'''simple docstring'''





import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, 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 NatBackbone, NatForImageClassification, NatModel
      from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor



class    UpperCamelCase :







    """simple docstring"""





    def __init__( self			:		Optional[int]				,				UpperCAmelCase_			:		List[Any]				,				UpperCAmelCase_			:		Union[str, Any]=1_3				,				UpperCAmelCase_			:		Tuple=6_4				,				UpperCAmelCase_			:		Tuple=4				,				UpperCAmelCase_			:		Union[str, Any]=3				,				UpperCAmelCase_			:		Tuple=1_6				,				UpperCAmelCase_			:		Any=[1, 2, 1]				,				UpperCAmelCase_			:		Optional[int]=[2, 4, 8]				,				UpperCAmelCase_			:		Dict=3				,				UpperCAmelCase_			:		Dict=2.0				,				UpperCAmelCase_			:		Dict=True				,				UpperCAmelCase_			:		List[Any]=0.0				,				UpperCAmelCase_			:		List[str]=0.0				,				UpperCAmelCase_			:		Union[str, Any]=0.1				,				UpperCAmelCase_			:		List[Any]="gelu"				,				UpperCAmelCase_			:		Optional[int]=True				,				UpperCAmelCase_			:		Dict=0.02				,				UpperCAmelCase_			:		Optional[int]=1e-5				,				UpperCAmelCase_			:		Any=True				,				UpperCAmelCase_			:		List[Any]=None				,				UpperCAmelCase_			:		str=True				,				UpperCAmelCase_			:		List[Any]=1_0				,				UpperCAmelCase_			:		Optional[int]=["stage1", "stage2"]				,				UpperCAmelCase_			:		Any=[1, 2]				,				):


         """simple docstring"""







         a :				int				=      parent
         a :				Tuple				=      batch_size
         a :				List[Any]				=      image_size
         a :				Union[str, Any]				=      patch_size
         a :				Any				=      num_channels
         a :				Tuple				=      embed_dim
         a :				Dict				=      depths
         a :				Optional[int]				=      num_heads
         a :				Tuple				=      kernel_size
         a :				Optional[int]				=      mlp_ratio
         a :				Any				=      qkv_bias
         a :				List[str]				=      hidden_dropout_prob
         a :				Dict				=      attention_probs_dropout_prob
         a :				Union[str, Any]				=      drop_path_rate
         a :				Optional[Any]				=      hidden_act
         a :				Optional[int]				=      patch_norm
         a :				Dict				=      layer_norm_eps
         a :				str				=      initializer_range
         a :				Optional[int]				=      is_training
         a :				int				=      scope
         a :				Optional[int]				=      use_labels
         a :				List[Any]				=      num_labels
         a :				Tuple				=      out_features
         a :				Optional[int]				=      out_indices





    def 					SCREAMING_SNAKE_CASE_			( self			:		int):


         """simple docstring"""







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

         a :				int				=      None
         if self.use_labels:
              a :				Optional[Any]				=      ids_tensor([self.batch_size]				,				self.num_labels)

         a :				int				=      self.get_config()

         return config, pixel_values, labels





    def 					SCREAMING_SNAKE_CASE_			( self			:		int):


         """simple docstring"""







         return NatConfig(
             num_labels=self.num_labels				,				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				,				kernel_size=self.kernel_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				,				patch_norm=self.patch_norm				,				layer_norm_eps=self.layer_norm_eps				,				initializer_range=self.initializer_range				,				out_features=self.out_features				,				out_indices=self.out_indices				,				)





    def 					SCREAMING_SNAKE_CASE_			( self			:		Any				,				UpperCAmelCase_			:		Union[str, Any]				,				UpperCAmelCase_			:		str				,				UpperCAmelCase_			:		Optional[int]):


         """simple docstring"""







         a :				Optional[int]				=      NatModel(config=__lowercase)
         model.to(__lowercase)
         model.eval()
         a :				Tuple				=      model(__lowercase)

         a :				Dict				=      (config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1))
         a :				Optional[int]				=      int(config.embed_dim * 2 ** (len(config.depths) - 1))

