





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 	A_       (a_			):
	UpperCAmelCase__					=			(UnCLIPScheduler,)
	def      _lowercase				(    self							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				{
				    '''num_train_timesteps''': 1_0_0_0,
				    '''variance_type''': '''fixed_small_log''',
				    '''clip_sample''': True,
				    '''clip_sample_range''': 1.0,
				    '''prediction_type''': '''epsilon''',
				}

				config.update(**_A		)
				return config
	def      _lowercase				(    self		):






				'''simple docstring'''







				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				for variance in ["fixed_small_log", "learned_range"]:
							self.check_over_configs(variance_type=_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				for clip_sample_range in [1, 5, 1_0, 2_0]:
							self.check_over_configs(clip_sample_range=_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				for prediction_type in ["epsilon", "sample"]:
							self.check_over_configs(prediction_type=_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				for time_step in [0, 5_0_0, 9_9_9]:
							for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
										if prev_timestep is not None and prev_timestep >= time_step:
													continue

										self.check_over_forward(time_step=_A							,					prev_timestep=_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.scheduler_classes[0]
				UpperCAmelCase   		=				self.get_scheduler_config(variance_type='''fixed_small_log'''		)
				UpperCAmelCase   		=				scheduler_class(**_A		)

				assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0000E-10		)		) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7		) - 0.0_54_96_25		)		) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9		) - 0.9_99_49_87		)		) < 1E-5
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.scheduler_classes[0]
				UpperCAmelCase   		=				self.get_scheduler_config(variance_type='''learned_range'''		)
				UpperCAmelCase   		=				scheduler_class(**_A		)

				UpperCAmelCase   		=				0.5

				assert scheduler._get_variance(1							,					predicted_variance=_A		) - -10.1_71_27_90 < 1E-5
				assert scheduler._get_variance(4_8_7							,					predicted_variance=_A		) - -5.7_99_80_52 < 1E-5
				assert scheduler._get_variance(9_9_9							,					predicted_variance=_A		) - -0.0_01_00_11 < 1E-5
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.scheduler_classes[0]
				UpperCAmelCase   		=				self.get_scheduler_config()
				UpperCAmelCase   		=				scheduler_class(**_A		)

				UpperCAmelCase   		=				scheduler.timesteps

				UpperCAmelCase   		=				self.dummy_model()
				UpperCAmelCase   		=				self.dummy_sample_deter
				UpperCAmelCase   		=				torch.manual_seed(0		)

				for i, t in enumerate(_A		):
							# 1. predict noise residual
							UpperCAmelCase   		=				model(_A							,					_A		)

							# 2. predict previous mean of sample x_t-1
							UpperCAmelCase   		=				scheduler.step(_A							,					_A							,					_A							,					generator=_A		).prev_sample

							UpperCAmelCase   		=				pred_prev_sample

				UpperCAmelCase   		=				torch.sum(torch.abs(_A		)		)
				UpperCAmelCase   		=				torch.mean(torch.abs(_A		)		)

				assert abs(result_sum.item() - 2_52.2_68_24_95		) < 1E-2
				assert abs(result_mean.item() - 0.3_28_47_43		) < 1E-3
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.scheduler_classes[0]
				UpperCAmelCase   		=				self.get_scheduler_config()
				UpperCAmelCase   		=				scheduler_class(**_A		)

				scheduler.set_timesteps(2_5		)

				UpperCAmelCase   		=				scheduler.timesteps

				UpperCAmelCase   		=				self.dummy_model()
				UpperCAmelCase   		=				self.dummy_sample_deter
				UpperCAmelCase   		=				torch.manual_seed(0		)

				for i, t in enumerate(_A		):
							# 1. predict noise residual
							UpperCAmelCase   		=				model(_A							,					_A		)

							if i + 1 == timesteps.shape[0]:
										UpperCAmelCase   		=				None
							else:
										UpperCAmelCase   		=				timesteps[i + 1]

							# 2. predict previous mean of sample x_t-1
							UpperCAmelCase   		=				scheduler.step(
							    _A							,					_A							,					_A							,					prev_timestep=_A							,					generator=_A		).prev_sample

							UpperCAmelCase   		=				pred_prev_sample

				UpperCAmelCase   		=				torch.sum(torch.abs(_A		)		)
				UpperCAmelCase   		=				torch.mean(torch.abs(_A		)		)

				assert abs(result_sum.item() - 2_58.2_04_49_83		) < 1E-2
				assert abs(result_mean.item() - 0.3_36_20_38		) < 1E-3
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass







	def      _lowercase				(    self		):






				'''simple docstring'''







				pass










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 	A_       :
	def __init__(    self							,					_A							,					_A=1_3							,					_A=3_0							,					_A=2							,					_A=3							,					_A=True							,					_A=True							,					_A=3_2							,					_A=2							,					_A=4							,					_A=3_7							,					_A="gelu"							,					_A=0.1							,					_A=0.1							,					_A=1_0							,					_A=0.02							,					_A=3							,					_A=None							,					):






				'''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
				UpperCAmelCase   		=				scope

				# 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      _lowercase				(    self		):






				'''simple docstring'''







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

				UpperCAmelCase   		=				None
				if self.use_labels:
							UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.type_sequence_label_size		)

				UpperCAmelCase   		=				self.get_config()

				return config, pixel_values, labels
	def      _lowercase				(    self		):






				'''simple docstring'''







				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=_A							,					initializer_range=self.initializer_range							,					)
	def      _lowercase				(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTModel(config=_A		)
				UpperCAmelCase   		=				model(_A							,					training=_A		)
				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   		=				self.image_size // 2
				UpperCAmelCase   		=				pixel_values[:, :, :image_size, :image_size]
				UpperCAmelCase   		=				model(_A							,					interpolate_pos_encoding=_A							,					training=_A		)
				UpperCAmelCase   		=				(image_size // self.patch_size) ** 2 + 1
				self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, seq_length, self.hidden_size)		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.type_sequence_label_size
				UpperCAmelCase   		=				TFViTForImageClassification(_A		)
				UpperCAmelCase   		=				model(_A							,					labels=_A							,					training=_A		)
				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   		=				self.image_size // 2
				UpperCAmelCase   		=				pixel_values[:, :, :image_size, :image_size]
				UpperCAmelCase   		=				model(_A							,					interpolate_pos_encoding=_A							,					training=_A		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)		)

				# test greyscale images
				UpperCAmelCase   		=				1
				UpperCAmelCase   		=				TFViTForImageClassification(_A		)
				UpperCAmelCase   		=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
				UpperCAmelCase   		=				model(_A		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.prepare_config_and_inputs()
				UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase   		=				config_and_inputs
				UpperCAmelCase   		=				{'''pixel_values''': pixel_values}
				return config, inputs_dict

@require_tf
class 	A_       (a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
	UpperCAmelCase__					=			(
	    {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
	    if is_tf_available()
	    else {}
	)

	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			False
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTModelTester(self		)
				UpperCAmelCase   		=				ConfigTester(self							,					config_class=_A							,					has_text_modality=_A							,					hidden_size=3_7		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.config_tester.run_common_tests()
	@unittest.skip(reason='''ViT does not use inputs_embeds'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







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






				'''simple docstring'''







				pass
	def      _lowercase				(    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(_A		)
							self.assertIsInstance(model.get_input_embeddings()							,					(tf.keras.layers.Layer)		)
							UpperCAmelCase   		=				model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(_A							,					tf.keras.layers.Layer		)		)
	def      _lowercase				(    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(_A		)
							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]							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_A		)







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTModel.from_pretrained('''google/vit-base-patch16-224'''		)
				self.assertIsNotNone(_A		)





def 						__SCREAMING_SNAKE_CASE  (				)  ->				Optional[Any]:







			'''simple docstring'''

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






@require_tf
@require_vision
class 	A_       (unittest.TestCase			):
	@cached_property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''		) if is_vision_available() else None







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''		)

				UpperCAmelCase   		=				self.default_image_processor
				UpperCAmelCase   		=				prepare_img()
				UpperCAmelCase   		=				image_processor(images=_A							,					return_tensors='''tf'''		)

				# forward pass
				UpperCAmelCase   		=				model(**_A		)

				# verify the logits
				UpperCAmelCase   		=				tf.TensorShape((1, 1_0_0_0)		)
				self.assertEqual(outputs.logits.shape							,					_A		)

				UpperCAmelCase   		=				tf.constant([-0.27_44, 0.82_15, -0.08_36]		)

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



