
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         =   '\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 							a		(				snake_case__:   Tuple		,  snake_case__:   Optional[int]		,  snake_case__:   Union[str, Any]=8   ):


				'''simple docstring'''
				lowercase_	    =   h // scale_factor**2
				if h % scale_factor**2 != 0:
								new_h += 1
				lowercase_	    =   w // scale_factor**2
				if w % scale_factor**2 != 0:
								new_w += 1
				return new_h * scale_factor, new_w * scale_factor


class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	def __init__(			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     MultilingualCLIP			,				SCREAMING_SNAKE_CASE_    :     XLMRobertaTokenizer			,				SCREAMING_SNAKE_CASE_    :     UNetaDConditionModel			,				SCREAMING_SNAKE_CASE_    :     Union[DDIMScheduler, DDPMScheduler]			,				SCREAMING_SNAKE_CASE_    :     VQModel			,				)			->	str:
					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_			,				)
					lowercase_	    =   2 ** (len(self.movq.config.block_out_channels			) - 1)
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			)			->	List[Any]:
					if latents is None:
									lowercase_	    =   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}'''			)
									lowercase_	    =   latents.to(SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   latents * scheduler.init_noise_sigma
					return latents
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]=None			,				)			->	List[str]:
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else 1
					# get prompt text embeddings
					lowercase_	    =   self.tokenizer(
					    SCREAMING_SNAKE_CASE_			,				padding='''max_length'''			,				truncation=SCREAMING_SNAKE_CASE_			,				max_length=7_7			,				return_attention_mask=SCREAMING_SNAKE_CASE_			,				add_special_tokens=SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			,				)

					lowercase_	    =   text_inputs.input_ids
					lowercase_	    =   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_			):
									lowercase_	    =   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}'''			)

					lowercase_	    =   text_input_ids.to(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   text_inputs.attention_mask.to(SCREAMING_SNAKE_CASE_			)

					lowercase_    ,      lowercase_	    =   self.text_encoder(
					    input_ids=SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_			,				dim=0			)
					lowercase_	    =   text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_			,				dim=0			)
					lowercase_	    =   text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_			,				dim=0			)

					if do_classifier_free_guidance:
									lowercase_	    =   42
									if negative_prompt is None:
													lowercase_	    =   [''''''] * 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_			):
													lowercase_	    =   [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:
													lowercase_	    =   negative_prompt

									lowercase_	    =   self.tokenizer(
									    SCREAMING_SNAKE_CASE_			,				padding='''max_length'''			,				max_length=7_7			,				truncation=SCREAMING_SNAKE_CASE_			,				return_attention_mask=SCREAMING_SNAKE_CASE_			,				add_special_tokens=SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			,				)
									lowercase_	    =   uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   uncond_input.attention_mask.to(SCREAMING_SNAKE_CASE_			)

									lowercase_    ,      lowercase_	    =   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

									lowercase_	    =   negative_prompt_embeds.shape[1]
									lowercase_	    =   negative_prompt_embeds.repeat(1			,				SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   negative_prompt_embeds.view(batch_size * num_images_per_prompt			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   uncond_text_encoder_hidden_states.shape[1]
									lowercase_	    =   uncond_text_encoder_hidden_states.repeat(1			,				SCREAMING_SNAKE_CASE_			,				1			)
									lowercase_	    =   uncond_text_encoder_hidden_states.view(
									    batch_size * num_images_per_prompt			,				SCREAMING_SNAKE_CASE_			,				-1			)
									lowercase_	    =   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
									lowercase_	    =   torch.cat([negative_prompt_embeds, prompt_embeds]			)
									lowercase_	    =   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]			)

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

					return prompt_embeds, text_encoder_hidden_states, text_mask
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     Any=0			)			->	int:
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError('''Please install accelerate via `pip install accelerate`'''			)

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

					lowercase_	    =   [
					    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 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict=0			)			->	Union[str, Any]:
					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.'''			)

					lowercase_	    =   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)

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

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

					# We'll offload the last model manually.
					lowercase_	    =   hook
	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 		_lowercase    (			self    :     Dict			)			->	Optional[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    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, List[str]]			,				SCREAMING_SNAKE_CASE_    :     Union[torch.FloatTensor, List[torch.FloatTensor]]			,				SCREAMING_SNAKE_CASE_    :     Union[torch.FloatTensor, List[torch.FloatTensor]]			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, List[str]]] = None			,				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			,				)			->	List[Any]:

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

					lowercase_	    =   self._execution_device

					lowercase_	    =   batch_size * num_images_per_prompt
					lowercase_	    =   guidance_scale > 1.0

					lowercase_    ,      lowercase_    ,      lowercase_	    =   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_			):
									lowercase_	    =   torch.cat(SCREAMING_SNAKE_CASE_			,				dim=0			)
					if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   torch.cat(SCREAMING_SNAKE_CASE_			,				dim=0			)

					if do_classifier_free_guidance:
									lowercase_	    =   image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_			,				dim=0			)
									lowercase_	    =   negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_			,				dim=0			)

					lowercase_	    =   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_			)
					lowercase_	    =   self.scheduler.timesteps

					lowercase_	    =   self.unet.config.in_channels

					lowercase_    ,      lowercase_	    =   get_new_h_w(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				self.movq_scale_factor			)

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

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

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

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

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)








"""simple docstring"""

import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
       from ... import PreTrainedTokenizerBase, TensorType

UpperCAmelCase__					=logging.get_logger(__name__)





class  lowerCamelCase__    (		_a     ):
      a			:  Optional[int]           =		"""vision-encoder-decoder"""
      a			:  Dict           =		True
      def __init__(		self						:						Dict   ,							**A_						:						List[Any]				):






       '''simple docstring'''


       super().__init__(**A_				)
       if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            F'''A configuraton of type {self.model_type} cannot be instantiated because '''
            F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''				)

       __lowercase						=						kwargs.pop("""encoder"""				)
       __lowercase						=						encoder_config.pop("""model_type"""				)
       __lowercase						=						kwargs.pop("""decoder"""				)
       __lowercase						=						decoder_config.pop("""model_type"""				)

       __lowercase						=						AutoConfig.for_model(A_   ,							**A_				)
       __lowercase						=						AutoConfig.for_model(A_   ,							**A_				)
       __lowercase						=						True
      @classmethod
      def SCREAMING_SNAKE_CASE_						(		cls						:						int   ,							A_						:						PretrainedConfig   ,							A_						:						PretrainedConfig   ,							**A_						:						Any				):






       '''simple docstring'''


       logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""				)
       __lowercase						=						True
       __lowercase						=						True

       return cls(encoder=encoder_config.to_dict()   ,							decoder=decoder_config.to_dict()   ,							**A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						copy.deepcopy(self.__dict__				)
       __lowercase						=						self.encoder.to_dict()
       __lowercase						=						self.decoder.to_dict()
       __lowercase						=						self.__class__.model_type
       return output






class  lowerCamelCase__    (		_a     ):
      a			:  Dict           =		version.parse("""1.11"""     )
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]				)
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       return 1e-4






      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}				)






class  lowerCamelCase__    (		_a     ):
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						OrderedDict()
       __lowercase						=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       __lowercase						=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       __lowercase						=						{0: """batch""", 1: """encoder_sequence"""}

       return common_inputs






      def SCREAMING_SNAKE_CASE_						(		self						:						str   ,							A_						:						"PreTrainedTokenizerBase"   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional["TensorType"] = None   ,							):






       '''simple docstring'''


       import torch

       __lowercase						=						OrderedDict()

       __lowercase						=						super().generate_dummy_inputs(
           A_   ,							batch_size=A_   ,							seq_length=A_   ,							is_pair=A_   ,							framework=A_				)

       __lowercase				,		__lowercase						=						dummy_input["""input_ids"""].shape
       __lowercase						=						(batch, encoder_sequence, self._config.encoder_hidden_size)
       __lowercase						=						dummy_input.pop("""input_ids"""				)
       __lowercase						=						dummy_input.pop("""attention_mask"""				)
       __lowercase						=						torch.zeros(A_				)

       return common_inputs






class  lowerCamelCase__    (		_a     ):
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_						(		self						:						str   ,							A_						:						PretrainedConfig				):






       '''simple docstring'''


       return VisionEncoderDecoderEncoderOnnxConfig(A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						PretrainedConfig   ,							A_						:						PretrainedConfig   ,							A_						:						str = "default"				):






       '''simple docstring'''


       __lowercase						=						encoder_config.hidden_size
       return VisionEncoderDecoderDecoderOnnxConfig(A_   ,							A_				)
