
"""simple docstring"""





import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
       from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor


class 					UpperCAmelCase_     :


      def __init__(       self		,      a		,      a=1_3		,      a=3_2		,      a=2		,      a=3		,      a=1_6		,      a=[1, 2, 1]		,      a=[2, 2, 4]		,      a=2		,      a=2.0		,      a=True		,      a=0.0		,      a=0.0		,      a=0.1		,      a="gelu"		,      a=False		,      a=True		,      a=0.02		,      a=1e-5		,      a=True		,      a=None		,      a=True		,      a=1_0		,      a=8		,      a=["stage1", "stage2"]		,      a=[1, 2]		,      )	-> Optional[Any]:
             lowercase__      :		int          =     parent
             lowercase__      :		int          =     batch_size
             lowercase__      :		Any          =     image_size
             lowercase__      :		Any          =     patch_size
             lowercase__      :		List[Any]          =     num_channels
             lowercase__      :		int          =     embed_dim
             lowercase__      :		Any          =     depths
             lowercase__      :		Union[str, Any]          =     num_heads
             lowercase__      :		List[Any]          =     window_size
             lowercase__      :		List[str]          =     mlp_ratio
             lowercase__      :		Any          =     qkv_bias
             lowercase__      :		Any          =     hidden_dropout_prob
             lowercase__      :		Tuple          =     attention_probs_dropout_prob
             lowercase__      :		List[str]          =     drop_path_rate
             lowercase__      :		Any          =     hidden_act
             lowercase__      :		Any          =     use_absolute_embeddings
             lowercase__      :		Union[str, Any]          =     patch_norm
             lowercase__      :		List[Any]          =     layer_norm_eps
             lowercase__      :		Optional[Any]          =     initializer_range
             lowercase__      :		Any          =     is_training
             lowercase__      :		Dict          =     scope
             lowercase__      :		Any          =     use_labels
             lowercase__      :		List[str]          =     type_sequence_label_size
             lowercase__      :		Union[str, Any]          =     encoder_stride
             lowercase__      :		Optional[int]          =     out_features
             lowercase__      :		Optional[Any]          =     out_indices


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		Optional[Any]          =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__      :		int          =     None
             if self.use_labels:
                    lowercase__      :		str          =     ids_tensor([self.batch_size]		,      self.type_sequence_label_size							)

             lowercase__      :		Optional[Any]          =     self.get_config()

             return config, pixel_values, labels


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             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		,      out_features=self.out_features		,      out_indices=self.out_indices		,      )


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a							)	-> str:
             lowercase__      :		Dict          =     SwinModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__      :		Any          =     model(a							)

             lowercase__      :		Optional[int]          =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
             lowercase__      :		List[Any]          =     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		,      a		,      a		,      a							)	-> Union[str, Any]:
             lowercase__      :		Dict          =     SwinBackbone(config=a							)
             model.to(a							)
             model.eval()
             lowercase__      :		int          =     model(a							)

             # verify hidden states
             self.parent.assertEqual(len(result.feature_maps							)		,      len(config.out_features							)							)
             self.parent.assertListEqual(list(result.feature_maps[0].shape							)		,      [self.batch_size, model.channels[0], 1_6, 1_6]							)

             # verify channels
             self.parent.assertEqual(len(model.channels							)		,      len(config.out_features							)							)

             # verify backbone works with out_features=None
             lowercase__      :		Any          =     None
             lowercase__      :		Tuple          =     SwinBackbone(config=a							)
             model.to(a							)
             model.eval()
             lowercase__      :		List[str]          =     model(a							)

             # verify feature maps
             self.parent.assertEqual(len(result.feature_maps							)		,      1							)
             self.parent.assertListEqual(list(result.feature_maps[0].shape							)		,      [self.batch_size, model.channels[-1], 4, 4]							)

             # verify channels
             self.parent.assertEqual(len(model.channels							)		,      1							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a							)	-> str:
             lowercase__      :		Union[str, Any]          =     SwinForMaskedImageModeling(config=a							)
             model.to(a							)
             model.eval()
             lowercase__      :		int          =     model(a							)
             self.parent.assertEqual(
                 result.logits.shape		,      (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

