

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


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

SCREAMING_SNAKE_CASE_          =   '\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 				__SCREAMING_SNAKE_CASE				(					lowerCAmelCase:	List[Any]	,   lowerCAmelCase:	int	,   lowerCAmelCase:	List[str]=8			) ->							Tuple:
				_UpperCAmelCase							:		str					=     h // scale_factor**2
				if h % scale_factor**2 != 0:
								new_h += 1
				_UpperCAmelCase							:		Tuple					=     w // scale_factor**2
				if w % scale_factor**2 != 0:
								new_w += 1
				return new_h * scale_factor, new_w * scale_factor





class 					a	(						__lowercase					):


						def __init__(							self    ,       A_    ,       A_    ,       A_    ,       A_    ,       A_    ,       ):







										'''simple docstring'''


										super().__init__()

										self.register_modules(
										    text_encoder=SCREAMING_SNAKE_CASE_    ,       tokenizer=SCREAMING_SNAKE_CASE_    ,       unet=SCREAMING_SNAKE_CASE_    ,       scheduler=SCREAMING_SNAKE_CASE_    ,       movq=SCREAMING_SNAKE_CASE_    ,       )
										_UpperCAmelCase							:		Dict					=     2 ** (len(self.movq.config.block_out_channels						) - 1)


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_    ,       A_    ,       A_    ,       A_    ,       A_						):







										'''simple docstring'''


										if latents is None:
														_UpperCAmelCase							:		Tuple					=     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							:		str					=     latents.to(SCREAMING_SNAKE_CASE_						)

										_UpperCAmelCase							:		List[str]					=     latents * scheduler.init_noise_sigma
										return latents


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_    ,       A_    ,       A_    ,       A_=None    ,       ):







										'''simple docstring'''


										_UpperCAmelCase							:		Optional[Any]					=     len(SCREAMING_SNAKE_CASE_						) if isinstance(SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_						) else 1
										# get prompt text embeddings
										_UpperCAmelCase							:		List[Any]					=     self.tokenizer(
										    SCREAMING_SNAKE_CASE_    ,       padding="max_length"    ,       truncation=SCREAMING_SNAKE_CASE_    ,       max_length=77    ,       return_attention_mask=SCREAMING_SNAKE_CASE_    ,       add_special_tokens=SCREAMING_SNAKE_CASE_    ,       return_tensors="pt"    ,       )

										_UpperCAmelCase							:		Any					=     text_inputs.input_ids
										_UpperCAmelCase							:		Union[str, Any]					=     self.tokenizer(SCREAMING_SNAKE_CASE_    ,       padding="longest"    ,       return_tensors="pt"						).input_ids

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

										_UpperCAmelCase							:		Dict					=     text_input_ids.to(SCREAMING_SNAKE_CASE_						)
										_UpperCAmelCase							:		int					=     text_inputs.attention_mask.to(SCREAMING_SNAKE_CASE_						)

										_UpperCAmelCase		,				_UpperCAmelCase							:		Optional[Any]					=     self.text_encoder(
										    input_ids=SCREAMING_SNAKE_CASE_    ,       attention_mask=SCREAMING_SNAKE_CASE_						)

										_UpperCAmelCase							:		Dict					=     prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_    ,       dim=0						)
										_UpperCAmelCase							:		int					=     text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_    ,       dim=0						)
										_UpperCAmelCase							:		Optional[Any]					=     text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_    ,       dim=0						)

										if do_classifier_free_guidance:
														_UpperCAmelCase							:		List[Any]					=     42
														if negative_prompt is None:
																		_UpperCAmelCase							:		Optional[int]					=     [""] * batch_size
														elif type(SCREAMING_SNAKE_CASE_						) is not type(SCREAMING_SNAKE_CASE_						):
																		raise TypeError(
																		    f'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_						)} !='
																		    f' {type(SCREAMING_SNAKE_CASE_						)}.'						)
														elif isinstance(SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_						):
																		_UpperCAmelCase							:		List[Any]					=     [negative_prompt]
														elif batch_size != len(SCREAMING_SNAKE_CASE_						):
																		raise ValueError(
																		    f'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_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:
																		_UpperCAmelCase							:		Union[str, Any]					=     negative_prompt

														_UpperCAmelCase							:		Union[str, Any]					=     self.tokenizer(
														    SCREAMING_SNAKE_CASE_    ,       padding="max_length"    ,       max_length=77    ,       truncation=SCREAMING_SNAKE_CASE_    ,       return_attention_mask=SCREAMING_SNAKE_CASE_    ,       add_special_tokens=SCREAMING_SNAKE_CASE_    ,       return_tensors="pt"    ,       )
														_UpperCAmelCase							:		List[Any]					=     uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_						)
														_UpperCAmelCase							:		Union[str, Any]					=     uncond_input.attention_mask.to(SCREAMING_SNAKE_CASE_						)

														_UpperCAmelCase		,				_UpperCAmelCase							:		Dict					=     self.text_encoder(
														    input_ids=SCREAMING_SNAKE_CASE_    ,       attention_mask=SCREAMING_SNAKE_CASE_						)

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

														_UpperCAmelCase							:		List[str]					=     negative_prompt_embeds.shape[1]
														_UpperCAmelCase							:		Dict					=     negative_prompt_embeds.repeat(1    ,       SCREAMING_SNAKE_CASE_						)
														_UpperCAmelCase							:		Optional[int]					=     negative_prompt_embeds.view(batch_size * num_images_per_prompt    ,       SCREAMING_SNAKE_CASE_						)

