


'''simple docstring'''


import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType







@require_torch
class        __magic_name__  :

     """simple docstring"""






     lowerCamelCase__     :   Dict						=							()
     lowerCamelCase__     :   Any						=							True


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       self.config_class

          # test default config
          __A  :					Dict						     =       config_class()
          self.assertIsNotNone(lowerCamelCase		)
          __A  :					List[str]						     =       ['stem'] + [f"stage{idx}" for idx in range(1 ,    len(config.depths		) + 1		)]
          self.assertEqual(config.stage_names ,    lowerCamelCase		)
          self.assertTrue(set(config.out_features		).issubset(set(config.stage_names		)		)		)

          # Test out_features and out_indices are correctly set
          # out_features and out_indices both None
          __A  :					Dict						     =       config_class(out_features=lowerCamelCase ,    out_indices=lowerCamelCase		)
          self.assertEqual(config.out_features ,    [config.stage_names[-1]]		)
          self.assertEqual(config.out_indices ,    [len(config.stage_names		) - 1]		)

          # out_features and out_indices both set
          __A  :					str						     =       config_class(out_features=["stem", "stage1"] ,    out_indices=[0, 1]		)
          self.assertEqual(config.out_features ,    ["stem", "stage1"]		)
          self.assertEqual(config.out_indices ,    [0, 1]		)

          # Only out_features set
          __A  :					Dict						     =       config_class(out_features=["stage1", "stage3"]		)
          self.assertEqual(config.out_features ,    ["stage1", "stage3"]		)
          self.assertEqual(config.out_indices ,    [1, 3]		)

          # Only out_indices set
          __A  :					Dict						     =       config_class(out_indices=[0, 2]		)
          self.assertEqual(config.out_features ,    [config.stage_names[0], config.stage_names[2]]		)
          self.assertEqual(config.out_indices ,    [0, 2]		)

          # Error raised when out_indices do not correspond to out_features
          with self.assertRaises(lowerCamelCase		):
               __A  :					Tuple						     =       config_class(out_features=["stage1", "stage2"] ,    out_indices=[0, 2]		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Dict						     =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A  :					Tuple						     =       model_class(lowerCamelCase		)
               __A  :					str						     =       inspect.signature(model.forward		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A  :					Dict						     =       [*signature.parameters.keys()]
               __A  :					Union[str, Any]						     =       ['pixel_values']
               self.assertListEqual(arg_names[:1] ,    lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Tuple						     =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A  :					Dict						     =       model_class(lowerCamelCase		)
               self.assertEqual(len(model.channels		) ,    len(config.out_features		)		)
               __A  :					int						     =       model.num_features
               __A  :					Tuple						     =       [config.stage_names.index(lowerCamelCase		) for feat in config.out_features]
               __A  :					Dict						     =       [num_features[idx] for idx in out_indices]
               self.assertListEqual(model.channels ,    lowerCamelCase		)

               __A  :					Any						     =       copy.deepcopy(lowerCamelCase		)
               __A  :					Optional[Any]						     =       None
               __A  :					Optional[int]						     =       model_class(lowerCamelCase		)
               self.assertEqual(len(model.channels		) ,    1		)
               self.assertListEqual(model.channels ,    [num_features[-1]]		)

               __A  :					Dict						     =       copy.deepcopy(lowerCamelCase		)
               __A  :					Optional[int]						     =       None
               __A  :					int						     =       model_class(lowerCamelCase		)
               self.assertEqual(len(model.channels		) ,    1		)
               self.assertListEqual(model.channels ,    [num_features[-1]]		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[str]						     =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A  :					int						     =       model_class(lowerCamelCase		)
               model.to(lowerCamelCase		)
               model.eval()
               __A  :					int						     =       model(**lowerCamelCase		)

               self.assertEqual(len(result.feature_maps		) ,    len(config.out_features		)		)
               self.assertEqual(len(model.channels		) ,    len(config.out_features		)		)
               self.assertEqual(len(result.feature_maps		) ,    len(config.out_indices		)		)
               self.assertEqual(len(model.channels		) ,    len(config.out_indices		)		)

               # Check output of last stage is taken if out_features=None, out_indices=None
               __A  :					List[str]						     =       copy.deepcopy(lowerCamelCase		)
               __A  :					Any						     =       None
               __A  :					Optional[int]						     =       model_class(lowerCamelCase		)
               model.to(lowerCamelCase		)
               model.eval()
               __A  :					Any						     =       model(**lowerCamelCase		)

               self.assertEqual(len(result.feature_maps		) ,    1		)
               self.assertEqual(len(model.channels		) ,    1		)

