
'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.swin.modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
      )


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor






class        UpperCAmelCase_       :

 '''simple docstring'''







 def __init__(				self		:     Tuple ,							snake_case__		:     List[Any] ,							snake_case__		:     Any=13 ,							snake_case__		:     Optional[Any]=32 ,							snake_case__		:     Optional[Any]=2 ,							snake_case__		:     Union[str, Any]=3 ,							snake_case__		:     Optional[Any]=16 ,							snake_case__		:     Optional[int]=[1, 2, 1] ,							snake_case__		:     Dict=[2, 2, 4] ,							snake_case__		:     List[str]=2 ,							snake_case__		:     List[str]=2.0 ,							snake_case__		:     Optional[Any]=True ,							snake_case__		:     str=0.0 ,							snake_case__		:     int=0.0 ,							snake_case__		:     Optional[int]=0.1 ,							snake_case__		:     Tuple="gelu" ,							snake_case__		:     Any=False ,							snake_case__		:     str=True ,							snake_case__		:     Optional[int]=0.02 ,							snake_case__		:     List[Any]=1e-5 ,							snake_case__		:     List[str]=True ,							snake_case__		:     List[Any]=None ,							snake_case__		:     int=True ,							snake_case__		:     Union[str, Any]=10 ,							snake_case__		:     Optional[Any]=8 ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       parent
   UpperCAmelCase__			:				Dict												=       batch_size
   UpperCAmelCase__			:				int												=       image_size
   UpperCAmelCase__			:				str												=       patch_size
   UpperCAmelCase__			:				Dict												=       num_channels
   UpperCAmelCase__			:				Optional[int]												=       embed_dim
   UpperCAmelCase__			:				Dict												=       depths
   UpperCAmelCase__			:				Tuple												=       num_heads
   UpperCAmelCase__			:				str												=       window_size
   UpperCAmelCase__			:				Optional[Any]												=       mlp_ratio
   UpperCAmelCase__			:				str												=       qkv_bias
   UpperCAmelCase__			:				Tuple												=       hidden_dropout_prob
   UpperCAmelCase__			:				int												=       attention_probs_dropout_prob
   UpperCAmelCase__			:				int												=       drop_path_rate
   UpperCAmelCase__			:				Any												=       hidden_act
   UpperCAmelCase__			:				Optional[Any]												=       use_absolute_embeddings
   UpperCAmelCase__			:				Tuple												=       patch_norm
   UpperCAmelCase__			:				Dict												=       layer_norm_eps
   UpperCAmelCase__			:				Tuple												=       initializer_range
   UpperCAmelCase__			:				str												=       is_training
   UpperCAmelCase__			:				Dict												=       scope
   UpperCAmelCase__			:				Optional[Any]												=       use_labels
   UpperCAmelCase__			:				Optional[Any]												=       type_sequence_label_size
   UpperCAmelCase__			:				Union[str, Any]												=       encoder_stride







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

   UpperCAmelCase__			:				List[Any]												=       None
   if self.use_labels:
     UpperCAmelCase__			:				Union[str, Any]												=       ids_tensor([self.batch_size] ,							self.type_sequence_label_size   )

   UpperCAmelCase__			:				Tuple												=       self.get_config()

   return config, pixel_values, labels







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   return SwinConfig(
       image_size=self.image_size ,							patch_size=self.patch_size ,							num_channels=self.num_channels ,							embed_dim=self.embed_dim ,							depths=self.depths ,							num_heads=self.num_heads ,							window_size=self.window_size ,							mlp_ratio=self.mlp_ratio ,							qkv_bias=self.qkv_bias ,							hidden_dropout_prob=self.hidden_dropout_prob ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob ,							drop_path_rate=self.drop_path_rate ,							hidden_act=self.hidden_act ,							use_absolute_embeddings=self.use_absolute_embeddings ,							path_norm=self.patch_norm ,							layer_norm_eps=self.layer_norm_eps ,							initializer_range=self.initializer_range ,							encoder_stride=self.encoder_stride ,							)







 def        UpperCamelCase     (				self		:     Dict ,							snake_case__		:     List[str] ,							snake_case__		:     Dict ,							snake_case__		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__			:				List[Any]												=       TFSwinModel(config=snake_case__   )
   UpperCAmelCase__			:				Dict												=       model(snake_case__   )

   UpperCAmelCase__			:				str												=       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
   UpperCAmelCase__			:				Dict												=       int(config.embed_dim * 2 ** (len(config.depths   ) - 1)   )

   self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, expected_seq_len, expected_dim)   )







 def        UpperCamelCase     (				self		:     List[Any] ,							snake_case__		:     str ,							snake_case__		:     Dict ,							snake_case__		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       TFSwinForMaskedImageModeling(config=snake_case__   )
   UpperCAmelCase__			:				List[str]												=       model(snake_case__   )
   self.parent.assertEqual(
       result.logits.shape ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

