


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,
)


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

lowerCAmelCase__    :str       =				'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''

def 			lowerCAmelCase__					(     a__:						Optional[int]     ,    a__:						List[Any]     ,    a__:						Dict=8			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							_UpperCAmelCase											=     width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor







class 						__a (							UpperCAmelCase     ):
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       )					->			Optional[int]:




									"""simple docstring"""





									super().__init__()

									self.register_modules(
									    unet=_SCREAMING_SNAKE_CASE     ,       scheduler=_SCREAMING_SNAKE_CASE     ,       movq=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     2 ** (len(self.movq.config.block_out_channels		) - 1)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									if latents is None:
																_UpperCAmelCase											=     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}'''		)
																_UpperCAmelCase											=     latents.to(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     latents * scheduler.init_noise_sigma
									return latents
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE=0		)					->			Union[str, Any]:




									"""simple docstring"""





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

									_UpperCAmelCase											=     torch.device(f'''cuda:{gpu_id}'''		)

									_UpperCAmelCase											=     [
									    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       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE=0		)					->			Dict:




									"""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.'		)

									_UpperCAmelCase											=     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)

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

									# We'll offload the last model manually.
									_UpperCAmelCase											=     hook
		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									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     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = 512     ,       _SCREAMING_SNAKE_CASE = 512     ,       _SCREAMING_SNAKE_CASE = 100     ,       _SCREAMING_SNAKE_CASE = 4.0     ,       _SCREAMING_SNAKE_CASE = 1     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = "pil"     ,       _SCREAMING_SNAKE_CASE = True     ,       )					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self._execution_device

									_UpperCAmelCase											=     guidance_scale > 1.0

									if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase											=     torch.cat(_SCREAMING_SNAKE_CASE     ,       dim=0		)
									_UpperCAmelCase											=     image_embeds.shape[0] * num_images_per_prompt
									if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase											=     torch.cat(_SCREAMING_SNAKE_CASE     ,       dim=0		)

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

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

									self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     self.scheduler.timesteps

									_UpperCAmelCase											=     self.unet.config.in_channels

									_UpperCAmelCase						,			_UpperCAmelCase											=     downscale_height_and_width(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       self.movq_scale_factor		)

									# create initial latent
									_UpperCAmelCase											=     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
																_UpperCAmelCase											=     torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

																_UpperCAmelCase											=     {'image_embeds': image_embeds}
																_UpperCAmelCase											=     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:
																							_UpperCAmelCase						,			_UpperCAmelCase											=     noise_pred.split(latents.shape[1]     ,       dim=1		)
																							_UpperCAmelCase						,			_UpperCAmelCase											=     noise_pred.chunk(2		)
																							_UpperCAmelCase						,			_UpperCAmelCase											=     variance_pred.chunk(2		)
																							_UpperCAmelCase											=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																							_UpperCAmelCase											=     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"]
																):
																							_UpperCAmelCase						,			_UpperCAmelCase											=     noise_pred.split(latents.shape[1]     ,       dim=1		)

																# compute the previous noisy sample x_t -> x_t-1
																_UpperCAmelCase											=     self.scheduler.step(
																    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       )[0]
									# post-processing
									_UpperCAmelCase											=     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"]:
																_UpperCAmelCase											=     image * 0.5 + 0.5
																_UpperCAmelCase											=     image.clamp(0     ,       1		)
																_UpperCAmelCase											=     image.cpu().permute(0     ,       2     ,       3     ,       1		).float().numpy()

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

									if not return_dict:
																return (image,)

									return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE		)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__    :Tuple       =				{
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Dict       =				['''SpeechT5Tokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[str]       =				[
		    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SpeechT5ForSpeechToText''',
		    '''SpeechT5ForSpeechToSpeech''',
		    '''SpeechT5ForTextToSpeech''',
		    '''SpeechT5Model''',
		    '''SpeechT5PreTrainedModel''',
		    '''SpeechT5HifiGan''',
		]

if TYPE_CHECKING:
		from .configuration_speechta import (
		    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
		    SpeechTaConfig,
		    SpeechTaHifiGanConfig,
		)
		from .feature_extraction_speechta import SpeechTaFeatureExtractor
		from .processing_speechta import SpeechTaProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speechta import SpeechTaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speechta import (
				    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechTaForSpeechToSpeech,
				    SpeechTaForSpeechToText,
				    SpeechTaForTextToSpeech,
				    SpeechTaHifiGan,
				    SpeechTaModel,
				    SpeechTaPreTrainedModel,
				)

else:
		import sys

		lowerCAmelCase__    :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
