






'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
						import numpy
						import tensorflow as tf

						from transformers.models.esm.modeling_tf_esm import (
						    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFEsmForMaskedLM,
						    TFEsmForSequenceClassification,
						    TFEsmForTokenClassification,
						    TFEsmModel,
						)






class 							__magic_name__     :







	"""simple docstring"""



	def __init__(							self  ,      _lowercase  ,      )						->  int:
							lowercase_      :   Dict        =			parent
							lowercase_      :   Dict        =			13
							lowercase_      :   Optional[Any]        =			7
							lowercase_      :   Union[str, Any]        =			True
							lowercase_      :   str        =			True
							lowercase_      :   Optional[Any]        =			True
							lowercase_      :   List[Any]        =			99
							lowercase_      :   Union[str, Any]        =			32
							lowercase_      :   str        =			2
							lowercase_      :   Optional[int]        =			4
							lowercase_      :   Optional[int]        =			37
							lowercase_      :   Any        =			'gelu'
							lowercase_      :   Tuple        =			0.1
							lowercase_      :   List[Any]        =			0.1
							lowercase_      :   int        =			512
							lowercase_      :   Tuple        =			16
							lowercase_      :   Any        =			2
							lowercase_      :   Tuple        =			0.02
							lowercase_      :   Union[str, Any]        =			3
							lowercase_      :   int        =			4
							lowercase_      :   Optional[Any]        =			None
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   Optional[int]        =			ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size       )

							lowercase_      :   Tuple        =			None
							if self.use_input_mask:
													lowercase_      :   Tuple        =			random_attention_mask([self.batch_size, self.seq_length]       )

							lowercase_      :   List[str]        =			None
							lowercase_      :   Tuple        =			None
							lowercase_      :   Dict        =			None
							if self.use_labels:
													lowercase_      :   Any        =			ids_tensor([self.batch_size]  ,      self.type_sequence_label_size       )
													lowercase_      :   Union[str, Any]        =			ids_tensor([self.batch_size, self.seq_length]  ,      self.num_labels       )
													lowercase_      :   int        =			ids_tensor([self.batch_size]  ,      self.num_choices       )

							lowercase_      :   str        =			EsmConfig(
							    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      pad_token_id=1  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      initializer_range=self.initializer_range  ,      )

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							(
							    (
							    lowercase_
							)  ,      (
							    lowercase_
							)  ,      (
							    lowercase_
							)  ,      (
							    lowercase_
							)  ,      (
							    lowercase_
							)  ,      (
							    lowercase_
							)  ,      
							)      :   Any        =			self.prepare_config_and_inputs()

							lowercase_      :   Dict        =			True
							lowercase_      :   Optional[Any]        =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
							lowercase_      :   Union[str, Any]        =			ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2       )

							return (
							    config,
							    input_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  str:
							lowercase_      :   int        =			TFEsmModel(config=_lowercase       )
							lowercase_      :   List[str]        =			{'input_ids': input_ids, 'attention_mask': input_mask}
							lowercase_      :   Tuple        =			model(_lowercase       )

							lowercase_      :   Dict        =			[input_ids, input_mask]
							lowercase_      :   List[Any]        =			model(_lowercase       )

							lowercase_      :   Tuple        =			model(_lowercase       )

							self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )						->  List[Any]:
							lowercase_      :   Union[str, Any]        =			True

							lowercase_      :   Tuple        =			TFEsmModel(config=_lowercase       )
							lowercase_      :   int        =			{
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'encoder_hidden_states': encoder_hidden_states,
							    'encoder_attention_mask': encoder_attention_mask,
							}
							lowercase_      :   Optional[int]        =			model(_lowercase       )

							lowercase_      :   str        =			[input_ids, input_mask]
							lowercase_      :   Union[str, Any]        =			model(_lowercase  ,      encoder_hidden_states=_lowercase       )

