



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

_lowerCAmelCase	:   Tuple    =   "\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_		(					snake_case__    ,			snake_case__    ,			snake_case__=8					)	->     List[Any]:



				"""simple docstring"""





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


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,):







						"""simple docstring"""
						super().__init__()

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







						"""simple docstring"""
						if latents is None:
										lowerCAmelCase__		  =  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}'						)
										lowerCAmelCase__		  =  latents.to(a_						)

						lowerCAmelCase__		  =  latents * scheduler.init_noise_sigma
						return latents
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_=None    ,):







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

						lowerCAmelCase__		  =  text_inputs.input_ids
						lowerCAmelCase__		  =  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_						):
										lowerCAmelCase__		  =  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__		  =  text_input_ids.to(a_						)
						lowerCAmelCase__		  =  text_inputs.attention_mask.to(a_						)

						lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.text_encoder(
						    input_ids=a_    ,attention_mask=a_						)

						lowerCAmelCase__		  =  prompt_embeds.repeat_interleave(a_    ,dim=0						)
						lowerCAmelCase__		  =  text_encoder_hidden_states.repeat_interleave(a_    ,dim=0						)
						lowerCAmelCase__		  =  text_mask.repeat_interleave(a_    ,dim=0						)

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

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

										lowerCAmelCase__  ,   lowerCAmelCase__		  =  self.text_encoder(
										    input_ids=a_    ,attention_mask=a_						)

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

										lowerCAmelCase__		  =  negative_prompt_embeds.shape[1]
										lowerCAmelCase__		  =  negative_prompt_embeds.repeat(1    ,a_						)
										lowerCAmelCase__		  =  negative_prompt_embeds.view(batch_size * num_images_per_prompt    ,a_						)

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

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

						return prompt_embeds, text_encoder_hidden_states, text_mask
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_=0						):







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

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

						lowerCAmelCase__		  =  [
						    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 						SCREAMING_SNAKE_CASE_					(							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.'						)

						lowerCAmelCase__		  =  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)

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

						if self.safety_checker is not None:
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  cpu_offload_with_hook(self.safety_checker    ,a_    ,prev_module_hook=a_						)

						# We'll offload the last model manually.
						lowerCAmelCase__		  =  hook
		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""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_ = 512    ,a_ = 512    ,a_ = 100    ,a_ = 4.0    ,a_ = 1    ,a_ = None    ,a_ = None    ,a_ = "pil"    ,a_ = True    ,):







						"""simple docstring"""

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

						lowerCAmelCase__		  =  self._execution_device

						lowerCAmelCase__		  =  batch_size * num_images_per_prompt
						lowerCAmelCase__		  =  guidance_scale > 1.0

						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  self._encode_prompt(
						    a_    ,a_    ,a_    ,a_    ,a_						)

						if isinstance(a_    ,a_						):
										lowerCAmelCase__		  =  torch.cat(a_    ,dim=0						)
						if isinstance(a_    ,a_						):
										lowerCAmelCase__		  =  torch.cat(a_    ,dim=0						)

						if do_classifier_free_guidance:
										lowerCAmelCase__		  =  image_embeds.repeat_interleave(a_    ,dim=0						)
										lowerCAmelCase__		  =  negative_image_embeds.repeat_interleave(a_    ,dim=0						)

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

						self.scheduler.set_timesteps(a_    ,device=a_						)
						lowerCAmelCase__		  =  self.scheduler.timesteps

						lowerCAmelCase__		  =  self.unet.config.in_channels

						lowerCAmelCase__  ,   lowerCAmelCase__		  =  get_new_h_w(a_    ,a_    ,self.movq_scale_factor						)

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

										lowerCAmelCase__		  =  {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
										lowerCAmelCase__		  =  self.unet(
										    sample=a_    ,timestep=a_    ,encoder_hidden_states=a_    ,added_cond_kwargs=a_    ,return_dict=a_    ,)[0]

										if do_classifier_free_guidance:
														lowerCAmelCase__  ,   lowerCAmelCase__		  =  noise_pred.split(latents.shape[1]    ,dim=1						)
														lowerCAmelCase__  ,   lowerCAmelCase__		  =  noise_pred.chunk(2						)
														lowerCAmelCase__  ,   lowerCAmelCase__		  =  variance_pred.chunk(2						)
														lowerCAmelCase__		  =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
														lowerCAmelCase__		  =  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__		  =  noise_pred.split(latents.shape[1]    ,dim=1						)

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

						if output_type == "pil":
										lowerCAmelCase__		  =  self.numpy_to_pil(a_						)

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=a_						)





from typing import Dict

from .base import GenericTensor, Pipeline


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_=None    ,a_=None    ,a_=None    ,**a_						):







						"""simple docstring"""
						if tokenize_kwargs is None:
										lowerCAmelCase__		  =  {}

						if truncation is not None:
										if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'						)
										lowerCAmelCase__		  =  truncation

						lowerCAmelCase__		  =  tokenize_kwargs

						lowerCAmelCase__		  =  {}
						if return_tensors is not None:
										lowerCAmelCase__		  =  return_tensors

						return preprocess_params, {}, postprocess_params
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,**a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.framework
						lowerCAmelCase__		  =  self.tokenizer(a_    ,return_tensors=a_    ,**a_						)
						return model_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model(**a_						)
						return model_outputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=False						):







						"""simple docstring"""
						# [0] is the first available tensor, logits or last_hidden_state.
						if return_tensors:
										return model_outputs[0]
						if self.framework == "pt":
										return model_outputs[0].tolist()
						elif self.framework == "tf":
										return model_outputs[0].numpy().tolist()



		def __call__(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return super().__call__(*a_    ,**a_						)
