


import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCAmelCase_   :				Optional[Any]						      = random.Random()







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Tuple=1.0						,					__A				:    List[Any]=None						,					__A				:    Optional[int]=None       )			->	Union[str, Any]:

       """simple docstring"""

       if rng is None:
              a_      :	Union[str, Any]							=						global_rng

       a_      :	List[Any]							=						[]
       for batch_idx in range(shape[0]       ):
              values.append([]       )
              for _ in range(shape[1]       ):
                     values[-1].append(rng.random() * scale       )

       return values
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Dict=7  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=4_0_0  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=2_0_0_0  ,							SCREAMING_SNAKE_CASE__       : str=1  ,							SCREAMING_SNAKE_CASE__       : Tuple=0.0  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=1_6_0_0_0  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=True  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=True  ,							)	->    int:
             a_      :	Tuple							=						parent
             a_      :	Any							=						batch_size
             a_      :	Optional[Any]							=						min_seq_length
             a_      :	Tuple							=						max_seq_length
             a_      :	str							=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             a_      :	Optional[int]							=						feature_size
             a_      :	str							=						padding_value
             a_      :	Dict							=						sampling_rate
             a_      :	int							=						return_attention_mask
             a_      :	Dict							=						do_normalize
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    str:
             return {
                 "feature_size": self.feature_size,
                 "padding_value": self.padding_value,
                 "sampling_rate": self.sampling_rate,
                 "return_attention_mask": self.return_attention_mask,
                 "do_normalize": self.do_normalize,
             }

      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Any=False  ,							SCREAMING_SNAKE_CASE__       : Dict=False  )	->    List[Any]:
             def _flatten(SCREAMING_SNAKE_CASE__       : str  ):
                    return list(itertools.chain(*SCREAMING_SNAKE_CASE__  )  )

             if equal_length:
                    a_      :	List[str]							=						floats_list((self.batch_size, self.max_seq_length)  )
             else:
                    # make sure that inputs increase in size
                    a_      :	Dict							=						[
                        _flatten(floats_list((x, self.feature_size)  )  )
                        for x in range(self.min_seq_length  ,							self.max_seq_length  ,							self.seq_length_diff  )
                    ]

             if numpify:
                    a_      :	List[str]							=						[np.asarray(SCREAMING_SNAKE_CASE__  ) for x in speech_inputs]

             return speech_inputs
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  List[Any]       =		WavaVecaFeatureExtractor
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Tuple:
             a_      :	Optional[int]							=						WavaVecaFeatureExtractionTester(self  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Dict  )	->    int:
             self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE__  ,							axis=0  ) < 1E-3  )  )
             self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE__  ,							axis=0  ) - 1  ) < 1E-3  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Optional[int]:
             # Tests that all call wrap to encode_plus and batch_encode_plus
             a_      :	Union[str, Any]							=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
             # create three inputs of length 800, 1000, and 1200
             a_      :	List[Any]							=						[floats_list((1, x)  )[0] for x in range(8_0_0  ,							1_4_0_0  ,							2_0_0  )]
             a_      :	Any							=						[np.asarray(SCREAMING_SNAKE_CASE__  ) for speech_input in speech_inputs]

             # Test not batched input
             a_      :	List[str]							=						feat_extract(speech_inputs[0]  ,							return_tensors='np'  ).input_values
             a_      :	Any							=						feat_extract(np_speech_inputs[0]  ,							return_tensors='np'  ).input_values
             self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-3  )  )

             # Test batched
             a_      :	Tuple							=						feat_extract(SCREAMING_SNAKE_CASE__  ,							return_tensors='np'  ).input_values
             a_      :	Tuple							=						feat_extract(SCREAMING_SNAKE_CASE__  ,							return_tensors='np'  ).input_values
             for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-3  )  )

             # Test 2-D numpy arrays are batched.
             a_      :	Union[str, Any]							=						[floats_list((1, x)  )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
             a_      :	Any							=						np.asarray(SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						feat_extract(SCREAMING_SNAKE_CASE__  ,							return_tensors='np'  ).input_values
             a_      :	int							=						feat_extract(SCREAMING_SNAKE_CASE__  ,							return_tensors='np'  ).input_values
             for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-3  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Dict:
             a_      :	str							=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
             a_      :	List[Any]							=						[floats_list((1, x)  )[0] for x in range(8_0_0  ,							1_4_0_0  ,							2_0_0  )]