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





    def 					SCREAMING_SNAKE_CASE_			( self			:		Optional[int]				,				UpperCAmelCase_			:		Tuple				,				UpperCAmelCase_			:		Any				,				UpperCAmelCase_			:		List[Any]):


         """simple docstring"""







         a :				Any				=      NatForImageClassification(__lowercase)
         model.to(__lowercase)
         model.eval()
         a :				int				=      model(__lowercase				,				labels=__lowercase)
         self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels))

         # test greyscale images
         a :				List[str]				=      1
         a :				Dict				=      NatForImageClassification(__lowercase)
         model.to(__lowercase)
         model.eval()

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





    def 					SCREAMING_SNAKE_CASE_			( self			:		Optional[Any]				,				UpperCAmelCase_			:		int				,				UpperCAmelCase_			:		Optional[Any]				,				UpperCAmelCase_			:		Tuple):


         """simple docstring"""







         a :				Tuple				=      NatBackbone(config=__lowercase)
         model.to(__lowercase)
         model.eval()
         a :				Union[str, Any]				=      model(__lowercase)

         # 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
         a :				Optional[Any]				=      None
         a :				str				=      NatBackbone(config=__lowercase)
         model.to(__lowercase)
         model.eval()
         a :				Tuple				=      model(__lowercase)

         # 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 					SCREAMING_SNAKE_CASE_			( self			:		List[Any]):


         """simple docstring"""







         a :				int				=      self.prepare_config_and_inputs()
         a :				List[Any]				=      config_and_inputs
         a :				Dict				=      {'pixel_values': pixel_values}
         return config, inputs_dict






@require_natten
@require_torch
class    UpperCamelCase (    a_  ,			a_  ,			unittest.TestCase    ):







    """simple docstring"""





    A			:  List[Any]            =       (
        (
            NatModel,
            NatForImageClassification,
            NatBackbone,
        )
        if is_torch_available()
        else ()
    )
    A			:  Dict            =       (
        {"feature-extraction": NatModel, "image-classification": NatForImageClassification}
        if is_torch_available()
        else {}
    )
    A			:  Union[str, Any]            =       False

    A			:  Any            =       False
    A			:  int            =       False
    A			:  Union[str, Any]            =       False
    A			:  Dict            =       False





    def 					SCREAMING_SNAKE_CASE_			( self			:		Optional[Any]):


         """simple docstring"""







         a :				List[Any]				=      NatModelTester(self)
         a :				Optional[Any]				=      ConfigTester(self				,				config_class=__lowercase				,				embed_dim=3_7)





    def 					SCREAMING_SNAKE_CASE_			( self			:		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 					SCREAMING_SNAKE_CASE_			( self			:		str):


         """simple docstring"""







         return





    def 					SCREAMING_SNAKE_CASE_			( self			:		Tuple):


         """simple docstring"""







         a :				Any				=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowercase)





    def 					SCREAMING_SNAKE_CASE_			( self			:		Optional[Any]):


         """simple docstring"""







         a :				List[Any]				=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowercase)





    def 					SCREAMING_SNAKE_CASE_			( self			:		Any):


         """simple docstring"""







         a :				Union[str, Any]				=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*__lowercase)





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


         """simple docstring"""







         pass





    @unittest.skip(reason='Nat does not use feedforward chunking')
    def 					SCREAMING_SNAKE_CASE_			( self			:		List[Any]):


         """simple docstring"""







         pass





    def 					SCREAMING_SNAKE_CASE_			( self			:		str):


         """simple docstring"""







         a :				Tuple				=      self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              a :				Any				=      model_class(__lowercase)
              self.assertIsInstance(model.get_input_embeddings()				,				(nn.Module))
              a :				List[Any]				=      model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__lowercase				,				nn.Linear))





    def 					SCREAMING_SNAKE_CASE_			( self			:		Optional[Any]):


         """simple docstring"""







         a :				Dict				=      self.model_tester.prepare_config_and_inputs_for_common()

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

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





    def 					SCREAMING_SNAKE_CASE_			( self			:		str):


