





'''simple docstring'''






import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      if isinstance(lowerCAmelCase							,     torch.Tensor  ):
            return image
      elif isinstance(lowerCAmelCase							,     PIL.Image.Image  ):
            _lowerCAmelCase												=							[image]

      if isinstance(image[0]							,     PIL.Image.Image  ):
            _lowerCAmelCase												=							[np.array(i.resize((w, h)							,     resample=PIL_INTERPOLATION["""lanczos"""]  )  )[None, :] for i in image]
            _lowerCAmelCase												=							np.concatenate(lowerCAmelCase							,     axis=0  )
            _lowerCAmelCase												=							np.array(lowerCAmelCase  ).astype(np.floataa  ) / 255.0
            _lowerCAmelCase												=							image.transpose(0							,     3							,     1							,     2  )
            _lowerCAmelCase												=							2.0 * image - 1.0
            _lowerCAmelCase												=							torch.from_numpy(lowerCAmelCase  )
      elif isinstance(image[0]							,     torch.Tensor  ):
            _lowerCAmelCase												=							torch.cat(lowerCAmelCase							,     dim=0  )
      return image
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=0.9_995  ):





      """simple docstring"""


      if not isinstance(lowerCAmelCase							,     np.ndarray  ):
            _lowerCAmelCase												=							True
            _lowerCAmelCase												=							va.device
            _lowerCAmelCase												=							va.cpu().numpy()
            _lowerCAmelCase												=							va.cpu().numpy()

      _lowerCAmelCase												=							np.sum(va * va / (np.linalg.norm(lowerCAmelCase  ) * np.linalg.norm(lowerCAmelCase  ))  )
      if np.abs(lowerCAmelCase  ) > DOT_THRESHOLD:
            _lowerCAmelCase												=							(1 - t) * va + t * va
      else:
            _lowerCAmelCase												=							np.arccos(lowerCAmelCase  )
            _lowerCAmelCase												=							np.sin(lowerCAmelCase  )
            _lowerCAmelCase												=							theta_a * t
            _lowerCAmelCase												=							np.sin(lowerCAmelCase  )
            _lowerCAmelCase												=							np.sin(theta_a - theta_t  ) / sin_theta_a
            _lowerCAmelCase												=							sin_theta_t / sin_theta_a
            _lowerCAmelCase												=							sa * va + sa * va

      if inputs_are_torch:
            _lowerCAmelCase												=							torch.from_numpy(lowerCAmelCase  ).to(lowerCAmelCase  )

      return va
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							F.normalize(lowerCAmelCase							,     dim=-1  )
      _lowerCAmelCase												=							F.normalize(lowerCAmelCase							,     dim=-1  )
      return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      for param in model.parameters():
            _lowerCAmelCase												=							value





class 			UpperCAmelCase					(      snake_case_       ):




      def __init__(      self      :			Dict      ,      __snake_case      :			AutoencoderKL      ,      __snake_case      :			CLIPTextModel      ,      __snake_case      :			CLIPModel      ,      __snake_case      :			CLIPTokenizer      ,      __snake_case      :			UNetaDConditionModel      ,      __snake_case      :			Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]      ,      __snake_case      :			CLIPFeatureExtractor      ,      __snake_case      :			str=None      ,      __snake_case      :			List[str]=None      ,      __snake_case      :			str=None      ,      )    ->				Union[str, Any]:
            super().__init__()
            self.register_modules(
                vae=__snake_case      ,      text_encoder=__snake_case      ,      clip_model=__snake_case      ,      tokenizer=__snake_case      ,      unet=__snake_case      ,      scheduler=__snake_case      ,      feature_extractor=__snake_case      ,      coca_model=__snake_case      ,      coca_tokenizer=__snake_case      ,      coca_transform=__snake_case      ,      )
            _lowerCAmelCase												=							(
                feature_extractor.size
                if isinstance(feature_extractor.size      ,      __snake_case  )
                else feature_extractor.size["""shortest_edge"""]
            )
            _lowerCAmelCase												=							transforms.Normalize(mean=feature_extractor.image_mean      ,      std=feature_extractor.image_std  )
            set_requires_grad(self.text_encoder      ,      __snake_case  )
            set_requires_grad(self.clip_model      ,      __snake_case  )




