

from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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






class 						__magic_name__    (       _snake_case	,						_snake_case    ):


 @register_to_config
 def __init__(						self		: Optional[Any]			,						lowerCAmelCase__		: bool			,						lowerCAmelCase__		: Optional[int] = None			,						lowerCAmelCase__		: Optional[int] = None    )    ->      Optional[int]:
   super().__init__()

   UpperCAmelCase					      =  learnable

   if self.learnable:
     assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
     assert length is not None, "learnable=True requires `length` to be set"

     UpperCAmelCase					      =  torch.zeros(lowerCAmelCase__			,						lowerCAmelCase__    )
   else:
     UpperCAmelCase					      =  None

   UpperCAmelCase					      =  torch.nn.Parameter(lowerCAmelCase__    )






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42


 def __init__(						self		: str			,						lowerCAmelCase__		: VQModel			,						lowerCAmelCase__		: CLIPTextModel			,						lowerCAmelCase__		: CLIPTokenizer			,						lowerCAmelCase__		: TransformeraDModel			,						lowerCAmelCase__		: VQDiffusionScheduler			,						lowerCAmelCase__		: LearnedClassifierFreeSamplingEmbeddings			,						)    ->      int:
   super().__init__()

   self.register_modules(
       vqvae=lowerCAmelCase__			,						transformer=lowerCAmelCase__			,						text_encoder=lowerCAmelCase__			,						tokenizer=lowerCAmelCase__			,						scheduler=lowerCAmelCase__			,						learned_classifier_free_sampling_embeddings=lowerCAmelCase__			,						)


 def _UpperCamelCase	(						self		: List[Any]			,						lowerCAmelCase__		: Optional[Any]			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: int    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  len(lowerCAmelCase__    ) if isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ) else 1

   # get prompt text embeddings
   UpperCAmelCase					      =  self.tokenizer(
       lowerCAmelCase__			,						padding="max_length"			,						max_length=self.tokenizer.model_max_length			,						return_tensors="pt"			,						)
   UpperCAmelCase					      =  text_inputs.input_ids

   if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
     UpperCAmelCase					      =  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
     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}"    )
     UpperCAmelCase					      =  text_input_ids[:, : self.tokenizer.model_max_length]
   UpperCAmelCase					      =  self.text_encoder(text_input_ids.to(self.device    )    )[0]

   # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
   # While CLIP does normalize the pooled output of the text transformer when combining
   # the image and text embeddings, CLIP does not directly normalize the last hidden state.
   #
   # CLIP normalizing the pooled output.
   # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
   UpperCAmelCase					      =  prompt_embeds / prompt_embeds.norm(dim=-1			,						keepdim=lowerCAmelCase__    )

   # duplicate text embeddings for each generation per prompt
   UpperCAmelCase					      =  prompt_embeds.repeat_interleave(lowerCAmelCase__			,						dim=0    )

   if do_classifier_free_guidance:
     if self.learned_classifier_free_sampling_embeddings.learnable:
       UpperCAmelCase					      =  self.learned_classifier_free_sampling_embeddings.embeddings
       UpperCAmelCase					      =  negative_prompt_embeds.unsqueeze(0    ).repeat(lowerCAmelCase__			,						1			,						1    )
     else:
       UpperCAmelCase					      =  [""] * batch_size

       UpperCAmelCase					      =  text_input_ids.shape[-1]
       UpperCAmelCase					      =  self.tokenizer(
           lowerCAmelCase__			,						padding="max_length"			,						max_length=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						return_tensors="pt"			,						)
       UpperCAmelCase					      =  self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]
       # See comment for normalizing text embeddings
       UpperCAmelCase					      =  negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1			,						keepdim=lowerCAmelCase__    )

     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
     UpperCAmelCase					      =  negative_prompt_embeds.shape[1]
     UpperCAmelCase					      =  negative_prompt_embeds.repeat(1			,						lowerCAmelCase__			,						1    )
     UpperCAmelCase					      =  negative_prompt_embeds.view(batch_size * num_images_per_prompt			,						lowerCAmelCase__			,						-1    )

     # 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([negative_prompt_embeds, prompt_embeds]    )

   return prompt_embeds


 @torch.no_grad()
 def __call__(						self		: Optional[Any]			,						lowerCAmelCase__		: Union[str, List[str]]			,						lowerCAmelCase__		: int = 1_0_0			,						lowerCAmelCase__		: float = 5.0			,						lowerCAmelCase__		: float = 1.0			,						lowerCAmelCase__		: int = 1			,						lowerCAmelCase__		: Optional[Union[torch.Generator, List[torch.Generator]]] = None			,						lowerCAmelCase__		: Optional[torch.FloatTensor] = None			,						lowerCAmelCase__		: Optional[str] = "pil"			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,						lowerCAmelCase__		: int = 1			,						)    ->      Union[ImagePipelineOutput, Tuple]:
   if isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ):
     UpperCAmelCase					      =  1
   elif isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ):
     UpperCAmelCase					      =  len(lowerCAmelCase__    )
   else:
     raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__    )}"    )

   UpperCAmelCase					      =  batch_size * num_images_per_prompt

   UpperCAmelCase					      =  guidance_scale > 1.0

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

   if (callback_steps is None) or (
       callback_steps is not None and (not isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ) or callback_steps <= 0)
   ):
     raise ValueError(
         f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
         f" {type(lowerCAmelCase__    )}."    )