							# Also check the case where encoder outputs are not passed
							lowercase_      :   List[Any]        =			model(_lowercase  ,      attention_mask=_lowercase       )

							self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Tuple:
							lowercase_      :   Optional[Any]        =			TFEsmForMaskedLM(config=_lowercase       )
							lowercase_      :   List[Any]        =			model([input_ids, input_mask]       )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.vocab_size)       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Union[str, Any]:
							lowercase_      :   Union[str, Any]        =			self.num_labels
							lowercase_      :   Dict        =			TFEsmForTokenClassification(config=_lowercase       )
							lowercase_      :   str        =			{'input_ids': input_ids, 'attention_mask': input_mask}
							lowercase_      :   int        =			model(_lowercase       )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.num_labels)       )







	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   Union[str, Any]        =			self.prepare_config_and_inputs()
							(
							    (
							    lowercase_
							)  ,      (
							    lowercase_
							)  ,      (
							    lowercase_
							)  ,      (
							    lowercase_
							)  ,      (
							    lowercase_
							)  ,      (
							    lowercase_
							)  ,      
							)      :   List[str]        =			config_and_inputs
							lowercase_      :   Optional[int]        =			{'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_tf
class 							__magic_name__     (	UpperCAmelCase_,						UpperCAmelCase_,						unittest.TestCase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  (
	    (
	        TFEsmModel,
	        TFEsmForMaskedLM,
	        TFEsmForSequenceClassification,
	        TFEsmForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE_					:      Tuple        =  (
	    {
	        'feature-extraction': TFEsmModel,
	        'fill-mask': TFEsmForMaskedLM,
	        'text-classification': TFEsmForSequenceClassification,
	        'token-classification': TFEsmForTokenClassification,
	        'zero-shot': TFEsmForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE_					:      List[Any]        =  False
	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  False
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   Optional[int]        =			TFEsmModelTester(self       )
							lowercase_      :   Any        =			ConfigTester(self  ,      config_class=_lowercase  ,      hidden_size=37       )
	def        lowerCamelCase__		(							self       )						->  str:
							self.config_tester.run_common_tests()
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   int        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Tuple:
							lowercase_      :   Optional[Any]        =			self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							lowercase_      :   Dict        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   Tuple        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_lowercase       )
	@slow
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase_      :   List[str]        =			TFEsmModel.from_pretrained(_lowercase       )
													self.assertIsNotNone(_lowercase       )
	@unittest.skip('Protein models do not support embedding resizing.'       )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							pass
	@unittest.skip('Protein models do not support embedding resizing.'       )
	def        lowerCamelCase__		(							self       )						->  Tuple:
							pass







	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_  ,      lowercase_      :   str        =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase_      :   List[Any]        =			model_class(_lowercase       )
													assert isinstance(model.get_input_embeddings()  ,      tf.keras.layers.Layer       )
													if model_class is TFEsmForMaskedLM:
																			# Output embedding test differs from the main test because they're a matrix, not a layer
																			lowercase_      :   Any        =			model.get_bias()
																			assert isinstance(_lowercase  ,      _lowercase       )
																			for k, v in name.items():
																									assert isinstance(_lowercase  ,      tf.Variable       )
													else:
																			lowercase_      :   Union[str, Any]        =			model.get_output_embeddings()
																			assert x is None
																			lowercase_      :   Tuple        =			model.get_bias()
																			assert name is None






@require_tf
class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	@slow
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							lowercase_      :   Optional[int]        =			TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D'       )

							lowercase_      :   Any        =			tf.constant([[0, 1, 2, 3, 4, 5]]       )
							lowercase_      :   Tuple        =			model(_lowercase       )[0]
							lowercase_      :   int        =			[1, 6, 33]
							self.assertEqual(list(output.numpy().shape       )  ,      _lowercase       )
							# compare the actual values for a slice.
							lowercase_      :   Union[str, Any]        =			tf.constant(
							    [
							        [
							            [8.92_15_18, -10.58_98_14, -6.4_67_13_07],
							            [-6.3_96_71_56, -13.91_13_77, -1.1_21_19_15],
							            [-7.78_12_47, -13.95_15_57, -3.74_05_92],
							        ]
							    ]       )
							self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()  ,      expected_slice.numpy()  ,      atol=1E-2       )       )







