
import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
 from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor
class       _lowerCAmelCase  :

 """simple docstring"""

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






      """simple docstring"""



      UpperCamelCase						    =					parent
      UpperCamelCase						    =					batch_size
      UpperCamelCase						    =					image_size
      UpperCamelCase						    =					patch_size
      UpperCamelCase						    =					num_channels
      UpperCamelCase						    =					embed_dim
      UpperCamelCase						    =					depths
      UpperCamelCase						    =					num_heads
      UpperCamelCase						    =					window_size
      UpperCamelCase						    =					mlp_ratio
      UpperCamelCase						    =					qkv_bias
      UpperCamelCase						    =					hidden_dropout_prob
      UpperCamelCase						    =					attention_probs_dropout_prob
      UpperCamelCase						    =					drop_path_rate
      UpperCamelCase						    =					hidden_act
      UpperCamelCase						    =					use_absolute_embeddings
      UpperCamelCase						    =					patch_norm
      UpperCamelCase						    =					layer_norm_eps
      UpperCamelCase						    =					initializer_range
      UpperCamelCase						    =					is_training
      UpperCamelCase						    =					scope
      UpperCamelCase						    =					use_labels
      UpperCamelCase						    =					type_sequence_label_size
      UpperCamelCase						    =					encoder_stride

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      UpperCamelCase						    =					None
      if self.use_labels:
           UpperCamelCase						    =					ids_tensor([self.batch_size]					, self.type_sequence_label_size   )

      UpperCamelCase						    =					self.get_config()

      return config, pixel_values, labels

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      return SwinvaConfig(
          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      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					SwinvaModel(config=SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
      UpperCamelCase						    =					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      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					SwinvaForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__   )
      self.parent.assertEqual(
          result.logits.shape					, (self.batch_size, self.num_channels, self.image_size, self.image_size)   )

      # test greyscale images
      UpperCamelCase						    =					1
      UpperCamelCase						    =					SwinvaForMaskedImageModeling(SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()

      UpperCamelCase						    =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__   )
      self.parent.assertEqual(result.logits.shape					, (self.batch_size, 1, self.image_size, self.image_size)   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Any   ):






      """simple docstring"""



      UpperCamelCase						    =					self.type_sequence_label_size
      UpperCamelCase						    =					SwinvaForImageClassification(SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, labels=SCREAMING_SNAKE_CASE__   )
      self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.type_sequence_label_size)   )






 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					self.prepare_config_and_inputs()
      UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					config_and_inputs
      UpperCamelCase						    =					{'pixel_values': pixel_values}
      return config, inputs_dict




@require_torch
class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Any       						=(
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 SCREAMING_SNAKE_CASE_				:					int       						=(
     {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

 SCREAMING_SNAKE_CASE_				:					List[Any]       						=False
 SCREAMING_SNAKE_CASE_				:					Any       						=False
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=False
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=False

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      UpperCamelCase						    =					SwinvaModelTester(self   )
      UpperCamelCase						    =					ConfigTester(self					, config_class=SCREAMING_SNAKE_CASE__					, embed_dim=37   )

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      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      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__   )

 @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'   )
 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      pass

 @unittest.skip(reason='Swinv2 does not use inputs_embeds'   )
 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
           self.assertIsInstance(model.get_input_embeddings()					, (nn.Module)   )
           UpperCamelCase						    =					model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__					, nn.Linear   )   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					inspect.signature(model.forward   )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase						    =					[*signature.parameters.keys()]

           UpperCamelCase						    =					['pixel_values']
           self.assertListEqual(arg_names[:1]					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					True

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					True
           UpperCamelCase						    =					False
           UpperCamelCase						    =					True
           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
           model.to(SCREAMING_SNAKE_CASE__   )
           model.eval()
           with torch.no_grad():
                UpperCamelCase						    =					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )   )
           UpperCamelCase						    =					outputs.attentions
           UpperCamelCase						    =					len(self.model_tester.depths   )
           self.assertEqual(len(SCREAMING_SNAKE_CASE__   )					, SCREAMING_SNAKE_CASE__   )

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           UpperCamelCase						    =					True
           UpperCamelCase						    =					config.window_size**2
           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
           model.to(SCREAMING_SNAKE_CASE__   )
           model.eval()
           with torch.no_grad():
                UpperCamelCase						    =					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )   )
           UpperCamelCase						    =					outputs.attentions
           self.assertEqual(len(SCREAMING_SNAKE_CASE__   )					, SCREAMING_SNAKE_CASE__   )

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

           # Check attention is always last and order is fine
           UpperCamelCase						    =					True
           UpperCamelCase						    =					True
           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
           model.to(SCREAMING_SNAKE_CASE__   )
           model.eval()
           with torch.no_grad():
                UpperCamelCase						    =					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )   )

           if hasattr(self.model_tester					, 'num_hidden_states_types'   ):
                UpperCamelCase						    =					self.model_tester.num_hidden_states_types
           else:
                # also another +1 for reshaped_hidden_states
                UpperCamelCase						    =					2
           self.assertEqual(out_len + added_hidden_states					, len(SCREAMING_SNAKE_CASE__   )   )

           UpperCamelCase						    =					outputs.attentions

           self.assertEqual(len(SCREAMING_SNAKE_CASE__   )					, SCREAMING_SNAKE_CASE__   )

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

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()

      with torch.no_grad():
           UpperCamelCase						    =					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )   )

