

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import (
		    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    MODEL_MAPPING,
		    LevitForImageClassification,
		    LevitForImageClassificationWithTeacher,
		    LevitModel,
		)
		from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import LevitImageProcessor


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.config_class(**self.inputs_dict							)
				self.parent.assertTrue(hasattr(__UpperCamelCase ,				'hidden_sizes'							)							)
				self.parent.assertTrue(hasattr(__UpperCamelCase ,				'num_attention_heads'							)							)







class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=13 ,				__UpperCamelCase=64 ,				__UpperCamelCase=3 ,				__UpperCamelCase=3 ,				__UpperCamelCase=2 ,				__UpperCamelCase=1 ,				__UpperCamelCase=16 ,				__UpperCamelCase=[1_28, 2_56, 3_84] ,				__UpperCamelCase=[4, 6, 8] ,				__UpperCamelCase=[2, 3, 4] ,				__UpperCamelCase=[16, 16, 16] ,				__UpperCamelCase=0 ,				__UpperCamelCase=[2, 2, 2] ,				__UpperCamelCase=[2, 2, 2] ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=2 ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= image_size
				snake_case_      		= num_channels
				snake_case_      		= kernel_size
				snake_case_      		= stride
				snake_case_      		= padding
				snake_case_      		= hidden_sizes
				snake_case_      		= num_attention_heads
				snake_case_      		= depths
				snake_case_      		= key_dim
				snake_case_      		= drop_path_rate
				snake_case_      		= patch_size
				snake_case_      		= attention_ratio
				snake_case_      		= mlp_ratio
				snake_case_      		= initializer_range
				snake_case_      		= [
				    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]
				snake_case_      		= is_training
				snake_case_      		= use_labels
				snake_case_      		= num_labels
				snake_case_      		= initializer_range


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

				snake_case_      		= None
				if self.use_labels:
					snake_case_      		= ids_tensor([self.batch_size] ,				self.num_labels							)

				snake_case_      		= self.get_config()
				return config, pixel_values, labels


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return LevitConfig(
				    image_size=self.image_size ,				num_channels=self.num_channels ,				kernel_size=self.kernel_size ,				stride=self.stride ,				padding=self.padding ,				patch_size=self.patch_size ,				hidden_sizes=self.hidden_sizes ,				num_attention_heads=self.num_attention_heads ,				depths=self.depths ,				key_dim=self.key_dim ,				drop_path_rate=self.drop_path_rate ,				mlp_ratio=self.mlp_ratio ,				attention_ratio=self.attention_ratio ,				initializer_range=self.initializer_range ,				down_ops=self.down_ops ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= LevitModel(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)
				snake_case_      		= (self.image_size, self.image_size)
				snake_case_		,       snake_case_      		= image_size[0], image_size[1]
				for _ in range(4							):
					snake_case_      		= floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
					snake_case_      		= floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
				self.parent.assertEqual(
				    result.last_hidden_state.shape ,				(self.batch_size, ceil(height / 4							) * ceil(width / 4							), self.hidden_sizes[-1]) ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_labels
				snake_case_      		= LevitForImageClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  (
			    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
			    if is_torch_available()
			    else ()
			)
			__A						  =  (
			    {
			        """feature-extraction""": LevitModel,
			        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= LevitModelTester(self							)
				snake_case_      		= ConfigTester(self ,				config_class=__UpperCamelCase ,				has_text_modality=__UpperCamelCase ,				hidden_size=37							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return


			@unittest.skip(reason='Levit does not use inputs_embeds'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			@unittest.skip(reason='Levit does not support input and output embeddings'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			@unittest.skip(reason='Levit does not output attentions'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

					snake_case_      		= ['pixel_values']
					self.assertListEqual(arg_names[:1] ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				def check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= model_class(__UpperCamelCase							)
					model.to(__UpperCamelCase							)
					model.eval()

					with torch.no_grad():
						snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)

					snake_case_      		= outputs.hidden_states

					snake_case_      		= len(self.model_tester.depths							) + 1
					self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

					snake_case_      		= (self.model_tester.image_size, self.model_tester.image_size)
					snake_case_		,       snake_case_      		= image_size[0], image_size[1]
					for _ in range(4							):
						snake_case_      		= floor(
						    (
						        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
						        / self.model_tester.stride
						    )
						    + 1							)
						snake_case_      		= floor(
						    (
						        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
						        / self.model_tester.stride
						    )
						    + 1							)
					# verify the first hidden states (first block)
					self.assertListEqual(
					    list(hidden_states[0].shape[-2:]							) ,				[
					        height * width,
					        self.model_tester.hidden_sizes[0],
					    ] ,				)

				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case_      		= True
					check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					snake_case_      		= True

					check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)


			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase=False							):

				"""simple docstring"""


				snake_case_      		= super()._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase ,				return_labels=__UpperCamelCase							)

				if return_labels:
					if model_class.__name__ == "LevitForImageClassificationWithTeacher":
						del inputs_dict["labels"]

				return inputs_dict


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				if not self.model_tester.is_training:
					return

				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()
				snake_case_      		= True

				for model_class in self.all_model_classes:
					# LevitForImageClassificationWithTeacher supports inference-only
					if (
					    model_class in get_values(__UpperCamelCase							)
					    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
					):
						continue
					snake_case_      		= model_class(__UpperCamelCase							)
					model.to(__UpperCamelCase							)
					model.train()
					snake_case_      		= self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase ,				return_labels=__UpperCamelCase							)
					snake_case_      		= model(**__UpperCamelCase							).loss
					loss.backward()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()
				if not self.model_tester.is_training:
					return

				snake_case_      		= False
				snake_case_      		= True

				for model_class in self.all_model_classes:
					if model_class in get_values(__UpperCamelCase							) or not model_class.supports_gradient_checkpointing:
						continue
					# LevitForImageClassificationWithTeacher supports inference-only
					if model_class.__name__ == "LevitForImageClassificationWithTeacher":
						continue
					snake_case_      		= model_class(__UpperCamelCase							)
					model.gradient_checkpointing_enable()
					model.to(__UpperCamelCase							)
					model.train()
					snake_case_      		= self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase ,				return_labels=__UpperCamelCase							)
					snake_case_      		= model(**__UpperCamelCase							).loss
					loss.backward()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				snake_case_      		= [
				    {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
				    {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
				    {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
				]

				for model_class in self.all_model_classes:
					if (
					    model_class
					    not in [
					        *get_values(__UpperCamelCase							),
					    ]
					    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
					):
						continue

					for problem_type in problem_types:
						with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}"""							):
							snake_case_      		= problem_type['title']
							snake_case_      		= problem_type['num_labels']

							snake_case_      		= model_class(__UpperCamelCase							)
							model.to(__UpperCamelCase							)
							model.train()

							snake_case_      		= self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase ,				return_labels=__UpperCamelCase							)

							if problem_type["num_labels"] > 1:
								snake_case_      		= inputs['labels'].unsqueeze(1							).repeat(1 ,				problem_type['num_labels']							)

							snake_case_      		= inputs['labels'].to(problem_type['dtype']							)

							# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
							# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
							# they have the same size." which is a symptom something in wrong for the regression problem.
							# See https://github.com/huggingface/transformers/issues/11780
							with warnings.catch_warnings(record=__UpperCamelCase							) as warning_list:
								snake_case_      		= model(**__UpperCamelCase							).loss
							for w in warning_list:
								if "Using a target size that is different to the input size" in str(w.message							):
									raise ValueError(
									    f"""Something is going wrong in the regression problem: intercepted {w.message}"""							)

							loss.backward()







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_      		= LevitModel.from_pretrained(__UpperCamelCase							)
					self.assertIsNotNone(__UpperCamelCase							)







def a():





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






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






			"""simple docstring"""




			@cached_property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
				    __UpperCamelCase							)

				snake_case_      		= self.default_image_processor
				snake_case_      		= prepare_img()
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='pt'							).to(__UpperCamelCase							)

				# forward pass
				with torch.no_grad():
					snake_case_      		= model(**__UpperCamelCase							)

				# verify the logits
				snake_case_      		= torch.Size((1, 10_00)							)
				self.assertEqual(outputs.logits.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor([1.0448, -0.3745, -1.8317]							).to(__UpperCamelCase							)

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



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= 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







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= ScoreSdeVeScheduler()

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase ,				return_dict=__UpperCamelCase							)[
				    0
				]

				snake_case_      		= image[0, -3:, -3:, -1]
				snake_case_      		= image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				snake_case_      		= np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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







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






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'google/ncsnpp-church-256'
				snake_case_      		= UNetaDModel.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=10 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= image[0, -3:, -3:, -1]

				assert image.shape == (1, 2_56, 2_56, 3)

				snake_case_      		= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

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