	@slow
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_      :   Dict        =			TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D'       )

							lowercase_      :   int        =			tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]       )
							lowercase_      :   str        =			model(_lowercase       )[0]
							# compare the actual values for a slice.
							lowercase_      :   Optional[int]        =			tf.constant(
							    [
							        [
							            [0.14_44_30_92, 0.54_12_53_27, 0.3_24_77_39],
							            [0.30_34_04_84, 0.00_52_66_76, 0.31_07_77_22],
							            [0.32_27_80_43, -0.24_98_70_96, 0.3_41_46_28],
							        ]
							    ]       )
							self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()  ,      expected_slice.numpy()  ,      atol=1E-4       )       )








'''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:      List[Any]  				=				logging.get_logger(__name__)  # pylint: disable=invalid-name

A:      Union[str, Any]  				=				"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"






def 	_UpperCAmelCase    (  a		:	Tuple						,			a		:	Union[str, Any]						,			a		:	List[Any]=8							)			->			Dict:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						lowercase_      :   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		:	Any						,			a		:	Dict=5_1_2						,			a		:	Optional[Any]=5_1_2							)			->			Tuple:





						"""simple docstring"""
						lowercase_      :   int        =			pil_image.resize((w, h)						,			resample=Image.BICUBIC						,			reducing_gap=1							)
						lowercase_      :   int        =			np.array(pil_image.convert('RGB'							)							)
						lowercase_      :   Optional[int]        =			arr.astype(np.floataa							) / 1_27.5 - 1
						lowercase_      :   Any        =			np.transpose(a						,			[2, 0, 1]							)
						lowercase_      :   Any        =			torch.from_numpy(a							).unsqueeze(0							)
						return image






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def __init__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )						->  List[Any]:
							super().__init__()

							self.register_modules(
							    unet=_lowercase  ,      scheduler=_lowercase  ,      movq=_lowercase  ,      )
							lowercase_      :   Dict        =			2 ** (len(self.movq.config.block_out_channels       ) - 1)
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  int:
							# get the original timestep using init_timestep
							lowercase_      :   List[Any]        =			min(int(num_inference_steps * strength       )  ,      _lowercase       )

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

							return timesteps, num_inference_steps - t_start
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase=None       )						->  Any:
							if not isinstance(_lowercase  ,      (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(_lowercase       )}"       )

							lowercase_      :   Dict        =			image.to(device=_lowercase  ,      dtype=_lowercase       )

							lowercase_      :   Dict        =			batch_size * num_images_per_prompt

							if image.shape[1] == 4:
													lowercase_      :   str        =			image

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

													elif isinstance(_lowercase  ,      _lowercase       ):
																			lowercase_      :   List[Any]        =			[
																			    self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(_lowercase       )
																			]
																			lowercase_      :   Union[str, Any]        =			torch.cat(_lowercase  ,      dim=0       )
													else:
																			lowercase_      :   Union[str, Any]        =			self.movq.encode(_lowercase       ).latent_dist.sample(_lowercase       )

													lowercase_      :   str        =			self.movq.config.scaling_factor * init_latents

							lowercase_      :   int        =			torch.cat([init_latents]  ,      dim=0       )

							lowercase_      :   Dict        =			init_latents.shape
							lowercase_      :   Dict        =			randn_tensor(_lowercase  ,      generator=_lowercase  ,      device=_lowercase  ,      dtype=_lowercase       )

							# get latents
							lowercase_      :   List[str]        =			self.scheduler.add_noise(_lowercase  ,      _lowercase  ,      _lowercase       )

							lowercase_      :   Optional[Any]        =			init_latents

							return latents
	def        lowerCamelCase__		(							self  ,      _lowercase=0       )						->  int:
							if is_accelerate_available():
													from accelerate import cpu_offload
							else:
													raise ImportError('Please install accelerate via `pip install accelerate`'       )

							lowercase_      :   List[Any]        =			torch.device(f"cuda:{gpu_id}"       )

							lowercase_      :   Optional[Any]        =			[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																			cpu_offload(_lowercase  ,      _lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase=0       )						->  int:
							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_      :   List[Any]        =			torch.device(f"cuda:{gpu_id}"       )