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			Optional[Union[str, int]] = "auto"  )    ->				Tuple:
            if slice_size == "auto":
                  # half the attention head size is usually a good trade-off between
                  # speed and memory
                  _lowerCAmelCase												=							self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(__snake_case  )




      def     lowercase__   (      self      :			Tuple  )    ->				Dict:
            self.enable_attention_slicing(__snake_case  )




      def     lowercase__   (      self      :			Dict  )    ->				List[Any]:
            set_requires_grad(self.vae      ,      __snake_case  )




      def     lowercase__   (      self      :			Dict  )    ->				Tuple:
            set_requires_grad(self.vae      ,      __snake_case  )




      def     lowercase__   (      self      :			Dict  )    ->				str:
            set_requires_grad(self.unet      ,      __snake_case  )




      def     lowercase__   (      self      :			Dict  )    ->				List[str]:
            set_requires_grad(self.unet      ,      __snake_case  )




      def     lowercase__   (      self      :			Any      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Any  )    ->				List[Any]:
            # get the original timestep using init_timestep
            _lowerCAmelCase												=							min(int(num_inference_steps * strength  )      ,      __snake_case  )

            _lowerCAmelCase												=							max(num_inference_steps - init_timestep      ,      0  )
            _lowerCAmelCase												=							self.scheduler.timesteps[t_start:]

            return timesteps, num_inference_steps - t_start




      def     lowercase__   (      self      :			str      ,      __snake_case      :			str      ,      __snake_case      :			Any      ,      __snake_case      :			List[Any]      ,      __snake_case      :			int      ,      __snake_case      :			str      ,      __snake_case      :			Union[str, Any]=None  )    ->				List[str]:
            if not isinstance(__snake_case      ,      torch.Tensor  ):
                  raise ValueError(f"`image` has to be of type `torch.Tensor` but is {type(__snake_case  )}"  )

            _lowerCAmelCase												=							image.to(device=__snake_case      ,      dtype=__snake_case  )

            if isinstance(__snake_case      ,      __snake_case  ):
                  _lowerCAmelCase												=							[
                      self.vae.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(__snake_case  )
                  ]
                  _lowerCAmelCase												=							torch.cat(__snake_case      ,      dim=0  )
            else:
                  _lowerCAmelCase												=							self.vae.encode(__snake_case  ).latent_dist.sample(__snake_case  )

            # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
            _lowerCAmelCase												=							0.1_82_15 * init_latents
            _lowerCAmelCase												=							init_latents.repeat_interleave(__snake_case      ,      dim=0  )

            _lowerCAmelCase												=							randn_tensor(init_latents.shape      ,      generator=__snake_case      ,      device=__snake_case      ,      dtype=__snake_case  )

            # get latents
            _lowerCAmelCase												=							self.scheduler.add_noise(__snake_case      ,      __snake_case      ,      __snake_case  )
            _lowerCAmelCase												=							init_latents

            return latents




      def     lowercase__   (      self      :			int      ,      __snake_case      :			Union[str, Any]  )    ->				int:
            _lowerCAmelCase												=							self.coca_transform(__snake_case  ).unsqueeze(0  )
            with torch.no_grad(), torch.cuda.amp.autocast():
                  _lowerCAmelCase												=							self.coca_model.generate(transformed_image.to(device=self.device      ,      dtype=self.coca_model.dtype  )  )
            _lowerCAmelCase												=							self.coca_tokenizer.decode(generated[0].cpu().numpy()  )
            return generated.split("""<end_of_text>"""  )[0].replace("""<start_of_text>"""      ,      """"""  ).rstrip(""" .,"""  )