             # test greyscale images
             lowercase__      :		List[Any]          =     1
             lowercase__      :		List[Any]          =     SwinForMaskedImageModeling(a							)
             model.to(a							)
             model.eval()

             lowercase__      :		Dict          =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
             lowercase__      :		Union[str, Any]          =     model(a							)
             self.parent.assertEqual(result.logits.shape		,      (self.batch_size, 1, self.image_size, self.image_size)							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a							)	-> str:
             lowercase__      :		Optional[int]          =     self.type_sequence_label_size
             lowercase__      :		Optional[Any]          =     SwinForImageClassification(a							)
             model.to(a							)
             model.eval()
             lowercase__      :		Dict          =     model(a		,      labels=a							)
             self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size)							)

             # test greyscale images
             lowercase__      :		Any          =     1
             lowercase__      :		Dict          =     SwinForImageClassification(a							)
             model.to(a							)
             model.eval()

             lowercase__      :		Any          =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
             lowercase__      :		List[Any]          =     model(a							)
             self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size)							)


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		Optional[int]          =     self.prepare_config_and_inputs()
             (
                 lowercase__				
             )      :		Any          =     config_and_inputs
             lowercase__      :		List[str]          =     {'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class 					UpperCAmelCase_     (   _a			,							_a			,							unittest.TestCase):
      lowerCamelCase__		:   List[str]			  =  (
          (
              SwinModel,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase__		:   Union[str, Any]			  =  (
          {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
          if is_torch_available()
          else {}
      )
      lowerCamelCase__		:   List[str]			  =  True

      lowerCamelCase__		:   List[str]			  =  False
      lowerCamelCase__		:   int			  =  False
      lowerCamelCase__		:   Any			  =  False


      def 		_UpperCAmelCase	(       self							)	-> str:
             lowercase__      :		Union[str, Any]          =     SwinModelTester(self							)
             lowercase__      :		Optional[int]          =     ConfigTester(self		,      config_class=a		,      embed_dim=3_7							)


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             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							)	-> Optional[Any]:
             return


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             lowercase__      :		List[Any]          =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'							)
      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             pass


      def 		_UpperCAmelCase	(       self							)	-> str:
             super().test_training_gradient_checkpointing()


      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             lowercase__      :		Tuple          =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_backbone(*a							)


      def 		_UpperCAmelCase	(       self							)	-> Any:
             lowercase__      :		Any          =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_image_modeling(*a							)


      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             lowercase__      :		Optional[Any]          =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a							)


      @unittest.skip(reason='Swin does not use inputs_embeds'							)
      def 		_UpperCAmelCase	(       self							)	-> int:
             pass


      @unittest.skip(reason='Swin Transformer does not use feedforward chunking'							)
      def 		_UpperCAmelCase	(       self							)	-> int:
             pass


      def 		_UpperCAmelCase	(       self							)	-> Any:
             lowercase__      :		Optional[int]          =     self.model_tester.prepare_config_and_inputs_for_common()

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


      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		int          =     self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__      :		Union[str, Any]          =     model_class(a							)
                    lowercase__      :		Optional[int]          =     inspect.signature(model.forward							)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    lowercase__      :		List[str]          =     [*signature.parameters.keys()]

                    lowercase__      :		List[str]          =     ['pixel_values']
                    self.assertListEqual(arg_names[:1]		,      a							)


      def 		_UpperCAmelCase	(       self							)	-> str:
             lowercase__      :		Union[str, Any]          =     self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__      :		List[Any]          =     True

             for model_class in self.all_model_classes:
                    lowercase__      :		List[str]          =     True
                    lowercase__      :		Optional[Any]          =     False
                    lowercase__      :		Union[str, Any]          =     True
                    lowercase__      :		Union[str, Any]          =     model_class(a							)
                    model.to(a							)
                    model.eval()
                    with torch.no_grad():
                           lowercase__      :		Optional[Any]          =     model(**self._prepare_for_class(a		,      a							)							)
                    lowercase__      :		List[str]          =     outputs.attentions
                    lowercase__      :		Tuple          =     len(self.model_tester.depths							)
                    self.assertEqual(len(a							)		,      a							)

