
from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

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

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


if is_tf_available():
							import tensorflow as tf

							from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
							from PIL import Image

							from transformers import ConvNextImageProcessor
class      A__       :





							def __init__(			self  :       Any					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       List[Any]=13					,			_UpperCAmelCase  :       Union[str, Any]=32					,			_UpperCAmelCase  :       Optional[Any]=3					,			_UpperCAmelCase  :       Optional[Any]=4					,			_UpperCAmelCase  :       Optional[Any]=[10, 20, 30, 40]					,			_UpperCAmelCase  :       Dict=[2, 2, 3, 2]					,			_UpperCAmelCase  :       Union[str, Any]=True					,			_UpperCAmelCase  :       str=True					,			_UpperCAmelCase  :       str=37					,			_UpperCAmelCase  :       List[str]="gelu"					,			_UpperCAmelCase  :       List[str]=10					,			_UpperCAmelCase  :       Optional[int]=0.02					,			_UpperCAmelCase  :       List[Any]=3					,			_UpperCAmelCase  :       List[Any]=None					,			)						->		str:



											"""simple docstring"""


											__lowercase								= parent
											__lowercase								= batch_size
											__lowercase								= image_size
											__lowercase								= num_channels
											__lowercase								= num_stages
											__lowercase								= hidden_sizes
											__lowercase								= depths
											__lowercase								= is_training
											__lowercase								= use_labels
											__lowercase								= intermediate_size
											__lowercase								= hidden_act
											__lowercase								= type_sequence_label_size
											__lowercase								= initializer_range
											__lowercase								= scope





							def       a__			(			self  :       int    )						->		List[Any]:



											"""simple docstring"""


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

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

											__lowercase								= self.get_config()

											return config, pixel_values, labels





							def       a__			(			self  :       List[str]    )						->		Tuple:



											"""simple docstring"""


											return ConvNextConfig(
											    num_channels=self.num_channels					,			hidden_sizes=self.hidden_sizes					,			depths=self.depths					,			num_stages=self.num_stages					,			hidden_act=self.hidden_act					,			is_decoder=_UpperCAmelCase					,			initializer_range=self.initializer_range					,			)





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       Any    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= TFConvNextModel(config=_UpperCAmelCase    )
											__lowercase								= model(_UpperCAmelCase					,			training=_UpperCAmelCase    )
											# expected last hidden states: B, C, H // 32, W // 32
											self.parent.assertEqual(
											    result.last_hidden_state.shape					,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,			)





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       Dict    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.type_sequence_label_size
											__lowercase								= TFConvNextForImageClassification(_UpperCAmelCase    )
											__lowercase								= model(_UpperCAmelCase					,			labels=_UpperCAmelCase					,			training=_UpperCAmelCase    )
											self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.type_sequence_label_size)    )





							def       a__			(			self  :       Union[str, Any]    )						->		str:



											"""simple docstring"""


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






@require_tf
class      A__       (						lowerCAmelCase__   ,						lowerCAmelCase__   ,						unittest.TestCase    ):
							lowerCAmelCase__		:						List[Any]         =							(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
							lowerCAmelCase__		:						List[Any]         =							(
							    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
							    if is_tf_available()
							    else {}
							)

							lowerCAmelCase__		:						Union[str, Any]         =							False
							lowerCAmelCase__		:						Union[str, Any]         =							False
							lowerCAmelCase__		:						Any         =							False
							lowerCAmelCase__		:						Optional[Any]         =							False
							lowerCAmelCase__		:						Optional[int]         =							False





							def       a__			(			self  :       List[Any]    )						->		str:



											"""simple docstring"""


											__lowercase								= TFConvNextModelTester(self    )
											__lowercase								= ConfigTester(
											    self					,			config_class=_UpperCAmelCase					,			has_text_modality=_UpperCAmelCase					,			hidden_size=37					,			)





							@unittest.skip(reason='ConvNext does not use inputs_embeds'    )
							def       a__			(			self  :       List[Any]    )						->		Tuple:



											"""simple docstring"""


											pass





							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices('GPU'    )    ) == 0					,			reason='TF does not support backprop for grouped convolutions on CPU.'					,			)
							@slow
							def       a__			(			self  :       Union[str, Any]    )						->		str:



											"""simple docstring"""


											super().test_keras_fit()





							@unittest.skip(reason='ConvNext does not support input and output embeddings'    )
							def       a__			(			self  :       Optional[Any]    )						->		Optional[Any]:



											"""simple docstring"""


											pass





							def       a__			(			self  :       List[Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											__lowercase		, __lowercase								= self.model_tester.prepare_config_and_inputs_for_common()

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

															__lowercase								= ['pixel_values']
															self.assertListEqual(arg_names[:1]					,			_UpperCAmelCase    )





							def       a__			(			self  :       str    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_UpperCAmelCase    )





