"""simple docstring"""







# 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		(			lowercase_  ):
   lowercase							=  42
   lowercase							=  42







class        UpperCamelCase		(			lowercase_						,      lowercase_  ):
   lowercase							=  1
   @register_to_config
   def __init__(			self     ,__UpperCamelCase = 2000     ,__UpperCamelCase = 0.15     ,__UpperCamelCase = 0.01     ,__UpperCamelCase = 1348.0     ,__UpperCamelCase = 1e-5     ,__UpperCamelCase = 1     ,)	->      int:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	sigma_max

        # setable values
        lowercase_	:				Union[str, Any]													=	None

        self.set_sigmas(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      torch.FloatTensor:


        '''simple docstring'''


        return sample
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = None			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	sampling_eps if sampling_eps is not None else self.config.sampling_eps

        lowercase_	:				Dict													=	torch.linspace(1     ,__UpperCamelCase     ,__UpperCamelCase     ,device=__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = None			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	sigma_min if sigma_min is not None else self.config.sigma_min
        lowercase_	:				Optional[Any]													=	sigma_max if sigma_max is not None else self.config.sigma_max
        lowercase_	:				Any													=	sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
             self.set_timesteps(__UpperCamelCase     ,__UpperCamelCase			)

        lowercase_	:				Any													=	sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        lowercase_	:				Tuple													=	torch.exp(torch.linspace(math.log(__UpperCamelCase			)     ,math.log(__UpperCamelCase			)     ,__UpperCamelCase			)			)
        lowercase_	:				Any													=	torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Tuple:


        '''simple docstring'''


        return torch.where(
            timesteps == 0     ,torch.zeros_like(t.to(timesteps.device			)			)     ,self.discrete_sigmas[timesteps - 1].to(timesteps.device			)     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = True     ,)	->      Union[SdeVeOutput, Tuple]:


        '''simple docstring'''


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

        lowercase_	:				List[str]													=	timestep * torch.ones(
            sample.shape[0]     ,device=sample.device			)  # torch.repeat_interleave(timestep, sample.shape[0])
        lowercase_	:				Any													=	(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
        lowercase_	:				str													=	timesteps.to(self.discrete_sigmas.device			)

        lowercase_	:				Optional[Any]													=	self.discrete_sigmas[timesteps].to(sample.device			)
        lowercase_	:				int													=	self.get_adjacent_sigma(__UpperCamelCase     ,__UpperCamelCase			).to(sample.device			)
        lowercase_	:				List[Any]													=	torch.zeros_like(__UpperCamelCase			)
        lowercase_	:				str													=	(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
        lowercase_	:				Tuple													=	diffusion.flatten()
        while len(diffusion.shape			) < len(sample.shape			):
             lowercase_	:				Any													=	diffusion.unsqueeze(-1			)
        lowercase_	:				Optional[Any]													=	drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        lowercase_	:				Optional[Any]													=	randn_tensor(
            sample.shape     ,layout=sample.layout     ,generator=__UpperCamelCase     ,device=sample.device     ,dtype=sample.dtype			)
        lowercase_	:				List[Any]													=	sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        lowercase_	:				Optional[Any]													=	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=__UpperCamelCase     ,prev_sample_mean=__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = True     ,)	->      Union[SchedulerOutput, Tuple]:


        '''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
        lowercase_	:				Optional[Any]													=	randn_tensor(sample.shape     ,layout=sample.layout     ,generator=__UpperCamelCase			).to(sample.device			)

        # compute step size from the model_output, the noise, and the snr
        lowercase_	:				Optional[int]													=	torch.norm(model_output.reshape(model_output.shape[0]     ,-1			)     ,dim=-1			).mean()
        lowercase_	:				Union[str, Any]													=	torch.norm(noise.reshape(noise.shape[0]     ,-1			)     ,dim=-1			).mean()
        lowercase_	:				Any													=	(self.config.snr * noise_norm / grad_norm) ** 2 * 2
        lowercase_	:				Optional[int]													=	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
        lowercase_	:				Tuple													=	step_size.flatten()
        while len(step_size.shape			) < len(sample.shape			):
             lowercase_	:				int													=	step_size.unsqueeze(-1			)
        lowercase_	:				Tuple													=	sample + step_size * model_output
        lowercase_	:				Dict													=	prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
             return (prev_sample,)

        return SchedulerOutput(prev_sample=__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,)	->      torch.FloatTensor:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	timesteps.to(original_samples.device			)
        lowercase_	:				Dict													=	self.discrete_sigmas.to(original_samples.device			)[timesteps]
        lowercase_	:				List[Any]													=	(
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(__UpperCamelCase			) * sigmas[:, None, None, None]
        )
        lowercase_	:				List[str]													=	noise + original_samples
        return noisy_samples







   def __len__(			self			)	->      int:


        '''simple docstring'''


        return self.config.num_train_timesteps


"""simple docstring"""







import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
       from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=30     ,__UpperCamelCase=2     ,__UpperCamelCase=3     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=32     ,__UpperCamelCase=5     ,__UpperCamelCase=4     ,__UpperCamelCase=37     ,__UpperCamelCase="gelu"     ,__UpperCamelCase=0.1     ,__UpperCamelCase=0.1     ,__UpperCamelCase=10     ,__UpperCamelCase=0.02     ,__UpperCamelCase=None     ,__UpperCamelCase=2     ,)	->      List[Any]:


        '''simple docstring'''


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

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase_	:				Any													=	(image_size // patch_size) ** 2
        lowercase_	:				Optional[int]													=	num_patches + 1
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase_	:				Tuple													=	None
        if self.use_labels:
             lowercase_	:				Dict													=	ids_tensor([self.batch_size]     ,self.type_sequence_label_size			)

        lowercase_	:				Any													=	self.get_config()

        return config, pixel_values, labels
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''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=__UpperCamelCase     ,initializer_range=self.initializer_range     ,encoder_stride=self.encoder_stride     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	ViTModel(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Optional[Any]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTForMaskedImageModeling(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				List[str]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(
            result.reconstruction.shape     ,(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

        # test greyscale images
        lowercase_	:				Any													=	1
        lowercase_	:				List[Any]													=	ViTForMaskedImageModeling(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()

        lowercase_	:				Dict													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				str													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.reconstruction.shape     ,(self.batch_size, 1, self.image_size, self.image_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.type_sequence_label_size
        lowercase_	:				Dict													=	ViTForImageClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				int													=	model(__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase_	:				Dict													=	1
        lowercase_	:				Any													=	ViTForImageClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()

        lowercase_	:				Dict													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				Dict													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)







   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


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







@require_torch
class        UpperCamelCase		(			lowercase_						,      lowercase_						,      unittest.TestCase  ):
   lowercase							=  (
       (
           ViTModel,
           ViTForImageClassification,
           ViTForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   lowercase							=  (
       {'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
       if is_torch_available()
       else {}
   )
   lowercase							=  True

   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTModelTester(self			)
        lowercase_	:				Dict													=	ConfigTester(self     ,config_class=__UpperCamelCase     ,has_text_modality=__UpperCamelCase     ,hidden_size=37			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   @unittest.skip(reason='ViT does not use inputs_embeds'			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				str													=	model_class(__UpperCamelCase			)
             self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)			)
             lowercase_	:				List[Any]													=	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__UpperCamelCase     ,nn.Linear			)			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Union[str, Any]													=	self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_	:				Tuple													=	['pixel_values']
             self.assertListEqual(arg_names[:1]     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase			)







   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_	:				List[Any]													=	ViTModel.from_pretrained(__UpperCamelCase			)
             self.assertIsNotNone(__UpperCamelCase			)






def 			lowercase__(       ):
     lowercase_	:				Any													=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image







@require_torch
@require_vision
class        UpperCamelCase		(			unittest.TestCase  ):
   @cached_property
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'			) if is_vision_available() else None
   @slow
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				str													=	ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'			).to(__UpperCamelCase			)

        lowercase_	:				Any													=	self.default_image_processor
        lowercase_	:				Dict													=	prepare_img()
        lowercase_	:				Tuple													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			).to(__UpperCamelCase			)

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

        # verify the logits
        lowercase_	:				Optional[Any]													=	torch.Size((1, 1000)			)
        self.assertEqual(outputs.logits.shape     ,__UpperCamelCase			)

        lowercase_	:				Optional[int]													=	torch.tensor([-0.2744, 0.8215, -0.0836]			).to(__UpperCamelCase			)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,__UpperCamelCase     ,atol=1e-4			)			)
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				str													=	ViTModel.from_pretrained('facebook/dino-vits8'			).to(__UpperCamelCase			)

        lowercase_	:				int													=	ViTImageProcessor.from_pretrained('facebook/dino-vits8'     ,size=480			)
        lowercase_	:				int													=	prepare_img()
        lowercase_	:				Dict													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			)
        lowercase_	:				int													=	inputs.pixel_values.to(__UpperCamelCase			)

        # forward pass
        with torch.no_grad():
             lowercase_	:				int													=	model(__UpperCamelCase     ,interpolate_pos_encoding=__UpperCamelCase			)

        # verify the logits
        lowercase_	:				Any													=	torch.Size((1, 3601, 384)			)
        self.assertEqual(outputs.last_hidden_state.shape     ,__UpperCamelCase			)

        lowercase_	:				Any													=	torch.tensor(
            [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]			).to(__UpperCamelCase			)

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







   @slow
   @require_accelerate
   @require_torch_gpu
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTModel.from_pretrained('facebook/dino-vits8'     ,torch_dtype=torch.floataa     ,device_map='auto'			)
        lowercase_	:				int													=	self.default_image_processor

        lowercase_	:				Optional[int]													=	prepare_img()
        lowercase_	:				Tuple													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			)
        lowercase_	:				Union[str, Any]													=	inputs.pixel_values.to(__UpperCamelCase			)

        # forward pass to make sure inference works in fp16
        with torch.no_grad():
             lowercase_	:				Dict													=	model(__UpperCamelCase			)

