






'''simple docstring'''







# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 					a__    :
     '''simple docstring'''







     _snake_case             =  42
     _snake_case             =  42

     # setable values
     _snake_case             =  42
     _snake_case             =  42
     _snake_case             =  None






     @classmethod
     def     _UpperCamelCase   (cls    :	Union[str, Any]				,				lowercase__    :	CommonSchedulerState				,				lowercase__    :	jnp.ndarray				,				lowercase__    :	jnp.ndarray				,				lowercase__    :	jnp.ndarray				,				)  ->       Any:
           return cls(
               common=lowercase__				,				final_alpha_cumprod=lowercase__				,				init_noise_sigma=lowercase__				,				timesteps=lowercase__				,				)







@dataclass
class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     _snake_case             =  42







class 					a__    (			lowerCamelCase ,						lowerCamelCase						):
     '''simple docstring'''







     _snake_case             =  [e.name for e in FlaxKarrasDiffusionSchedulers]

     _snake_case             =  42






     @property
     def     _UpperCamelCase   (self    :	str					)  ->       int:
           return True






     @register_to_config
     def __init__(self    :	Tuple				,				lowercase__    :	int = 1000				,				lowercase__    :	float = 0.00_01				,				lowercase__    :	float = 0.02				,				lowercase__    :	str = "linear"				,				lowercase__    :	Optional[jnp.ndarray] = None				,				lowercase__    :	bool = True				,				lowercase__    :	int = 0				,				lowercase__    :	str = "epsilon"				,				lowercase__    :	jnp.dtype = jnp.floataa				,				)  ->       List[Any]:
           __A		   =      dtype






     def     _UpperCamelCase   (self    :	str				,				lowercase__    :	Optional[CommonSchedulerState] = None					)  ->       DDIMSchedulerState:
           if common is None:
                 __A		   =      CommonSchedulerState.create(self					)

           # At every step in ddim, we are looking into the previous alphas_cumprod
           # For the final step, there is no previous alphas_cumprod because we are already at 0
           # `set_alpha_to_one` decides whether we set this parameter simply to one or
           # whether we use the final alpha of the "non-previous" one.
           __A		   =      (
               jnp.array(1.0				,				dtype=self.dtype					) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
           )

           # standard deviation of the initial noise distribution
           __A		   =      jnp.array(1.0				,				dtype=self.dtype					)

           __A		   =      jnp.arange(0				,				self.config.num_train_timesteps					).round()[::-1]

           return DDIMSchedulerState.create(
               common=lowercase__				,				final_alpha_cumprod=lowercase__				,				init_noise_sigma=lowercase__				,				timesteps=lowercase__				,				)






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	DDIMSchedulerState				,				lowercase__    :	jnp.ndarray				,				lowercase__    :	Optional[int] = None					)  ->       jnp.ndarray:
           return sample






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	DDIMSchedulerState				,				lowercase__    :	int				,				lowercase__    :	Tuple = ()					)  ->       DDIMSchedulerState:
           __A		   =      self.config.num_train_timesteps // num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # rounding to avoid issues when num_inference_step is power of 3
           __A		   =      (jnp.arange(0				,				lowercase__					) * step_ratio).round()[::-1] + self.config.steps_offset

           return state.replace(
               num_inference_steps=lowercase__				,				timesteps=lowercase__				,				)






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	DDIMSchedulerState				,				lowercase__    :	List[str]				,				lowercase__    :	Dict					)  ->       Optional[int]:
           __A		   =      state.common.alphas_cumprod[timestep]
           __A		   =      jnp.where(
               prev_timestep >= 0				,				state.common.alphas_cumprod[prev_timestep]				,				state.final_alpha_cumprod					)
           __A		   =      1 - alpha_prod_t
           __A		   =      1 - alpha_prod_t_prev

           __A		   =      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

           return variance






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	DDIMSchedulerState				,				lowercase__    :	jnp.ndarray				,				lowercase__    :	int				,				lowercase__    :	jnp.ndarray				,				lowercase__    :	float = 0.0				,				lowercase__    :	bool = True				,				)  ->       Union[FlaxDDIMSchedulerOutput, Tuple]:
           if state.num_inference_steps is None:
                 raise ValueError(
                     "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"					)

           # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
           # Ideally, read DDIM paper in-detail understanding

           # Notation (<variable name> -> <name in paper>
           # - pred_noise_t -> e_theta(x_t, t)
           # - pred_original_sample -> f_theta(x_t, t) or x_0
           # - std_dev_t -> sigma_t
           # - eta -> η
           # - pred_sample_direction -> "direction pointing to x_t"
           # - pred_prev_sample -> "x_t-1"

