


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        lowerCamelCase__	(							unittest.TestCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=13      ,						snake_case=30      ,						snake_case=2      ,						snake_case=3      ,						snake_case=True      ,						snake_case=True      ,						snake_case=32      ,						snake_case=5      ,						snake_case=4      ,						snake_case=37      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=10      ,						snake_case=0.02      ,						):

						'''simple docstring'''


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

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




			def 				snake_case__				(							self     ):

						'''simple docstring'''


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

						UpperCamelCase__					=       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=snake_case      ,						initializer_range=self.initializer_range      ,						)

						return config, pixel_values




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       FlaxViTModel(config=snake_case     )
						UpperCamelCase__					=       model(snake_case     )
						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
						UpperCamelCase__					=       (self.image_size, self.image_size)
						UpperCamelCase__					=       (self.patch_size, self.patch_size)
						UpperCamelCase__					=       (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      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


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

						# test greyscale images
						UpperCamelCase__					=       1
						UpperCamelCase__					=       FlaxViTForImageClassification(snake_case     )

						UpperCamelCase__					=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
						UpperCamelCase__					=       model(snake_case     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.prepare_config_and_inputs()
						(
						    (
						    UpperCamelCase__
						),      (
						    UpperCamelCase__
						),      
						)					=       config_and_inputs
						UpperCamelCase__					=       {"pixel_values": pixel_values}
						return config, inputs_dict







@require_flax
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       FlaxViTModelTester(self     )
						UpperCamelCase__					=       ConfigTester(self      ,						config_class=snake_case      ,						has_text_modality=snake_case      ,						hidden_size=37     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						self.config_tester.run_common_tests()




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCamelCase__					=       ["pixel_values"]
									self.assertListEqual(arg_names[:1]      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__     ):
												UpperCamelCase__					=       self._prepare_for_class(snake_case      ,						snake_case     )
												UpperCamelCase__					=       model_class(snake_case     )

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

												with self.subTest("JIT Enabled"     ):
															UpperCamelCase__					=       model_jitted(**snake_case     ).to_tuple()

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







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


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






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
						import jax







@require_flax
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		FlaxAutoencoderKL




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       4
						UpperCamelCase__					=       3
						UpperCamelCase__					=       (32, 32)

						UpperCamelCase__					=       jax.random.PRNGKey(0     )
						UpperCamelCase__					=       jax.random.uniform(snake_case      ,						((batch_size, num_channels) + sizes)     )

						return {"sample": image, "prng_key": prng_key}







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {
						    "block_out_channels": [32, 64],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 4,
						}
						UpperCamelCase__					=       self.dummy_input
						return init_dict, inputs_dict