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

                    self.assertListEqual(
                        list(attentions[0].shape[-3:]							)		,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,      )
                    lowercase__      :		Optional[int]          =     len(a							)

                    # Check attention is always last and order is fine
                    lowercase__      :		Optional[Any]          =     True
                    lowercase__      :		List[Any]          =     True
                    lowercase__      :		Union[str, Any]          =     model_class(a							)
                    model.to(a							)
                    model.eval()
                    with torch.no_grad():
                           lowercase__      :		Tuple          =     model(**self._prepare_for_class(a		,      a							)							)

                    # also another +1 for reshaped_hidden_states
                    lowercase__      :		Any          =     1 if model_class.__name__ == 'SwinBackbone' else 2
                    self.assertEqual(out_len + added_hidden_states		,      len(a							)							)

                    lowercase__      :		List[Any]          =     outputs.attentions

                    self.assertEqual(len(a							)		,      a							)

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


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a							)	-> Dict:
             lowercase__      :		Union[str, Any]          =     model_class(a							)
             model.to(a							)
             model.eval()

             with torch.no_grad():
                    lowercase__      :		Tuple          =     model(**self._prepare_for_class(a		,      a							)							)

             lowercase__      :		Optional[int]          =     outputs.hidden_states

             lowercase__      :		Any          =     getattr(
                 self.model_tester		,      'expected_num_hidden_layers'		,      len(self.model_tester.depths							) + 1							)
             self.assertEqual(len(a							)		,      a							)

             # Swin has a different seq_length
             lowercase__      :		Union[str, Any]          =     (
                 config.patch_size
                 if isinstance(config.patch_size		,      collections.abc.Iterable							)
                 else (config.patch_size, config.patch_size)
             )

             lowercase__      :		List[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]		,      )

             if not model_class.__name__ == "SwinBackbone":
                    lowercase__      :		Any          =     outputs.reshaped_hidden_states
                    self.assertEqual(len(a							)		,      a							)

                    lowercase__      :		Union[str, Any]          =     reshaped_hidden_states[0].shape
                    lowercase__      :		Optional[int]          =     (
                        reshaped_hidden_states[0].view(a		,      a		,      height * width							).permute(0		,      2		,      1							)
                    )
                    self.assertListEqual(
                        list(reshaped_hidden_states.shape[-2:]							)		,      [num_patches, self.model_tester.embed_dim]		,      )


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		str          =     self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__      :		Union[str, Any]          =     (
                 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:
                    lowercase__      :		List[Any]          =     True
                    self.check_hidden_states_output(a		,      a		,      a		,      a							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__      :		List[str]          =     True

                    self.check_hidden_states_output(a		,      a		,      a		,      a							)


      def 		_UpperCAmelCase	(       self							)	-> Any:
             lowercase__      :		Union[str, Any]          =     self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__      :		Any          =     3

             lowercase__      :		List[str]          =     (
                 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)
             )
             lowercase__      :		int          =     (
                 config.patch_size
                 if isinstance(config.patch_size		,      collections.abc.Iterable							)
                 else (config.patch_size, config.patch_size)
             )

             lowercase__      :		int          =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
             lowercase__      :		List[Any]          =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__      :		Union[str, Any]          =     True
                    self.check_hidden_states_output(a		,      a		,      a		,      (padded_height, padded_width)							)


      @slow
      def 		_UpperCAmelCase	(       self							)	-> int:
             for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__      :		Dict          =     SwinModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)


      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             lowercase__      :		Dict          =     self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__      :		List[str]          =     _config_zero_init(a							)
             for model_class in self.all_model_classes:
                    lowercase__      :		Union[str, Any]          =     model_class(config=a							)
                    for name, param in model.named_parameters():
                           if "embeddings" 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 					UpperCAmelCase_     (   unittest.TestCase):


