


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 			_lowercase (							unittest.TestCase):


       """simple docstring"""




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


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



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

             lowerCamelCase__		:				str    =  self.get_config()

             return config, pixel_values





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''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							:			List[Any]					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  FlaxRegNetModel(config=__lowerCamelCase       )
             lowerCamelCase__		:				Optional[Any]    =  model(__lowerCamelCase       )

             # 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							:			Any					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  self.num_labels
             lowerCamelCase__		:				Any    =  FlaxRegNetForImageClassification(config=__lowerCamelCase       )
             lowerCamelCase__		:				str    =  model(__lowerCamelCase       )
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



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







@require_flax
class 			_lowercase (							lowercase__							,  unittest.TestCase):


       """simple docstring"""




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

       A__											=			False
       A__											=			False
       A__											=			False





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  FlaxRegNetModelTester(self       )
             lowerCamelCase__		:				int    =  ConfigTester(self					,      config_class=__lowerCamelCase					,      has_text_modality=__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			str       ):


             '''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							:			str       ):


             '''simple docstring'''



             return





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase       )





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


             '''simple docstring'''



             pass





       @unittest.skip(reason="RegNet does not support input and output embeddings"       )
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



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

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

                   lowerCamelCase__		:				List[Any]    =  ["pixel_values"]
                   self.assertListEqual(arg_names[:1]					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             def check_hidden_states_output(__lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			Any       ):
                   lowerCamelCase__		:				List[Any]    =  model_class(__lowerCamelCase       )

                   lowerCamelCase__		:				int    =  model(**self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase       )       )

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

                   lowerCamelCase__		:				Any    =  self.model_tester.num_stages
                   self.assertEqual(len(__lowerCamelCase       )					,      expected_num_stages + 1       )

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

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				Union[str, Any]    =  True
                   check_hidden_states_output(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   lowerCamelCase__		:				Dict    =  True

                   check_hidden_states_output(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				Dict    =  self.model_tester.prepare_config_and_inputs_for_common()

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

                         @jax.jit
                         def model_jitted(__lowerCamelCase							:			Union[str, Any]					,      **__lowerCamelCase							:			Optional[int]       ):
                               return model(pixel_values=__lowerCamelCase					,      **__lowerCamelCase       )

                         with self.subTest("JIT Enabled"       ):
                               lowerCamelCase__		:				Any    =  model_jitted(**__lowerCamelCase       ).to_tuple()

                         with self.subTest("JIT Disabled"       ):
                               with jax.disable_jit():
                                     lowerCamelCase__		:				Dict    =  model_jitted(**__lowerCamelCase       ).to_tuple()

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



def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				str    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
      return image







@require_flax
class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       @cached_property
       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



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





       @slow
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"       )

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

             lowerCamelCase__		:				Optional[Any]    =  model(**__lowerCamelCase       )

             # verify the logits
             lowerCamelCase__		:				Any    =  (1, 1000)
             self.assertEqual(outputs.logits.shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				str    =  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]					,      __lowerCamelCase					,      atol=1E-4       )       )






class 			_lowercase :  # Public class to implement a graph


       """simple docstring"""




       def __init__(   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  row
             lowerCamelCase__		:				Optional[Any]    =  col
             lowerCamelCase__		:				Union[str, Any]    =  graph





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             return (
                 0 <= i < self.ROW
                 and 0 <= j < self.COL
                 and not visited[i][j]
                 and self.graph[i][j]
             )





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
             lowerCamelCase__		:				Tuple    =  [-1, 0, 1, -1, 1, -1, 0, 1]
             lowerCamelCase__		:				List[Any]    =  True  # Make those cells visited
             for k in range(8       ):
                   if self.is_safe(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       ):
                         self.diffs(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):  # And finally, count all islands.


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  [[False for j in range(self.COL       )] for i in range(self.ROW       )]
             lowerCamelCase__		:				Optional[Any]    =  0
             for i in range(self.ROW       ):
                   for j in range(self.COL       ):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                               self.diffs(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )
                               count += 1
             return count


