




import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor

class        lowercase      :







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					Tuple=13      ,     _lowerCamelCase   :					int=30      ,     _lowerCamelCase   :					List[Any]=2      ,     _lowerCamelCase   :					Dict=3      ,     _lowerCamelCase   :					Optional[Any]=True      ,     _lowerCamelCase   :					Any=True      ,     _lowerCamelCase   :					Tuple=32      ,     _lowerCamelCase   :					Optional[int]=5      ,     _lowerCamelCase   :					Any=4      ,     _lowerCamelCase   :					List[str]=37      ,     _lowerCamelCase   :					Optional[Any]="gelu"      ,     _lowerCamelCase   :					List[Any]=0.1      ,     _lowerCamelCase   :					List[str]=0.1      ,     _lowerCamelCase   :					Union[str, Any]=10      ,     _lowerCamelCase   :					Dict=0.02      ,     _lowerCamelCase   :					Optional[int]=None      ,     ):



					"""simple docstring"""

					A_  :				int	   =		parent
					A_  :				Any	   =		batch_size
					A_  :				List[Any]	   =		image_size
					A_  :				int	   =		patch_size
					A_  :				Tuple	   =		num_channels
					A_  :				List[str]	   =		is_training
					A_  :				List[Any]	   =		use_labels
					A_  :				List[str]	   =		hidden_size
					A_  :				Union[str, Any]	   =		num_hidden_layers
					A_  :				Dict	   =		num_attention_heads
					A_  :				str	   =		intermediate_size
					A_  :				int	   =		hidden_act
					A_  :				str	   =		hidden_dropout_prob
					A_  :				List[str]	   =		attention_probs_dropout_prob
					A_  :				List[str]	   =		type_sequence_label_size
					A_  :				Any	   =		initializer_range
					A_  :				Tuple	   =		scope

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







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

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

					A_  :				Optional[int]	   =		None
					if self.use_labels:
						A_  :				Optional[Any]	   =		ids_tensor([self.batch_size]      ,     self.type_sequence_label_size       )

					A_  :				Tuple	   =		self.get_config()

					return config, pixel_values, labels







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					return ViTMSNConfig(
					    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      ,     initializer_range=self.initializer_range      ,     )







				def 					a_  (							self   :					str      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					A_  :				Any	   =		ViTMSNModel(config=_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()
					A_  :				List[str]	   =		model(_lowerCamelCase       )
					self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)       )







				def 					a_  (							self   :					Optional[Any]      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Dict	   =		self.type_sequence_label_size
					A_  :				str	   =		ViTMSNForImageClassification(_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()
					A_  :				int	   =		model(_lowerCamelCase      ,     labels=_lowerCamelCase       )
					print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''       )
					print('''Labels: {labels}'''       )
					self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.type_sequence_label_size)       )

					# test greyscale images
					A_  :				Optional[int]	   =		1
					A_  :				Dict	   =		ViTMSNForImageClassification(_lowerCamelCase       )
					model.to(_lowerCamelCase       )
					model.eval()

					A_  :				int	   =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
					A_  :				Optional[Any]	   =		model(_lowerCamelCase       )
					self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.type_sequence_label_size)       )







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

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

@require_torch
class        lowercase      (    __UpperCAmelCase     ,	__UpperCAmelCase     ,	unittest.TestCase):
				__lowerCAmelCase    :    List[str]         =   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				__lowerCAmelCase    :    Dict         =   (
				    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__lowerCAmelCase    :    List[Any]         =   False
				__lowerCAmelCase    :    str         =   False
				__lowerCAmelCase    :    Optional[Any]         =   False
				__lowerCAmelCase    :    Any         =   False







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		ViTMSNModelTester(self       )
					A_  :				Dict	   =		ConfigTester(self      ,     config_class=_lowerCamelCase      ,     has_text_modality=_lowerCamelCase      ,     hidden_size=37       )







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					self.config_tester.run_common_tests()







				@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''       )
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					pass







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Tuple	   =		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						A_  :				List[str]	   =		model_class(_lowerCamelCase       )
						self.assertIsInstance(model.get_input_embeddings()      ,     (nn.Module)       )
						A_  :				str	   =		model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(_lowerCamelCase      ,     nn.Linear       )       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		self.model_tester.prepare_config_and_inputs_for_common()

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

						A_  :				Any	   =		['''pixel_values''']
						self.assertListEqual(arg_names[:1]      ,     _lowerCamelCase       )







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_lowerCamelCase       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				str	   =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase       )







				@slow
				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						A_  :				List[Any]	   =		ViTMSNModel.from_pretrained(_lowerCamelCase       )
						self.assertIsNotNone(_lowerCamelCase       )

def 		lowercase_      (		):




	"""simple docstring"""






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

@require_torch
@require_vision
class        lowercase      (    unittest.TestCase):







				@cached_property
				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''       ) if is_vision_available() else None







				@slow
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					torch.manual_seed(2       )
					A_  :				Dict	   =		ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''       ).to(_lowerCamelCase       )

					A_  :				Dict	   =		self.default_image_processor
					A_  :				int	   =		prepare_img()
					A_  :				Dict	   =		image_processor(images=_lowerCamelCase      ,     return_tensors='''pt'''       ).to(_lowerCamelCase       )

					# forward pass
					with torch.no_grad():
						A_  :				str	   =		model(**_lowerCamelCase       )

					# verify the logits
					A_  :				str	   =		torch.Size((1, 10_00)       )
					self.assertEqual(outputs.logits.shape      ,     _lowerCamelCase       )

