
"""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


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

_a														=		"""
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")
        >>> pipe.to(\"cuda\")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save(\"cat.png\")
        ```
"""



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case=8     )							->		Any:


					"""simple docstring"""





					_UpperCamelCase										=  h // scale_factor**2
					if h % scale_factor**2 != 0:
										new_h += 1
					_UpperCamelCase										=  w // scale_factor**2
					if w % scale_factor**2 != 0:
										new_w += 1
					return new_h * scale_factor, new_w * scale_factor






class    _UpperCAmelCase(   lowerCamelCase     ):



		def __init__( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		)	->	Union[str, Any]:






							'''simple docstring'''
							super().__init__()

							self.register_modules(
							    text_encoder=__a    ,		tokenizer=__a    ,		unet=__a    ,		scheduler=__a    ,		movq=__a    ,		)
							_UpperCamelCase										=  2 ** (len(self.movq.config.block_out_channels) - 1)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	str:






							'''simple docstring'''
							if latents is None:
												_UpperCamelCase										=  randn_tensor(__a    ,		generator=__a    ,		device=__a    ,		dtype=__a)
							else:
												if latents.shape != shape:
																	raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''')
												_UpperCamelCase										=  latents.to(__a)

							_UpperCamelCase										=  latents * scheduler.init_noise_sigma
							return latents



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a=None    ,		)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  len(__a) if isinstance(__a    ,		__a) else 1
							# get prompt text embeddings
							_UpperCamelCase										=  self.tokenizer(
							    __a    ,		padding='''max_length'''    ,		truncation=__a    ,		max_length=77    ,		return_attention_mask=__a    ,		add_special_tokens=__a    ,		return_tensors='''pt'''    ,		)

							_UpperCamelCase										=  text_inputs.input_ids
							_UpperCamelCase										=  self.tokenizer(__a    ,		padding='''longest'''    ,		return_tensors='''pt''').input_ids

							if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__a    ,		__a):
												_UpperCamelCase										=  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}''')

							_UpperCamelCase										=  text_input_ids.to(__a)
							_UpperCamelCase										=  text_inputs.attention_mask.to(__a)

							_UpperCamelCase			,  _UpperCamelCase										=  self.text_encoder(
							    input_ids=__a    ,		attention_mask=__a)

							_UpperCamelCase										=  prompt_embeds.repeat_interleave(__a    ,		dim=0)
							_UpperCamelCase										=  text_encoder_hidden_states.repeat_interleave(__a    ,		dim=0)
							_UpperCamelCase										=  text_mask.repeat_interleave(__a    ,		dim=0)

							if do_classifier_free_guidance:
												_UpperCamelCase										=  42
												if negative_prompt is None:
																	_UpperCamelCase										=  [''''''] * batch_size
												elif type(__a) is not type(__a):
																	raise TypeError(
																	    F'''`negative_prompt` should be the same type to `prompt`, but got {type(__a)} !='''
																	    F''' {type(__a)}.''')
												elif isinstance(__a    ,		__a):
																	_UpperCamelCase										=  [negative_prompt]
												elif batch_size != len(__a):
																	raise ValueError(
																	    F'''`negative_prompt`: {negative_prompt} has batch size {len(__a)}, but `prompt`:'''
																	    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																	    ''' the batch size of `prompt`.''')
												else:
																	_UpperCamelCase										=  negative_prompt

												_UpperCamelCase										=  self.tokenizer(
												    __a    ,		padding='''max_length'''    ,		max_length=77    ,		truncation=__a    ,		return_attention_mask=__a    ,		add_special_tokens=__a    ,		return_tensors='''pt'''    ,		)
												_UpperCamelCase										=  uncond_input.input_ids.to(__a)
												_UpperCamelCase										=  uncond_input.attention_mask.to(__a)

												_UpperCamelCase			,  _UpperCamelCase										=  self.text_encoder(
												    input_ids=__a    ,		attention_mask=__a)

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

												_UpperCamelCase										=  negative_prompt_embeds.shape[1]
												_UpperCamelCase										=  negative_prompt_embeds.repeat(1    ,		__a)
												_UpperCamelCase										=  negative_prompt_embeds.view(batch_size * num_images_per_prompt    ,		__a)

