


import copy
import inspect

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




@require_torch
class    A_   :







 """simple docstring"""





 _UpperCamelCase				: int      =					()
 _UpperCamelCase				: str      =					True
 def   _UpperCamelCase    (			self					: int				)    ->			List[str]:
      UpperCAmelCase						:				int		  =			self.config_class

      # test default config
      UpperCAmelCase						:				Dict		  =			config_class()
      self.assertIsNotNone(_a				)
      UpperCAmelCase						:				str		  =			["stem"] + [f'stage{idx}' for idx in range(1       ,    len(config.depths				) + 1				)]
      self.assertEqual(config.stage_names       ,    _a				)
      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
      UpperCAmelCase						:				List[str]		  =			config_class(out_features=_a       ,    out_indices=_a				)
      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
      UpperCAmelCase						:				List[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
      UpperCAmelCase						:				List[str]		  =			config_class(out_features=["stage1", "stage3"]				)
      self.assertEqual(config.out_features       ,    ["stage1", "stage3"]				)
      self.assertEqual(config.out_indices       ,    [1, 3]				)

      # Only out_indices set
      UpperCAmelCase						:				Tuple		  =			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(_a				):
           UpperCAmelCase						:				List[str]		  =			config_class(out_features=["stage1", "stage2"]       ,    out_indices=[0, 2]				)
 def   _UpperCamelCase    (			self					: int				)    ->			str:
      UpperCAmelCase  , UpperCAmelCase						:				Tuple		  =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCAmelCase						:				List[str]		  =			model_class(_a				)
           UpperCAmelCase						:				Optional[Any]		  =			inspect.signature(model.forward				)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCAmelCase						:				str		  =			[*signature.parameters.keys()]
           UpperCAmelCase						:				List[str]		  =			["pixel_values"]
           self.assertListEqual(arg_names[:1]       ,    _a				)
 def   _UpperCamelCase    (			self					: str				)    ->			Any:
      UpperCAmelCase  , UpperCAmelCase						:				Optional[Any]		  =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCAmelCase						:				str		  =			model_class(_a				)
           self.assertEqual(len(model.channels				)       ,    len(config.out_features				)				)
           UpperCAmelCase						:				Optional[int]		  =			model.num_features
           UpperCAmelCase						:				List[Any]		  =			[config.stage_names.index(_a				) for feat in config.out_features]
           UpperCAmelCase						:				List[str]		  =			[num_features[idx] for idx in out_indices]
           self.assertListEqual(model.channels       ,    _a				)

           UpperCAmelCase						:				Tuple		  =			copy.deepcopy(_a				)
           UpperCAmelCase						:				Union[str, Any]		  =			None
           UpperCAmelCase						:				str		  =			model_class(_a				)
           self.assertEqual(len(model.channels				)       ,    1				)
           self.assertListEqual(model.channels       ,    [num_features[-1]]				)

           UpperCAmelCase						:				Any		  =			copy.deepcopy(_a				)
           UpperCAmelCase						:				Tuple		  =			None
           UpperCAmelCase						:				Optional[Any]		  =			model_class(_a				)
           self.assertEqual(len(model.channels				)       ,    1				)
           self.assertListEqual(model.channels       ,    [num_features[-1]]				)
 def   _UpperCamelCase    (			self					: Any				)    ->			List[Any]:
      UpperCAmelCase  , UpperCAmelCase						:				Dict		  =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCAmelCase						:				Any		  =			model_class(_a				)
           model.to(_a				)
           model.eval()
           UpperCAmelCase						:				List[str]		  =			model(**_a				)

           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
           UpperCAmelCase						:				Union[str, Any]		  =			copy.deepcopy(_a				)
           UpperCAmelCase						:				Tuple		  =			None
           UpperCAmelCase						:				List[str]		  =			model_class(_a				)
           model.to(_a				)
           model.eval()
           UpperCAmelCase						:				List[str]		  =			model(**_a				)

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

           UpperCAmelCase						:				Union[str, Any]		  =			copy.deepcopy(_a				)
           UpperCAmelCase						:				List[Any]		  =			None
           UpperCAmelCase						:				Any		  =			model_class(_a				)
           model.to(_a				)
           model.eval()
           UpperCAmelCase						:				int		  =			model(**_a				)

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

           # Check backbone can be initialized with fresh weights
           UpperCAmelCase						:				Tuple		  =			copy.deepcopy(_a				)
           UpperCAmelCase						:				int		  =			False
           UpperCAmelCase						:				Dict		  =			model_class(_a				)
           model.to(_a				)
           model.eval()
           UpperCAmelCase						:				Dict		  =			model(**_a				)
 def   _UpperCamelCase    (			self					: List[str]				)    ->			Dict:
      UpperCAmelCase  , UpperCAmelCase						:				List[Any]		  =			self.model_tester.prepare_config_and_inputs_for_common()

      for backbone_class in self.all_model_classes:
           UpperCAmelCase						:				Any		  =			backbone_class(_a				)

