

from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor







class 	lowercase    :


		"""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=2	,       __snake_case=4	,       __snake_case=37	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=10	,       __snake_case=0.0_2	,       __snake_case=3	,       __snake_case=None	,       ):
			_UpperCamelCase   :		Tuple			=						parent
			_UpperCamelCase   :		str			=						batch_size
			_UpperCamelCase   :		Tuple			=						image_size
			_UpperCamelCase   :		int			=						patch_size
			_UpperCamelCase   :		List[Any]			=						num_channels
			_UpperCamelCase   :		Dict			=						is_training
			_UpperCamelCase   :		Optional[Any]			=						use_labels
			_UpperCamelCase   :		int			=						hidden_size
			_UpperCamelCase   :		Dict			=						num_hidden_layers
			_UpperCamelCase   :		str			=						num_attention_heads
			_UpperCamelCase   :		Optional[int]			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_act
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		Any			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Tuple			=						type_sequence_label_size
			_UpperCamelCase   :		Dict			=						initializer_range
			_UpperCamelCase   :		Optional[Any]			=						scope

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


		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			_UpperCamelCase   :		List[Any]			=						None
			if self.use_labels:
				_UpperCamelCase   :		Union[str, Any]			=						ids_tensor([self.batch_size]	,       self.type_sequence_label_size)

			_UpperCamelCase   :		Any			=						self.get_config()

			return config, pixel_values, labels


		def 		A__					(				self):
			return 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	,       )


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[str]			=						TFViTModel(config=__snake_case)
			_UpperCamelCase   :		int			=						model(__snake_case	,       training=__snake_case)
			self.parent.assertEqual(result.last_hidden_state.shape	,       (self.batch_size, self.seq_length, self.hidden_size))

			# Test with an image with different size than the one specified in config.
			_UpperCamelCase   :		Any			=						self.image_size // 2
			_UpperCamelCase   :		List[str]			=						pixel_values[:, :, :image_size, :image_size]
			_UpperCamelCase   :		Tuple			=						model(__snake_case	,       interpolate_pos_encoding=__snake_case	,       training=__snake_case)
			_UpperCamelCase   :		Any			=						(image_size // self.patch_size) ** 2 + 1
			self.parent.assertEqual(result.last_hidden_state.shape	,       (self.batch_size, seq_length, self.hidden_size))


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		int			=						self.type_sequence_label_size
			_UpperCamelCase   :		int			=						TFViTForImageClassification(__snake_case)
			_UpperCamelCase   :		Optional[Any]			=						model(__snake_case	,       labels=__snake_case	,       training=__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.type_sequence_label_size))

			# Test with an image with different size than the one specified in config.
			_UpperCamelCase   :		Dict			=						self.image_size // 2
			_UpperCamelCase   :		List[str]			=						pixel_values[:, :, :image_size, :image_size]
			_UpperCamelCase   :		List[str]			=						model(__snake_case	,       interpolate_pos_encoding=__snake_case	,       training=__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.type_sequence_label_size))

			# test greyscale images
			_UpperCamelCase   :		Union[str, Any]			=						1
			_UpperCamelCase   :		List[str]			=						TFViTForImageClassification(__snake_case)
			_UpperCamelCase   :		int			=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
			_UpperCamelCase   :		List[Any]			=						model(__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.type_sequence_label_size))






		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						self.prepare_config_and_inputs()
			_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						config_and_inputs
			_UpperCamelCase   :		Optional[int]			=						{'pixel_values': pixel_values}
			return config, inputs_dict







@require_tf
class 	lowercase    (				_lowercase    ,							_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
		a__					       =  (
		    {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
		    if is_tf_available()
		    else {}
		)

		a__					       =  False
		a__					       =  False
		a__					       =  False


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						TFViTModelTester(self)
			_UpperCamelCase   :		Union[str, Any]			=						ConfigTester(self	,       config_class=__snake_case	,       has_text_modality=__snake_case	,       hidden_size=37)


		def 		A__					(				self):
			self.config_tester.run_common_tests()


		@unittest.skip(reason='ViT does not use inputs_embeds')
		def 		A__					(				self):
			pass


		@unittest.skip(reason='ViT does not use inputs_embeds')
		def 		A__					(				self):
			pass


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase   :		int			=						model_class(__snake_case)
				self.assertIsInstance(model.get_input_embeddings()	,       (tf.keras.layers.Layer))
				_UpperCamelCase   :		int			=						model.get_output_embeddings()
				self.assertTrue(x is None or isinstance(__snake_case	,       tf.keras.layers.Layer))


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase   :		List[Any]			=						model_class(__snake_case)
				_UpperCamelCase   :		Optional[int]			=						inspect.signature(model.call)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				_UpperCamelCase   :		Union[str, Any]			=						[*signature.parameters.keys()]

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


		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		int			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						TFViTModel.from_pretrained('google/vit-base-patch16-224')
			self.assertIsNotNone(__snake_case)






def 					lowerCamelCase_     (							)						->				List[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image







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


		"""simple docstring"""




		@cached_property
		def 		A__					(				self):
			return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224') if is_vision_available() else None






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224')

			_UpperCamelCase   :		Dict			=						self.default_image_processor
			_UpperCamelCase   :		List[Any]			=						prepare_img()
			_UpperCamelCase   :		List[str]			=						image_processor(images=__snake_case	,       return_tensors='tf')

			# forward pass
			_UpperCamelCase   :		Optional[Any]			=						model(**__snake_case)

			# verify the logits
			_UpperCamelCase   :		int			=						tf.TensorShape((1, 10_00))
			self.assertEqual(outputs.logits.shape	,       __snake_case)

			_UpperCamelCase   :		Dict			=						tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6])

			tf.debugging.assert_near(outputs.logits[0, :3]	,       __snake_case	,       atol=1e-4)







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCAmelCase__								=		False







class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case=32):
			set_seed(0)
			_UpperCamelCase   :		int			=						UNetaDModel(sample_size=__snake_case	,       in_channels=3	,       out_channels=3)
			_UpperCamelCase   :		str			=						torch.optim.SGD(model.parameters()	,       lr=0.0_0_0_1)
			return model, optimizer






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
			_UpperCamelCase   :		List[Any]			=						DDPMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )
			_UpperCamelCase   :		List[Any]			=						DDIMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )

			assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

			# shared batches for DDPM and DDIM
			set_seed(0)
			_UpperCamelCase   :		Optional[Any]			=						[torch.randn((4, 3, 32, 32)).clip(-1	,       1).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		str			=						[torch.randn((4, 3, 32, 32)).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		int			=						[torch.randint(0	,       10_00	,       (4,)).long().to(__snake_case) for _ in range(4)]

			# train with a DDPM scheduler
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		int			=						ddpm_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Any			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		str			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			# recreate the model and optimizer, and retry with DDIM
			_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		Dict			=						ddim_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Dict			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		Tuple			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))
			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))




