



'''simple docstring'''





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_     ):
     UpperCamelCase__		    =				42
     UpperCamelCase__		    =				42







class     _UpperCAmelCase			(       UpperCamelCase_			,      UpperCamelCase_     ):
     UpperCamelCase__		    =				1




     @register_to_config
     def __init__(				self ,						a__ = 2_0_0_0 ,						a__ = 0.1_5 ,						a__ = 0.0_1 ,						a__ = 1_3_4_8.0 ,						a__ = 1e-5 ,						a__ = 1 ,						):
         A__		 =					sigma_max

         # setable values
         A__		 =					None

         self.set_sigmas(__a ,						__a ,						__a ,						__a)




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         return sample




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = None):
         A__		 =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

         A__		 =					torch.linspace(1 ,						__a ,						__a ,						device=__a)




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = None ,						a__ = None):
         A__		 =					sigma_min if sigma_min is not None else self.config.sigma_min
         A__		 =					sigma_max if sigma_max is not None else self.config.sigma_max
         A__		 =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
         if self.timesteps is None:
             self.set_timesteps(__a ,						__a)

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




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




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ = None ,						a__ = True ,						):






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

         A__		 =					timestep * torch.ones(
             sample.shape[0] ,						device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
         A__		 =					(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
         A__		 =					timesteps.to(self.discrete_sigmas.device)

         A__		 =					self.discrete_sigmas[timesteps].to(sample.device)
         A__		 =					self.get_adjacent_sigma(__a ,						__a).to(sample.device)
         A__		 =					torch.zeros_like(__a)
         A__		 =					(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
         A__		 =					diffusion.flatten()
         while len(diffusion.shape) < len(sample.shape):
             A__		 =					diffusion.unsqueeze(-1)
         A__		 =					drift - diffusion**2 * model_output

         #  equation 6: sample noise for the diffusion term of
         A__		 =					randn_tensor(
             sample.shape ,						layout=sample.layout ,						generator=__a ,						device=sample.device ,						dtype=sample.dtype)
         A__		 =					sample - drift  # subtract because `dt` is a small negative timestep
         # TODO is the variable diffusion the correct scaling term for the noise?
         A__		 =					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=__a ,						prev_sample_mean=__a)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = None ,						a__ = True ,						):






         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
         A__		 =					randn_tensor(sample.shape ,						layout=sample.layout ,						generator=__a).to(sample.device)

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

         if not return_dict:
             return (prev_sample,)

         return SchedulerOutput(prev_sample=__a)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					timesteps.to(original_samples.device)
         A__		 =					self.discrete_sigmas.to(original_samples.device)[timesteps]
         A__		 =					(
             noise * sigmas[:, None, None, None]
             if noise is not None
             else torch.randn_like(__a) * sigmas[:, None, None, None]
         )
         A__		 =					noise + original_samples
         return noisy_samples




     def __len__(				self):
         return self.config.num_train_timesteps





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:



   class     _UpperCAmelCase			:




        @staticmethod
        def 		snake_case_		(				*a__ ,						**a__):
            pass



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Image	)->  str:
    A__		 =					hashlib.mda(image.tobytes()	)
    return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):
     UpperCamelCase__		    =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					DepthEstimationPipeline(model=a__ ,						image_processor=a__)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
         self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)} ,						a__)
         import datasets

         A__		 =					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,						'''image''' ,						split='''test''')
         A__		 =					depth_estimator(
             [
                 Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 # RGBA
                 dataset[0]['''file'''],
                 # LA
                 dataset[1]['''file'''],
                 # L
                 dataset[2]['''file'''],
             ])
         self.assertEqual(
             [
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
             ] ,						a__ ,						)




     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF''')
     def 		snake_case_		(				self):
         pass




     @slow
     @require_torch
     def 		snake_case_		(				self):
         A__		 =					'''Intel/dpt-large'''
         A__		 =					pipeline('''depth-estimation''' ,						model=a__)
         A__		 =					depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
         A__		 =					hashimage(outputs['''depth'''])

         # This seems flaky.
         # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()) ,						2_9.3_0_4)
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()) ,						2.6_6_2)




     @require_torch
     def 		snake_case_		(				self):
         # This is highly irregular to have no small tests.
         self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')
