






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     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   if isinstance(__lowercase    ,      torch.Tensor):
      return image
   elif isinstance(__lowercase    ,      PIL.Image.Image):
      UpperCamelCase_									=     [image]

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






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase=0.9_995):
   if not isinstance(__lowercase    ,      np.ndarray):
      UpperCamelCase_									=     True
      UpperCamelCase_									=     va.device
      UpperCamelCase_									=     va.cpu().numpy()
      UpperCamelCase_									=     va.cpu().numpy()

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

   if inputs_are_torch:
      UpperCamelCase_									=     torch.from_numpy(__lowercase).to(__lowercase)

   return va






def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     F.normalize(__lowercase    ,      dim=-1)
   UpperCamelCase_									=     F.normalize(__lowercase    ,      dim=-1)
   return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)






def     _snake_case    (__lowercase    ,      __lowercase):
   for param in model.parameters():
      UpperCamelCase_									=     value


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		)			-> Tuple:
     super().__init__()
     self.register_modules(
         vae=_UpperCAmelCase		,		text_encoder=_UpperCAmelCase		,		clip_model=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase		,		unet=_UpperCAmelCase		,		scheduler=_UpperCAmelCase		,		feature_extractor=_UpperCAmelCase		,		coca_model=_UpperCAmelCase		,		coca_tokenizer=_UpperCAmelCase		,		coca_transform=_UpperCAmelCase		,		)
     UpperCamelCase_									=     (
         feature_extractor.size
         if isinstance(feature_extractor.size		,		_UpperCAmelCase )
         else feature_extractor.size['shortest_edge']
     )
     UpperCamelCase_									=     transforms.Normalize(mean=feature_extractor.image_mean		,		std=feature_extractor.image_std )
     set_requires_grad(self.text_encoder		,		_UpperCAmelCase )
     set_requires_grad(self.clip_model		,		_UpperCAmelCase )




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




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     self.enable_attention_slicing(_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> int:
     set_requires_grad(self.vae		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     set_requires_grad(self.vae		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     set_requires_grad(self.unet		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[str]:
     set_requires_grad(self.unet		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Any:
     # get the original timestep using init_timestep
     UpperCamelCase_									=     min(int(num_inference_steps * strength )		,		_UpperCAmelCase )

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

     return timesteps, num_inference_steps - t_start




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=None )			-> str:
     if not isinstance(_UpperCAmelCase		,		torch.Tensor ):
        raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(_UpperCAmelCase )}""" )

     UpperCamelCase_									=     image.to(device=_UpperCAmelCase		,		dtype=_UpperCAmelCase )

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

     # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
     UpperCamelCase_									=     0.1_8_2_1_5 * init_latents
     UpperCamelCase_									=     init_latents.repeat_interleave(_UpperCAmelCase		,		dim=0 )

     UpperCamelCase_									=     randn_tensor(init_latents.shape		,		generator=_UpperCAmelCase		,		device=_UpperCAmelCase		,		dtype=_UpperCAmelCase )

     # get latents
     UpperCamelCase_									=     self.scheduler.add_noise(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )
     UpperCamelCase_									=     init_latents

     return latents




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




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Union[str, Any]:
     UpperCamelCase_									=     self.feature_extractor.preprocess(_UpperCAmelCase )
     UpperCamelCase_									=     torch.from_numpy(clip_image_input['pixel_values'][0] ).unsqueeze(0 ).to(self.device ).half()
     UpperCamelCase_									=     self.clip_model.get_image_features(_UpperCAmelCase )
     UpperCamelCase_									=     image_embeddings_clip / image_embeddings_clip.norm(p=2		,		dim=-1		,		keepdim=_UpperCAmelCase )
     UpperCamelCase_									=     image_embeddings_clip.repeat_interleave(_UpperCAmelCase		,		dim=0 )
     return image_embeddings_clip




  @torch.enable_grad()
  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)			-> List[Any]:
     UpperCamelCase_									=     latents.detach().requires_grad_()

     UpperCamelCase_									=     self.scheduler.scale_model_input(_UpperCAmelCase		,		_UpperCAmelCase )

     # predict the noise residual
     UpperCamelCase_									=     self.unet(_UpperCAmelCase		,		_UpperCAmelCase		,		encoder_hidden_states=_UpperCAmelCase ).sample

     if isinstance(self.scheduler		,		(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
        UpperCamelCase_									=     self.scheduler.alphas_cumprod[timestep]
        UpperCamelCase_									=     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
        UpperCamelCase_									=     (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

        UpperCamelCase_									=     torch.sqrt(_UpperCAmelCase )
        UpperCamelCase_									=     pred_original_sample * (fac) + latents * (1 - fac)
     elif isinstance(self.scheduler		,		_UpperCAmelCase ):
        UpperCamelCase_									=     self.scheduler.sigmas[index]
        UpperCamelCase_									=     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
     UpperCamelCase_									=     1 / 0.1_8_2_1_5 * sample
     UpperCamelCase_									=     self.vae.decode(_UpperCAmelCase ).sample
     UpperCamelCase_									=     (image / 2 + 0.5).clamp(0		,		1 )