      @cached_property
      def 		_UpperCAmelCase	(       self							)	-> str:
             return (
                 AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
                 if is_vision_available()
                 else None
             )


      @slow
      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             lowercase__      :		Any          =     SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							).to(a							)
             lowercase__      :		List[str]          =     self.default_image_processor

             lowercase__      :		Dict          =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             lowercase__      :		str          =     image_processor(images=a		,      return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__      :		Any          =     model(**a							)

             # verify the logits
             lowercase__      :		Optional[int]          =     torch.Size((1, 1_0_0_0)							)
             self.assertEqual(outputs.logits.shape		,      a							)
             lowercase__      :		Union[str, Any]          =     torch.tensor([-0.0_948, -0.6_454, -0.0_921]							).to(a							)
             self.assertTrue(torch.allclose(outputs.logits[0, :3]		,      a		,      atol=1e-4							)							)





@require_torch
class 					UpperCAmelCase_     (   unittest.TestCase			,							_a):
      lowerCamelCase__		:   List[Any]			  =  (SwinBackbone,) if is_torch_available() else ()
      lowerCamelCase__		:   Dict			  =  SwinConfig


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             lowercase__      :		Any          =     SwinModelTester(self							)


"""simple docstring"""





from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 					UpperCAmelCase_     (   _a):


      def __init__(       self		,      a		,      a		,      a		,      a		,      a		,      a = 0.93		,      a = True		,      )	-> Any:
             lowercase__      :		Union[str, Any]          =     mixing_coeff
             lowercase__      :		Any          =     leapfrog_steps

             super().__init__()
             self.register_modules(
                 vae=a		,      text_encoder=a		,      tokenizer=a		,      unet=a		,      scheduler=a		,      )

             lowercase__      :		List[str]          =     2 ** (len(self.vae.config.block_out_channels							) - 1)
             lowercase__      :		Union[str, Any]          =     VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)


      def 		_UpperCAmelCase	(       self		,      a		,      a = None		,      a = False							)	-> Optional[int]:
             lowercase__      :		str          =     self.tokenizer(
                 a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

             lowercase__      :		str          =     self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

             lowercase__      :		Any          =     prompt_embeds.to(dtype=self.text_encoder.dtype		,      device=self.device							)

             if do_classifier_free_guidance:
                    lowercase__      :		Tuple          =     '' if negative_prompt is None else negative_prompt

                    lowercase__      :		int          =     self.tokenizer(
                        a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

                    lowercase__      :		str          =     self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

                    lowercase__      :		str          =     torch.cat([negative_prompt_embeds, prompt_embeds]							)

             return prompt_embeds


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> Optional[Any]:
             lowercase__      :		int          =     self.mixing_coeff * x + (1 - self.mixing_coeff) * y
             lowercase__      :		Tuple          =     self.mixing_coeff * y + (1 - self.mixing_coeff) * x

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> List[str]:
             lowercase__      :		str          =     (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
             lowercase__      :		Union[str, Any]          =     (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a							)	-> List[str]:
             # as self.alphas_cumprod is always in cpu
             lowercase__      :		Any          =     int(a							)

             lowercase__      :		Any          =     self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

             return alpha_prod, 1 - alpha_prod


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> int:
             lowercase__      :		List[Any]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[Any]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		str          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Tuple          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[int]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

             lowercase__      :		int          =     (base - b_t * model_output) / a_t

             return model_input, next_model_input.to(base.dtype							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> Tuple:
             lowercase__      :		Optional[int]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[int]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		List[Any]          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Union[str, Any]          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[Any]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
             lowercase__      :		List[str]          =     a_t * base + b_t * model_output

             return model_input, next_model_input.to(base.dtype							)


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     1 / self.vae.config.scaling_factor * latents
             lowercase__      :		Union[str, Any]          =     self.vae.decode(a							).sample
             lowercase__      :		int          =     (image / 2 + 0.5).clamp(0		,      1							)
             return image