         """simple docstring"""







         self.skipTest('Nat\'s attention operation is handled entirely by NATTEN.')





    def 					SCREAMING_SNAKE_CASE_			( self			:		str				,				UpperCAmelCase_			:		Dict				,				UpperCAmelCase_			:		List[Any]				,				UpperCAmelCase_			:		Union[str, Any]				,				UpperCAmelCase_			:		int):


         """simple docstring"""







         a :				Optional[Any]				=      model_class(__lowercase)
         model.to(__lowercase)
         model.eval()

         with torch.no_grad():
              a :				Optional[int]				=      model(**self._prepare_for_class(__lowercase				,				__lowercase))

         a :				Tuple				=      outputs.hidden_states

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

         # Nat has a different seq_length
         a :				Any				=      (
             config.patch_size
             if isinstance(config.patch_size				,				collections.abc.Iterable)
             else (config.patch_size, config.patch_size)
         )

         a :				List[Any]				=      image_size[0] // patch_size[0]
         a :				Any				=      image_size[1] // patch_size[1]

         self.assertListEqual(
             list(hidden_states[0].shape[-3:])				,				[height, width, self.model_tester.embed_dim]				,				)

         if model_class.__name__ != "NatBackbone":
              a :				Dict				=      outputs.reshaped_hidden_states
              self.assertEqual(len(__lowercase)				,				__lowercase)

              a :				Optional[int]				=      reshaped_hidden_states[0].shape
              a :				Dict				=      (
                  reshaped_hidden_states[0].view(__lowercase				,				__lowercase				,				__lowercase				,				__lowercase).permute(0				,				2				,				3				,				1)
              )
              self.assertListEqual(
                  list(reshaped_hidden_states.shape[-3:])				,				[height, width, self.model_tester.embed_dim]				,				)





    def 					SCREAMING_SNAKE_CASE_			( self			:		Any):


         """simple docstring"""







         a :				str				=      self.model_tester.prepare_config_and_inputs_for_common()

         a :				List[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:
              a :				str				=      True
              self.check_hidden_states_output(__lowercase				,				__lowercase				,				__lowercase				,				__lowercase)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              a :				Union[str, Any]				=      True

              self.check_hidden_states_output(__lowercase				,				__lowercase				,				__lowercase				,				__lowercase)





    @slow
    def 					SCREAMING_SNAKE_CASE_			( self			:		int):


         """simple docstring"""







         for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              a :				Optional[Any]				=      NatModel.from_pretrained(__lowercase)
              self.assertIsNotNone(__lowercase)







    def 					SCREAMING_SNAKE_CASE_			( self			:		Optional[int]):


         """simple docstring"""







         a :				str				=      self.model_tester.prepare_config_and_inputs_for_common()

         a :				str				=      _config_zero_init(__lowercase)
         for model_class in self.all_model_classes:
              a :				int				=      model_class(config=__lowercase)
              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_natten
@require_vision
@require_torch
class    UpperCamelCase (    unittest.TestCase    ):







    """simple docstring"""





    @cached_property
    def 					SCREAMING_SNAKE_CASE_			( self			:		Dict):


         """simple docstring"""







         return AutoImageProcessor.from_pretrained('shi-labs/nat-mini-in1k-224') if is_vision_available() else None







    @slow
    def 					SCREAMING_SNAKE_CASE_			( self			:		Optional[Any]):


         """simple docstring"""







         a :				str				=      NatForImageClassification.from_pretrained('shi-labs/nat-mini-in1k-224').to(__lowercase)
         a :				int				=      self.default_image_processor

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

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

         # verify the logits
         a :				Union[str, Any]				=      torch.Size((1, 1_0_0_0))
         self.assertEqual(outputs.logits.shape				,				__lowercase)
         a :				List[Any]				=      torch.tensor([0.38_05, -0.86_76, -0.39_12]).to(__lowercase)
         self.assertTrue(torch.allclose(outputs.logits[0, :3]				,				__lowercase				,				atol=1e-4))






@require_torch
@require_natten
class    UpperCamelCase (    unittest.TestCase  ,			a_    ):







