



import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def __init__(self	:				List[Any]    ,      a__	:				List[str]    ,      a__	:				int=13    ,      a__	:				Optional[int]=30    ,      a__	:				int=2    ,      a__	:				List[Any]=3    ,      a__	:				str=True    ,      a__	:				int=True    ,      a__	:				str=32    ,      a__	:				Optional[int]=5    ,      a__	:				Optional[Any]=4    ,      a__	:				Any=37    ,      a__	:				str="gelu"    ,      a__	:				Any=0.1    ,      a__	:				List[Any]=0.1    ,      a__	:				int=10    ,      a__	:				str=0.0_2    ,      ):





         """simple docstring"""



         __snake_case								=       parent
         __snake_case								=       batch_size
         __snake_case								=       image_size
         __snake_case								=       patch_size
         __snake_case								=       num_channels
         __snake_case								=       is_training
         __snake_case								=       use_labels
         __snake_case								=       hidden_size
         __snake_case								=       num_hidden_layers
         __snake_case								=       num_attention_heads
         __snake_case								=       intermediate_size
         __snake_case								=       hidden_act
         __snake_case								=       hidden_dropout_prob
         __snake_case								=       attention_probs_dropout_prob
         __snake_case								=       type_sequence_label_size
         __snake_case								=       initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __snake_case								=       (image_size // patch_size) ** 2
         __snake_case								=       num_patches + 1


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

         __snake_case								=       ViTConfig(
             image_size=self.image_size    ,      patch_size=self.patch_size    ,      num_channels=self.num_channels    ,      hidden_size=self.hidden_size    ,      num_hidden_layers=self.num_hidden_layers    ,      num_attention_heads=self.num_attention_heads    ,      intermediate_size=self.intermediate_size    ,      hidden_act=self.hidden_act    ,      hidden_dropout_prob=self.hidden_dropout_prob    ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,      is_decoder=lowercase_    ,      initializer_range=self.initializer_range    ,      )

         return config, pixel_values


      def        a			(self	:				Tuple    ,      a__	:				List[Any]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       FlaxViTModel(config=lowercase_				)
         __snake_case								=       model(lowercase_				)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         __snake_case								=       (self.image_size, self.image_size)
         __snake_case								=       (self.patch_size, self.patch_size)
         __snake_case								=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, num_patches + 1, self.hidden_size)				)


      def        a			(self	:				int    ,      a__	:				List[Any]    ,      a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       self.type_sequence_label_size
         __snake_case								=       FlaxViTForImageClassification(config=lowercase_				)
         __snake_case								=       model(lowercase_				)
         self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)				)

         # test greyscale images
         __snake_case								=       1
         __snake_case								=       FlaxViTForImageClassification(lowercase_				)

         __snake_case								=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
         __snake_case								=       model(lowercase_				)


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





         """simple docstring"""



         __snake_case								=       self.prepare_config_and_inputs()
         (
             __snake_case
         )								=       config_and_inputs
         __snake_case								=       {'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class 		SCREAMING_SNAKE_CASE__    (	a_						,				unittest.TestCase  ):
      A_						:    Any							=   (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       FlaxViTModelTester(self				)
         __snake_case								=       ConfigTester(self    ,      config_class=lowercase_    ,      has_text_modality=lowercase_    ,      hidden_size=37				)


      def        a			(self	:				Dict				):





         """simple docstring"""



         self.config_tester.run_common_tests()


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase_				)


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowercase_				)


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs_for_common()

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

            __snake_case								=       ['''pixel_values''']
            self.assertListEqual(arg_names[:1]    ,      lowercase_				)


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs_for_common()

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

               @jax.jit
               def model_jitted(a__	:				List[Any]    ,      **a__	:				Optional[int]				):
                  return model(pixel_values=lowercase_    ,      **lowercase_				)

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

               with self.subTest('''JIT Disabled'''				):
                  with jax.disable_jit():
                     __snake_case								=       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				)


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





         """simple docstring"""



         for model_class_name in self.all_model_classes:
            __snake_case								=       model_class_name.from_pretrained('''google/vit-base-patch16-224'''				)
            __snake_case								=       model(np.ones((1, 3, 224, 224)				)				)
            self.assertIsNotNone(lowercase_				)








class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				str    ,      a__	:				list				):





         """simple docstring"""



         __snake_case								=       set_counts
         __snake_case								=       max(a__				)
         __snake_case								=       len(a__				)
         __snake_case								=       [1] * num_sets
         __snake_case								=       list(range(a__				)				)


      def        a			(self	:				str    ,      a__	:				int    ,      a__	:				int				):





         """simple docstring"""



         __snake_case								=       self.get_parent(a__				)
         __snake_case								=       self.get_parent(a__				)

         if src_parent == dst_parent:
            return False

         if self.ranks[dst_parent] >= self.ranks[src_parent]:
            self.set_counts[dst_parent] += self.set_counts[src_parent]
            __snake_case								=       0
            __snake_case								=       dst_parent
            if self.ranks[dst_parent] == self.ranks[src_parent]:
               self.ranks[dst_parent] += 1
            __snake_case								=       self.set_counts[dst_parent]
         else:
            self.set_counts[src_parent] += self.set_counts[dst_parent]
            __snake_case								=       0
            __snake_case								=       src_parent
            __snake_case								=       self.set_counts[src_parent]

         __snake_case								=       max(self.max_set    ,      a__				)
         return True


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





         """simple docstring"""



         if self.parents[disj_set] == disj_set:
            return disj_set
         __snake_case								=       self.get_parent(self.parents[disj_set]				)
         return self.parents[disj_set]

