


'''simple docstring'''




from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


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

_lowerCAmelCase  :		int		     =	'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				List[str]					,   _A    :				List[Any]=8				)    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= h // scale_factor**2
	if h % scale_factor**2 != 0:
		new_h += 1
	lowerCAmelCase					:    Optional[Any]      							= w // scale_factor**2
	if w % scale_factor**2 != 0:
		new_w += 1
	return new_h * scale_factor, new_w * scale_factor






class  lowerCAmelCase				(				a							):



	def __init__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					):
		super().__init__()

		self.register_modules(
		    text_encoder=snake_case__ ,					tokenizer=snake_case__ ,					unet=snake_case__ ,					scheduler=snake_case__ ,					movq=snake_case__ ,					)
		lowerCAmelCase					:    int      							= 2 ** (len(self.movq.config.block_out_channels    ) - 1)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		if latents is None:
			lowerCAmelCase					:    Any      							= randn_tensor(snake_case__ ,					generator=snake_case__ ,					device=snake_case__ ,					dtype=snake_case__    )
		else:
			if latents.shape != shape:
				raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"    )
			lowerCAmelCase					:    Optional[Any]      							= latents.to(snake_case__    )

		lowerCAmelCase					:    Optional[int]      							= latents * scheduler.init_noise_sigma
		return latents



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__=None ,					):
		lowerCAmelCase					:    Dict      							= len(snake_case__    ) if isinstance(snake_case__ ,					snake_case__    ) else 1
		# get prompt text embeddings
		lowerCAmelCase					:    Union[str, Any]      							= self.tokenizer(
		    snake_case__ ,					padding='max_length' ,					truncation=snake_case__ ,					max_length=77 ,					return_attention_mask=snake_case__ ,					add_special_tokens=snake_case__ ,					return_tensors='pt' ,					)

		lowerCAmelCase					:    int      							= text_inputs.input_ids
		lowerCAmelCase					:    Union[str, Any]      							= self.tokenizer(snake_case__ ,					padding='longest' ,					return_tensors='pt'    ).input_ids

		if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    List[Any]      							= self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]    )
			logger.warning(
			    'The following part of your input was truncated because CLIP can only handle sequences up to'
			    f" {self.tokenizer.model_max_length} tokens: {removed_text}"    )

		lowerCAmelCase					:    List[Any]      							= text_input_ids.to(snake_case__    )
		lowerCAmelCase					:    List[str]      							= text_inputs.attention_mask.to(snake_case__    )

		lowerCAmelCase							,   lowerCAmelCase					:    Union[str, Any]      							= self.text_encoder(
		    input_ids=snake_case__ ,					attention_mask=snake_case__    )

		lowerCAmelCase					:    str      							= prompt_embeds.repeat_interleave(snake_case__ ,					dim=0    )
		lowerCAmelCase					:    List[Any]      							= text_encoder_hidden_states.repeat_interleave(snake_case__ ,					dim=0    )
		lowerCAmelCase					:    List[str]      							= text_mask.repeat_interleave(snake_case__ ,					dim=0    )

		if do_classifier_free_guidance:
			lowerCAmelCase					:    List[str]
			if negative_prompt is None:
				lowerCAmelCase					:    Optional[Any]      							= [''] * batch_size
			elif type(snake_case__    ) is not type(snake_case__    ):
				raise TypeError(
				    f"`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__    )} !="
				    f" {type(snake_case__    )}."    )
			elif isinstance(snake_case__ ,					snake_case__    ):
				lowerCAmelCase					:    Optional[Any]      							= [negative_prompt]
			elif batch_size != len(snake_case__    ):
				raise ValueError(
				    f"`negative_prompt`: {negative_prompt} has batch size {len(snake_case__    )}, but `prompt`:"
				    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
				    ' the batch size of `prompt`.'    )
			else:
				lowerCAmelCase					:    Dict      							= negative_prompt