    """simple docstring"""





    A			:  Dict            =       (NatBackbone,) if is_torch_available() else ()
    A			:  Union[str, Any]            =       NatConfig





    def 					SCREAMING_SNAKE_CASE_			( self			:		Dict):


         """simple docstring"""







         a :				List[str]				=      NatModelTester(self)







from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


UpperCamelCase__              =						getLogger(__name__)


UpperCamelCase__              =						4
UpperCamelCase__              =						7

UpperCamelCase__              =						{
    'float16': torch.floataa,
    'float32': torch.floataa,
}






def 					snake_case					(							A			)							->							Optional[int]:




	'''simple docstring'''


	if isinstance(A						,   torch.Tensor			):
		return image
	elif isinstance(A						,   PIL.Image.Image			):
		lowerCamelCase_							:		List[str]							   =							[image]

	if isinstance(image[0]						,   PIL.Image.Image			):
		lowerCamelCase_   ,			lowerCamelCase_							:		Any							   =							image[0].size
		lowerCamelCase_   ,			lowerCamelCase_							:		str							   =							(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

		lowerCamelCase_							:		List[Any]							   =							[np.array(i.resize((w, h)			)			)[None, :] for i in image]
		lowerCamelCase_							:		Dict							   =							np.concatenate(A						,   axis=0			)
		lowerCamelCase_							:		Any							   =							np.array(A			).astype(np.floataa			) / 255.0
		lowerCamelCase_							:		Any							   =							image.transpose(0						,   3						,   1						,   2			)
		lowerCamelCase_							:		List[Any]							   =							2.0 * image - 1.0
		lowerCamelCase_							:		str							   =							torch.from_numpy(A			)
	elif isinstance(image[0]						,   torch.Tensor			):
		lowerCamelCase_							:		Union[str, Any]							   =							torch.cat(A						,   dim=0			)

	return image



class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase = 350 ,       ):







						'''simple docstring'''




						super().__init__(
						    vae=__lowercase ,       text_encoder=__lowercase ,       tokenizer=__lowercase ,       unet=__lowercase ,       low_res_scheduler=__lowercase ,       scheduler=__lowercase ,       safety_checker=__lowercase ,       feature_extractor=__lowercase ,       watermarker=__lowercase ,       max_noise_level=__lowercase ,       )

					def __call__(				self ,       __lowercase ,       __lowercase ,       __lowercase = 75 ,       __lowercase = 9.0 ,       __lowercase = 20 ,       __lowercase = None ,       __lowercase = 1 ,       __lowercase = 0.0 ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = "pil" ,       __lowercase = True ,       __lowercase = None ,       __lowercase = 1 ,       ):







						'''simple docstring'''




						self.check_inputs(__lowercase ,       __lowercase ,       __lowercase ,       __lowercase					)

						# 2. Define call parameters
						if prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		Any							   =							1
						elif prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		List[Any]							   =							len(__lowercase					)
						else:
							lowerCamelCase_							:		Optional[Any]							   =							prompt_embeds.shape[0]

						lowerCamelCase_							:		List[Any]							   =							self._execution_device
						# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
						# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
						# corresponds to doing no classifier free guidance.
						lowerCamelCase_							:		int							   =							guidance_scale > 1.0

						# 3. Encode input prompt
						lowerCamelCase_							:		Optional[int]							   =							self._encode_prompt(
						    __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       prompt_embeds=__lowercase ,       negative_prompt_embeds=__lowercase ,       )

						lowerCamelCase_							:		str							   =							ORT_TO_PT_TYPE[str(text_embeddings.dtype					)]

						# 4. Preprocess image
						lowerCamelCase_							:		List[str]							   =							preprocess(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							image.cpu()

						# 5. set timesteps
						self.scheduler.set_timesteps(__lowercase ,       device=__lowercase					)
						lowerCamelCase_							:		int							   =							self.scheduler.timesteps

