
"""simple docstring"""
import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        CpmAntConfig,
        CpmAntForCausalLM,
        CpmAntModel,
        CpmAntTokenizer,
    )






@require_torch
class   _UpperCAmelCase					:
    def __init__(     self    ,     snake_case_    ,     snake_case_=2    ,     snake_case_=8    ,     snake_case_=True    ,     snake_case_=False    ,     snake_case_=False    ,     snake_case_=False    ,     snake_case_=False    ,     snake_case_=99    ,     snake_case_=32    ,     snake_case_=3    ,     snake_case_=4    ,     snake_case_=37    ,     snake_case_=32    ,     snake_case_=1_28    ,     snake_case_=8    ,     snake_case_=8    ,     snake_case_=8    ,     snake_case_=1.0    ,     snake_case_=True    ,     ):
      _snake_case  :    Optional[Any]            = parent
      _snake_case  :    str            = batch_size
      _snake_case  :    Union[str, Any]            = seq_length
      _snake_case  :    Optional[int]            = is_training
      _snake_case  :    Optional[Any]            = use_token_type_ids
      _snake_case  :    List[str]            = use_input_mask
      _snake_case  :    Any            = use_labels
      _snake_case  :    Tuple            = use_mc_token_ids
      _snake_case  :    Dict            = vocab_size
      _snake_case  :    List[str]            = hidden_size
      _snake_case  :    Any            = num_hidden_layers
      _snake_case  :    int            = num_attention_heads
      _snake_case  :    List[str]            = intermediate_size
      _snake_case  :    Union[str, Any]            = num_buckets
      _snake_case  :    List[str]            = max_distance
      _snake_case  :    Any            = prompt_length
      _snake_case  :    Dict            = prompt_types
      _snake_case  :    str            = segment_types
      _snake_case  :    List[str]            = init_std
      _snake_case  :    Tuple            = return_dict
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = {}
      _snake_case  :    List[Any]            = ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size  ).type(torch.intaa  )
      _snake_case  :    Union[str, Any]            = False

      _snake_case  :    Union[str, Any]            = self.get_config()

      return (config, input_ids)
    def        lowerCamelCase__						(     self  ):
      return CpmAntConfig(
          vocab_size=self.vocab_size    ,     hidden_size=self.hidden_size    ,     num_hidden_layers=self.num_hidden_layers    ,     num_attention_heads=self.num_attention_heads    ,     dim_ff=self.intermediate_size    ,     position_bias_num_buckets=self.num_buckets    ,     position_bias_max_distance=self.max_distance    ,     prompt_types=self.prompt_types    ,     prompt_length=self.prompt_length    ,     segment_types=self.segment_types    ,     use_cache=snake_case_    ,     init_std=self.init_std    ,     return_dict=self.return_dict    ,     )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     *snake_case_  ):
      _snake_case  :    Union[str, Any]            = CpmAntModel(config=snake_case_  )
      model.to(snake_case_  )
      model.eval()

      _snake_case  :    List[Any]            = model(**snake_case_  ).last_hidden_state

      self.parent.assertEqual(hidden_states.shape    ,     (self.batch_size, self.seq_length, config.hidden_size)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     *snake_case_  ):
      _snake_case  :    List[Any]            = CpmAntForCausalLM(snake_case_  )
      model.to(snake_case_  )
      _snake_case  :    Union[str, Any]            = input_ids["input_ids"].to(snake_case_  )
      model.eval()

      _snake_case  :    List[str]            = model(**snake_case_  )
      self.parent.assertEqual(
          model_output.logits.shape    ,     (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)    ,     )


    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    Any            = self.prepare_config_and_inputs()
      return config, inputs_dict






@require_torch
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
    __lowercase				:			Optional[int]	=       (
        {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
    )

