
"""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_       :       Optional[int]      						=  logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase_       :       List[Any]      						=  """
    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 _A						(						lowercase	,   lowercase	,   lowercase=8		):







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






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 def __init__(		self			,   __A			,   __A			,   __A			,   __A			,   __A			,   ) ->					Any:
     super().__init__()

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A						) ->					List[Any]:
     if latents is None:
         a					=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}'''						)
         a					=latents.to(__A						)

     a					=latents * scheduler.init_noise_sigma
     return latents


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A=None			,   ) ->					List[Any]:
     a					=len(__A						) if isinstance(__A			,   __A						) else 1
     # get prompt text embeddings
     a					=self.tokenizer(
         __A			,   padding='''max_length'''			,   truncation=__A			,   max_length=77			,   return_attention_mask=__A			,   add_special_tokens=__A			,   return_tensors='''pt'''			,   )

     a					=text_inputs.input_ids
     a					=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						):
         a					=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}'''						)

     a					=text_input_ids.to(__A						)
     a					=text_inputs.attention_mask.to(__A						)

     a		,				a					=self.text_encoder(
         input_ids=__A			,   attention_mask=__A						)

     a					=prompt_embeds.repeat_interleave(__A			,   dim=0						)
     a					=text_encoder_hidden_states.repeat_interleave(__A			,   dim=0						)
     a					=text_mask.repeat_interleave(__A			,   dim=0						)

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

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

         a		,				a					=self.text_encoder(
             input_ids=__A			,   attention_mask=__A						)

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

         a					=negative_prompt_embeds.shape[1]
         a					=negative_prompt_embeds.repeat(1			,   __A						)
         a					=negative_prompt_embeds.view(batch_size * num_images_per_prompt			,   __A						)

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

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

     return prompt_embeds, text_encoder_hidden_states, text_mask


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=0						) ->					Any:
     if is_accelerate_available():
         from accelerate import cpu_offload
     else:
         raise ImportError('''Please install accelerate via `pip install accelerate`'''						)

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

     a					=[
         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						) ->					Optional[int]:
     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.'''						)

     a					=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)

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

     if self.safety_checker is not None:
         a		,				a					=cpu_offload_with_hook(self.safety_checker			,   __A			,   prev_module_hook=__A						)

     # We'll offload the last model manually.
     a					=hook


 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     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			,   ) ->					int:

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

     a					=self._execution_device

     a					=batch_size * num_images_per_prompt
     a					=guidance_scale > 1.0

     a		,				a		,				a					=self._encode_prompt(
         __A			,   __A			,   __A			,   __A			,   __A						)

     if isinstance(__A			,   __A						):
         a					=torch.cat(__A			,   dim=0						)
     if isinstance(__A			,   __A						):
         a					=torch.cat(__A			,   dim=0						)

     if do_classifier_free_guidance:
         a					=image_embeds.repeat_interleave(__A			,   dim=0						)
         a					=negative_image_embeds.repeat_interleave(__A			,   dim=0						)

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

     self.scheduler.set_timesteps(__A			,   device=__A						)
     a					=self.scheduler.timesteps

     a					=self.unet.config.in_channels

     a		,				a					=get_new_h_w(__A			,   __A			,   self.movq_scale_factor						)

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

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

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

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

     if output_type == "pil":
         a					=self.numpy_to_pil(__A						)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=__A						)



"""simple docstring"""







from __future__ import annotations

from typing import Generic, TypeVar

lowerCamelCase_       :       List[Any]      						=  TypeVar("""T""")






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self			,   __A						) ->					None:
     a					=data
     a					=self
     a					=0






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self						) ->					None:
     # map from node name to the node object
     a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					None:
     # create a new set with x as its member
     a					=DisjointSetTreeNode(__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					DisjointSetTreeNode[T]:
     # find the set x belongs to (with path-compression)
     a					=self.map[data]
     if elem_ref != elem_ref.parent:
         a					=self.find_set(elem_ref.parent.data						)
     return elem_ref.parent


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					None:
     # helper function for union operation
     if nodea.rank > nodea.rank:
         a					=nodea
     else:
         a					=nodea
         if nodea.rank == nodea.rank:
             nodea.rank += 1





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					None:
     # merge 2 disjoint sets
     self.link(self.find_set(__A						)			,   self.find_set(__A						)						)






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self						) ->					None:
     # connections: map from the node to the neighbouring nodes (with weights)
     a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					None:
     # add a node ONLY if its not present in the graph
     if node not in self.connections:
         a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					None:
     # add an edge with the given weight
     self.add_node(__A						)
     self.add_node(__A						)
     a					=weight
     a					=weight





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					GraphUndirectedWeighted[T]:
     a					=[]
     a					=set()
     for start in self.connections:
         for end in self.connections[start]:
             if (start, end) not in seen:
                 seen.add((end, start)						)
                 edges.append((start, end, self.connections[start][end])						)
     edges.sort(key=lambda __A						: x[2]						)

     # creating the disjoint set
     a					=DisjointSetTree[T]()
     for node in self.connections:
         disjoint_set.make_set(__A						)

     # MST generation
     a					=0
     a					=0
     a					=GraphUndirectedWeighted[T]()
     while num_edges < len(self.connections						) - 1:
         a		,				a		,				a					=edges[index]
         index += 1
         a					=disjoint_set.find_set(__A						)
         a					=disjoint_set.find_set(__A						)
         if parent_u != parent_v:
             num_edges += 1
             graph.add_edge(__A			,   __A			,   __A						)
             disjoint_set.union(__A			,   __A						)
     return graph