   # test greyscale images
   UpperCAmelCase__			:				Tuple												=       1
   UpperCAmelCase__			:				int												=       TFSwinForMaskedImageModeling(snake_case__   )

   UpperCAmelCase__			:				List[str]												=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
   UpperCAmelCase__			:				Tuple												=       model(snake_case__   )
   self.parent.assertEqual(result.logits.shape ,							(self.batch_size, 1, self.image_size, self.image_size)   )







 def        UpperCamelCase     (				self		:     Dict ,							snake_case__		:     Optional[Any] ,							snake_case__		:     Any ,							snake_case__		:     Optional[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[Any]												=       self.type_sequence_label_size
   UpperCAmelCase__			:				Optional[int]												=       TFSwinForImageClassification(snake_case__   )
   UpperCAmelCase__			:				Union[str, Any]												=       model(snake_case__ ,							labels=snake_case__   )
   self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.type_sequence_label_size)   )

   # test greyscale images
   UpperCAmelCase__			:				Optional[int]												=       1
   UpperCAmelCase__			:				List[str]												=       TFSwinForImageClassification(snake_case__   )
   UpperCAmelCase__			:				Any												=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
   UpperCAmelCase__			:				int												=       model(snake_case__   )
   self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.type_sequence_label_size)   )







 def        UpperCamelCase     (				self		:     str   ):



   '''simple docstring'''




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






@require_tf
class        UpperCAmelCase_       (						A				,							A				,							unittest.TestCase							):

 '''simple docstring'''
 lowercase_				:						List[str]				=       (
     (
         TFSwinModel,
         TFSwinForImageClassification,
         TFSwinForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 lowercase_				:						Any				=       (
     {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
     if is_tf_available()
     else {}
 )

 lowercase_				:						Any				=       False
 lowercase_				:						List[Any]				=       False
 lowercase_				:						Optional[Any]				=       False
 lowercase_				:						Optional[Any]				=       False







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Optional[Any]												=       TFSwinModelTester(self   )
   UpperCAmelCase__			:				Optional[int]												=       ConfigTester(self ,							config_class=snake_case__ ,							embed_dim=37   )







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   self.create_and_test_config_common_properties()
   self.config_tester.create_and_test_config_to_json_string()
   self.config_tester.create_and_test_config_to_json_file()
   self.config_tester.create_and_test_config_from_and_save_pretrained()
   self.config_tester.create_and_test_config_with_num_labels()
   self.config_tester.check_config_can_be_init_without_params()
   self.config_tester.check_config_arguments_init()







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   return







 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*snake_case__   )







 def        UpperCamelCase     (				self		:     str   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__   )







 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				List[Any]												=       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*snake_case__   )







 @unittest.skip(reason="Swin does not use inputs_embeds"   )
 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   pass







 def        UpperCamelCase     (				self		:     str   ):



   '''simple docstring'''




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

   for model_class in self.all_model_classes:
     UpperCAmelCase__			:				int												=       model_class(snake_case__   )
     self.assertIsInstance(model.get_input_embeddings() ,							tf.keras.layers.Layer   )
     UpperCAmelCase__			:				Optional[int]												=       model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(snake_case__ ,							tf.keras.layers.Dense   )   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




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

   for model_class in self.all_model_classes:
     UpperCAmelCase__			:				List[str]												=       model_class(snake_case__   )
     UpperCAmelCase__			:				Dict												=       inspect.signature(model.call   )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     UpperCAmelCase__			:				Optional[Any]												=       [*signature.parameters.keys()]

     UpperCAmelCase__			:				Union[str, Any]												=       ["pixel_values"]
     self.assertListEqual(arg_names[:1] ,							snake_case__   )







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__					, UpperCAmelCase__			:				Union[str, Any]												=       self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__			:				Optional[Any]												=       True

   for model_class in self.all_model_classes:
     UpperCAmelCase__			:				List[str]												=       True
     UpperCAmelCase__			:				Tuple												=       False
     UpperCAmelCase__			:				str												=       True
     UpperCAmelCase__			:				Optional[int]												=       model_class(snake_case__   )
     UpperCAmelCase__			:				Optional[Any]												=       model(**self._prepare_for_class(snake_case__ ,							snake_case__   )   )
     UpperCAmelCase__			:				List[str]												=       outputs.attentions
     UpperCAmelCase__			:				Tuple												=       len(self.model_tester.depths   )
     self.assertEqual(len(snake_case__   ) ,							snake_case__   )