												_UpperCamelCase										=  uncond_text_encoder_hidden_states.shape[1]
												_UpperCamelCase										=  uncond_text_encoder_hidden_states.repeat(1    ,		__a    ,		1)
												_UpperCamelCase										=  uncond_text_encoder_hidden_states.view(
												    batch_size * num_images_per_prompt    ,		__a    ,		-1)
												_UpperCamelCase										=  uncond_text_mask.repeat_interleave(__a    ,		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
												_UpperCamelCase										=  torch.cat([negative_prompt_embeds, prompt_embeds])
												_UpperCamelCase										=  torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])

												_UpperCamelCase										=  torch.cat([uncond_text_mask, text_mask])

							return prompt_embeds, text_encoder_hidden_states, text_mask



		def 						UpperCAmelCase							( self    ,		__a=0)	->	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.text_encoder,
							    self.movq,
							]
							for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																	cpu_offload(__a    ,		__a)



		def 						UpperCAmelCase							( self    ,		__a=0)	->	Optional[Any]:






							'''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=__a)
												torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							_UpperCamelCase										=  None
							for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
												_UpperCamelCase			,  _UpperCamelCase										=  cpu_offload_with_hook(__a    ,		__a    ,		prev_module_hook=__a)

							if self.safety_checker is not None:
												_UpperCamelCase			,  _UpperCamelCase										=  cpu_offload_with_hook(self.safety_checker    ,		__a    ,		prev_module_hook=__a)

							# 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)	->	List[str]:






							'''simple docstring'''
							if not hasattr(self.unet    ,		'''_hf_hook'''):
												return self.device
							for module in self.unet.modules():
												if (
												    hasattr(__a    ,		'''_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(__a)
		def __call__( self    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		__a = 5_12    ,		__a = 5_12    ,		__a = 1_00    ,		__a = 4.0    ,		__a = 1    ,		__a = None    ,		__a = None    ,		__a = "pil"    ,		__a = True    ,		)	->	Tuple:






							'''simple docstring'''

							if isinstance(__a    ,		__a):
												_UpperCamelCase										=  1
							elif isinstance(__a    ,		__a):
												_UpperCamelCase										=  len(__a)
							else:
												raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__a)}''')

							_UpperCamelCase										=  self._execution_device

							_UpperCamelCase										=  batch_size * num_images_per_prompt
							_UpperCamelCase										=  guidance_scale > 1.0

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  self._encode_prompt(
							    __a    ,		__a    ,		__a    ,		__a    ,		__a)

							if isinstance(__a    ,		__a):
												_UpperCamelCase										=  torch.cat(__a    ,		dim=0)
							if isinstance(__a    ,		__a):
												_UpperCamelCase										=  torch.cat(__a    ,		dim=0)

							if do_classifier_free_guidance:
												_UpperCamelCase										=  image_embeds.repeat_interleave(__a    ,		dim=0)
												_UpperCamelCase										=  negative_image_embeds.repeat_interleave(__a    ,		dim=0)

							_UpperCamelCase										=  torch.cat([negative_image_embeds, image_embeds]    ,		dim=0).to(
							    dtype=prompt_embeds.dtype    ,		device=__a)

							self.scheduler.set_timesteps(__a    ,		device=__a)
							_UpperCamelCase										=  self.scheduler.timesteps

							_UpperCamelCase										=  self.unet.config.in_channels

							_UpperCamelCase			,  _UpperCamelCase										=  get_new_h_w(__a    ,		__a    ,		self.movq_scale_factor)

							# create initial latent
							_UpperCamelCase										=  self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)    ,		text_encoder_hidden_states.dtype    ,		__a    ,		__a    ,		__a    ,		self.scheduler    ,		)

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

												_UpperCamelCase										=  {'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
												_UpperCamelCase										=  self.unet(
												    sample=__a    ,		timestep=__a    ,		encoder_hidden_states=__a    ,		added_cond_kwargs=__a    ,		return_dict=__a    ,		)[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(
												    __a    ,		__a    ,		__a    ,		generator=__a    ,		).prev_sample
							# post-processing
							_UpperCamelCase										=  self.movq.decode(__a    ,		force_not_quantize=__a)['''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(__a)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=__a)




"""simple docstring"""





import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)