     UpperCamelCase_									=     transforms.Resize(self.feature_extractor_size )(_UpperCAmelCase )
     UpperCamelCase_									=     self.normalize(_UpperCAmelCase ).to(latents.dtype )

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

     UpperCamelCase_									=     spherical_dist_loss(_UpperCAmelCase		,		_UpperCAmelCase ).mean() * clip_guidance_scale

     UpperCamelCase_									=     -torch.autograd.grad(_UpperCAmelCase		,		_UpperCAmelCase )[0]

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




  @torch.no_grad()
  def __call__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = 512		,		_UpperCAmelCase = 512		,		_UpperCAmelCase = 0.6		,		_UpperCAmelCase = 50		,		_UpperCAmelCase = 7.5		,		_UpperCAmelCase = 1		,		_UpperCAmelCase = 0.0		,		_UpperCAmelCase = 100		,		_UpperCAmelCase = None		,		_UpperCAmelCase = "pil"		,		_UpperCAmelCase = True		,		_UpperCAmelCase = 0.8		,		_UpperCAmelCase = 0.1		,		_UpperCAmelCase = 0.1		,		)			-> int:
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ) and len(_UpperCAmelCase ) != batch_size:
        raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(_UpperCAmelCase )} 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(_UpperCAmelCase		,		torch.Generator ) and batch_size > 1:
        UpperCamelCase_									=     [generator] + [None] * (batch_size - 1)

     UpperCamelCase_									=     [
         ('model', self.coca_model is None),
         ('tokenizer', self.coca_tokenizer is None),
         ('transform', self.coca_transform is None),
     ]
     UpperCamelCase_									=     [x[0] for x in coca_is_none if x[1]]
     UpperCamelCase_									=     ', '.join(_UpperCAmelCase )
     # generate prompts with coca model if prompt is None
     if content_prompt is None:
        if len(_UpperCAmelCase ):
           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.""" )
        UpperCamelCase_									=     self.get_image_description(_UpperCAmelCase )
     if style_prompt is None:
        if len(_UpperCAmelCase ):
           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.""" )
        UpperCamelCase_									=     self.get_image_description(_UpperCAmelCase )

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

     UpperCamelCase_									=     self.tokenizer(
         _UpperCAmelCase		,		padding='max_length'		,		max_length=self.tokenizer.model_max_length		,		truncation=_UpperCAmelCase		,		return_tensors='pt'		,		)
     UpperCamelCase_									=     self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]

     UpperCamelCase_									=     slerp(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )

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

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

     self.scheduler.set_timesteps(_UpperCAmelCase		,		**_UpperCAmelCase )
     # 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 )

     UpperCamelCase_		,      UpperCamelCase_									=     self.get_timesteps(_UpperCAmelCase		,		_UpperCAmelCase		,		self.device )
     UpperCamelCase_									=     timesteps[:1].repeat(_UpperCAmelCase )

     # Preprocess image
     UpperCamelCase_									=     preprocess(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )
     UpperCamelCase_									=     self.prepare_latents(
         _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		text_embeddings.dtype		,		self.device		,		_UpperCAmelCase )

     UpperCamelCase_									=     preprocess(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )
     UpperCamelCase_									=     self.prepare_latents(
         _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		text_embeddings.dtype		,		self.device		,		_UpperCAmelCase )

     UpperCamelCase_									=     slerp(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )

     if clip_guidance_scale > 0:
        UpperCamelCase_									=     self.get_clip_image_embeddings(_UpperCAmelCase		,		_UpperCAmelCase )
        UpperCamelCase_									=     self.get_clip_image_embeddings(_UpperCAmelCase		,		_UpperCAmelCase )
        UpperCamelCase_									=     slerp(
            _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )

     # 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.
     UpperCamelCase_									=     guidance_scale > 1.0
     # get unconditional embeddings for classifier free guidance
     if do_classifier_free_guidance:
        UpperCamelCase_									=     content_text_input.input_ids.shape[-1]
        UpperCamelCase_									=     self.tokenizer(['']		,		padding='max_length'		,		max_length=_UpperCAmelCase		,		return_tensors='pt' )
        UpperCamelCase_									=     self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
        # duplicate unconditional embeddings for each generation per prompt
        UpperCamelCase_									=     uncond_embeddings.repeat_interleave(_UpperCAmelCase		,		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
        UpperCamelCase_									=     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`.
     UpperCamelCase_									=     (batch_size, self.unet.config.in_channels, height // 8, width // 8)
     UpperCamelCase_									=     text_embeddings.dtype
     if latents is None:
        if self.device.type == "mps":
           # randn does not work reproducibly on mps
           UpperCamelCase_									=     torch.randn(_UpperCAmelCase		,		generator=_UpperCAmelCase		,		device='cpu'		,		dtype=_UpperCAmelCase ).to(
               self.device )
        else:
           UpperCamelCase_									=     torch.randn(_UpperCAmelCase		,		generator=_UpperCAmelCase		,		device=self.device		,		dtype=_UpperCAmelCase )
     else:
        if latents.shape != latents_shape:
           raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
        UpperCamelCase_									=     latents.to(self.device )