														_UpperCAmelCase							:		int					=     uncond_text_encoder_hidden_states.shape[1]
														_UpperCAmelCase							:		int					=     uncond_text_encoder_hidden_states.repeat(1    ,       SCREAMING_SNAKE_CASE_    ,       1						)
														_UpperCAmelCase							:		List[str]					=     uncond_text_encoder_hidden_states.view(
														    batch_size * num_images_per_prompt    ,       SCREAMING_SNAKE_CASE_    ,       -1						)
														_UpperCAmelCase							:		int					=     uncond_text_mask.repeat_interleave(SCREAMING_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
														_UpperCAmelCase							:		List[str]					=     torch.cat([negative_prompt_embeds, prompt_embeds]						)
														_UpperCAmelCase							:		Optional[int]					=     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]						)

														_UpperCAmelCase							:		str					=     torch.cat([uncond_text_mask, text_mask]						)

										return prompt_embeds, text_encoder_hidden_states, text_mask


						def 							_UpperCAmelCase					(							self    ,       A_=0						):







										'''simple docstring'''


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

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

										_UpperCAmelCase							:		List[str]					=     [
										    self.unet,
										    self.text_encoder,
										    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    ,       A_=0						):







										'''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							:		Union[str, Any]					=     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							:		List[Any]					=     None
										for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
														_UpperCAmelCase		,				_UpperCAmelCase							:		List[Any]					=     cpu_offload_with_hook(SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_    ,       prev_module_hook=SCREAMING_SNAKE_CASE_						)

										if self.safety_checker is not None:
														_UpperCAmelCase		,				_UpperCAmelCase							:		str					=     cpu_offload_with_hook(self.safety_checker    ,       SCREAMING_SNAKE_CASE_    ,       prev_module_hook=SCREAMING_SNAKE_CASE_						)

										# We'll offload the last model manually.
										_UpperCAmelCase							:		Any					=     hook


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def 							_UpperCAmelCase					(							self						):







										'''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    ,       A_    ,       A_    ,       A_    ,       A_ = None    ,       A_ = 512    ,       A_ = 512    ,       A_ = 100    ,       A_ = 4.0    ,       A_ = 1    ,       A_ = None    ,       A_ = None    ,       A_ = "pil"    ,       A_ = True    ,       ):







										'''simple docstring'''


										if isinstance(SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_						):
														_UpperCAmelCase							:		Optional[int]					=     1
										elif isinstance(SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_						):
														_UpperCAmelCase							:		Optional[int]					=     len(SCREAMING_SNAKE_CASE_						)
										else:
														raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_						)}'						)

										_UpperCAmelCase							:		Optional[int]					=     self._execution_device

										_UpperCAmelCase							:		str					=     batch_size * num_images_per_prompt
										_UpperCAmelCase							:		str					=     guidance_scale > 1.0

										_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase							:		int					=     self._encode_prompt(
										    SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_						)

										if isinstance(SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_						):
														_UpperCAmelCase							:		Optional[int]					=     torch.cat(SCREAMING_SNAKE_CASE_    ,       dim=0						)
										if isinstance(SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_						):
														_UpperCAmelCase							:		str					=     torch.cat(SCREAMING_SNAKE_CASE_    ,       dim=0						)

										if do_classifier_free_guidance:
														_UpperCAmelCase							:		List[Any]					=     image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_    ,       dim=0						)
														_UpperCAmelCase							:		Optional[Any]					=     negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_    ,       dim=0						)

										_UpperCAmelCase							:		Optional[int]					=     torch.cat([negative_image_embeds, image_embeds]    ,       dim=0						).to(
										    dtype=prompt_embeds.dtype    ,       device=SCREAMING_SNAKE_CASE_						)

										self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    ,       device=SCREAMING_SNAKE_CASE_						)
										_UpperCAmelCase							:		str					=     self.scheduler.timesteps

										_UpperCAmelCase							:		Optional[Any]					=     self.unet.config.in_channels

										_UpperCAmelCase		,				_UpperCAmelCase							:		List[Any]					=     get_new_h_w(SCREAMING_SNAKE_CASE_    ,       SCREAMING_SNAKE_CASE_    ,       self.movq_scale_factor						)

										# create initial latent
										_UpperCAmelCase							:		Tuple					=     self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)    ,       text_encoder_hidden_states.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							:		Optional[int]					=     torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

														_UpperCAmelCase							:		str					=     {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
														_UpperCAmelCase							:		int					=     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							:		Optional[Any]					=     noise_pred.split(latents.shape[1]    ,       dim=1						)
																		_UpperCAmelCase		,				_UpperCAmelCase							:		Dict					=     noise_pred.chunk(2						)
																		_UpperCAmelCase		,				_UpperCAmelCase							:		Dict					=     variance_pred.chunk(2						)
																		_UpperCAmelCase							:		Optional[Any]					=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																		_UpperCAmelCase							:		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"]
														):
																		_UpperCAmelCase		,				_UpperCAmelCase							:		int					=     noise_pred.split(latents.shape[1]    ,       dim=1						)

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

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

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_						)




"""simple docstring"""




from ..utils import DummyObject, requires_backends





class 			snake_case  (				metaclass=__lowercase ):
							UpperCAmelCase__      =   ['''note_seq''']

							def __init__(self							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									requires_backends(self							,							['''note_seq''']     )

							@classmethod
							def _lowercase       (cls							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									requires_backends(cls							,							['''note_seq''']     )







							@classmethod
							def _lowercase       (cls							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									requires_backends(cls							,							['''note_seq''']     )