      def     lowercase__   (      self      :			List[Any]      ,      __snake_case      :			int      ,      __snake_case      :			int  )    ->				Optional[Any]:
            _lowerCAmelCase												=							self.feature_extractor.preprocess(__snake_case  )
            _lowerCAmelCase												=							torch.from_numpy(clip_image_input["""pixel_values"""][0]  ).unsqueeze(0  ).to(self.device  ).half()
            _lowerCAmelCase												=							self.clip_model.get_image_features(__snake_case  )
            _lowerCAmelCase												=							image_embeddings_clip / image_embeddings_clip.norm(p=2      ,      dim=-1      ,      keepdim=__snake_case  )
            _lowerCAmelCase												=							image_embeddings_clip.repeat_interleave(__snake_case      ,      dim=0  )
            return image_embeddings_clip




      @torch.enable_grad()
      def     lowercase__   (      self      :			Any      ,      __snake_case      :			List[Any]      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			str      ,      __snake_case      :			Dict      ,      __snake_case      :			List[Any]      ,      __snake_case      :			List[Any]      ,      )    ->				List[str]:
            _lowerCAmelCase												=							latents.detach().requires_grad_()

            _lowerCAmelCase												=							self.scheduler.scale_model_input(__snake_case      ,      __snake_case  )

            # predict the noise residual
            _lowerCAmelCase												=							self.unet(__snake_case      ,      __snake_case      ,      encoder_hidden_states=__snake_case  ).sample

            if isinstance(self.scheduler      ,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)  ):
                  _lowerCAmelCase												=							self.scheduler.alphas_cumprod[timestep]
                  _lowerCAmelCase												=							1 - alpha_prod_t
                  # compute predicted original sample from predicted noise also called
                  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
                  _lowerCAmelCase												=							(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

                  _lowerCAmelCase												=							torch.sqrt(__snake_case  )
                  _lowerCAmelCase												=							pred_original_sample * (fac) + latents * (1 - fac)
            elif isinstance(self.scheduler      ,      __snake_case  ):
                  _lowerCAmelCase												=							self.scheduler.sigmas[index]
                  _lowerCAmelCase												=							latents - sigma * noise_pred
            else:
                  raise ValueError(f"scheduler type {type(self.scheduler  )} not supported"  )

            # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
            _lowerCAmelCase												=							1 / 0.1_82_15 * sample
            _lowerCAmelCase												=							self.vae.decode(__snake_case  ).sample
            _lowerCAmelCase												=							(image / 2 + 0.5).clamp(0      ,      1  )

            _lowerCAmelCase												=							transforms.Resize(self.feature_extractor_size  )(__snake_case  )
            _lowerCAmelCase												=							self.normalize(__snake_case  ).to(latents.dtype  )

            _lowerCAmelCase												=							self.clip_model.get_image_features(__snake_case  )
            _lowerCAmelCase												=							image_embeddings_clip / image_embeddings_clip.norm(p=2      ,      dim=-1      ,      keepdim=__snake_case  )

            _lowerCAmelCase												=							spherical_dist_loss(__snake_case      ,      __snake_case  ).mean() * clip_guidance_scale

            _lowerCAmelCase												=							-torch.autograd.grad(__snake_case      ,      __snake_case  )[0]

            if isinstance(self.scheduler      ,      __snake_case  ):
                  _lowerCAmelCase												=							latents.detach() + grads * (sigma**2)
                  _lowerCAmelCase												=							noise_pred_original
            else:
                  _lowerCAmelCase												=							noise_pred_original - torch.sqrt(__snake_case  ) * grads
            return noise_pred, latents