     # scale the initial noise by the standard deviation required by the scheduler
     UpperCamelCase_									=     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]
     UpperCamelCase_									=     'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
     UpperCamelCase_									=     {}
     if accepts_eta:
        UpperCamelCase_									=     eta

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

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

           # predict the noise residual
           UpperCamelCase_									=     self.unet(_UpperCAmelCase		,		_UpperCAmelCase		,		encoder_hidden_states=_UpperCAmelCase ).sample

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

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

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
     UpperCamelCase_									=     1 / 0.1_8_2_1_5 * latents
     UpperCamelCase_									=     self.vae.decode(_UpperCAmelCase ).sample

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

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

     if not return_dict:
        return (image, None)

     return StableDiffusionPipelineOutput(images=_UpperCAmelCase		,		nsfw_content_detected=_UpperCAmelCase )













from typing import Any






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validation(
       __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      )
   # Creates data structures and fill initial step
   UpperCamelCase_									=     {}
   UpperCamelCase_									=     {}
   for state in states_space:
      UpperCamelCase_									=     observations_space[0]
      UpperCamelCase_									=     (
          initial_probabilities[state] * emission_probabilities[state][observation]
      )
      UpperCamelCase_									=     None

   # Fills the data structure with the probabilities of
   # different transitions and pointers to previous states
   for o in range(1    ,      len(__lowercase)):
      UpperCamelCase_									=     observations_space[o]
      UpperCamelCase_									=     observations_space[o - 1]
      for state in states_space:
         # Calculates the argmax for probability function
         UpperCamelCase_									=     ''
         UpperCamelCase_									=     -1
         for k_state in states_space:
            UpperCamelCase_									=     (
                probabilities[(k_state, prior_observation)]
                * transition_probabilities[k_state][state]
                * emission_probabilities[state][observation]
            )
            if probability > max_probability:
               UpperCamelCase_									=     probability
               UpperCamelCase_									=     k_state

            # Update probabilities and pointers dicts
         UpperCamelCase_									=     (
             probabilities[(arg_max, prior_observation)]
             * transition_probabilities[arg_max][state]
             * emission_probabilities[state][observation]
         )

         UpperCamelCase_									=     arg_max

    # The final observation
   UpperCamelCase_									=     observations_space[len(__lowercase) - 1]

   # argmax for given final observation
   UpperCamelCase_									=     ''
   UpperCamelCase_									=     -1
   for k_state in states_space:
      UpperCamelCase_									=     probabilities[(k_state, final_observation)]
      if probability > max_probability:
         UpperCamelCase_									=     probability
         UpperCamelCase_									=     k_state
   UpperCamelCase_									=     arg_max

   # Process pointers backwards
   UpperCamelCase_									=     last_state
   UpperCamelCase_									=     []
   for o in range(len(__lowercase) - 1    ,      -1    ,      -1):
      result.append(__lowercase)
      UpperCamelCase_									=     pointers[previous, observations_space[o]]
   result.reverse()

   return result






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validate_not_empty(
       __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      )
   _validate_lists(__lowercase    ,      __lowercase)
   _validate_dicts(
       __lowercase    ,      __lowercase    ,      __lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   if not all(
       [
           observations_space,
           states_space,
           initial_probabilities,
           transition_probabilities,
           emission_probabilities,
       ]):
      raise ValueError('There\'s an empty parameter')






def     _snake_case    (__lowercase    ,      __lowercase):
   _validate_list(__lowercase    ,      'observations_space')
   _validate_list(__lowercase    ,      'states_space')






def     _snake_case    (__lowercase    ,      __lowercase):
   if not isinstance(_object    ,      __lowercase):
      UpperCamelCase_									=     f"""{var_name} must be a list"""
      raise ValueError(__lowercase)
   else:
      for x in _object:
         if not isinstance(__lowercase    ,      __lowercase):
            UpperCamelCase_									=     f"""{var_name} must be a list of strings"""
            raise ValueError(__lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validate_dict(__lowercase    ,      'initial_probabilities'    ,      __lowercase)
   _validate_nested_dict(__lowercase    ,      'transition_probabilities')
   _validate_nested_dict(__lowercase    ,      'emission_probabilities')






def     _snake_case    (__lowercase    ,      __lowercase):
   _validate_dict(_object    ,      __lowercase    ,      __lowercase)
   for x in _object.values():
      _validate_dict(__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase = False):
   if not isinstance(_object    ,      __lowercase):
      UpperCamelCase_									=     f"""{var_name} must be a dict"""
      raise ValueError(__lowercase)
   if not all(isinstance(__lowercase    ,      __lowercase) for x in _object):
      UpperCamelCase_									=     f"""{var_name} all keys must be strings"""
      raise ValueError(__lowercase)
   if not all(isinstance(__lowercase    ,      __lowercase) for x in _object.values()):
      UpperCamelCase_									=     'nested dictionary ' if nested else ''
      UpperCamelCase_									=     f"""{var_name} {nested_text}all values must be {value_type.__name__}"""
      raise ValueError(__lowercase)


if __name__ == "__main__":
 from doctest import testmod

 testmod()