					A_  :				Optional[int]	   =		torch.tensor([-0.0803, -0.4454, -0.2375]       ).to(_lowerCamelCase       )

					self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,     _lowerCamelCase      ,     atol=1E-4       )       )











"""simple docstring"""

import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_lowerCamelCase  :			Any        =       False

class        lowercase      (    unittest.TestCase):







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					return 12







				@property
				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					return 12







				@property
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					return 32







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					torch.manual_seed(0       )
					A_  :				Union[str, Any]	   =		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      ,     num_vq_embeddings=self.num_embed      ,     vq_embed_dim=3      ,     )
					return model







				@property
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				int	   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
					return tokenizer







				@property
				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					torch.manual_seed(0       )
					A_  :				Optional[int]	   =		CLIPTextConfig(
					    bos_token_id=0      ,     eos_token_id=2      ,     hidden_size=self.text_embedder_hidden_size      ,     intermediate_size=37      ,     layer_norm_eps=1E-05      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     pad_token_id=1      ,     vocab_size=10_00      ,     )
					return CLIPTextModel(_lowerCamelCase       )







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					torch.manual_seed(0       )

					A_  :				Optional[Any]	   =		12
					A_  :				Optional[int]	   =		12

					A_  :				int	   =		{
					    '''attention_bias''': True,
					    '''cross_attention_dim''': 32,
					    '''attention_head_dim''': height * width,
					    '''num_attention_heads''': 1,
					    '''num_vector_embeds''': self.num_embed,
					    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
					    '''norm_num_groups''': 32,
					    '''sample_size''': width,
					    '''activation_fn''': '''geglu-approximate''',
					}

					A_  :				Tuple	   =		TransformeraDModel(**_lowerCamelCase       )
					return model







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		'''cpu'''

					A_  :				Union[str, Any]	   =		self.dummy_vqvae
					A_  :				str	   =		self.dummy_text_encoder
					A_  :				List[Any]	   =		self.dummy_tokenizer
					A_  :				int	   =		self.dummy_transformer
					A_  :				Any	   =		VQDiffusionScheduler(self.num_embed       )
					A_  :				Optional[Any]	   =		LearnedClassifierFreeSamplingEmbeddings(learnable=_lowerCamelCase       )

					A_  :				Dict	   =		VQDiffusionPipeline(
					    vqvae=_lowerCamelCase      ,     text_encoder=_lowerCamelCase      ,     tokenizer=_lowerCamelCase      ,     transformer=_lowerCamelCase      ,     scheduler=_lowerCamelCase      ,     learned_classifier_free_sampling_embeddings=_lowerCamelCase      ,     )
					A_  :				List[Any]	   =		pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				List[Any]	   =		'''teddy bear playing in the pool'''

					A_  :				List[Any]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[Any]	   =		pipe([prompt]      ,     generator=_lowerCamelCase      ,     num_inference_steps=2      ,     output_type='''np'''       )
					A_  :				Any	   =		output.images

					A_  :				List[str]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[Any]	   =		pipe(
					    [prompt]      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     return_dict=_lowerCamelCase      ,     num_inference_steps=2       )[0]

					A_  :				Optional[int]	   =		image[0, -3:, -3:, -1]
					A_  :				Optional[int]	   =		image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					A_  :				Optional[int]	   =		np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		'''cpu'''

					A_  :				int	   =		self.dummy_vqvae
					A_  :				List[str]	   =		self.dummy_text_encoder
					A_  :				Optional[Any]	   =		self.dummy_tokenizer
					A_  :				Any	   =		self.dummy_transformer
					A_  :				Any	   =		VQDiffusionScheduler(self.num_embed       )
					A_  :				Optional[int]	   =		LearnedClassifierFreeSamplingEmbeddings(
					    learnable=_lowerCamelCase      ,     hidden_size=self.text_embedder_hidden_size      ,     length=tokenizer.model_max_length       )

					A_  :				int	   =		VQDiffusionPipeline(
					    vqvae=_lowerCamelCase      ,     text_encoder=_lowerCamelCase      ,     tokenizer=_lowerCamelCase      ,     transformer=_lowerCamelCase      ,     scheduler=_lowerCamelCase      ,     learned_classifier_free_sampling_embeddings=_lowerCamelCase      ,     )
					A_  :				List[Any]	   =		pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				Any	   =		'''teddy bear playing in the pool'''

					A_  :				str	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				Optional[Any]	   =		pipe([prompt]      ,     generator=_lowerCamelCase      ,     num_inference_steps=2      ,     output_type='''np'''       )
					A_  :				Tuple	   =		output.images

					A_  :				Optional[int]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[str]	   =		pipe(
					    [prompt]      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     return_dict=_lowerCamelCase      ,     num_inference_steps=2       )[0]

					A_  :				Optional[int]	   =		image[0, -3:, -3:, -1]
					A_  :				Optional[Any]	   =		image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					A_  :				str	   =		np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2.0
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2

@slow
@require_torch_gpu
class        lowercase      (    unittest.TestCase):







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				str	   =		load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''       )

					A_  :				int	   =		VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''       )
					A_  :				Tuple	   =		pipeline.to(_lowerCamelCase       )
					pipeline.set_progress_bar_config(disable=_lowerCamelCase       )

					# requires GPU generator for gumbel softmax
					# don't use GPU generator in tests though
					A_  :				Dict	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				Union[str, Any]	   =		pipeline(
					    '''teddy bear playing in the pool'''      ,     num_images_per_prompt=1      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     )

					A_  :				Optional[int]	   =		output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image       ).max() < 2.0