      @torch.no_grad()
      def __call__(      self      :			Optional[Any]      ,      __snake_case      :			Union[torch.FloatTensor, PIL.Image.Image]      ,      __snake_case      :			Union[torch.FloatTensor, PIL.Image.Image]      ,      __snake_case      :			Optional[str] = None      ,      __snake_case      :			Optional[str] = None      ,      __snake_case      :			Optional[int] = 5_12      ,      __snake_case      :			Optional[int] = 5_12      ,      __snake_case      :			float = 0.6      ,      __snake_case      :			Optional[int] = 50      ,      __snake_case      :			Optional[float] = 7.5      ,      __snake_case      :			Optional[int] = 1      ,      __snake_case      :			float = 0.0      ,      __snake_case      :			Optional[float] = 1_00      ,      __snake_case      :			Optional[torch.Generator] = None      ,      __snake_case      :			Optional[str] = "pil"      ,      __snake_case      :			bool = True      ,      __snake_case      :			float = 0.8      ,      __snake_case      :			float = 0.1      ,      __snake_case      :			float = 0.1      ,      )    ->				Optional[Any]:
            if isinstance(__snake_case      ,      __snake_case  ) and len(__snake_case  ) != batch_size:
                  raise ValueError(f"You have passed {batch_size} batch_size, but only {len(__snake_case  )} generators."  )

            if height % 8 != 0 or width % 8 != 0:
                  raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}."  )

            if isinstance(__snake_case      ,      torch.Generator  ) and batch_size > 1:
                  _lowerCAmelCase												=							[generator] + [None] * (batch_size - 1)

            _lowerCAmelCase												=							[
                ("""model""", self.coca_model is None),
                ("""tokenizer""", self.coca_tokenizer is None),
                ("""transform""", self.coca_transform is None),
            ]
            _lowerCAmelCase												=							[x[0] for x in coca_is_none if x[1]]
            _lowerCAmelCase												=							""", """.join(__snake_case  )
            # generate prompts with coca model if prompt is None
            if content_prompt is None:
                  if len(__snake_case  ):
                        raise ValueError(
                            f"Content prompt is None and CoCa [{coca_is_none_str}] is None."
                            f"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."  )
                  _lowerCAmelCase												=							self.get_image_description(__snake_case  )
            if style_prompt is None:
                  if len(__snake_case  ):
                        raise ValueError(
                            f"Style prompt is None and CoCa [{coca_is_none_str}] is None."
                            f" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."  )
                  _lowerCAmelCase												=							self.get_image_description(__snake_case  )

            # get prompt text embeddings for content and style
            _lowerCAmelCase												=							self.tokenizer(
                __snake_case      ,      padding="""max_length"""      ,      max_length=self.tokenizer.model_max_length      ,      truncation=__snake_case      ,      return_tensors="""pt"""      ,      )
            _lowerCAmelCase												=							self.text_encoder(content_text_input.input_ids.to(self.device  )  )[0]

            _lowerCAmelCase												=							self.tokenizer(
                __snake_case      ,      padding="""max_length"""      ,      max_length=self.tokenizer.model_max_length      ,      truncation=__snake_case      ,      return_tensors="""pt"""      ,      )
            _lowerCAmelCase												=							self.text_encoder(style_text_input.input_ids.to(self.device  )  )[0]

            _lowerCAmelCase												=							slerp(__snake_case      ,      __snake_case      ,      __snake_case  )

            # duplicate text embeddings for each generation per prompt
            _lowerCAmelCase												=							text_embeddings.repeat_interleave(__snake_case      ,      dim=0  )

            # set timesteps
            _lowerCAmelCase												=							"""offset""" in set(inspect.signature(self.scheduler.set_timesteps  ).parameters.keys()  )
            _lowerCAmelCase												=							{}
            if accepts_offset:
                  _lowerCAmelCase												=							1

            self.scheduler.set_timesteps(__snake_case      ,      **__snake_case  )
            # Some schedulers like PNDM have timesteps as arrays
            # It's more optimized to move all timesteps to correct device beforehand
            self.scheduler.timesteps.to(self.device  )

            _lowerCAmelCase					,							_lowerCAmelCase												=							self.get_timesteps(__snake_case      ,      __snake_case      ,      self.device  )
            _lowerCAmelCase												=							timesteps[:1].repeat(__snake_case  )