               __A  :					Any						     =       copy.deepcopy(lowerCamelCase		)
               __A  :					Dict						     =       None
               __A  :					int						     =       model_class(lowerCamelCase		)
               model.to(lowerCamelCase		)
               model.eval()
               __A  :					int						     =       model(**lowerCamelCase		)

               self.assertEqual(len(result.feature_maps		) ,    1		)
               self.assertEqual(len(model.channels		) ,    1		)

               # Check backbone can be initialized with fresh weights
               __A  :					Union[str, Any]						     =       copy.deepcopy(lowerCamelCase		)
               __A  :					Tuple						     =       False
               __A  :					Any						     =       model_class(lowerCamelCase		)
               model.to(lowerCamelCase		)
               model.eval()
               __A  :					Any						     =       model(**lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       self.model_tester.prepare_config_and_inputs_for_common()

          for backbone_class in self.all_model_classes:
               __A  :					int						     =       backbone_class(lowerCamelCase		)

               self.assertTrue(hasattr(lowerCamelCase ,    "backbone_type"		)		)
               self.assertTrue(hasattr(lowerCamelCase ,    "stage_names"		)		)
               self.assertTrue(hasattr(lowerCamelCase ,    "num_features"		)		)
               self.assertTrue(hasattr(lowerCamelCase ,    "out_indices"		)		)
               self.assertTrue(hasattr(lowerCamelCase ,    "out_features"		)		)
               self.assertTrue(hasattr(lowerCamelCase ,    "out_feature_channels"		)		)
               self.assertTrue(hasattr(lowerCamelCase ,    "channels"		)		)

               self.assertIsInstance(backbone.backbone_type ,    lowerCamelCase		)
               # Verify num_features has been initialized in the backbone init
               self.assertIsNotNone(backbone.num_features		)
               self.assertTrue(len(backbone.channels		) == len(backbone.out_indices		)		)
               self.assertTrue(len(backbone.stage_names		) == len(backbone.num_features		)		)
               self.assertTrue(len(backbone.channels		) <= len(backbone.num_features		)		)
               self.assertTrue(len(backbone.out_feature_channels		) == len(backbone.stage_names		)		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       self.model_tester.prepare_config_and_inputs_for_common()
          __A  :					Any						     =       inputs_dict['pixel_values'].shape[0]

          for backbone_class in self.all_model_classes:
               __A  :					List[Any]						     =       backbone_class(lowerCamelCase		)
               backbone.to(lowerCamelCase		)
               backbone.eval()

               __A  :					Union[str, Any]						     =       backbone(**lowerCamelCase		)

               # Test default outputs and verify feature maps
               self.assertIsInstance(outputs.feature_maps ,    lowerCamelCase		)
               self.assertTrue(len(outputs.feature_maps		) == len(backbone.channels		)		)
               for feature_map, n_channels in zip(outputs.feature_maps ,    backbone.channels		):
                    self.assertTrue(feature_map.shape[:2] ,    (batch_size, n_channels)		)
               self.assertIsNone(outputs.hidden_states		)
               self.assertIsNone(outputs.attentions		)

               # Test output_hidden_states=True
               __A  :					List[str]						     =       backbone(**lowerCamelCase ,    output_hidden_states=lowerCamelCase		)
               self.assertIsNotNone(outputs.hidden_states		)
               self.assertTrue(len(outputs.hidden_states		) ,    len(backbone.stage_names		)		)
               for hidden_state, n_channels in zip(outputs.hidden_states ,    backbone.channels		):
                    self.assertTrue(hidden_state.shape[:2] ,    (batch_size, n_channels)		)

               # Test output_attentions=True
               if self.has_attentions:
                    __A  :					Union[str, Any]						     =       backbone(**lowerCamelCase ,    output_attentions=lowerCamelCase		)
                    self.assertIsNotNone(outputs.attentions		)





'''simple docstring'''


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

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left







@flax.struct.dataclass
class        __magic_name__  :

     """simple docstring"""






     lowerCamelCase__						=							None
     lowerCamelCase__						=							None
     lowerCamelCase__						=							None


     @classmethod
     def lowerCAmelCase__     (      cls		):