    __lowercase				:			Union[str, Any]	=       False
    __lowercase				:			int	=       False
    __lowercase				:			List[str]	=       False
    __lowercase				:			List[Any]	=       False
    __lowercase				:			Optional[Any]	=       False
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = CpmAntModelTester(self  )
      _snake_case  :    List[str]            = ConfigTester(self    ,     config_class=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      self.config_tester.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.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()
    def        lowerCamelCase__						(     self  ):
      unittest.skip("CPMAnt doesn't support input_embeds."  )(self.test_inputs_embeds  )
    def        lowerCamelCase__						(     self  ):
      unittest.skip(
          "CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model."  )(self.test_retain_grad_hidden_states_attentions  )
    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    Optional[Any]            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_cpmant_model(snake_case_    ,     snake_case_  )


    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    Optional[int]            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_lm_head_model(snake_case_    ,     snake_case_  )






@require_torch
class   _UpperCAmelCase					(       unittest.TestCase):
    @tooslow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = "今天天气真好！"
      _snake_case  :    str            = "openbmb/cpm-ant-10b"
      _snake_case  :    str            = CpmAntModel.from_pretrained(snake_case_  )
      _snake_case  :    List[Any]            = CpmAntTokenizer.from_pretrained(snake_case_  )
      _snake_case  :    Dict            = tokenizer(snake_case_    ,     return_tensors="pt"  )
      _snake_case  :    List[str]            = model(**snake_case_  ).last_hidden_state

      _snake_case  :    Tuple            = torch.tensor(
          [[[6.1708, 5.9244, 1.0835], [6.5207, 6.2893, -11.3324], [-1.0107, -0.0576, -5.9577]]]    ,     )
      self.assertTrue(torch.allclose(hidden_states[:, :3, :3]    ,     snake_case_    ,     atol=1E-2  )  )






@require_torch
class   _UpperCAmelCase					(       unittest.TestCase):
    @tooslow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = "今天天气真好！"
      _snake_case  :    Union[str, Any]            = "openbmb/cpm-ant-10b"
      _snake_case  :    int            = CpmAntForCausalLM.from_pretrained(snake_case_  )
      _snake_case  :    List[str]            = CpmAntTokenizer.from_pretrained(snake_case_  )
      _snake_case  :    Union[str, Any]            = tokenizer(snake_case_    ,     return_tensors="pt"  )
      _snake_case  :    List[str]            = model(**snake_case_  ).logits

      _snake_case  :    List[Any]            = torch.tensor(
          [[[-6.4267, -6.4083, -6.3958], [-5.8802, -5.9447, -5.7811], [-5.3896, -5.4820, -5.4295]]]    ,     )
      self.assertTrue(torch.allclose(hidden_states[:, :3, :3]    ,     snake_case_    ,     atol=1E-2  )  )
    @tooslow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = "openbmb/cpm-ant-10b"
      _snake_case  :    str            = CpmAntForCausalLM.from_pretrained(snake_case_  )
      _snake_case  :    List[str]            = CpmAntTokenizer.from_pretrained(snake_case_  )
      _snake_case  :    Optional[Any]            = "今天天气不错，"
      _snake_case  :    Any            = "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"
      _snake_case  :    List[str]            = tokenizer(snake_case_    ,     return_tensors="pt"  )
      _snake_case  :    List[str]            = model.generate(**snake_case_  )
      _snake_case  :    str            = tokenizer.batch_decode(snake_case_  )
      self.assertEqual(snake_case_    ,     snake_case_  )


    @tooslow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = "openbmb/cpm-ant-10b"
      _snake_case  :    str            = CpmAntForCausalLM.from_pretrained(snake_case_  )
      _snake_case  :    str            = CpmAntTokenizer.from_pretrained(snake_case_  )
      _snake_case  :    Union[str, Any]            = ["今天天气不错，", "新年快乐，万事如意！"]
      _snake_case  :    Dict            = [
          "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的",
          "新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的",
      ]
      _snake_case  :    List[str]            = tokenizer(snake_case_    ,     return_tensors="pt"    ,     padding=snake_case_  )
      _snake_case  :    Any            = model.generate(**snake_case_  )
      _snake_case  :    List[str]            = tokenizer.batch_decode(snake_case_  )
      self.assertEqual(snake_case_    ,     snake_case_  )



"""simple docstring"""

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,
)


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

_A				=					'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> 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"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'
def 						UpperCAmelCase   (						a_,		a_,		a_=8     ):