            # Preprocess image
            _lowerCAmelCase												=							preprocess(__snake_case      ,      __snake_case      ,      __snake_case  )
            _lowerCAmelCase												=							self.prepare_latents(
                __snake_case      ,      __snake_case      ,      __snake_case      ,      text_embeddings.dtype      ,      self.device      ,      __snake_case  )

            _lowerCAmelCase												=							preprocess(__snake_case      ,      __snake_case      ,      __snake_case  )
            _lowerCAmelCase												=							self.prepare_latents(
                __snake_case      ,      __snake_case      ,      __snake_case      ,      text_embeddings.dtype      ,      self.device      ,      __snake_case  )

            _lowerCAmelCase												=							slerp(__snake_case      ,      __snake_case      ,      __snake_case  )

            if clip_guidance_scale > 0:
                  _lowerCAmelCase												=							self.get_clip_image_embeddings(__snake_case      ,      __snake_case  )
                  _lowerCAmelCase												=							self.get_clip_image_embeddings(__snake_case      ,      __snake_case  )
                  _lowerCAmelCase												=							slerp(
                      __snake_case      ,      __snake_case      ,      __snake_case  )

            # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
            # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
            # corresponds to doing no classifier free guidance.
            _lowerCAmelCase												=							guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                  _lowerCAmelCase												=							content_text_input.input_ids.shape[-1]
                  _lowerCAmelCase												=							self.tokenizer([""""""]      ,      padding="""max_length"""      ,      max_length=__snake_case      ,      return_tensors="""pt"""  )
                  _lowerCAmelCase												=							self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]
                  # duplicate unconditional embeddings for each generation per prompt
                  _lowerCAmelCase												=							uncond_embeddings.repeat_interleave(__snake_case      ,      dim=0  )

                  # 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([uncond_embeddings, text_embeddings]  )

            # get the initial random noise unless the user supplied it

            # Unlike in other pipelines, latents need to be generated in the target device
            # for 1-to-1 results reproducibility with the CompVis implementation.
            # However this currently doesn't work in `mps`.
            _lowerCAmelCase												=							(batch_size, self.unet.config.in_channels, height // 8, width // 8)
            _lowerCAmelCase												=							text_embeddings.dtype
            if latents is None:
                  if self.device.type == "mps":
                        # randn does not work reproducibly on mps
                        _lowerCAmelCase												=							torch.randn(__snake_case      ,      generator=__snake_case      ,      device="""cpu"""      ,      dtype=__snake_case  ).to(
                            self.device  )
                  else:
                        _lowerCAmelCase												=							torch.randn(__snake_case      ,      generator=__snake_case      ,      device=self.device      ,      dtype=__snake_case  )
            else:
                  if latents.shape != latents_shape:
                        raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"  )
                  _lowerCAmelCase												=							latents.to(self.device  )

            # scale the initial noise by the standard deviation required by the scheduler
            _lowerCAmelCase												=							latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            _lowerCAmelCase												=							"""eta""" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
            _lowerCAmelCase												=							{}
            if accepts_eta:
                  _lowerCAmelCase												=							eta

            # check if the scheduler accepts generator
            _lowerCAmelCase												=							"""generator""" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
            if accepts_generator:
                  _lowerCAmelCase												=							generator

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

                        # predict the noise residual
                        _lowerCAmelCase												=							self.unet(__snake_case      ,      __snake_case      ,      encoder_hidden_states=__snake_case  ).sample

                        # perform classifier free guidance
                        if do_classifier_free_guidance:
                              _lowerCAmelCase					,							_lowerCAmelCase												=							noise_pred.chunk(2  )
                              _lowerCAmelCase												=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                        # perform clip guidance
                        if clip_guidance_scale > 0:
                              _lowerCAmelCase												=							(
                                  text_embeddings.chunk(2  )[1] if do_classifier_free_guidance else text_embeddings
                              )
                              _lowerCAmelCase					,							_lowerCAmelCase												=							self.cond_fn(
                                  __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      )