			lowerCAmelCase					:    int      							= self.tokenizer(
			    snake_case__ ,					padding='max_length' ,					max_length=77 ,					truncation=snake_case__ ,					return_attention_mask=snake_case__ ,					add_special_tokens=snake_case__ ,					return_tensors='pt' ,					)
			lowerCAmelCase					:    List[str]      							= uncond_input.input_ids.to(snake_case__    )
			lowerCAmelCase					:    Optional[Any]      							= uncond_input.attention_mask.to(snake_case__    )

			lowerCAmelCase							,   lowerCAmelCase					:    Optional[Any]      							= self.text_encoder(
			    input_ids=snake_case__ ,					attention_mask=snake_case__    )

			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

			lowerCAmelCase					:    Tuple      							= negative_prompt_embeds.shape[1]
			lowerCAmelCase					:    Dict      							= negative_prompt_embeds.repeat(1 ,					snake_case__    )
			lowerCAmelCase					:    Union[str, Any]      							= negative_prompt_embeds.view(batch_size * num_images_per_prompt ,					snake_case__    )

			lowerCAmelCase					:    List[Any]      							= uncond_text_encoder_hidden_states.shape[1]
			lowerCAmelCase					:    str      							= uncond_text_encoder_hidden_states.repeat(1 ,					snake_case__ ,					1    )
			lowerCAmelCase					:    Optional[int]      							= uncond_text_encoder_hidden_states.view(
			    batch_size * num_images_per_prompt ,					snake_case__ ,					-1    )
			lowerCAmelCase					:    Any      							= uncond_text_mask.repeat_interleave(snake_case__ ,					dim=0    )

			# done duplicates

			# For classifier free guidance, we need to do two forward passes.
			# Here we concatenate the unconditional and text embeddings into a single batch
			# to avoid doing two forward passes
			lowerCAmelCase					:    Dict      							= torch.cat([negative_prompt_embeds, prompt_embeds]    )
			lowerCAmelCase					:    Optional[Any]      							= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]    )

			lowerCAmelCase					:    List[Any]      							= torch.cat([uncond_text_mask, text_mask]    )

		return prompt_embeds, text_encoder_hidden_states, text_mask



	def 				lowercase     (					self ,					snake_case__=0    ):
		if is_accelerate_available():
			from accelerate import cpu_offload
		else:
			raise ImportError('Please install accelerate via `pip install accelerate`'    )

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

		lowerCAmelCase					:    Any      							= [
		    self.unet,
		    self.text_encoder,
		    self.movq,
		]
		for cpu_offloaded_model in models:
			if cpu_offloaded_model is not None:
				cpu_offload(snake_case__ ,					snake_case__    )



	def 				lowercase     (					self ,					snake_case__=0    ):
		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					:    List[Any]      							= torch.device(f"cuda:{gpu_id}"    )

		if self.device.type != "cpu":
			self.to('cpu' ,					silence_dtype_warnings=snake_case__    )
			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.text_encoder, self.unet, self.movq]:
			lowerCAmelCase							,   lowerCAmelCase					:    str      							= cpu_offload_with_hook(snake_case__ ,					snake_case__ ,					prev_module_hook=snake_case__    )

		if self.safety_checker is not None:
			lowerCAmelCase							,   lowerCAmelCase					:    Optional[int]      							= cpu_offload_with_hook(self.safety_checker ,					snake_case__ ,					prev_module_hook=snake_case__    )

		# We'll offload the last model manually.
		lowerCAmelCase					:    int      							= hook



	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 				lowercase     (					self    ):
		if not hasattr(self.unet ,					'_hf_hook'    ):
			return self.device
		for module in self.unet.modules():
			if (
			    hasattr(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(snake_case__    )
	def __call__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ = None ,					snake_case__ = 512 ,					snake_case__ = 512 ,					snake_case__ = 100 ,					snake_case__ = 4.0 ,					snake_case__ = 1 ,					snake_case__ = None ,					snake_case__ = None ,					snake_case__ = "pil" ,					snake_case__ = True ,					):

		if isinstance(snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    Dict      							= 1
		elif isinstance(snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    int      							= len(snake_case__    )
		else:
			raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(snake_case__    )}"    )

		lowerCAmelCase					:    str      							= self._execution_device

		lowerCAmelCase					:    Any      							= batch_size * num_images_per_prompt
		lowerCAmelCase					:    Dict      							= guidance_scale > 1.0