		'''simple docstring'''


		lowerCamelCase	:     List[str]            =       height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowerCamelCase	:     List[str]            =       width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor
def 						UpperCAmelCase   (						a_,		a_=512,		a_=512     ):





		'''simple docstring'''


		lowerCamelCase	:     Union[str, Any]            =       pil_image.resize((w, h),		resample=Image.BICUBIC,		reducing_gap=1     )
		lowerCamelCase	:     Tuple            =       np.array(pil_image.convert('RGB'     )     )
		lowerCamelCase	:     List[str]            =       arr.astype(np.floataa     ) / 1_2_7.5 - 1
		lowerCamelCase	:     Optional[int]            =       np.transpose(a_,		[2, 0, 1]     )
		lowerCamelCase	:     int            =       torch.from_numpy(a_     ).unsqueeze(0     )
		return image






class 			_lowercase  (							__UpperCAmelCase						):




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					)						->				Optional[Any]:
			super().__init__()

			self.register_modules(
			    unet=UpperCAmelCase_						,					scheduler=UpperCAmelCase_						,					movq=UpperCAmelCase_						,					)
			lowerCamelCase	:     Any            =       2 ** (len(self.movq.config.block_out_channels   ) - 1)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				str:
			# get the original timestep using init_timestep
			lowerCamelCase	:     List[str]            =       min(int(num_inference_steps * strength   )						,					UpperCAmelCase_   )

			lowerCamelCase	:     Dict            =       max(num_inference_steps - init_timestep						,					0   )
			lowerCamelCase	:     Optional[Any]            =       self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=None   )						->				Union[str, Any]:
			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_   )}"""   )

			lowerCamelCase	:     Optional[Any]            =       image.to(device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       batch_size * num_images_per_prompt

			if image.shape[1] == 4:
					lowerCamelCase	:     Any            =       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_   ):
							lowerCamelCase	:     List[Any]            =       [
							    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(UpperCAmelCase_   )
							]
							lowerCamelCase	:     List[Any]            =       torch.cat(UpperCAmelCase_						,					dim=0   )
					else:
							lowerCamelCase	:     Any            =       self.movq.encode(UpperCAmelCase_   ).latent_dist.sample(UpperCAmelCase_   )

					lowerCamelCase	:     List[Any]            =       self.movq.config.scaling_factor * init_latents

			lowerCamelCase	:     List[str]            =       torch.cat([init_latents]						,					dim=0   )

			lowerCamelCase	:     List[Any]            =       init_latents.shape
			lowerCamelCase	:     str            =       randn_tensor(UpperCAmelCase_						,					generator=UpperCAmelCase_						,					device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )

			# get latents
			lowerCamelCase	:     Union[str, Any]            =       self.scheduler.add_noise(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       init_latents

			return latents




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0   )						->				Tuple:
			if is_accelerate_available():
					from accelerate import cpu_offload
			else:
					raise ImportError('Please install accelerate via `pip install accelerate`'   )

			lowerCamelCase	:     Dict            =       torch.device(F"""cuda:{gpu_id}"""   )

			lowerCamelCase	:     Optional[int]            =       [
			    self.unet,
			    self.movq,
			]
			for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
							cpu_offload(UpperCAmelCase_						,					UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0   )						->				Optional[Any]:
			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.'   )

			lowerCamelCase	:     str            =       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)

			lowerCamelCase	:     Optional[int]            =       None
			for cpu_offloaded_model in [self.unet, self.movq]:
					lowerCamelCase      ,       lowerCamelCase	:     Any            =       cpu_offload_with_hook(UpperCAmelCase_						,					UpperCAmelCase_						,					prev_module_hook=UpperCAmelCase_   )

