
'''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     ):

     """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				,		):

       '''simple docstring'''

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


     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       a_   :						int   =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       a_   :						List[str]   =				self.get_config()

       return config, pixel_values


     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       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 				_lowerCAmelCase	(   self				,		lowerCAmelCase_				,		lowerCAmelCase_			):

       '''simple docstring'''

       a_   :						Any   =				FlaxRegNetModel(config=snake_case__			)
       a_   :						Any   =				model(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 				_lowerCAmelCase	(   self				,		lowerCAmelCase_				,		lowerCAmelCase_			):

       '''simple docstring'''

       a_   :						Tuple   =				self.num_labels
       a_   :						Optional[int]   =				FlaxRegNetForImageClassification(config=snake_case__			)
       a_   :						Optional[int]   =				model(snake_case__			)
       self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)			)






     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       a_   :						Dict   =				self.prepare_config_and_inputs()
       a_   :						str   =				config_and_inputs
       a_   :						Optional[int]   =				{"pixel_values": pixel_values}
       return config, inputs_dict







@require_flax
class 			_UpperCAmelCase			(			A_						,unittest.TestCase     ):

     """simple docstring"""

     a_     						=       (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

     a_     						=       False
     a_     						=       False
     a_     						=       False


     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       a_   :						Optional[Any]   =				FlaxRegNetModelTester(self			)
       a_   :						Dict   =				ConfigTester(self				,		config_class=snake_case__				,		has_text_modality=snake_case__			)


     def 				_lowerCAmelCase	(   self			):

       '''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 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       return


     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       a_   :						str   =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case__			)


     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       a_   :						str   =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case__			)


     @unittest.skip(reason="""RegNet does not use inputs_embeds"""			)
     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       pass


     @unittest.skip(reason="""RegNet does not support input and output embeddings"""			)
     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       pass


     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       a_   :						Union[str, Any]   =				self.model_tester.prepare_config_and_inputs_for_common()

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

         a_   :						Tuple   =				["pixel_values"]
         self.assertListEqual(arg_names[:1]				,		snake_case__			)


     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       def check_hidden_states_output(lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_			):
         a_   :						List[str]   =				model_class(snake_case__			)

         a_   :						Union[str, Any]   =				model(**self._prepare_for_class(snake_case__				,		snake_case__			)			)

         a_   :						Any   =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

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

       a_   :						Optional[Any]   =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         a_   :						Tuple   =				True
         check_hidden_states_output(snake_case__				,		snake_case__				,		snake_case__			)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         a_   :						Optional[int]   =				True

         check_hidden_states_output(snake_case__				,		snake_case__				,		snake_case__			)






     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       a_   :						Optional[Any]   =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__			):
           a_   :						Optional[Any]   =				self._prepare_for_class(snake_case__				,		snake_case__			)
           a_   :						List[Any]   =				model_class(snake_case__			)

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

           with self.subTest("""JIT Enabled"""			):
             a_   :						Union[str, Any]   =				model_jitted(**snake_case__			).to_tuple()

           with self.subTest("""JIT Disabled"""			):
             with jax.disable_jit():
               a_   :						Optional[int]   =				model_jitted(**snake_case__			).to_tuple()

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







def 						_snake_case    (   ):
  """simple docstring"""







  a_   :						List[str]   =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
  return image




@require_flax
class 			_UpperCAmelCase			(			unittest.TestCase     ):

     """simple docstring"""

     @cached_property
     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""			) if is_vision_available() else None






     @slow
     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       a_   :						List[str]   =				FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""			)

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

       a_   :						Optional[int]   =				model(**snake_case__			)

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

       a_   :						Optional[int]   =				jnp.array([-0.4180, -1.5051, -3.4836]			)

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




from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

__lowerCamelCase				=			TypeVar("""T""")






class     UpperCAmelCase						(		Generic[T]					):



   def __init__(self						:   int       ,		snake_case__						:   list[T]       ,		snake_case__						:   Callable[[T, T], T]					)						->      None:


          '''simple docstring'''


          snake_case			:  Any | T     							= None

          snake_case			:  int     							= len(snake_case__					)
          snake_case			:  list[T]     							= [any_type for _ in range(self.N					)] + arr
          snake_case			:  str     							= fnc
          self.build()



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      None:


          '''simple docstring'''


          for p in range(self.N - 1       ,		0       ,		-1					):
                 snake_case			:  int     							= self.fn(self.st[p * 2]       ,		self.st[p * 2 + 1]					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict       ,		snake_case__						:   int       ,		snake_case__						:   T					)						->      None:


          '''simple docstring'''


          p += self.N
          snake_case			:  int     							= v
          while p > 1:
                 snake_case			:  List[str]     							= p // 2
                 snake_case			:  Tuple     							= self.fn(self.st[p * 2]       ,		self.st[p * 2 + 1]					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   int       ,		snake_case__						:   int					)						->      T | None:  # noqa: E741


          '''simple docstring'''


          snake_case							,   snake_case			:  Optional[int]     							= l + self.N, r + self.N

          snake_case			:  T | None     							= None
          while l <= r:
                 if l % 2 == 1:
                        snake_case			:  List[str]     							= self.st[l] if res is None else self.fn(snake_case__       ,		self.st[l]					)
                 if r % 2 == 0:
                        snake_case			:  int     							= self.st[r] if res is None else self.fn(snake_case__       ,		self.st[r]					)
                 snake_case							,   snake_case			:  str     							= (l + 1) // 2, (r - 1) // 2
          return res


if __name__ == "__main__":
   from functools import reduce

   __lowerCamelCase				=			[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

   __lowerCamelCase				=			{
       0: 7,
       1: 2,
       2: 6,
       3: -14,
       4: 5,
       5: 4,
       6: 7,
       7: -10,
       8: 9,
       9: 10,
       10: 12,
       11: 1,
   }

   __lowerCamelCase				=			SegmentTree(test_array, min)
   __lowerCamelCase				=			SegmentTree(test_array, max)
   __lowerCamelCase				=			SegmentTree(test_array, lambda a, b: a + b)


   def      UpperCamelCase   (       ):
          for i in range(len(__lowerCamelCase       )       ):
                 for j in range(__lowerCamelCase							,   len(__lowerCamelCase       )       ):
                        snake_case			:  int     							= reduce(__lowerCamelCase							,   test_array[i : j + 1]       )
                        snake_case			:  Tuple     							= reduce(__lowerCamelCase							,   test_array[i : j + 1]       )
                        snake_case			:  Union[str, Any]     							= reduce(lambda __lowerCamelCase							,   __lowerCamelCase       : a + b							,   test_array[i : j + 1]       )
                        assert min_range == min_segment_tree.query(__lowerCamelCase							,   __lowerCamelCase       )
                        assert max_range == max_segment_tree.query(__lowerCamelCase							,   __lowerCamelCase       )
                        assert sum_range == sum_segment_tree.query(__lowerCamelCase							,   __lowerCamelCase       )

   test_all_segments()

   for index, value in test_updates.items():
      __lowerCamelCase				=			value
      min_segment_tree.update(index, value)
      max_segment_tree.update(index, value)
      sum_segment_tree.update(index, value)
      test_all_segments()