          '''simple docstring'''





          return cls()







@dataclass
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							42
     lowerCamelCase__						=							42
     lowerCamelCase__						=							None







class        __magic_name__  (					lowerCAmelCase						,		lowerCAmelCase   ):

     """simple docstring"""






     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return True


     @register_to_config
     def __init__(      self ,    lowerCamelCase = 2000 ,    lowerCamelCase = 0.15 ,    lowerCamelCase = 0.01 ,    lowerCamelCase = 1348.0 ,    lowerCamelCase = 1E-5 ,    lowerCamelCase = 1 ,    ):


          '''simple docstring'''





          pass


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       ScoreSdeVeSchedulerState.create()
          return self.set_sigmas(
              lowerCamelCase ,    self.config.num_train_timesteps ,    self.config.sigma_min ,    self.config.sigma_max ,    self.config.sampling_eps ,    )


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = () ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					Tuple						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

          __A  :					Optional[int]						     =       jnp.linspace(1 ,    lowerCamelCase ,    lowerCamelCase		)
          return state.replace(timesteps=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = None ,    lowerCamelCase = None ,    lowerCamelCase = None ,    ):


          '''simple docstring'''





          __A  :					str						     =       sigma_min if sigma_min is not None else self.config.sigma_min
          __A  :					Union[str, Any]						     =       sigma_max if sigma_max is not None else self.config.sigma_max
          __A  :					Dict						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if state.timesteps is None:
               __A  :					Union[str, Any]						     =       self.set_timesteps(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)

          __A  :					Dict						     =       jnp.exp(jnp.linspace(jnp.log(lowerCamelCase		) ,    jnp.log(lowerCamelCase		) ,    lowerCamelCase		)		)
          __A  :					int						     =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]		)

          return state.replace(discrete_sigmas=lowerCamelCase ,    sigmas=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          return jnp.where(timesteps == 0 ,    jnp.zeros_like(lowerCamelCase		) ,    state.discrete_sigmas[timesteps - 1]		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = True ,    ):


          '''simple docstring'''





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

          __A  :					Any						     =       timestep * jnp.ones(
              sample.shape[0] ,    )
          __A  :					Any						     =       (timestep * (len(state.timesteps		) - 1)).long()

          __A  :					Any						     =       state.discrete_sigmas[timesteps]
          __A  :					Union[str, Any]						     =       self.get_adjacent_sigma(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          __A  :					List[str]						     =       jnp.zeros_like(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       (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  :					List[Any]						     =       diffusion.flatten()
          __A  :					List[Any]						     =       broadcast_to_shape_from_left(lowerCamelCase ,    sample.shape		)
          __A  :					str						     =       drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          __A  :					Any						     =       random.split(lowerCamelCase ,    num=1		)
          __A  :					Union[str, Any]						     =       random.normal(key=lowerCamelCase ,    shape=sample.shape		)
          __A  :					Dict						     =       sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          __A  :					int						     =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

          if not return_dict:
               return (prev_sample, prev_sample_mean, state)

          return FlaxSdeVeOutput(prev_sample=lowerCamelCase ,    prev_sample_mean=lowerCamelCase ,    state=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = True ,    ):


          '''simple docstring'''





          if state.timesteps is None:
               raise ValueError(
                   "`state.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  :					int						     =       random.split(lowerCamelCase ,    num=1		)
          __A  :					int						     =       random.normal(key=lowerCamelCase ,    shape=sample.shape		)

          # compute step size from the model_output, the noise, and the snr
          __A  :					Optional[Any]						     =       jnp.linalg.norm(lowerCamelCase		)
          __A  :					Dict						     =       jnp.linalg.norm(lowerCamelCase		)
          __A  :					Any						     =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
          __A  :					List[str]						     =       step_size * jnp.ones(sample.shape[0]		)

          # compute corrected sample: model_output term and noise term
          __A  :					Optional[Any]						     =       step_size.flatten()
          __A  :					Optional[int]						     =       broadcast_to_shape_from_left(lowerCamelCase ,    sample.shape		)
          __A  :					Dict						     =       sample + step_size * model_output
          __A  :					List[Any]						     =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
               return (prev_sample, state)

          return FlaxSdeVeOutput(prev_sample=lowerCamelCase ,    state=lowerCamelCase		)


     def __len__(      self		):


          '''simple docstring'''





          return self.config.num_train_timesteps