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     UpperCAmelCase__			:				Optional[int]												=       True
     UpperCAmelCase__			:				Dict												=       config.window_size**2
     UpperCAmelCase__			:				int												=       model_class(snake_case__   )
     UpperCAmelCase__			:				str												=       model(**self._prepare_for_class(snake_case__ ,							snake_case__   )   )
     UpperCAmelCase__			:				int												=       outputs.attentions
     self.assertEqual(len(snake_case__   ) ,							snake_case__   )

     self.assertListEqual(
         list(attentions[0].shape[-3:]   ) ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,							)
     UpperCAmelCase__			:				Dict												=       len(snake_case__   )

     # Check attention is always last and order is fine
     UpperCAmelCase__			:				Dict												=       True
     UpperCAmelCase__			:				Any												=       True
     UpperCAmelCase__			:				Tuple												=       model_class(snake_case__   )
     UpperCAmelCase__			:				Optional[int]												=       model(**self._prepare_for_class(snake_case__ ,							snake_case__   )   )

     if hasattr(self.model_tester ,							"num_hidden_states_types"   ):
       UpperCAmelCase__			:				Optional[Any]												=       self.model_tester.num_hidden_states_types
     else:
       # also another +1 for reshaped_hidden_states
       UpperCAmelCase__			:				Dict												=       2
     self.assertEqual(out_len + added_hidden_states ,							len(snake_case__   )   )

     UpperCAmelCase__			:				Optional[int]												=       outputs.attentions

     self.assertEqual(len(snake_case__   ) ,							snake_case__   )

     self.assertListEqual(
         list(self_attentions[0].shape[-3:]   ) ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,							)







 def        UpperCamelCase     (				self		:     Any ,							snake_case__		:     Dict ,							snake_case__		:     Any ,							snake_case__		:     Any ,							snake_case__		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Dict												=       model_class(snake_case__   )
   UpperCAmelCase__			:				List[str]												=       model(**self._prepare_for_class(snake_case__ ,							snake_case__   )   )
   UpperCAmelCase__			:				Dict												=       outputs.hidden_states

   UpperCAmelCase__			:				str												=       getattr(
       self.model_tester ,							"expected_num_hidden_layers" ,							len(self.model_tester.depths   ) + 1   )
   self.assertEqual(len(snake_case__   ) ,							snake_case__   )

   # Swin has a different seq_length
   UpperCAmelCase__			:				Dict												=       to_atuple(config.patch_size   )

   UpperCAmelCase__			:				Optional[Any]												=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

   self.assertListEqual(
       list(hidden_states[0].shape[-2:]   ) ,							[num_patches, self.model_tester.embed_dim] ,							)

   UpperCAmelCase__			:				int												=       outputs.reshaped_hidden_states
   self.assertEqual(len(snake_case__   ) ,							snake_case__   )

   UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__			:				List[str]												=       reshaped_hidden_states[0].shape

   UpperCAmelCase__			:				Union[str, Any]												=       tf.reshape(reshaped_hidden_states[0] ,							(batch_size, num_channels, height * width)   )
   UpperCAmelCase__			:				Optional[int]												=       tf.transpose(snake_case__ ,							(0, 2, 1)   )

   self.assertListEqual(
       list(reshaped_hidden_states.shape[-2:]   ) ,							[num_patches, self.model_tester.embed_dim] ,							)







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__					, UpperCAmelCase__			:				int												=       self.model_tester.prepare_config_and_inputs_for_common()

   UpperCAmelCase__			:				Dict												=       to_atuple(self.model_tester.image_size   )

   for model_class in self.all_model_classes:
     UpperCAmelCase__			:				Any												=       True
     self.check_hidden_states_output(snake_case__ ,							snake_case__ ,							snake_case__ ,							snake_case__   )

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     UpperCAmelCase__			:				Any												=       True

     self.check_hidden_states_output(snake_case__ ,							snake_case__ ,							snake_case__ ,							snake_case__   )







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__					, UpperCAmelCase__			:				Tuple												=       self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__			:				List[Any]												=       3

   UpperCAmelCase__			:				Dict												=       to_atuple(self.model_tester.image_size   )
   UpperCAmelCase__			:				List[str]												=       to_atuple(config.patch_size   )

   UpperCAmelCase__			:				str												=       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
   UpperCAmelCase__			:				Union[str, Any]												=       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

   for model_class in self.all_model_classes:
     UpperCAmelCase__			:				int												=       True
     self.check_hidden_states_output(snake_case__ ,							snake_case__ ,							snake_case__ ,							(padded_height, padded_width)   )

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     UpperCAmelCase__			:				Tuple												=       True
     self.check_hidden_states_output(snake_case__ ,							snake_case__ ,							snake_case__ ,							(padded_height, padded_width)   )







 @slow
 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase__			:				Tuple												=       TFSwinModel.from_pretrained(snake_case__   )
     self.assertIsNotNone(snake_case__   )






@require_vision
@require_tf
class        UpperCAmelCase_       (						unittest.TestCase							):