							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices('GPU'    )    ) == 0					,			reason='TF does not support backprop for grouped convolutions on CPU.'					,			)
							def       a__			(			self  :       Optional[int]    )						->		str:



											"""simple docstring"""


											super().test_dataset_conversion()





							def       a__			(			self  :       int    )						->		List[Any]:



											"""simple docstring"""


											def check_hidden_states_output(_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       str    ):
															__lowercase								= model_class(_UpperCAmelCase    )

															__lowercase								= model(**self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )    )
															__lowercase								= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															__lowercase								= self.model_tester.num_stages
															self.assertEqual(len(_UpperCAmelCase    )					,			expected_num_stages + 1    )

															# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]    )					,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,			)

											__lowercase		, __lowercase								= self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															__lowercase								= True
															check_hidden_states_output(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

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

															check_hidden_states_output(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )





							def       a__			(			self  :       Union[str, Any]    )						->		List[Any]:



											"""simple docstring"""


											__lowercase		, __lowercase								= self.model_tester.prepare_config_and_inputs_for_common()

											def check_equivalence(_UpperCAmelCase  :       Dict					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       Optional[int]={}    ):
															__lowercase								= model(_UpperCAmelCase					,			return_dict=_UpperCAmelCase					,			**_UpperCAmelCase    )
															__lowercase								= model(_UpperCAmelCase					,			return_dict=_UpperCAmelCase					,			**_UpperCAmelCase    ).to_tuple()

															def recursive_check(_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       Optional[int]    ):
																			if isinstance(_UpperCAmelCase					,			(List, Tuple)    ):
																							for tuple_iterable_value, dict_iterable_value in zip(_UpperCAmelCase					,			_UpperCAmelCase    ):
																											recursive_check(_UpperCAmelCase					,			_UpperCAmelCase    )
																			elif tuple_object is None:
																							return
																			else:
																							self.assertTrue(
																							    all(tf.equal(_UpperCAmelCase					,			_UpperCAmelCase    )    )					,			msg=(
																							        'Tuple and dict output are not equal. Difference:'
																							        f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object    )    )}"""
																							    )					,			)

																			recursive_check(_UpperCAmelCase					,			_UpperCAmelCase    )

											for model_class in self.all_model_classes:
															__lowercase								= model_class(_UpperCAmelCase    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			{'output_hidden_states': True}    )

															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															__lowercase								= self._prepare_for_class(_UpperCAmelCase					,			_UpperCAmelCase					,			return_labels=_UpperCAmelCase    )
															check_equivalence(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			{'output_hidden_states': True}    )





							def       a__			(			self  :       Dict    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase    )





							@slow
							def       a__			(			self  :       Tuple    )						->		Any:



											"""simple docstring"""


											__lowercase								= TFConvNextModel.from_pretrained('facebook/convnext-tiny-224'    )
											self.assertIsNotNone(_UpperCAmelCase    )






def 							__SCREAMING_SNAKE_CASE       (		)					->						List[str]:
				__lowercase								= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
				return image
@require_tf
@require_vision
class      A__       (						unittest.TestCase    ):





							@cached_property
							def       a__			(			self  :       List[Any]    )						->		int:



											"""simple docstring"""


											return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224'    ) if is_vision_available() else None





							@slow
							def       a__			(			self  :       List[Any]    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'    )

											__lowercase								= self.default_image_processor
											__lowercase								= prepare_img()
											__lowercase								= image_processor(images=_UpperCAmelCase					,			return_tensors='tf'    )

											# forward pass
											__lowercase								= model(**_UpperCAmelCase    )

											# verify the logits
											__lowercase								= tf.TensorShape((1, 10_00)    )
											self.assertEqual(outputs.logits.shape					,			_UpperCAmelCase    )

											__lowercase								= tf.constant([-0.0_260, -0.4_739, 0.1_911]    )

											tf.debugging.assert_near(outputs.logits[0, :3]					,			_UpperCAmelCase					,			atol=1e-4    )







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE__							=    """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				Dict   ,							SCREAMING_SNAKE_CASE	:				Any=8      )					->						Any:
				__lowercase								= height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				__lowercase								= width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				List[Any]   ,							SCREAMING_SNAKE_CASE	:				Any=512   ,							SCREAMING_SNAKE_CASE	:				List[str]=512      )					->						str:
				__lowercase								= pil_image.resize((w, h)   ,							resample=Image.BICUBIC   ,							reducing_gap=1      )
				__lowercase								= np.array(pil_image.convert('RGB'      )      )
				__lowercase								= arr.astype(np.floataa      ) / 127.5 - 1
				__lowercase								= np.transpose(SCREAMING_SNAKE_CASE   ,							[2, 0, 1]      )
				__lowercase								= torch.from_numpy(SCREAMING_SNAKE_CASE      ).unsqueeze(0      )
				return image
class      A__       (						lowerCAmelCase__    ):





							def __init__(			self  :       str					,			_UpperCAmelCase  :       UNetaDConditionModel					,			_UpperCAmelCase  :       DDPMScheduler					,			_UpperCAmelCase  :       VQModel					,			)						->		Optional[Any]:



											"""simple docstring"""


											super().__init__()

											self.register_modules(
											    unet=_UpperCAmelCase					,			scheduler=_UpperCAmelCase					,			movq=_UpperCAmelCase					,			)
											__lowercase								= 2 ** (len(self.movq.config.block_out_channels    ) - 1)





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       Dict					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       Dict    )						->		List[str]:



											"""simple docstring"""


											__lowercase								= min(int(num_inference_steps * strength    )					,			_UpperCAmelCase    )

											__lowercase								= max(num_inference_steps - init_timestep					,			0    )
											__lowercase								= self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       List[Any]=None    )						->		Optional[Any]:



											"""simple docstring"""


											if not isinstance(_UpperCAmelCase					,			(torch.Tensor, PIL.Image.Image, list)    ):
															raise ValueError(
															    f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCAmelCase    )}"""    )

											__lowercase								= image.to(device=_UpperCAmelCase					,			dtype=_UpperCAmelCase    )

											__lowercase								= batch_size * num_images_per_prompt

											if image.shape[1] == 4:
															__lowercase								= image

											else:
															if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ) and len(_UpperCAmelCase    ) != batch_size:
																			raise ValueError(
																			    f"""You have passed a list of generators of length {len(_UpperCAmelCase    )}, but requested an effective batch"""
																			    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

															elif isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
																			__lowercase								= [
																			    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(_UpperCAmelCase    )
																			]
																			__lowercase								= torch.cat(_UpperCAmelCase					,			dim=0    )
															else:
																			__lowercase								= self.movq.encode(_UpperCAmelCase    ).latent_dist.sample(_UpperCAmelCase    )

															__lowercase								= self.movq.config.scaling_factor * init_latents

											__lowercase								= torch.cat([init_latents]					,			dim=0    )

											__lowercase								= init_latents.shape
											__lowercase								= randn_tensor(_UpperCAmelCase					,			generator=_UpperCAmelCase					,			device=_UpperCAmelCase					,			dtype=_UpperCAmelCase    )

											# get latents
											__lowercase								= self.scheduler.add_noise(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

											__lowercase								= init_latents

											return latents





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       Dict=0    )						->		List[str]:



											"""simple docstring"""


											if is_accelerate_available():
															from accelerate import cpu_offload
											else:
															raise ImportError('Please install accelerate via `pip install accelerate`'    )

											__lowercase								= torch.device(f"""cuda:{gpu_id}"""    )

											__lowercase								= [
											    self.unet,
											    self.movq,
											]
											for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																			cpu_offload(_UpperCAmelCase					,			_UpperCAmelCase    )





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       Tuple=0    )						->		List[str]:



											"""simple docstring"""


											if is_accelerate_available() and is_accelerate_version('>='					,			'0.17.0.dev0'    ):
															from accelerate import cpu_offload_with_hook
											else:
															raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'    )

											__lowercase								= torch.device(f"""cuda:{gpu_id}"""    )

											if self.device.type != "cpu":
															self.to('cpu'					,			silence_dtype_warnings=_UpperCAmelCase    )
															torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											__lowercase								= None
											for cpu_offloaded_model in [self.unet, self.movq]:
															__lowercase		, __lowercase								= cpu_offload_with_hook(_UpperCAmelCase					,			_UpperCAmelCase					,			prev_module_hook=_UpperCAmelCase    )

											# We'll offload the last model manually.
											__lowercase								= hook





							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def       a__			(			self  :       int    )						->		Optional[int]:



											"""simple docstring"""


											if not hasattr(self.unet					,			'_hf_hook'    ):
															return self.device
											for module in self.unet.modules():
															if (
															    hasattr(_UpperCAmelCase					,			'_hf_hook'    )
															    and hasattr(module._hf_hook					,			'execution_device'    )
															    and module._hf_hook.execution_device is not None
															):
																			return torch.device(module._hf_hook.execution_device    )
											return self.device





							@torch.no_grad()
							@replace_example_docstring(_UpperCAmelCase    )
							def __call__(			self  :       Optional[Any]					,			_UpperCAmelCase  :       Union[torch.FloatTensor, List[torch.FloatTensor]]					,			_UpperCAmelCase  :       Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]					,			_UpperCAmelCase  :       Union[torch.FloatTensor, List[torch.FloatTensor]]					,			_UpperCAmelCase  :       torch.FloatTensor					,			_UpperCAmelCase  :       int = 5_12					,			_UpperCAmelCase  :       int = 5_12					,			_UpperCAmelCase  :       int = 1_00					,			_UpperCAmelCase  :       float = 4.0					,			_UpperCAmelCase  :       float = 0.3					,			_UpperCAmelCase  :       int = 1					,			_UpperCAmelCase  :       Optional[Union[torch.Generator, List[torch.Generator]]] = None					,			_UpperCAmelCase  :       Optional[str] = "pil"					,			_UpperCAmelCase  :       bool = True					,			)						->		Union[str, Any]:



											"""simple docstring"""


											__lowercase								= self._execution_device

											__lowercase								= guidance_scale > 1.0

											if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= torch.cat(_UpperCAmelCase					,			dim=0    )
											if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= torch.cat(_UpperCAmelCase					,			dim=0    )
											if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= torch.cat(_UpperCAmelCase					,			dim=0    )

											__lowercase								= image_embeds.shape[0]

											if do_classifier_free_guidance:
															__lowercase								= image_embeds.repeat_interleave(_UpperCAmelCase					,			dim=0    )
															__lowercase								= negative_image_embeds.repeat_interleave(_UpperCAmelCase					,			dim=0    )
															__lowercase								= hint.repeat_interleave(_UpperCAmelCase					,			dim=0    )

											__lowercase								= torch.cat([negative_image_embeds, image_embeds]					,			dim=0    ).to(dtype=self.unet.dtype					,			device=_UpperCAmelCase    )
											__lowercase								= torch.cat([hint, hint]					,			dim=0    ).to(dtype=self.unet.dtype					,			device=_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [image]
											if not all(isinstance(_UpperCAmelCase					,			(PIL.Image.Image, torch.Tensor)    ) for i in image    ):
															raise ValueError(
															    f"""Input is in incorrect format: {[type(_UpperCAmelCase    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""    )

											__lowercase								= torch.cat([prepare_image(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    ) for i in image]					,			dim=0    )
											__lowercase								= image.to(dtype=image_embeds.dtype					,			device=_UpperCAmelCase    )

											__lowercase								= self.movq.encode(_UpperCAmelCase    )['latents']
											__lowercase								= latents.repeat_interleave(_UpperCAmelCase					,			dim=0    )
											self.scheduler.set_timesteps(_UpperCAmelCase					,			device=_UpperCAmelCase    )
											__lowercase		, __lowercase								= self.get_timesteps(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )
											__lowercase								= timesteps[:1].repeat(batch_size * num_images_per_prompt    )
											__lowercase		, __lowercase								= downscale_height_and_width(_UpperCAmelCase					,			_UpperCAmelCase					,			self.movq_scale_factor    )
											__lowercase								= self.prepare_latents(
											    _UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			image_embeds.dtype					,			_UpperCAmelCase					,			_UpperCAmelCase    )
											for i, t in enumerate(self.progress_bar(_UpperCAmelCase    )    ):
															# expand the latents if we are doing classifier free guidance
															__lowercase								= torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

															__lowercase								= {'image_embeds': image_embeds, 'hint': hint}
															__lowercase								= self.unet(
															    sample=_UpperCAmelCase					,			timestep=_UpperCAmelCase					,			encoder_hidden_states=_UpperCAmelCase					,			added_cond_kwargs=_UpperCAmelCase					,			return_dict=_UpperCAmelCase					,			)[0]

															if do_classifier_free_guidance:
																			__lowercase		, __lowercase								= noise_pred.split(latents.shape[1]					,			dim=1    )
																			__lowercase		, __lowercase								= noise_pred.chunk(2    )
																			__lowercase		, __lowercase								= variance_pred.chunk(2    )
																			__lowercase								= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			__lowercase								= torch.cat([noise_pred, variance_pred_text]					,			dim=1    )

															if not (
															    hasattr(self.scheduler.config					,			'variance_type'    )
															    and self.scheduler.config.variance_type in ["learned", "learned_range"]
															):
																			__lowercase		, __lowercase								= noise_pred.split(latents.shape[1]					,			dim=1    )

															# compute the previous noisy sample x_t -> x_t-1

															__lowercase								= self.scheduler.step(
															    _UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			generator=_UpperCAmelCase					,			)[0]

											# post-processing
											__lowercase								= self.movq.decode(_UpperCAmelCase					,			force_not_quantize=_UpperCAmelCase    )['sample']

											if output_type not in ["pt", "np", "pil"]:
															raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""    )

											if output_type in ["np", "pil"]:
															__lowercase								= image * 0.5 + 0.5
															__lowercase								= image.clamp(0					,			1    )
															__lowercase								= image.cpu().permute(0					,			2					,			3					,			1    ).float().numpy()

											if output_type == "pil":
															__lowercase								= self.numpy_to_pil(_UpperCAmelCase    )

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=_UpperCAmelCase    )