						# 5. Add noise to image
						lowerCamelCase_							:		Optional[Any]							   =							torch.tensor([noise_level] ,       dtype=torch.long ,       device=__lowercase					)
						lowerCamelCase_							:		List[Any]							   =							torch.randn(image.shape ,       generator=__lowercase ,       device=__lowercase ,       dtype=__lowercase					)
						lowerCamelCase_							:		Tuple							   =							self.low_res_scheduler.add_noise(__lowercase ,       __lowercase ,       __lowercase					)

						lowerCamelCase_							:		Dict							   =							2 if do_classifier_free_guidance else 1
						lowerCamelCase_							:		Any							   =							np.concatenate([image] * batch_multiplier * num_images_per_prompt					)
						lowerCamelCase_							:		str							   =							np.concatenate([noise_level] * image.shape[0]					)

						# 6. Prepare latent variables
						lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							image.shape[2:]
						lowerCamelCase_							:		Tuple							   =							self.prepare_latents(
						    batch_size * num_images_per_prompt ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       )

						# 7. Check that sizes of image and latents match
						lowerCamelCase_							:		List[str]							   =							image.shape[1]
						if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
							raise ValueError(
							    'Incorrect configuration settings! The config of `pipeline.unet` expects'
							    f" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"
							    f" `num_channels_image`: {num_channels_image} "
							    f" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"
							    ' `pipeline.unet` or your `image` input.'					)

						# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
						lowerCamelCase_							:		Dict							   =							self.prepare_extra_step_kwargs(__lowercase ,       __lowercase					)

						lowerCamelCase_							:		int							   =							next(
						    (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep') ,       'tensor(float)'					)
						lowerCamelCase_							:		Any							   =							ORT_TO_NP_TYPE[timestep_dtype]

						# 9. Denoising loop
						lowerCamelCase_							:		List[Any]							   =							len(__lowercase					) - num_inference_steps * self.scheduler.order
						with self.progress_bar(total=__lowercase					) as progress_bar:
							for i, t in enumerate(__lowercase					):
								# expand the latents if we are doing classifier free guidance
								lowerCamelCase_							:		Optional[int]							   =							np.concatenate([latents] * 2					) if do_classifier_free_guidance else latents

								# concat latents, mask, masked_image_latents in the channel dimension
								lowerCamelCase_							:		List[Any]							   =							self.scheduler.scale_model_input(__lowercase ,       __lowercase					)
								lowerCamelCase_							:		Optional[Any]							   =							np.concatenate([latent_model_input, image] ,       axis=1					)

								# timestep to tensor
								lowerCamelCase_							:		Optional[int]							   =							np.array([t] ,       dtype=__lowercase					)

								# predict the noise residual
								lowerCamelCase_							:		List[str]							   =							self.unet(
								    sample=__lowercase ,       timestep=__lowercase ,       encoder_hidden_states=__lowercase ,       class_labels=noise_level.astype(np.intaa					) ,       )[0]

								# perform guidance
								if do_classifier_free_guidance:
									lowerCamelCase_   ,			lowerCamelCase_							:		int							   =							np.split(__lowercase ,       2					)
								lowerCamelCase_							:		Tuple							   =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

								# compute the previous noisy sample x_t -> x_t-1
								lowerCamelCase_							:		str							   =							self.scheduler.step(
								    torch.from_numpy(__lowercase					) ,       __lowercase ,       __lowercase ,       **__lowercase					).prev_sample

								# call the callback, if provided
								if i == len(__lowercase					) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
									progress_bar.update()
									if callback is not None and i % callback_steps == 0:
										callback(__lowercase ,       __lowercase ,       __lowercase					)

        # 10. Post-processing
						lowerCamelCase_							:		Dict							   =							self.decode_latents(latents.float()					)

						# 11. Convert to PIL
						if output_type == "pil":
							lowerCamelCase_							:		List[str]							   =							self.numpy_to_pil(__lowercase					)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=__lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							1 / 0.0_83_33 * latents
						lowerCamelCase_							:		List[Any]							   =							self.vae(latent_sample=__lowercase					)[0]
						lowerCamelCase_							:		List[Any]							   =							np.clip(image / 2 + 0.5 ,       0 ,       1					)
						lowerCamelCase_							:		List[Any]							   =							image.transpose((0, 2, 3, 1)					)
						return image