             a_      :	Optional[Any]							=						['longest', 'max_length', 'do_not_pad']
             a_      :	Any							=						[None, 1_6_0_0, None]
             for max_length, padding in zip(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Optional[int]							=						feat_extract(SCREAMING_SNAKE_CASE__  ,							padding=SCREAMING_SNAKE_CASE__  ,							max_length=SCREAMING_SNAKE_CASE__  ,							return_tensors='np'  )
                    a_      :	Dict							=						processed.input_values

                    self._check_zero_mean_unit_variance(input_values[0][:8_0_0]  )
                    self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6  )
                    self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]  )
                    self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6  )
                    self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    int:
             a_      :	List[str]							=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
             a_      :	Dict							=						range(8_0_0  ,							1_4_0_0  ,							2_0_0  )
             a_      :	str							=						[floats_list((1, x)  )[0] for x in lengths]

             a_      :	Dict							=						['longest', 'max_length', 'do_not_pad']
             a_      :	Optional[int]							=						[None, 1_6_0_0, None]

             for max_length, padding in zip(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Tuple							=						feat_extract(SCREAMING_SNAKE_CASE__  ,							max_length=SCREAMING_SNAKE_CASE__  ,							padding=SCREAMING_SNAKE_CASE__  )
                    a_      :	Tuple							=						processed.input_values

                    self._check_zero_mean_unit_variance(input_values[0][:8_0_0]  )
                    self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]  )
                    self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Tuple:
             a_      :	Optional[int]							=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
             a_      :	str							=						[floats_list((1, x)  )[0] for x in range(8_0_0  ,							1_4_0_0  ,							2_0_0  )]
             a_      :	Tuple							=						feat_extract(
                 SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ,							max_length=1_0_0_0  ,							padding='max_length'  ,							return_tensors='np'  )
             a_      :	str							=						processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :8_0_0]  )
             self._check_zero_mean_unit_variance(input_values[1]  )
             self._check_zero_mean_unit_variance(input_values[2]  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    int:
             a_      :	Any							=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
             a_      :	Tuple							=						[floats_list((1, x)  )[0] for x in range(8_0_0  ,							1_4_0_0  ,							2_0_0  )]
             a_      :	Tuple							=						feat_extract(
                 SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ,							max_length=1_0_0_0  ,							padding='longest'  ,							return_tensors='np'  )
             a_      :	str							=						processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :8_0_0]  )
             self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]  )
             self._check_zero_mean_unit_variance(input_values[2]  )

             # make sure that if max_length < longest -> then pad to max_length
             self.assertTrue(input_values.shape == (3, 1_0_0_0)  )

             a_      :	List[Any]							=						[floats_list((1, x)  )[0] for x in range(8_0_0  ,							1_4_0_0  ,							2_0_0  )]
             a_      :	List[str]							=						feat_extract(
                 SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ,							max_length=2_0_0_0  ,							padding='longest'  ,							return_tensors='np'  )
             a_      :	List[Any]							=						processed.input_values

             self._check_zero_mean_unit_variance(input_values[0, :8_0_0]  )
             self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]  )
             self._check_zero_mean_unit_variance(input_values[2]  )

             # make sure that if max_length > longest -> then pad to longest
             self.assertTrue(input_values.shape == (3, 1_2_0_0)  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    List[str]:
             import torch

             a_      :	Any							=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
             a_      :	Any							=						np.random.rand(1_0_0  ).astype(np.floataa  )
             a_      :	Tuple							=						np_speech_inputs.tolist()

             for inputs in [py_speech_inputs, np_speech_inputs]:
                    a_      :	Optional[int]							=						feature_extractor.pad([{'input_values': inputs}]  ,							return_tensors='np'  )
                    self.assertTrue(np_processed.input_values.dtype == np.floataa  )
                    a_      :	Tuple							=						feature_extractor.pad([{'input_values': inputs}]  ,							return_tensors='pt'  )
                    self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )

      @slow
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Optional[int]:
             # this test makes sure that models that are using
             # group norm don't have their feature extractor return the
             # attention_mask
             for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
                    a_      :	str							=						WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__  )
                    a_      :	str							=						WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__  )

                    # only "layer" feature extraction norm should make use of
                    # attention_mask
                    self.assertEqual(feat_extract.return_attention_mask  ,							config.feat_extract_norm == 'layer'  )






from typing import List, Optional, Union

import torch

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,
)


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

UpperCAmelCase_   :				str						      = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\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 = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\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        >>> 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        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\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  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Tuple						,					__A				:    Tuple=8       )			->	Dict:

       """simple docstring"""

       a_      :	Optional[int]							=						height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       a_      :	int							=						width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : UNetaDConditionModel  ,							SCREAMING_SNAKE_CASE__       : DDPMScheduler  ,							SCREAMING_SNAKE_CASE__       : VQModel  ,							)	->    Any:
             super().__init__()

             self.register_modules(
                 unet=SCREAMING_SNAKE_CASE__  ,							scheduler=SCREAMING_SNAKE_CASE__  ,							movq=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	List[str]							=						2 ** (len(self.movq.config.block_out_channels  ) - 1)
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    str:
             if latents is None:
                    a_      :	List[str]							=						randn_tensor(SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							device=SCREAMING_SNAKE_CASE__  ,							dtype=SCREAMING_SNAKE_CASE__  )
             else:
                    if latents.shape != shape:
                           raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
                    a_      :	Any							=						latents.to(SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						latents * scheduler.init_noise_sigma
             return latents
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0  )	->    Dict:
             if is_accelerate_available():
                    from accelerate import cpu_offload
             else:
                    raise ImportError('Please install accelerate via `pip install accelerate`'  )

             a_      :	Union[str, Any]							=						torch.device(F"""cuda:{gpu_id}"""  )

             a_      :	Optional[Any]							=						[
                 self.unet,
                 self.movq,
             ]
             for cpu_offloaded_model in models:
                    if cpu_offloaded_model is not None:
                           cpu_offload(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0  )	->    Dict:
             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.'  )

             a_      :	Tuple							=						torch.device(F"""cuda:{gpu_id}"""  )

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

             a_      :	Dict							=						None
             for cpu_offloaded_model in [self.unet, self.movq]:
                    a_							,	a_      :	Optional[Any]							=						cpu_offload_with_hook(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							prev_module_hook=SCREAMING_SNAKE_CASE__  )

             # We'll offload the last model manually.
             a_      :	str							=						hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[Any]:
             if not hasattr(self.unet  ,							'_hf_hook'  ):
                    return self.device
             for module in self.unet.modules():
                    if (
                        hasattr(SCREAMING_SNAKE_CASE__  ,							'_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(SCREAMING_SNAKE_CASE__  )
      def __call__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							SCREAMING_SNAKE_CASE__       : Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							SCREAMING_SNAKE_CASE__       : torch.FloatTensor  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 1_0_0  ,							SCREAMING_SNAKE_CASE__       : float = 4.0  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[torch.FloatTensor] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[str] = "pil"  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							)	->    int:
             a_      :	Dict							=						self._execution_device

             a_      :	int							=						guidance_scale > 1.0

             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Union[str, Any]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )
             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Optional[Any]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )
             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Optional[int]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )

             a_      :	Dict							=						image_embeds.shape[0] * num_images_per_prompt

             if do_classifier_free_guidance:
                    a_      :	Optional[Any]							=						image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )
                    a_      :	Optional[Any]							=						negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )
                    a_      :	int							=						hint.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )

             a_      :	int							=						torch.cat([negative_image_embeds, image_embeds]  ,							dim=0  ).to(dtype=self.unet.dtype  ,							device=SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						torch.cat([hint, hint]  ,							dim=0  ).to(dtype=self.unet.dtype  ,							device=SCREAMING_SNAKE_CASE__  )
             self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  ,							device=SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						self.scheduler.timesteps

             a_      :	Optional[int]							=						self.movq.config.latent_channels

             a_							,	a_      :	Dict							=						downscale_height_and_width(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							self.movq_scale_factor  )

             # create initial latent
             a_      :	int							=						self.prepare_latents(
                 (batch_size, num_channels_latents, height, width)  ,							image_embeds.dtype  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							self.scheduler  ,							)

             for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__  )  ):
                    # expand the latents if we are doing classifier free guidance
                    a_      :	int							=						torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

                    a_      :	int							=						{'image_embeds': image_embeds, 'hint': hint}
                    a_      :	str							=						self.unet(
                        sample=SCREAMING_SNAKE_CASE__  ,							timestep=SCREAMING_SNAKE_CASE__  ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__  ,							added_cond_kwargs=SCREAMING_SNAKE_CASE__  ,							return_dict=SCREAMING_SNAKE_CASE__  ,							)[0]

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

                    # compute the previous noisy sample x_t -> x_t-1
                    a_      :	str							=						self.scheduler.step(
                        SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							)[0]
             # post-processing
             a_      :	str							=						self.movq.decode(SCREAMING_SNAKE_CASE__  ,							force_not_quantize=SCREAMING_SNAKE_CASE__  )['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"]:
                    a_      :	str							=						image * 0.5 + 0.5
                    a_      :	Union[str, Any]							=						image.clamp(0  ,							1  )
                    a_      :	Optional[int]							=						image.cpu().permute(0  ,							2  ,							3  ,							1  ).float().numpy()

             if output_type == "pil":
                    a_      :	List[str]							=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__  )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__  )