		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    Any      							= self._encode_prompt(
		    snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    )

		if isinstance(snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    Union[str, Any]      							= torch.cat(snake_case__ ,					dim=0    )
		if isinstance(snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    List[str]      							= torch.cat(snake_case__ ,					dim=0    )

		if do_classifier_free_guidance:
			lowerCAmelCase					:    List[Any]      							= image_embeds.repeat_interleave(snake_case__ ,					dim=0    )
			lowerCAmelCase					:    List[str]      							= negative_image_embeds.repeat_interleave(snake_case__ ,					dim=0    )

		lowerCAmelCase					:    Optional[Any]      							= torch.cat([negative_image_embeds, image_embeds] ,					dim=0    ).to(
		    dtype=prompt_embeds.dtype ,					device=snake_case__    )

		self.scheduler.set_timesteps(snake_case__ ,					device=snake_case__    )
		lowerCAmelCase					:    Tuple      							= self.scheduler.timesteps

		lowerCAmelCase					:    List[str]      							= self.unet.config.in_channels

		lowerCAmelCase							,   lowerCAmelCase					:    Tuple      							= get_new_h_w(snake_case__ ,					snake_case__ ,					self.movq_scale_factor    )

		# create initial latent
		lowerCAmelCase					:    Dict      							= self.prepare_latents(
		    (batch_size, num_channels_latents, height, width) ,					text_encoder_hidden_states.dtype ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					self.scheduler ,					)

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

			lowerCAmelCase					:    Union[str, Any]      							= {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
			lowerCAmelCase					:    int      							= self.unet(
			    sample=snake_case__ ,					timestep=snake_case__ ,					encoder_hidden_states=snake_case__ ,					added_cond_kwargs=snake_case__ ,					return_dict=snake_case__ ,					)[0]

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

			# compute the previous noisy sample x_t -> x_t-1
			lowerCAmelCase					:    Dict      							= self.scheduler.step(
			    snake_case__ ,					snake_case__ ,					snake_case__ ,					generator=snake_case__ ,					).prev_sample
		# post-processing
		lowerCAmelCase					:    Dict      							= self.movq.decode(snake_case__ ,					force_not_quantize=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"]:
			lowerCAmelCase					:    Tuple      							= image * 0.5 + 0.5
			lowerCAmelCase					:    Union[str, Any]      							= image.clamp(0 ,					1    )
			lowerCAmelCase					:    List[Any]      							= image.cpu().permute(0 ,					2 ,					3 ,					1    ).float().numpy()

		if output_type == "pil":
			lowerCAmelCase					:    int      							= self.numpy_to_pil(snake_case__    )

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=snake_case__    )




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase  :		int		     =	{
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		Tuple		     =	[
				    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'NezhaForNextSentencePrediction',
				    'NezhaForMaskedLM',
				    'NezhaForPreTraining',
				    'NezhaForMultipleChoice',
				    'NezhaForQuestionAnswering',
				    'NezhaForSequenceClassification',
				    'NezhaForTokenClassification',
				    'NezhaModel',
				    'NezhaPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nezha import (
								    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NezhaForMaskedLM,
								    NezhaForMultipleChoice,
								    NezhaForNextSentencePrediction,
								    NezhaForPreTraining,
								    NezhaForQuestionAnswering,
								    NezhaForSequenceClassification,
								    NezhaForTokenClassification,
								    NezhaModel,
								    NezhaPreTrainedModel,
								)


else:
				import sys

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