					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase = None ,       __lowercase = None ,       ):







						'''simple docstring'''




						if prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		Tuple							   =							1
						elif prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		Union[str, Any]							   =							len(__lowercase					)
						else:
							lowerCamelCase_							:		Any							   =							prompt_embeds.shape[0]

						if prompt_embeds is None:
							lowerCamelCase_							:		Dict							   =							self.tokenizer(
							    __lowercase ,       padding='max_length' ,       max_length=self.tokenizer.model_max_length ,       truncation=__lowercase ,       return_tensors='pt' ,       )
							lowerCamelCase_							:		List[str]							   =							text_inputs.input_ids
							lowerCamelCase_							:		Tuple							   =							self.tokenizer(__lowercase ,       padding='longest' ,       return_tensors='pt'					).input_ids

							if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
							    __lowercase ,       __lowercase					):
								lowerCamelCase_							:		Any							   =							self.tokenizer.batch_decode(
								    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]					)
								logger.warning(
								    'The following part of your input was truncated because CLIP can only handle sequences up to'
								    f" {self.tokenizer.model_max_length} tokens: {removed_text}"					)

							# no positional arguments to text_encoder
							lowerCamelCase_							:		Tuple							   =							self.text_encoder(
							    input_ids=text_input_ids.int().to(__lowercase					) ,       )
							lowerCamelCase_							:		List[str]							   =							prompt_embeds[0]

						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Optional[Any]							   =							prompt_embeds.shape
						# duplicate text embeddings for each generation per prompt, using mps friendly method
						lowerCamelCase_							:		str							   =							prompt_embeds.repeat(1 ,       __lowercase					)
						lowerCamelCase_							:		Optional[int]							   =							prompt_embeds.reshape(bs_embed * num_images_per_prompt ,       __lowercase ,       -1					)

						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance and negative_prompt_embeds is None:
							lowerCamelCase_							:		List[str]
							if negative_prompt is None:
								lowerCamelCase_							:		Dict							   =							[''] * batch_size
							elif type(__lowercase					) is not type(__lowercase					):
								raise TypeError(
								    f"`negative_prompt` should be the same type to `prompt`, but got {type(__lowercase					)} !="
								    f" {type(__lowercase					)}."					)
							elif isinstance(__lowercase ,       __lowercase					):
								lowerCamelCase_							:		Union[str, Any]							   =							[negative_prompt]
							elif batch_size != len(__lowercase					):
								raise ValueError(
								    f"`negative_prompt`: {negative_prompt} has batch size {len(__lowercase					)}, but `prompt`:"
								    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
								    ' the batch size of `prompt`.'					)
							else:
								lowerCamelCase_							:		Any							   =							negative_prompt

							lowerCamelCase_							:		str							   =							text_input_ids.shape[-1]
							lowerCamelCase_							:		str							   =							self.tokenizer(
							    __lowercase ,       padding='max_length' ,       max_length=__lowercase ,       truncation=__lowercase ,       return_tensors='pt' ,       )

							# if hasattr(uncond_input, "attention_mask"):
							#     attention_mask = uncond_input.attention_mask.to(device)
							# else:
							#     attention_mask = None

							lowerCamelCase_							:		Dict							   =							self.text_encoder(
							    input_ids=uncond_input.input_ids.int().to(__lowercase					) ,       )
							lowerCamelCase_							:		Optional[Any]							   =							uncond_embeddings[0]

						if do_classifier_free_guidance:
							lowerCamelCase_							:		Union[str, Any]							   =							uncond_embeddings.shape[1]
							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							lowerCamelCase_							:		Any							   =							uncond_embeddings.repeat(1 ,       __lowercase					)
							lowerCamelCase_							:		Union[str, Any]							   =							uncond_embeddings.reshape(batch_size * num_images_per_prompt ,       __lowercase ,       -1					)

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							lowerCamelCase_							:		Any							   =							np.concatenate([uncond_embeddings, prompt_embeds]					)

						return prompt_embeds