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

							lowercase_      :   Tuple        =			None
							for cpu_offloaded_model in [self.unet, self.movq]:
													lowercase_  ,      lowercase_      :   Dict        =			cpu_offload_with_hook(_lowercase  ,      _lowercase  ,      prev_module_hook=_lowercase       )

							# We'll offload the last model manually.
							lowercase_      :   List[str]        =			hook
	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def        lowerCamelCase__		(							self       )						->  List[str]:
							if not hasattr(self.unet  ,      '_hf_hook'       ):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(_lowercase  ,      '_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(_lowercase       )
	def __call__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = 512  ,      _lowercase = 512  ,      _lowercase = 100  ,      _lowercase = 4.0  ,      _lowercase = 0.3  ,      _lowercase = 1  ,      _lowercase = None  ,      _lowercase = "pil"  ,      _lowercase = True  ,      )						->  str:
							lowercase_      :   List[Any]        =			self._execution_device

							lowercase_      :   List[Any]        =			guidance_scale > 1.0

							if isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   Union[str, Any]        =			torch.cat(_lowercase  ,      dim=0       )
							lowercase_      :   Optional[Any]        =			image_embeds.shape[0]
							if isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   List[str]        =			torch.cat(_lowercase  ,      dim=0       )

							if do_classifier_free_guidance:
													lowercase_      :   List[str]        =			image_embeds.repeat_interleave(_lowercase  ,      dim=0       )
													lowercase_      :   Dict        =			negative_image_embeds.repeat_interleave(_lowercase  ,      dim=0       )

							lowercase_      :   Dict        =			torch.cat([negative_image_embeds, image_embeds]  ,      dim=0       ).to(dtype=self.unet.dtype  ,      device=_lowercase       )

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

							lowercase_      :   List[Any]        =			torch.cat([prepare_image(_lowercase  ,      _lowercase  ,      _lowercase       ) for i in image]  ,      dim=0       )
							lowercase_      :   Dict        =			image.to(dtype=image_embeds.dtype  ,      device=_lowercase       )

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

													lowercase_      :   str        =			{'image_embeds': image_embeds}
													lowercase_      :   str        =			self.unet(
													    sample=_lowercase  ,      timestep=_lowercase  ,      encoder_hidden_states=_lowercase  ,      added_cond_kwargs=_lowercase  ,      return_dict=_lowercase  ,      )[0]

													if do_classifier_free_guidance:
																			lowercase_  ,      lowercase_      :   Dict        =			noise_pred.split(latents.shape[1]  ,      dim=1       )
																			lowercase_  ,      lowercase_      :   Optional[int]        =			noise_pred.chunk(2       )
																			lowercase_  ,      lowercase_      :   Tuple        =			variance_pred.chunk(2       )
																			lowercase_      :   List[str]        =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			lowercase_      :   Tuple        =			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_      :   List[str]        =			noise_pred.split(latents.shape[1]  ,      dim=1       )

													# compute the previous noisy sample x_t -> x_t-1
													lowercase_      :   Dict        =			self.scheduler.step(
													    _lowercase  ,      _lowercase  ,      _lowercase  ,      generator=_lowercase  ,      )[0]

							# post-processing
							lowercase_      :   Any        =			self.movq.decode(_lowercase  ,      force_not_quantize=_lowercase       )['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_      :   Dict        =			image * 0.5 + 0.5
													lowercase_      :   Dict        =			image.clamp(0  ,      1       )
													lowercase_      :   int        =			image.cpu().permute(0  ,      2  ,      3  ,      1       ).float().numpy()

							if output_type == "pil":
													lowercase_      :   int        =			self.numpy_to_pil(_lowercase       )

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=_lowercase       )
