
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 				UpperCAmelCase						( unittest.TestCase       ):

					def __init__(      self   :       List[str]			,   lowerCAmelCase   :       Optional[int]			,   lowerCAmelCase   :       List[str]=13			,   lowerCAmelCase   :       Any=30			,   lowerCAmelCase   :       Optional[int]=2			,   lowerCAmelCase   :       int=3			,   lowerCAmelCase   :       List[Any]=True			,   lowerCAmelCase   :       List[Any]=True			,   lowerCAmelCase   :       Optional[int]=32			,   lowerCAmelCase   :       Any=5			,   lowerCAmelCase   :       Dict=4			,   lowerCAmelCase   :       List[str]=37			,   lowerCAmelCase   :       Optional[int]="gelu"			,   lowerCAmelCase   :       List[str]=0.1			,   lowerCAmelCase   :       List[Any]=0.1			,   lowerCAmelCase   :       List[Any]=10			,   lowerCAmelCase   :       List[str]=0.02			,   ):
											lowercase							:     Tuple									=	parent
											lowercase							:     Optional[Any]									=	batch_size
											lowercase							:     Tuple									=	image_size
											lowercase							:     Union[str, Any]									=	patch_size
											lowercase							:     Any									=	num_channels
											lowercase							:     List[Any]									=	is_training
											lowercase							:     Optional[Any]									=	use_labels
											lowercase							:     Dict									=	hidden_size
											lowercase							:     Optional[Any]									=	num_hidden_layers
											lowercase							:     Any									=	num_attention_heads
											lowercase							:     int									=	intermediate_size
											lowercase							:     List[str]									=	hidden_act
											lowercase							:     Union[str, Any]									=	hidden_dropout_prob
											lowercase							:     List[Any]									=	attention_probs_dropout_prob
											lowercase							:     Any									=	type_sequence_label_size
											lowercase							:     Optional[int]									=	initializer_range

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

					def 							_lowerCAmelCase						(      self   :       Any       ):
											lowercase							:     int									=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

											lowercase							:     Union[str, Any]									=	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=_UpperCAmelCase			,   initializer_range=self.initializer_range			,   )

											return config, pixel_values

					def 							_lowerCAmelCase						(      self   :       Optional[Any]			,   lowerCAmelCase   :       str			,   lowerCAmelCase   :       Any       ):
											lowercase							:     int									=	FlaxViTModel(config=_UpperCAmelCase       )
											lowercase							:     str									=	model(_UpperCAmelCase       )
											# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
											lowercase							:     Any									=	(self.image_size, self.image_size)
											lowercase							:     Dict									=	(self.patch_size, self.patch_size)
											lowercase							:     Any									=	(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 							_lowerCAmelCase						(      self   :       int			,   lowerCAmelCase   :       Optional[Any]			,   lowerCAmelCase   :       int       ):
											lowercase							:     Optional[int]									=	self.type_sequence_label_size
											lowercase							:     int									=	FlaxViTForImageClassification(config=_UpperCAmelCase       )
											lowercase							:     Tuple									=	model(_UpperCAmelCase       )
											self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.type_sequence_label_size)       )

											# test greyscale images
											lowercase							:     Dict									=	1
											lowercase							:     List[str]									=	FlaxViTForImageClassification(_UpperCAmelCase       )

											lowercase							:     List[Any]									=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											lowercase							:     Any									=	model(_UpperCAmelCase       )




					def 							_lowerCAmelCase						(      self   :       List[str]       ):
											lowercase							:     int									=	self.prepare_config_and_inputs()
											(
											    lowercase   
											)							:     List[str]									=	config_and_inputs
											lowercase							:     str									=	{'pixel_values': pixel_values}
											return config, inputs_dict