			# We'll offload the last model manually.
			lowerCamelCase	:     Optional[Any]            =       hook




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def      _UpperCamelCase       (					self   )						->				List[Any]:
			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						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_ = 512						,					UpperCAmelCase_ = 512						,					UpperCAmelCase_ = 100						,					UpperCAmelCase_ = 4.0						,					UpperCAmelCase_ = 0.3						,					UpperCAmelCase_ = 1						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = "pil"						,					UpperCAmelCase_ = True						,					)						->				Any:
			lowerCamelCase	:     List[Any]            =       self._execution_device

			lowerCamelCase	:     int            =       guidance_scale > 1.0

			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Dict            =       torch.cat(UpperCAmelCase_						,					dim=0   )
			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     List[Any]            =       torch.cat(UpperCAmelCase_						,					dim=0   )
			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     List[str]            =       torch.cat(UpperCAmelCase_						,					dim=0   )

			lowerCamelCase	:     Optional[Any]            =       image_embeds.shape[0]

			if do_classifier_free_guidance:
					lowerCamelCase	:     str            =       image_embeds.repeat_interleave(UpperCAmelCase_						,					dim=0   )
					lowerCamelCase	:     Tuple            =       negative_image_embeds.repeat_interleave(UpperCAmelCase_						,					dim=0   )
					lowerCamelCase	:     Optional[Any]            =       hint.repeat_interleave(UpperCAmelCase_						,					dim=0   )

			lowerCamelCase	:     Optional[Any]            =       torch.cat([negative_image_embeds, image_embeds]						,					dim=0   ).to(dtype=self.unet.dtype						,					device=UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       torch.cat([hint, hint]						,					dim=0   ).to(dtype=self.unet.dtype						,					device=UpperCAmelCase_   )

			if not isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Dict            =       [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"""   )

			lowerCamelCase	:     int            =       torch.cat([prepare_image(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   ) for i in image]						,					dim=0   )
			lowerCamelCase	:     Tuple            =       image.to(dtype=image_embeds.dtype						,					device=UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       self.movq.encode(UpperCAmelCase_   )['latents']
			lowerCamelCase	:     Tuple            =       latents.repeat_interleave(UpperCAmelCase_						,					dim=0   )
			self.scheduler.set_timesteps(UpperCAmelCase_						,					device=UpperCAmelCase_   )
			lowerCamelCase      ,       lowerCamelCase	:     str            =       self.get_timesteps(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       timesteps[:1].repeat(batch_size * num_images_per_prompt   )
			lowerCamelCase      ,       lowerCamelCase	:     int            =       downscale_height_and_width(UpperCAmelCase_						,					UpperCAmelCase_						,					self.movq_scale_factor   )
			lowerCamelCase	:     List[Any]            =       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
					lowerCamelCase	:     List[Any]            =       torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

					lowerCamelCase	:     Tuple            =       {'image_embeds': image_embeds, 'hint': hint}
					lowerCamelCase	:     List[Any]            =       self.unet(
					    sample=UpperCAmelCase_						,					timestep=UpperCAmelCase_						,					encoder_hidden_states=UpperCAmelCase_						,					added_cond_kwargs=UpperCAmelCase_						,					return_dict=UpperCAmelCase_						,					)[0]

					if do_classifier_free_guidance:
							lowerCamelCase      ,       lowerCamelCase	:     int            =       noise_pred.split(latents.shape[1]						,					dim=1   )
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       noise_pred.chunk(2   )
							lowerCamelCase      ,       lowerCamelCase	:     Any            =       variance_pred.chunk(2   )
							lowerCamelCase	:     Any            =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
							lowerCamelCase	:     Any            =       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"]
					):
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       noise_pred.split(latents.shape[1]						,					dim=1   )

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

					lowerCamelCase	:     Union[str, Any]            =       self.scheduler.step(
					    UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					generator=UpperCAmelCase_						,					)[0]

			# post-processing
			lowerCamelCase	:     Optional[Any]            =       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"]:
					lowerCamelCase	:     Optional[Any]            =       image * 0.5 + 0.5
					lowerCamelCase	:     Any            =       image.clamp(0						,					1   )
					lowerCamelCase	:     Any            =       image.cpu().permute(0						,					2						,					3						,					1   ).float().numpy()

			if output_type == "pil":
					lowerCamelCase	:     Tuple            =       self.numpy_to_pil(UpperCAmelCase_   )

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=UpperCAmelCase_   )






