
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class   UpperCAmelCase_					(		UpperCamelCase__			):






						'''simple docstring'''






						_lowercase		:				List[str]									=       4_2
						_lowercase		:				List[Any]									=       4_2






class   UpperCAmelCase_					(		UpperCamelCase__	,							UpperCamelCase__			):






						'''simple docstring'''






						_lowercase		:				Optional[int]									=       1




						@register_to_config
						def __init__( self   ,       _lowercase = 2_000   ,       _lowercase = 0.15   ,       _lowercase = 0.01   ,       _lowercase = 1348.0   ,       _lowercase = 1e-5   ,       _lowercase = 1   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			sigma_max

												# setable values
												_lowerCAmelCase  						=			None

												self.set_sigmas(__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase = None				):

												"""simple docstring"""



												return sample




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase = None   ,       _lowercase = None				):

												"""simple docstring"""



												_lowerCAmelCase  						=			sampling_eps if sampling_eps is not None else self.config.sampling_eps

												_lowerCAmelCase  						=			torch.linspace(1   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       device=__lowerCamelCase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None				):

												"""simple docstring"""



												_lowerCAmelCase  						=			sigma_min if sigma_min is not None else self.config.sigma_min
												_lowerCAmelCase  						=			sigma_max if sigma_max is not None else self.config.sigma_max
												_lowerCAmelCase  						=			sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if self.timesteps is None:
																		self.set_timesteps(__lowerCamelCase   ,       __lowerCamelCase				)

												_lowerCAmelCase  						=			sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
												_lowerCAmelCase  						=			torch.exp(torch.linspace(math.log(__lowerCamelCase				)   ,       math.log(__lowerCamelCase				)   ,       __lowerCamelCase				)				)
												_lowerCAmelCase  						=			torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												return torch.where(
												    timesteps == 0   ,       torch.zeros_like(t.to(timesteps.device				)				)   ,       self.discrete_sigmas[timesteps - 1].to(timesteps.device				)   ,       )




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase = None   ,       _lowercase = True   ,       ):

												"""simple docstring"""



												if self.timesteps is None:
																		raise ValueError(
																		    """`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""				)

												_lowerCAmelCase  						=			timestep * torch.ones(
												    sample.shape[0]   ,       device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
												_lowerCAmelCase  						=			(timestep * (len(self.timesteps				) - 1)).long()

												# mps requires indices to be in the same device, so we use cpu as is the default with cuda
												_lowerCAmelCase  						=			timesteps.to(self.discrete_sigmas.device				)

												_lowerCAmelCase  						=			self.discrete_sigmas[timesteps].to(sample.device				)
												_lowerCAmelCase  						=			self.get_adjacent_sigma(__lowerCamelCase   ,       __lowerCamelCase				).to(sample.device				)
												_lowerCAmelCase  						=			torch.zeros_like(__lowerCamelCase				)
												_lowerCAmelCase  						=			(sigma**2 - adjacent_sigma**2) ** 0.5

												# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
												# also equation 47 shows the analog from SDE models to ancestral sampling methods
												_lowerCAmelCase  						=			diffusion.flatten()
												while len(diffusion.shape				) < len(sample.shape				):
																		_lowerCAmelCase  						=			diffusion.unsqueeze(-1				)
												_lowerCAmelCase  						=			drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												_lowerCAmelCase  						=			randn_tensor(
												    sample.shape   ,       layout=sample.layout   ,       generator=__lowerCamelCase   ,       device=sample.device   ,       dtype=sample.dtype				)
												_lowerCAmelCase  						=			sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												_lowerCAmelCase  						=			prev_sample_mean + diffusion * noise  # add impact of diffusion field g

												if not return_dict:
																		return (prev_sample, prev_sample_mean)

												return SdeVeOutput(prev_sample=__lowerCamelCase   ,       prev_sample_mean=__lowerCamelCase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase = None   ,       _lowercase = True   ,       ):

												"""simple docstring"""



												if self.timesteps is None:
																		raise ValueError(
																		    """`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""				)

												# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
												# sample noise for correction
												_lowerCAmelCase  						=			randn_tensor(sample.shape   ,       layout=sample.layout   ,       generator=__lowerCamelCase				).to(sample.device				)

												# compute step size from the model_output, the noise, and the snr
												_lowerCAmelCase  						=			torch.norm(model_output.reshape(model_output.shape[0]   ,       -1				)   ,       dim=-1				).mean()
												_lowerCAmelCase  						=			torch.norm(noise.reshape(noise.shape[0]   ,       -1				)   ,       dim=-1				).mean()
												_lowerCAmelCase  						=			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
												_lowerCAmelCase  						=			step_size * torch.ones(sample.shape[0]				).to(sample.device				)
												# self.repeat_scalar(step_size, sample.shape[0])

												# compute corrected sample: model_output term and noise term
												_lowerCAmelCase  						=			step_size.flatten()
												while len(step_size.shape				) < len(sample.shape				):
																		_lowerCAmelCase  						=			step_size.unsqueeze(-1				)
												_lowerCAmelCase  						=			sample + step_size * model_output
												_lowerCAmelCase  						=			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																		return (prev_sample,)

												return SchedulerOutput(prev_sample=__lowerCamelCase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			timesteps.to(original_samples.device				)
												_lowerCAmelCase  						=			self.discrete_sigmas.to(original_samples.device				)[timesteps]
												_lowerCAmelCase  						=			(
												    noise * sigmas[:, None, None, None]
												    if noise is not None
												    else torch.randn_like(__lowerCamelCase				) * sigmas[:, None, None, None]
												)
												_lowerCAmelCase  						=			noise + original_samples
												return noisy_samples




						def __len__( self				):

												"""simple docstring"""



												return self.config.num_train_timesteps


'''simple docstring'''



import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
						import torch

						from transformers import UperNetForSemanticSegmentation
						from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase=13   ,       _lowercase=32   ,       _lowercase=3   ,       _lowercase=4   ,       _lowercase=[10, 20, 30, 40]   ,       _lowercase=[2, 2, 3, 2]   ,       _lowercase=True   ,       _lowercase=True   ,       _lowercase=37   ,       _lowercase="gelu"   ,       _lowercase=10   ,       _lowercase=0.02   ,       _lowercase=["stage2", "stage3", "stage4"]   ,       _lowercase=3   ,       _lowercase=None   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			batch_size
												_lowerCAmelCase  						=			image_size
												_lowerCAmelCase  						=			num_channels
												_lowerCAmelCase  						=			num_stages
												_lowerCAmelCase  						=			hidden_sizes
												_lowerCAmelCase  						=			depths
												_lowerCAmelCase  						=			is_training
												_lowerCAmelCase  						=			use_labels
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			type_sequence_label_size
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			out_features
												_lowerCAmelCase  						=			num_labels
												_lowerCAmelCase  						=			scope
												_lowerCAmelCase  						=			num_stages




						def  _lowercase     ( self				):

												"""simple docstring"""



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

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

												_lowerCAmelCase  						=			self.get_config()

												return config, pixel_values, labels




						def  _lowercase     ( self				):

												"""simple docstring"""



												return ConvNextConfig(
												    num_channels=self.num_channels   ,       num_stages=self.num_stages   ,       hidden_sizes=self.hidden_sizes   ,       depths=self.depths   ,       is_training=self.is_training   ,       intermediate_size=self.intermediate_size   ,       hidden_act=self.hidden_act   ,       out_features=self.out_features   ,       )




						def  _lowercase     ( self				):

												"""simple docstring"""



												return UperNetConfig(
												    backbone_config=self.get_backbone_config()   ,       hidden_size=512   ,       pool_scales=[1, 2, 3, 6]   ,       use_auxiliary_head=_lowercase   ,       auxiliary_loss_weight=0.4   ,       auxiliary_in_channels=40   ,       auxiliary_channels=256   ,       auxiliary_num_convs=1   ,       auxiliary_concat_input=_lowercase   ,       loss_ignore_index=255   ,       num_labels=self.num_labels   ,       )




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			UperNetForSemanticSegmentation(config=_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			model(_lowercase				)
												self.parent.assertEqual(
												    result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size, self.image_size)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.prepare_config_and_inputs()
												(
												    (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   (
												    _lowerCAmelCase
												)						,   
												)  						=			config_and_inputs
												_lowerCAmelCase  						=			{"""pixel_values""": pixel_values}
												return config, inputs_dict