@require_flax
class 				UpperCAmelCase						( _UpperCamelCase    ,			unittest.TestCase       ):
					a__:			Any      	=					(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()

					def 							_lowerCAmelCase						(      self   :       Optional[int]       ):
											lowercase							:     List[str]									=	FlaxViTModelTester(self       )
											lowercase							:     Optional[Any]									=	ConfigTester(self			,   config_class=_UpperCAmelCase			,   has_text_modality=_UpperCAmelCase			,   hidden_size=37       )

					def 							_lowerCAmelCase						(      self   :       int       ):
											self.config_tester.run_common_tests()

					def 							_lowerCAmelCase						(      self   :       Optional[Any]       ):
											lowercase							:     Optional[Any]									=	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_UpperCAmelCase       )

					def 							_lowerCAmelCase						(      self   :       str       ):
											lowercase							:     Dict									=	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase       )

					def 							_lowerCAmelCase						(      self   :       List[Any]       ):
											lowercase							:     Any									=	self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase							:     Tuple									=	['pixel_values']
																	self.assertListEqual(arg_names[:1]			,   _UpperCAmelCase       )

					def 							_lowerCAmelCase						(      self   :       Dict       ):
											lowercase							:     List[Any]									=	self.model_tester.prepare_config_and_inputs_for_common()

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

																							@jax.jit
																							def model_jitted(lowerCAmelCase   :       Dict			,   **lowerCAmelCase   :       Dict       ):
																													return model(pixel_values=_UpperCAmelCase			,   **_UpperCAmelCase       )

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

																							with self.subTest('''JIT Disabled'''       ):
																													with jax.disable_jit():
																																			lowercase							:     Union[str, Any]									=	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       )




					@slow
					def 							_lowerCAmelCase						(      self   :       List[str]       ):
											for model_class_name in self.all_model_classes:
																	lowercase							:     Tuple									=	model_class_name.from_pretrained('''google/vit-base-patch16-224'''       )
																	lowercase							:     Optional[int]									=	model(np.ones((1, 3, 224, 224)       )       )
																	self.assertIsNotNone(_UpperCAmelCase       )







'''simple docstring'''

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class       __magic_name__		(       unittest.TestCase  ):







							@property
							def 				__lowercase     (				self   :			int						):
									torch.manual_seed(0						)
									_a	: int								=				UNetaDModel(
									    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=3      ,out_channels=3      ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,)
									return model







							@property
							def 				__lowercase     (				self   :			Any						):
									torch.manual_seed(0						)
									_a	: int								=				VQModel(
									    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,latent_channels=3      ,)
									return model







							@property
							def 				__lowercase     (				self   :			Union[str, Any]						):
									torch.manual_seed(0						)
									_a	: Dict								=				CLIPTextConfig(
									    bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=1000      ,)
									return CLIPTextModel(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Dict						):
									_a	: Union[str, Any]								=				self.dummy_uncond_unet
									_a	: Union[str, Any]								=				DDIMScheduler()
									_a	: Union[str, Any]								=				self.dummy_vq_model

									_a	: List[Any]								=				LDMPipeline(unet=_UpperCAmelCase      ,vqvae=_UpperCAmelCase      ,scheduler=_UpperCAmelCase						)
									ldm.to(_UpperCAmelCase						)
									ldm.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: int								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='numpy'						).images

									_a	: Tuple								=				torch.manual_seed(0						)
									_a	: List[Any]								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='numpy'      ,return_dict=_UpperCAmelCase						)[0]

									_a	: Any								=				image[0, -3:, -3:, -1]
									_a	: List[Any]								=				image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: Union[str, Any]								=				np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]						)
									_a	: Any								=				1E-2 if torch_device != 'mps' else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < tolerance

@slow
@require_torch
class       __magic_name__		(       unittest.TestCase  ):







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Dict								=				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'						)
									ldm.to(_UpperCAmelCase						)
									ldm.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: str								=				torch.manual_seed(0						)
									_a	: Optional[Any]								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=5      ,output_type='numpy'						).images

									_a	: Optional[Any]								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)
									_a	: Union[str, Any]								=				np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]						)
									_a	: List[str]								=				1E-2 if torch_device != 'mps' else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance




