    str    					=  hidden_sizes
            UpperCAmelCase_						:    Optional[int]    					=  depths
            UpperCAmelCase_						:    str    					=  is_training
            UpperCAmelCase_						:    Union[str, Any]    					=  use_labels
            UpperCAmelCase_						:    Union[str, Any]    					=  hidden_act
            UpperCAmelCase_						:    str    					=  num_labels
            UpperCAmelCase_						:    Union[str, Any]    					=  scope
            UpperCAmelCase_						:    int    					=  len(a_ )




       def   a      ( self       : int )->					Any:



            """simple docstring"""

            UpperCAmelCase_						:    Union[str, Any]    					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

            UpperCAmelCase_						:    Dict    					=  self.get_config()

            return config, pixel_values




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



            """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       : Union[str, Any]     ,   a_       : Union[str, Any]     ,   a_       : List[str] )->					int:



            """simple docstring"""

            UpperCAmelCase_						:    List[str]    					=  FlaxResNetModel(config=a_ )
            UpperCAmelCase_						:    Any    					=  model(a_ )

            # 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       : Optional[int]     ,   a_       : str     ,   a_       : Dict )->					str:



            """simple docstring"""

            UpperCAmelCase_						:    str    					=  self.num_labels
            UpperCAmelCase_						:    Any    					=  FlaxResNetForImageClassification(config=a_ )
            UpperCAmelCase_						:    Dict    					=  model(a_ )
            self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels) )






       def   a      ( self       : Tuple )->					Any:



            """simple docstring"""

            UpperCAmelCase_						:    List[Any]    					=  self.prepare_config_and_inputs()
            UpperCAmelCase_						:    int    					=  config_and_inputs
            UpperCAmelCase_						:    str    					=  {"""pixel_values""": pixel_values}
            return config, inputs_dict



@require_flax
class 		UpperCAmelCase_	(lowerCamelCase_  , unittest.TestCase						):
       """simple docstring"""




       UpperCamelCase_						:     List[Any]				   =    (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

       UpperCamelCase_						:     Any				   =    False
       UpperCamelCase_						:     Optional[int]				   =    False
       UpperCamelCase_						:     List[str]				   =    False




       def   a      ( self       : Dict )->					None:



            """simple docstring"""

            UpperCAmelCase_						:    Optional[Any]    					=  FlaxResNetModelTester(self )
            UpperCAmelCase_						:    Union[str, Any]    					=  ConfigTester(self     ,   config_class=a_     ,   has_text_modality=a_ )




       def   a      ( self       : int )->					Tuple:



            """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       : Tuple )->					Optional[Any]:



            """simple docstring"""

            return




       def   a      ( self       : Any )->					int:



            """simple docstring"""

            UpperCAmelCase_						:    Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*a_ )




       def   a      ( self       : str )->					List[str]:



            """simple docstring"""

            UpperCAmelCase_						:    Optional[Any]    					=  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*a_ )




       @unittest.skip(reason="""ResNet does not use inputs_embeds""" )
       def   a      ( self       : Any )->					Union[str, Any]:



            """simple docstring"""

            pass




       @unittest.skip(reason="""ResNet does not support input and output embeddings""" )
       def   a      ( self       : Any )->					Optional[Any]:



            """simple docstring"""

            pass




       def   a      ( self       : Dict )->					Dict:



            """simple docstring"""

            UpperCAmelCase_						:    Any    					=  self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 UpperCAmelCase_						:    List[Any]    					=  model_class(a_ )
                 UpperCAmelCase_						:    Optional[int]    					=  inspect.signature(model.__call__ )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 UpperCAmelCase_						:    str    					=  [*signature.parameters.keys()]

                 UpperCAmelCase_						:    Any    					=  ["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]     ,   a_ )




       def   a      ( self       : Union[str, Any] )->					int:



            """simple docstring"""

            def check_hidden_states_output(a_       : int     ,   a_       : Tuple     ,   a_       : Optional[Any] ):
                 UpperCAmelCase_						:    Optional[Any]    					=  model_class(a_ )

                 UpperCAmelCase_						:    Any    					=  model(**self._prepare_for_class(a_     ,   a_ ) )

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

                 UpperCAmelCase_						:    Optional[int]    					=  self.model_tester.num_stages
                 self.assertEqual(len(a_ )     ,   expected_num_stages + 1 )




       @unittest.skip(reason="""ResNet does not use feedforward chunking""" )
       def   a      ( self       : str )->					Union[str, Any]:



            """simple docstring"""

            pass






       def   a      ( self       : Dict )->					List[str]:



            """simple docstring"""

            UpperCAmelCase_						:    Tuple    					=  self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__ ):
                      UpperCAmelCase_						:    List[str]    					=  self._prepare_for_class(a_     ,   a_ )
                      UpperCAmelCase_						:    int    					=  model_class(a_ )

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

                      with self.subTest("""JIT Enabled""" ):
                           UpperCAmelCase_						:    Tuple    					=  model_jitted(**a_ ).to_tuple()

                      with self.subTest("""JIT Disabled""" ):
                           with jax.disable_jit():
                                UpperCAmelCase_						:    int    					=  model_jitted(**a_ ).to_tuple()

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




def 			A_			(					)     ->							List[str]:


     """simple docstring"""







     UpperCAmelCase_						:    Optional[Any]    					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
     return image



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




       @cached_property
       def   a      ( self       : List[str] )->					Optional[Any]:



            """simple docstring"""

            return AutoImageProcessor.from_pretrained("""microsoft/resnet-50""" ) if is_vision_available() else None






       @slow
       def   a      ( self       : Optional[Any] )->					Optional[int]:



            """simple docstring"""

            UpperCAmelCase_						:    Union[str, Any]    					=  FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50""" )

            UpperCAmelCase_						:    str    					=  self.default_image_processor
            UpperCAmelCase_						:    Union[str, Any]    					=  prepare_img()
            UpperCAmelCase_						:    List[Any]    					=  image_processor(images=a_     ,   return_tensors="""np""" )

            UpperCAmelCase_						:    Dict    					=  model(**a_ )

            # verify the logits
            UpperCAmelCase_						:    Union[str, Any]    					=  (1, 10_00)
            self.assertEqual(outputs.logits.shape     ,   a_ )

            UpperCAmelCase_						:    Any    					=  jnp.array([-11.1069, -9.7877, -8.3777] )

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



"""simple docstring"""







from math import radians as angle_to_radians
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
lowercase_						    =					9.8_0665




def 			A_			(					lowercase						, lowercase      )     ->							None:


     """simple docstring"""







     if not isinstance(lowercase						, (int, float)      ):
          raise TypeError("""Invalid velocity. Should be a positive number."""      )

     if not isinstance(lowercase						, (int, float)      ):
          raise TypeError("""Invalid angle. Range is 1-90 degrees."""      )

     # Ensure valid angle
     if angle > 90 or angle < 1:
          raise ValueError("""Invalid angle. Range is 1-90 degrees."""      )

     # Ensure valid velocity
     if init_velocity < 0:
          raise ValueError("""Invalid velocity. Should be a positive number."""      )




def 			A_			(					lowercase						, lowercase      )     ->							float:


     """simple docstring"""







     check_args(lowercase						, lowercase      )
     UpperCAmelCase_						:    Any    					=  angle_to_radians(2 * angle      )
     return round(init_velocity**2 * sin(lowercase      ) / g						, 2      )




def 			A_			(					lowercase						, lowercase      )     ->							float:


     """simple docstring"""







     check_args(lowercase						, lowercase      )
     UpperCAmelCase_						:    Union[str, Any]    					=  angle_to_radians(lowercase      )
     return round(init_velocity**2 * sin(lowercase      ) ** 2 / (2 * g)						, 2      )




def 			A_			(					lowercase						, lowercase      )     ->							float:


     """simple docstring"""







     check_args(lowercase						, lowercase      )
     UpperCAmelCase_						:    Optional[int]    					=  angle_to_radians(lowercase      )
     return round(2 * init_velocity * sin(lowercase      ) / g						, 2      )




def 			A_			(					)     ->							None:


     """simple docstring"""







     UpperCAmelCase_					,UpperCAmelCase_						:    Optional[int]    					=  25, 20
     assert horizontal_distance(lowercase						, lowercase      ) == 40.97
     assert max_height(lowercase						, lowercase      ) == 3.73
     assert total_time(lowercase						, lowercase      ) == 1.74


if __name__ == "__main__":
       from doctest import testmod

       testmod()

       # Get input from user
       lowercase_						    =					float(input("Initial Velocity: ").strip())

       # Get input from user
       lowercase_						    =					float(input("angle: ").strip())

       # Print results
       print()
       print("Results: ")
       print(f"""Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]""")
       print(f"""Maximum Height: {max_height(init_vel, angle)!s} [m]""")
       print(f"""Total Time: {total_time(init_vel, angle)!s} [s]""")

