



'''simple docstring'''

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 __snake_case( unittest.TestCase   ):


						'''simple docstring'''



						def __init__(   self      ,			A_      ,			A_=13      ,			A_=30      ,			A_=2      ,			A_=3      ,			A_=True      ,			A_=True      ,			A_=32      ,			A_=5      ,			A_=4      ,			A_=37      ,			A_="gelu"      ,			A_=0.1      ,			A_=0.1      ,			A_=10      ,			A_=0.0_2      ,			)      ->      List[Any]:
							lowerCAmelCase       =      parent
							lowerCAmelCase       =      batch_size
							lowerCAmelCase       =      image_size
							lowerCAmelCase       =      patch_size
							lowerCAmelCase       =      num_channels
							lowerCAmelCase       =      is_training
							lowerCAmelCase       =      use_labels
							lowerCAmelCase       =      hidden_size
							lowerCAmelCase       =      num_hidden_layers
							lowerCAmelCase       =      num_attention_heads
							lowerCAmelCase       =      intermediate_size
							lowerCAmelCase       =      hidden_act
							lowerCAmelCase       =      hidden_dropout_prob
							lowerCAmelCase       =      attention_probs_dropout_prob
							lowerCAmelCase       =      type_sequence_label_size
							lowerCAmelCase       =      initializer_range

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


						def 							__snake_case       (   self )      ->      Optional[Any]:
							lowerCAmelCase       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

							lowerCAmelCase       =      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=A_      ,			initializer_range=self.initializer_range      ,			)

							return config, pixel_values


						def 							__snake_case       (   self      ,			A_      ,			A_ )      ->      List[str]:
							lowerCAmelCase       =      FlaxViTModel(config=A_ )
							lowerCAmelCase       =      model(A_ )
							# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
							lowerCAmelCase       =      (self.image_size, self.image_size)
							lowerCAmelCase       =      (self.patch_size, self.patch_size)
							lowerCAmelCase       =      (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 							__snake_case       (   self      ,			A_      ,			A_ )      ->      Tuple:
							lowerCAmelCase       =      self.type_sequence_label_size
							lowerCAmelCase       =      FlaxViTForImageClassification(config=A_ )
							lowerCAmelCase       =      model(A_ )
							self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.type_sequence_label_size) )

							# test greyscale images
							lowerCAmelCase       =      1
							lowerCAmelCase       =      FlaxViTForImageClassification(A_ )

							lowerCAmelCase       =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
							lowerCAmelCase       =      model(A_ )




						def 							__snake_case       (   self )      ->      Optional[int]:
							lowerCAmelCase       =      self.prepare_config_and_inputs()
							(
							    lowerCAmelCase
							)       =      config_and_inputs
							lowerCAmelCase       =      {"pixel_values": pixel_values}
							return config, inputs_dict







@require_flax
class __snake_case( __A						,				unittest.TestCase   ):


						'''simple docstring'''



						UpperCAmelCase				:       str 	=						(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


						def 							__snake_case       (   self )      ->      None:
							lowerCAmelCase       =      FlaxViTModelTester(self )
							lowerCAmelCase       =      ConfigTester(self      ,			config_class=A_      ,			has_text_modality=A_      ,			hidden_size=37 )


						def 							__snake_case       (   self )      ->      List[str]:
							self.config_tester.run_common_tests()


						def 							__snake_case       (   self )      ->      Dict:
							lowerCAmelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_ )


						def 							__snake_case       (   self )      ->      Dict:
							lowerCAmelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_ )


						def 							__snake_case       (   self )      ->      str:
							lowerCAmelCase       =      self.model_tester.prepare_config_and_inputs_for_common()

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

								lowerCAmelCase       =      ["pixel_values"]
								self.assertListEqual(arg_names[:1]      ,			A_ )


						def 							__snake_case       (   self )      ->      Any:
							lowerCAmelCase       =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__ ):
									lowerCAmelCase       =      self._prepare_for_class(A_      ,			A_ )
									lowerCAmelCase       =      model_class(A_ )

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

									with self.subTest("""JIT Enabled""" ):
										lowerCAmelCase       =      model_jitted(**A_ ).to_tuple()

									with self.subTest("""JIT Disabled""" ):
										with jax.disable_jit():
											lowerCAmelCase       =      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 )




						@slow
						def 							__snake_case       (   self )      ->      List[Any]:

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_      =     {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Swinv2ForImageClassification',
			    'Swinv2ForMaskedImageModeling',
			    'Swinv2Model',
			    'Swinv2PreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)