      UpperCamelCase						    =					outputs.hidden_states

      UpperCamelCase						    =					getattr(
          self.model_tester					, 'expected_num_hidden_layers'					, len(self.model_tester.depths   ) + 1   )
      self.assertEqual(len(SCREAMING_SNAKE_CASE__   )					, SCREAMING_SNAKE_CASE__   )

      # Swinv2 has a different seq_length
      UpperCamelCase						    =					(
          config.patch_size
          if isinstance(config.patch_size					, collections.abc.Iterable   )
          else (config.patch_size, config.patch_size)
      )

      UpperCamelCase						    =					(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						    =					outputs.reshaped_hidden_states
      self.assertEqual(len(SCREAMING_SNAKE_CASE__   )					, SCREAMING_SNAKE_CASE__   )

      UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					reshaped_hidden_states[0].shape
      UpperCamelCase						    =					(
          reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, height * width   ).permute(0					, 2					, 1   )
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:]   )					, [num_patches, self.model_tester.embed_dim]					, )

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      UpperCamelCase						    =					(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size					, collections.abc.Iterable   )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					True
           self.check_hidden_states_output(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           UpperCamelCase						    =					True

           self.check_hidden_states_output(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					3

      UpperCamelCase						    =					(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size					, collections.abc.Iterable   )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      UpperCamelCase						    =					(
          config.patch_size
          if isinstance(config.patch_size					, collections.abc.Iterable   )
          else (config.patch_size, config.patch_size)
      )

      UpperCamelCase						    =					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      UpperCamelCase						    =					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					True
           self.check_hidden_states_output(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, (padded_height, padded_width)   )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           UpperCamelCase						    =					True
           self.check_hidden_states_output(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, (padded_height, padded_width)   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__   )

 @slow
 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase						    =					SwinvaModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
           self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      UpperCamelCase						    =					_config_zero_init(SCREAMING_SNAKE_CASE__   )
      for model_class in self.all_model_classes:
           UpperCamelCase						    =					model_class(config=SCREAMING_SNAKE_CASE__   )
           for name, param in model.named_parameters():
                if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1e9).round() / 1e9).item()					, [0.0, 1.0]					, msg=F'Parameter {name} of model {model_class} seems not properly initialized'					, )




@require_vision
@require_torch
class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""

 @cached_property
 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      return (
          AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'   )
          if is_vision_available()
          else None
      )






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






      """simple docstring"""



      UpperCamelCase						    =					SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'   ).to(
          SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.default_image_processor

      UpperCamelCase						    =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
      UpperCamelCase						    =					image_processor(images=SCREAMING_SNAKE_CASE__					, return_tensors='pt'   ).to(SCREAMING_SNAKE_CASE__   )

      # forward pass
      with torch.no_grad():
           UpperCamelCase						    =					model(**SCREAMING_SNAKE_CASE__   )

      # verify the logits
      UpperCamelCase						    =					torch.Size((1, 10_00)   )
      self.assertEqual(outputs.logits.shape					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.tensor([-0.3947, -0.4306, 0.0026]   ).to(SCREAMING_SNAKE_CASE__   )
      self.assertTrue(torch.allclose(outputs.logits[0, :3]					, SCREAMING_SNAKE_CASE__					, atol=1e-4   )   )




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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class       _lowerCAmelCase  :

 """simple docstring"""
 # setable values
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None  # sigma(t_i)

 @classmethod
 def      __lowerCAmelCase  (						cls			:			Optional[Any]   ):






      """simple docstring"""



      return cls()




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					KarrasVeSchedulerState




class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      return True

 @register_to_config
 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			float = 0.02					, SCREAMING_SNAKE_CASE__			:			float = 1_00					, SCREAMING_SNAKE_CASE__			:			float = 1.007					, SCREAMING_SNAKE_CASE__			:			float = 80					, SCREAMING_SNAKE_CASE__			:			float = 0.05					, SCREAMING_SNAKE_CASE__			:			float = 50					, ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      return KarrasVeSchedulerState.create()

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple = ()   ):






      """simple docstring"""



      UpperCamelCase						    =					jnp.arange(0					, SCREAMING_SNAKE_CASE__   )[::-1].copy()
      UpperCamelCase						    =					[
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=SCREAMING_SNAKE_CASE__					, schedule=jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.floataa   )					, timesteps=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			random.KeyArray					, ):






      """simple docstring"""



      if self.config.s_min <= sigma <= self.config.s_max:
           UpperCamelCase						    =					min(self.config.s_churn / state.num_inference_steps					, 2**0.5 - 1   )
      else:
           UpperCamelCase						    =					0

      # sample eps ~ N(0, S_noise^2 * I)
      UpperCamelCase						    =					random.split(SCREAMING_SNAKE_CASE__					, num=1   )
      UpperCamelCase						    =					self.config.s_noise * random.normal(key=SCREAMING_SNAKE_CASE__					, shape=sample.shape   )
      UpperCamelCase						    =					sigma + gamma * sigma
      UpperCamelCase						    =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_hat + sigma_hat * model_output
      UpperCamelCase						    =					(sample_hat - pred_original_sample) / sigma_hat
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
           return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_prev + sigma_prev * model_output
      UpperCamelCase						    =					(sample_prev - pred_original_sample) / sigma_prev
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
           return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      raise NotImplementedError()