@require_torch
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Dict									=       (UperNetForSemanticSegmentation,) if is_torch_available() else ()
						_lowercase		:				Dict									=       {'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
						_lowercase		:				Dict									=       False
						_lowercase		:				Optional[Any]									=       False
						_lowercase		:				List[str]									=       False
						_lowercase		:				Union[str, Any]									=       False
						_lowercase		:				List[str]									=       False
						_lowercase		:				List[Any]									=       False




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			UperNetModelTester(self				)
												_lowerCAmelCase  						=			ConfigTester(self   ,       config_class=_lowercase   ,       has_text_modality=_lowercase   ,       hidden_size=37				)




						def  _lowercase     ( 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  _lowercase     ( self				):

												"""simple docstring"""



												return




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

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

																		_lowerCAmelCase  						=			["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase				)




						@unittest.skip(reason="""UperNet does not use inputs_embeds"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@unittest.skip(reason="""UperNet does not support input and output embeddings"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@unittest.skip(reason="""UperNet does not have a base model"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@unittest.skip(reason="""UperNet does not have a base model"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@require_torch_multi_gpu
						@unittest.skip(reason="""UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




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

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												def check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			model_class(_lowercase				)
																		model.to(_lowercase				)
																		model.eval()

																		with torch.no_grad():
																								_lowerCAmelCase  						=			model(**self._prepare_for_class(_lowercase   ,       _lowercase				)				)

																		_lowerCAmelCase  						=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		_lowerCAmelCase  						=			self.model_tester.num_stages
																		self.assertEqual(len(_lowercase				)   ,       expected_num_stages + 1				)

																		# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:]				)   ,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]   ,       )

												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		_lowerCAmelCase  						=			True
																		check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				)

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

																		check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

												_lowerCAmelCase  						=			_config_zero_init(_lowercase				)
												_lowerCAmelCase  						=			_config_zero_init(configs_no_init.backbone_config				)
												for model_class in self.all_model_classes:
																		_lowerCAmelCase  						=			model_class(config=_lowercase				)
																		for name, param in model.named_parameters():
																								if param.requires_grad:
																														self.assertIn(
																														    ((param.data.mean() * 1e9).round() / 1e9).item()   ,       [0.0, 1.0]   ,       msg=F'Parameter {name} of model {model_class} seems not properly initialized'   ,       )




						@unittest.skip(reason="""UperNet does not have tied weights"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowerCAmelCase  						=			UperNetForSemanticSegmentation.from_pretrained(_lowercase				)
																		self.assertIsNotNone(_lowercase				)






def A				():
						_lowerCAmelCase  						=			hf_hub_download(
						    repo_id="""hf-internal-testing/fixtures_ade20k"""			,     repo_type="""dataset"""			,     filename="""ADE_val_00000001.jpg"""		)
						_lowerCAmelCase  						=			Image.open(__lowerCamelCase		).convert("""RGB"""		)
						return image





@require_torch
@require_vision
@slow
class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny"""				)
												_lowerCAmelCase  						=			UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny"""				).to(_lowercase				)

												_lowerCAmelCase  						=			prepare_img()
												_lowerCAmelCase  						=			processor(images=_lowercase   ,       return_tensors="""pt"""				).to(_lowercase				)

												with torch.no_grad():
																		_lowerCAmelCase  						=			model(**_lowercase				)

												_lowerCAmelCase  						=			torch.Size((1, model.config.num_labels, 512, 512)				)
												self.assertEqual(outputs.logits.shape   ,       _lowercase				)

												_lowerCAmelCase  						=			torch.tensor(
												    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]				).to(_lowercase				)
												self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,       _lowercase   ,       atol=1e-4				)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""				)
												_lowerCAmelCase  						=			UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""				).to(_lowercase				)

												_lowerCAmelCase  						=			prepare_img()
												_lowerCAmelCase  						=			processor(images=_lowercase   ,       return_tensors="""pt"""				).to(_lowercase				)

												with torch.no_grad():
																		_lowerCAmelCase  						=			model(**_lowercase				)

												_lowerCAmelCase  						=			torch.Size((1, model.config.num_labels, 512, 512)				)
												self.assertEqual(outputs.logits.shape   ,       _lowercase				)

												_lowerCAmelCase  						=			torch.tensor(
												    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]				).to(_lowercase				)
												self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,       _lowercase   ,       atol=1e-4				)				)