                        # compute the previous noisy sample x_t -> x_t-1
                        _lowerCAmelCase												=							self.scheduler.step(__snake_case      ,      __snake_case      ,      __snake_case      ,      **__snake_case  ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
            _lowerCAmelCase												=							1 / 0.1_82_15 * latents
            _lowerCAmelCase												=							self.vae.decode(__snake_case  ).sample

            _lowerCAmelCase												=							(image / 2 + 0.5).clamp(0      ,      1  )
            _lowerCAmelCase												=							image.cpu().permute(0      ,      2      ,      3      ,      1  ).numpy()

            if output_type == "pil":
                  _lowerCAmelCase												=							self.numpy_to_pil(__snake_case  )

            if not return_dict:
                  return (image, None)

            return StableDiffusionPipelineOutput(images=__snake_case      ,      nsfw_content_detected=__snake_case  )













'''simple docstring'''






# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

A__    :       List[str]              =TypeVar('''T''')




class 			UpperCAmelCase					(      Generic[T]       ):




      def __init__(      self      :			Tuple      ,      __snake_case      :			bool = True  )    ->				None:
            _lowerCAmelCase												=							{}  # dictionary of lists
            _lowerCAmelCase												=							directed




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			T      ,      __snake_case      :			T  )    ->				GraphAdjacencyList[T]:

            if not self.directed:  # For undirected graphs
                  # if both source vertex and destination vertex are both present in the
                  # adjacency list, add destination vertex to source vertex list of adjacent
                  # vertices and add source vertex to destination vertex list of adjacent
                  # vertices.
                  if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                        self.adj_list[destination_vertex].append(__snake_case  )
                  # if only source vertex is present in adjacency list, add destination vertex
                  # to source vertex list of adjacent vertices, then create a new vertex with
                  # destination vertex as key and assign a list containing the source vertex
                  # as it's first adjacent vertex.
                  elif source_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                        _lowerCAmelCase												=							[source_vertex]
                  # if only destination vertex is present in adjacency list, add source vertex
                  # to destination vertex list of adjacent vertices, then create a new vertex
                  # with source vertex as key and assign a list containing the source vertex
                  # as it's first adjacent vertex.
                  elif destination_vertex in self.adj_list:
                        self.adj_list[destination_vertex].append(__snake_case  )
                        _lowerCAmelCase												=							[destination_vertex]
                  # if both source vertex and destination vertex are not present in adjacency
                  # list, create a new vertex with source vertex as key and assign a list
                  # containing the destination vertex as it's first adjacent vertex also
                  # create a new vertex with destination vertex as key and assign a list
                  # containing the source vertex as it's first adjacent vertex.
                  else:
                        _lowerCAmelCase												=							[destination_vertex]
                        _lowerCAmelCase												=							[source_vertex]
            else:  # For directed graphs
                  # if both source vertex and destination vertex are present in adjacency
                  # list, add destination vertex to source vertex list of adjacent vertices.
                  if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                  # if only source vertex is present in adjacency list, add destination
                  # vertex to source vertex list of adjacent vertices and create a new vertex
                  # with destination vertex as key, which has no adjacent vertex
                  elif source_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                        _lowerCAmelCase												=							[]
                  # if only destination vertex is present in adjacency list, create a new
                  # vertex with source vertex as key and assign a list containing destination
                  # vertex as first adjacent vertex
                  elif destination_vertex in self.adj_list:
                        _lowerCAmelCase												=							[destination_vertex]
                  # if both source vertex and destination vertex are not present in adjacency
                  # list, create a new vertex with source vertex as key and a list containing
                  # destination vertex as it's first adjacent vertex. Then create a new vertex
                  # with destination vertex as key, which has no adjacent vertex
                  else:
                        _lowerCAmelCase												=							[destination_vertex]
                        _lowerCAmelCase												=							[]

            return self




      def __repr__(      self      :			int  )    ->				str:
            return pformat(self.adj_list  )