   # get the initial completely masked latents unless the user supplied it

   UpperCAmelCase					      =  (batch_size, self.transformer.num_latent_pixels)
   if latents is None:
     UpperCAmelCase					      =  self.transformer.num_vector_embeds - 1
     UpperCAmelCase					      =  torch.full(lowerCAmelCase__			,						lowerCAmelCase__    ).to(self.device    )
   else:
     if latents.shape != latents_shape:
       raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"    )
     if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
       raise ValueError(
           "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
           f" {self.transformer.num_vector_embeds - 1} (inclusive)."    )
     UpperCAmelCase					      =  latents.to(self.device    )

   # set timesteps
   self.scheduler.set_timesteps(lowerCAmelCase__			,						device=self.device    )

   UpperCAmelCase					      =  self.scheduler.timesteps.to(self.device    )

   UpperCAmelCase					      =  latents

   for i, t in enumerate(self.progress_bar(lowerCAmelCase__    )    ):
     # expand the sample if we are doing classifier free guidance
     UpperCAmelCase					      =  torch.cat([sample] * 2    ) if do_classifier_free_guidance else sample

     # predict the un-noised image
     # model_output == `log_p_x_0`
     UpperCAmelCase					      =  self.transformer(lowerCAmelCase__			,						encoder_hidden_states=lowerCAmelCase__			,						timestep=lowerCAmelCase__    ).sample

     if do_classifier_free_guidance:
       UpperCAmelCase      ,	UpperCAmelCase					      =  model_output.chunk(2    )
       UpperCAmelCase					      =  model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
       model_output -= torch.logsumexp(lowerCAmelCase__			,						dim=1			,						keepdim=lowerCAmelCase__    )

     UpperCAmelCase					      =  self.truncate(lowerCAmelCase__			,						lowerCAmelCase__    )

     # remove `log(0)`'s (`-inf`s)
     UpperCAmelCase					      =  model_output.clamp(-7_0    )

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase					      =  self.scheduler.step(lowerCAmelCase__			,						timestep=lowerCAmelCase__			,						sample=lowerCAmelCase__			,						generator=lowerCAmelCase__    ).prev_sample

     # call the callback, if provided
     if callback is not None and i % callback_steps == 0:
       callback(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )

   UpperCAmelCase					      =  self.vqvae.config.vq_embed_dim
   UpperCAmelCase					      =  (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
   UpperCAmelCase					      =  self.vqvae.quantize.get_codebook_entry(lowerCAmelCase__			,						shape=lowerCAmelCase__    )
   UpperCAmelCase					      =  self.vqvae.decode(lowerCAmelCase__			,						force_not_quantize=lowerCAmelCase__    ).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(lowerCAmelCase__    )

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Union[str, Any]			,						lowerCAmelCase__		: torch.FloatTensor			,						lowerCAmelCase__		: float    )    ->      torch.FloatTensor:
   UpperCAmelCase      ,	UpperCAmelCase					      =  torch.sort(lowerCAmelCase__			,						1			,						descending=lowerCAmelCase__    )
   UpperCAmelCase					      =  torch.exp(lowerCAmelCase__    )
   UpperCAmelCase					      =  sorted_p_x_0.cumsum(dim=1    ) < truncation_rate

   # Ensure that at least the largest probability is not zeroed out
   UpperCAmelCase					      =  torch.full_like(keep_mask[:, 0:1, :]			,						lowerCAmelCase__    )
   UpperCAmelCase					      =  torch.cat((all_true, keep_mask)			,						dim=1    )
   UpperCAmelCase					      =  keep_mask[:, :-1, :]

   UpperCAmelCase					      =  keep_mask.gather(1			,						indices.argsort(1    )    )

   UpperCAmelCase					      =  log_p_x_0.clone()

   UpperCAmelCase					      =  -torch.inf  # -inf = log(0)

   return rv







def      _lowerCAmelCase(	__A   ,       __A			):
  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def      _lowerCAmelCase(	__A   ,       __A=0			):
  return sorted(__A   ,       key=lambda __A			: x[column]			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

  for i in range(points_counts - 1			):
    for j in range(i + 1   ,       __A			):
      UpperCAmelCase					      =  euclidean_distance_sqr(points[i]   ,       points[j]			)
      if current_dis < min_dis:
        UpperCAmelCase					      =  current_dis
  return min_dis
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

  for i in range(min(6   ,       points_counts - 1			)   ,       __A			):
    for j in range(max(0   ,       i - 6			)   ,       __A			):
      UpperCAmelCase					      =  euclidean_distance_sqr(points[i]   ,       points[j]			)
      if current_dis < min_dis:
        UpperCAmelCase					      =  current_dis
  return min_dis
def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):

  # base case
  if points_counts <= 3:
    return dis_between_closest_pair(__A   ,       __A			)

  # recursion
  UpperCAmelCase					      =  points_counts // 2
  UpperCAmelCase					      =  closest_pair_of_points_sqr(
      __A   ,       points_sorted_on_y[:mid]   ,       __A			)
  UpperCAmelCase					      =  closest_pair_of_points_sqr(
      __A   ,       points_sorted_on_y[mid:]   ,       points_counts - mid			)
  UpperCAmelCase					      =  min(__A   ,       __A			)
  UpperCAmelCase					      =  []
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]			) < closest_pair_dis:
      cross_strip.append(__A			)

  UpperCAmelCase					      =  dis_between_closest_in_strip(
      __A   ,       len(__A			)   ,       __A			)
  return min(__A   ,       __A			)
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=0			)
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=1			)
  return (
      closest_pair_of_points_sqr(
          __A   ,       __A   ,       __A			)
  ) ** 0.5


if __name__ == "__main__":
      lowerCAmelCase__       = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
      print("Distance:", closest_pair_of_points(points, len(points)))