           # 1. get previous step value (=t-1)
           __A		   =      timestep - self.config.num_train_timesteps // state.num_inference_steps

           __A		   =      state.common.alphas_cumprod
           __A		   =      state.final_alpha_cumprod

           # 2. compute alphas, betas
           __A		   =      alphas_cumprod[timestep]
           __A		   =      jnp.where(prev_timestep >= 0				,				alphas_cumprod[prev_timestep]				,				lowercase__					)

           __A		   =      1 - alpha_prod_t

           # 3. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           if self.config.prediction_type == "epsilon":
                 __A		   =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                 __A		   =      model_output
           elif self.config.prediction_type == "sample":
                 __A		   =      model_output
                 __A		   =      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
           elif self.config.prediction_type == "v_prediction":
                 __A		   =      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                 __A		   =      (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
           else:
                 raise ValueError(
                     f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
                     " `v_prediction`"					)

           # 4. compute variance: "sigma_t(η)" -> see formula (16)
           # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
           __A		   =      self._get_variance(lowercase__				,				lowercase__				,				lowercase__					)
           __A		   =      eta * variance ** 0.5

           # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           __A		   =      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

           # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           __A		   =      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

           if not return_dict:
                 return (prev_sample, state)

           return FlaxDDIMSchedulerOutput(prev_sample=lowercase__				,				state=lowercase__					)






     def     _UpperCamelCase   (self    :	Dict				,				lowercase__    :	DDIMSchedulerState				,				lowercase__    :	jnp.ndarray				,				lowercase__    :	jnp.ndarray				,				lowercase__    :	jnp.ndarray				,				)  ->       jnp.ndarray:
           return add_noise_common(state.common				,				lowercase__				,				lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	DDIMSchedulerState				,				lowercase__    :	jnp.ndarray				,				lowercase__    :	jnp.ndarray				,				lowercase__    :	jnp.ndarray				,				)  ->       jnp.ndarray:
           return get_velocity_common(state.common				,				lowercase__				,				lowercase__				,				lowercase__					)






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










'''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 					a__    :
     '''simple docstring'''







     _snake_case             =  ()
     _snake_case             =  True






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Union[str, Any]:
           __A		   =      self.config_class

           # test default config
           __A		   =      config_class()
           self.assertIsNotNone(lowercase__					)
           __A		   =      ["stem"] + [f"stage{idx}" for idx in range(1				,				len(config.depths					) + 1					)]
           self.assertEqual(config.stage_names				,				lowercase__					)
           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		   =      config_class(out_features=lowercase__				,				out_indices=lowercase__					)
           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		   =      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		   =      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		   =      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(lowercase__					):
                 __A		   =      config_class(out_features=["stage1", "stage2"]				,				out_indices=[0, 2]					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Optional[Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 __A		   =      model_class(lowercase__					)
                 __A		   =      inspect.signature(model.forward					)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 __A		   =      [*signature.parameters.keys()]
                 __A		   =      ["pixel_values"]
                 self.assertListEqual(arg_names[:1]				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Union[str, Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

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

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 self.assertEqual(len(model.channels					)				,				1					)
                 self.assertListEqual(model.channels				,				[num_features[-1]]					)

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 self.assertEqual(len(model.channels					)				,				1					)
                 self.assertListEqual(model.channels				,				[num_features[-1]]					)






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Optional[Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

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

                 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		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)

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

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)

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

                 # Check backbone can be initialized with fresh weights
                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      False
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Optional[int]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for backbone_class in self.all_model_classes:
                 __A		   =      backbone_class(lowercase__					)

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

                 self.assertIsInstance(backbone.backbone_type				,				lowercase__					)
                 # 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    :	Tuple					)  ->       int:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()
           __A		   =      inputs_dict["pixel_values"].shape[0]

           for backbone_class in self.all_model_classes:
                 __A		   =      backbone_class(lowercase__					)
                 backbone.to(lowercase__					)
                 backbone.eval()

                 __A		   =      backbone(**lowercase__					)

                 # Test default outputs and verify feature maps
                 self.assertIsInstance(outputs.feature_maps				,				lowercase__					)
                 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		   =      backbone(**lowercase__				,				output_hidden_states=lowercase__					)
                 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		   =      backbone(**lowercase__				,				output_attentions=lowercase__					)
                       self.assertIsNotNone(outputs.attentions					)