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

           self.assertIsInstance(backbone.backbone_type       ,    _a				)
           # 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   _UpperCamelCase    (			self					: List[Any]				)    ->			List[Any]:
      UpperCAmelCase  , UpperCAmelCase						:				Any		  =			self.model_tester.prepare_config_and_inputs_for_common()
      UpperCAmelCase						:				List[Any]		  =			inputs_dict["pixel_values"].shape[0]

      for backbone_class in self.all_model_classes:
           UpperCAmelCase						:				Tuple		  =			backbone_class(_a				)
           backbone.to(_a				)
           backbone.eval()

           UpperCAmelCase						:				Optional[int]		  =			backbone(**_a				)

           # Test default outputs and verify feature maps
           self.assertIsInstance(outputs.feature_maps       ,    _a				)
           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
           UpperCAmelCase						:				List[Any]		  =			backbone(**_a       ,    output_hidden_states=_a				)
           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:
                UpperCAmelCase						:				List[Any]		  =			backbone(**_a       ,    output_attentions=_a				)
                self.assertIsNotNone(outputs.attentions				)







# 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    A_   :







 """simple docstring"""





 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 @classmethod
 def   _UpperCamelCase    (			cls					: Union[str, Any]				)    ->			int:
      return cls()




@dataclass
class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: ScoreSdeVeSchedulerState
 _UpperCamelCase				: jnp.ndarray
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None




class    A_   ( a_      ,						a_     ):







 """simple docstring"""





 @property
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			List[str]:
      return True
 @register_to_config
 def __init__(			self					: List[str]       ,    _a					: int = 2_0_0_0       ,    _a					: float = 0.1_5       ,    _a					: float = 0.0_1       ,    _a					: float = 1_3_4_8.0       ,    _a					: float = 1E-5       ,    _a					: int = 1       ,    )    ->			List[Any]:
      pass
 def   _UpperCamelCase    (			self					: int				)    ->			List[Any]:
      UpperCAmelCase						:				str		  =			ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          _a       ,    self.config.num_train_timesteps       ,    self.config.sigma_min       ,    self.config.sigma_max       ,    self.config.sampling_eps       ,    )
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: Tuple = ()       ,    _a					: float = None				)    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				Tuple		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps

      UpperCAmelCase						:				Dict		  =			jnp.linspace(1       ,    _a       ,    _a				)
      return state.replace(timesteps=_a				)
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: float = None       ,    _a					: float = None       ,    _a					: float = None       ,    )    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				str		  =			sigma_min if sigma_min is not None else self.config.sigma_min
      UpperCAmelCase						:				Any		  =			sigma_max if sigma_max is not None else self.config.sigma_max
      UpperCAmelCase						:				Any		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
           UpperCAmelCase						:				List[Any]		  =			self.set_timesteps(_a       ,    _a       ,    _a				)

      UpperCAmelCase						:				List[Any]		  =			jnp.exp(jnp.linspace(jnp.log(_a				)       ,    jnp.log(_a				)       ,    _a				)				)
      UpperCAmelCase						:				List[str]		  =			jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]				)

      return state.replace(discrete_sigmas=_a       ,    sigmas=_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: Optional[int]       ,    _a					: Optional[int]       ,    _a					: Union[str, Any]				)    ->			List[str]:
      return jnp.where(timesteps == 0       ,    jnp.zeros_like(_a				)       ,    state.discrete_sigmas[timesteps - 1]				)
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: int       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

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

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

      #  equation 6: sample noise for the diffusion term of
      UpperCAmelCase						:				List[str]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)
      UpperCAmelCase						:				Any		  =			sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      UpperCAmelCase						:				str		  =			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=_a       ,    prev_sample_mean=_a       ,    state=_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      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
      UpperCAmelCase						:				Optional[int]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)

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

      # compute corrected sample: model_output term and noise term
      UpperCAmelCase						:				Optional[Any]		  =			step_size.flatten()
      UpperCAmelCase						:				Union[str, Any]		  =			broadcast_to_shape_from_left(_a       ,    sample.shape				)
      UpperCAmelCase						:				int		  =			sample + step_size * model_output
      UpperCAmelCase						:				int		  =			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
           return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=_a       ,    state=_a				)

 def __len__(			self					: Any				)    ->			Optional[int]:
      return self.config.num_train_timesteps