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      a = None		,      )	-> Dict:
             lowercase__      :		List[str]          =     guidance_scale > 1.0

             lowercase__      :		Union[str, Any]          =     image.to(device=self.device		,      dtype=text_embeds.dtype							)
             lowercase__      :		Tuple          =     self.vae.encode(a							).latent_dist.sample(a							)

             lowercase__      :		Optional[Any]          =     self.vae.config.scaling_factor * latent

             lowercase__      :		Tuple          =     [latent.clone(), latent.clone()]

             for i, t in tqdm(enumerate(a							)		,      total=len(a							)							):
                    lowercase__      :		Tuple          =     self.noise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

                    # j - model_input index, k - base index
                    for j in range(2							):
                           lowercase__      :		Any          =     j ^ 1

                           if self.leapfrog_steps:
                                  if i % 2 == 0:
                                         lowercase__   ,				lowercase__      :		Tuple          =     j, k

                           lowercase__      :		Union[str, Any]          =     coupled_latents[j]
                           lowercase__      :		Tuple          =     coupled_latents[k]

                           lowercase__      :		Dict          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		Union[str, Any]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

                           if do_classifier_free_guidance:
                                  lowercase__   ,				lowercase__      :		Any          =     noise_pred.chunk(2							)
                                  lowercase__      :		Any          =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                           lowercase__   ,				lowercase__      :		Optional[Any]          =     self.noise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		str          =     model_input

             return coupled_latents


      @torch.no_grad()
      def __call__(       self		,      a		,      a		,      a		,      a = 3.0		,      a = 5_0		,      a = 0.8		,      a = None		,      a = None		,      a = "pil"		,      )	-> Dict:
             lowercase__      :		List[Any]          =     guidance_scale > 1.0

             lowercase__      :		List[str]          =     self.image_processor.preprocess(a							)

             lowercase__      :		Union[str, Any]          =     self._encode_prompt(a		,      a		,      a							)
             lowercase__      :		int          =     self._encode_prompt(a		,      a		,      a							)

             self.scheduler.set_timesteps(a		,      self.device							)

             lowercase__      :		Dict          =     num_inference_steps - int(num_inference_steps * strength							)
             lowercase__      :		str          =     self.scheduler.timesteps[t_limit:]
             lowercase__      :		int          =     fwd_timesteps.flip(0							)

             lowercase__      :		int          =     self.prepare_latents(a		,      a		,      a		,      a		,      a							)

             for i, t in tqdm(enumerate(a							)		,      total=len(a							)							):
                    # j - model_input index, k - base index
                    for k in range(2							):
                           lowercase__      :		Optional[Any]          =     k ^ 1

                           if self.leapfrog_steps:
                                  if i % 2 == 1:
                                         lowercase__   ,				lowercase__      :		int          =     j, k

                           lowercase__      :		Optional[int]          =     coupled_latents[j]
                           lowercase__      :		int          =     coupled_latents[k]

                           lowercase__      :		List[Any]          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		List[str]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

                           if do_classifier_free_guidance:
                                  lowercase__   ,				lowercase__      :		int          =     noise_pred.chunk(2							)
                                  lowercase__      :		Any          =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                           lowercase__   ,				lowercase__      :		List[str]          =     self.denoise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		List[str]          =     model_input

                    lowercase__      :		Union[str, Any]          =     self.denoise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

             # either one is fine
             lowercase__      :		Optional[Any]          =     coupled_latents[0]

             if output_type not in ["latent", "pt", "np", "pil"]:
                    lowercase__      :		Any          =     (
                        f"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
                        '`pil`, `np`, `pt`, `latent`'
                    )
                    deprecate('Unsupported output_type'		,      '1.0.0'		,      a		,      standard_warn=a							)
                    lowercase__      :		str          =     'np'

             if output_type == "latent":
                    lowercase__      :		List[str]          =     final_latent
             else:
                    lowercase__      :		Optional[int]          =     self.decode_latents(a							)
                    lowercase__      :		Optional[int]          =     self.image_processor.postprocess(a		,      output_type=a							)

             return image