 '''simple docstring'''







 @cached_property
 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   return (
       AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"   )
       if is_vision_available()
       else None
   )







 @slow
 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"   )
   UpperCAmelCase__			:				int												=       self.default_image_processor

   UpperCAmelCase__			:				List[Any]												=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
   UpperCAmelCase__			:				Union[str, Any]												=       image_processor(images=snake_case__ ,							return_tensors="tf"   )

   # forward pass
   UpperCAmelCase__			:				Optional[Any]												=       model(snake_case__   )

   # verify the logits
   UpperCAmelCase__			:				str												=       tf.TensorShape((1, 10_00)   )
   self.assertEqual(outputs.logits.shape ,							snake_case__   )
   UpperCAmelCase__			:				List[str]												=       tf.constant([-0.0948, -0.6454, -0.0921]   )
   self.assertTrue(np.allclose(outputs.logits[0, :3] ,							snake_case__ ,							atol=1e-4   )   )





'''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        UpperCAmelCase_       :

 '''simple docstring'''
 # setable values
 lowercase_				:						Optional[jnp.ndarray]				=       None
 lowercase_				:						Optional[jnp.ndarray]				=       None
 lowercase_				:						Optional[jnp.ndarray]				=       None







 @classmethod
 def        UpperCamelCase     (				cls		:     List[str]   ):



   '''simple docstring'''




   return cls()






@dataclass
class        UpperCAmelCase_       (						A							):

 '''simple docstring'''
 lowercase_				:						ScoreSdeVeSchedulerState
 lowercase_				:						jnp.ndarray
 lowercase_				:						Optional[jnp.ndarray]				=       None






class        UpperCAmelCase_       (						A				,							A							):

 '''simple docstring'''







 @property
 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   return True







 @register_to_config
 def __init__(				self		:     Optional[Any] ,							snake_case__		:     int = 20_00 ,							snake_case__		:     float = 0.15 ,							snake_case__		:     float = 0.01 ,							snake_case__		:     float = 1348.0 ,							snake_case__		:     float = 1e-5 ,							snake_case__		:     int = 1 ,							):



   '''simple docstring'''




   pass







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       ScoreSdeVeSchedulerState.create()
   return self.set_sigmas(
       snake_case__ ,							self.config.num_train_timesteps ,							self.config.sigma_min ,							self.config.sigma_max ,							self.config.sampling_eps ,							)







 def        UpperCamelCase     (				self		:     List[Any] ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     int ,							snake_case__		:     Tuple = () ,							snake_case__		:     float = None   ):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

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







 def        UpperCamelCase     (				self		:     str ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     int ,							snake_case__		:     float = None ,							snake_case__		:     float = None ,							snake_case__		:     float = None ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       sigma_min if sigma_min is not None else self.config.sigma_min
   UpperCAmelCase__			:				Dict												=       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__			:				Optional[int]												=       self.set_timesteps(snake_case__ ,							snake_case__ ,							snake_case__   )

   UpperCAmelCase__			:				int												=       jnp.exp(jnp.linspace(jnp.log(snake_case__   ) ,							jnp.log(snake_case__   ) ,							snake_case__   )   )
   UpperCAmelCase__			:				Optional[int]												=       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]   )

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







 def        UpperCamelCase     (				self		:     List[str] ,							snake_case__		:     str ,							snake_case__		:     Tuple ,							snake_case__		:     Dict   ):



   '''simple docstring'''




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







 def        UpperCamelCase     (				self		:     Optional[int] ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     int ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     random.KeyArray ,							snake_case__		:     bool = 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"   )

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

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

   #  equation 6: sample noise for the diffusion term of
   UpperCAmelCase__			:				List[str]												=       random.split(snake_case__ ,							num=1   )
   UpperCAmelCase__			:				Dict												=       random.normal(key=snake_case__ ,							shape=sample.shape   )
   UpperCAmelCase__			:				Optional[int]												=       sample - drift  # subtract because `dt` is a small negative timestep
   # TODO is the variable diffusion the correct scaling term for the noise?
   UpperCAmelCase__			:				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=snake_case__ ,							prev_sample_mean=snake_case__ ,							state=snake_case__   )







 def        UpperCamelCase     (				self		:     Any ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     random.KeyArray ,							snake_case__		:     bool = 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
   UpperCAmelCase__			:				str												=       random.split(snake_case__ ,							num=1   )
   UpperCAmelCase__			:				Tuple												=       random.normal(key=snake_case__ ,							shape=sample.shape   )

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

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

   if not return_dict:
     return (prev_sample, state)

   return FlaxSdeVeOutput(prev_sample=snake_case__ ,							state=snake_case__   )







 def __len__(				self		:     List[str]   ):



   '''simple docstring'''




   return self.config.num_train_timesteps



