




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	):

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

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        lowerCAmelCase__				:  Tuple		       =     self.get_config()

        return config, pixel_values

      def 	__magic_name__(   self		):
        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 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     FlaxRegNetModel(config=__UpperCAmelCase		)
        lowerCAmelCase__				:  Dict		       =     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 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Dict		       =     self.num_labels
        lowerCAmelCase__				:  Union[str, Any]		       =     FlaxRegNetForImageClassification(config=__UpperCAmelCase		)
        lowerCAmelCase__				:  List[str]		       =     model(__UpperCAmelCase		)
        self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     self.prepare_config_and_inputs()
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Dict		       =     config_and_inputs
        lowerCAmelCase__				:  Union[str, Any]		       =     {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_flax
class  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      A__		     =		(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

      A__		     =		False
      A__		     =		False
      A__		     =		False

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     FlaxRegNetModelTester(self		)
        lowerCAmelCase__				:  Tuple		       =     ConfigTester(self      ,				config_class=__UpperCAmelCase      ,				has_text_modality=__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        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 	__magic_name__(   self		):
        return

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Dict		       =     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 	__magic_name__(   self		):
        pass

      @unittest.skip(reason='''RegNet does not support input and output embeddings'''		)
      def 	__magic_name__(   self		):
        pass

      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Tuple		       =     self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase__				:  Any		       =     ['''pixel_values''']
          self.assertListEqual(arg_names[:1]      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        def check_hidden_states_output(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  Tuple		       =     model_class(__UpperCAmelCase		)

          lowerCAmelCase__				:  List[str]		       =     model(**self._prepare_for_class(__UpperCAmelCase      ,				__UpperCAmelCase		)		)

          lowerCAmelCase__				:  List[Any]		       =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          lowerCAmelCase__				:  Dict		       =     self.model_tester.num_stages
          self.assertEqual(len(__UpperCAmelCase		)      ,				expected_num_stages + 1		)

        lowerCAmelCase__       ,			lowerCAmelCase__				:  str		       =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

          check_hidden_states_output(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

            with self.subTest('''JIT Enabled'''		):
              lowerCAmelCase__				:  List[Any]		       =     model_jitted(**__UpperCAmelCase		).to_tuple()

            with self.subTest('''JIT Disabled'''		):
              with jax.disable_jit():
                lowerCAmelCase__				:  int		       =     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      __lowerCAmelCase			(					)     ->	List[str]:
  lowerCAmelCase__				:  Dict		       =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
  return image






@require_flax
class  _lowerCAmelCase     (  unittest.TestCase	):

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







      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[int]		       =     FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''		)

        lowerCAmelCase__				:  Union[str, Any]		       =     self.default_image_processor
        lowerCAmelCase__				:  Dict		       =     prepare_img()
        lowerCAmelCase__				:  Union[str, Any]		       =     image_processor(images=__UpperCAmelCase      ,				return_tensors='''np'''		)

        lowerCAmelCase__				:  int		       =     model(**__UpperCAmelCase		)

        # verify the logits
        lowerCAmelCase__				:  Optional[Any]		       =     (1, 1000)
        self.assertEqual(outputs.logits.shape      ,				__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[Any]		       =     jnp.array([-0.4180, -1.5051, -3.4836]		)

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







from collections import Counter
from timeit import timeit



def      __lowerCAmelCase			(					UpperCamelCase = ""	,      )     ->	bool:
  return sum(c % 2 for c in Counter(input_str.replace(''' '''	,      ''''''      ).lower()      ).values()      ) < 2



def      __lowerCAmelCase			(					UpperCamelCase = ""      )     ->	bool:
  if len(UpperCamelCase      ) == 0:
    return True
  lowerCAmelCase__				:  str		       =     input_str.replace(''' '''	,      ''''''      ).lower()
  # character_freq_dict: Stores the frequency of every character in the input string
  lowerCAmelCase__				:  dict[str, int]		       =     {}

  for character in lower_case_input_str:
    lowerCAmelCase__				:  int		       =     character_freq_dict.get(UpperCamelCase	,      0      ) + 1
  lowerCAmelCase__				:  Any		       =     0

  for character_count in character_freq_dict.values():
    if character_count % 2:
      odd_char += 1
  if odd_char > 1:
    return False
  return True



def      __lowerCAmelCase			(					UpperCamelCase = ""      )     ->	None:
  print('''\nFor string = '''	,      UpperCamelCase	,      ''':'''      )
  print(
      '''> can_string_be_rearranged_as_palindrome_counter()'''	,      '''\tans ='''	,      can_string_be_rearranged_as_palindrome_counter(UpperCamelCase      )	,      '''\ttime ='''	,      timeit(
          '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''	,      setup='''import __main__ as z'''	,      )	,      '''seconds'''	,      )
  print(
      '''> can_string_be_rearranged_as_palindrome()'''	,      '''\tans ='''	,      can_string_be_rearranged_as_palindrome(UpperCamelCase      )	,      '''\ttime ='''	,      timeit(
          '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''	,      setup='''import __main__ as z'''	,      )	,      '''seconds'''	,      )


if __name__ == "__main__":
     lowerCAmelCase_					    = input(
         """Enter string to determine if it can be rearranged as a palindrome or not: """
     ).strip()
     benchmark(check_str)
     lowerCAmelCase_					    = can_string_be_rearranged_as_palindrome_counter(check_str)
     print(F"""{check_str} can {"" if status else "not "}be rearranged as a palindrome""")

