
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 							A__		(	__SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE):
      @register_to_config
      def __init__(      self    ,  __magic_name__    ,  __magic_name__ = None    ,  __magic_name__ = None      ):
       super().__init__()

       lowerCamelCase		:	int							=      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"

        lowerCamelCase		:	List[Any]							=      torch.zeros(__magic_name__    ,  __magic_name__      )
       else:
        lowerCamelCase		:	List[Any]							=      None

       lowerCamelCase		:	List[Any]							=      torch.nn.Parameter(__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    VQModel
      _UpperCAmelCase				:    CLIPTextModel
      _UpperCAmelCase				:    CLIPTokenizer
      _UpperCAmelCase				:    TransformeraDModel
      _UpperCAmelCase				:    LearnedClassifierFreeSamplingEmbeddings
      _UpperCAmelCase				:    VQDiffusionScheduler
      def __init__(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  ):
       super().__init__()

       self.register_modules(
           vqvae=__magic_name__    ,  transformer=__magic_name__    ,  text_encoder=__magic_name__    ,  tokenizer=__magic_name__    ,  scheduler=__magic_name__    ,  learned_classifier_free_sampling_embeddings=__magic_name__    ,  )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	int							=      len(__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else 1

       # get prompt text embeddings
       lowerCamelCase		:	Tuple							=      self.tokenizer(
           __magic_name__    ,  padding="""max_length"""    ,  max_length=self.tokenizer.model_max_length    ,  return_tensors="""pt"""    ,  )
       lowerCamelCase		:	Dict							=      text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        lowerCamelCase		:	Union[str, Any]							=      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}'''      )
        lowerCamelCase		:	Union[str, Any]							=      text_input_ids[:, : self.tokenizer.model_max_length]
       lowerCamelCase		:	Any							=      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
       lowerCamelCase		:	Tuple							=      prompt_embeds / prompt_embeds.norm(dim=-1    ,  keepdim=__magic_name__      )

       # duplicate text embeddings for each generation per prompt
       lowerCamelCase		:	Optional[Any]							=      prompt_embeds.repeat_interleave(__magic_name__    ,  dim=0      )

       if do_classifier_free_guidance:
        if self.learned_classifier_free_sampling_embeddings.learnable:
         lowerCamelCase		:	Any							=      self.learned_classifier_free_sampling_embeddings.embeddings
         lowerCamelCase		:	List[str]							=      negative_prompt_embeds.unsqueeze(0      ).repeat(__magic_name__    ,  1    ,  1      )
        else:
         lowerCamelCase		:	Optional[int]							=      [""""""] * batch_size

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

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        lowerCamelCase		:	Dict							=      negative_prompt_embeds.shape[1]
        lowerCamelCase		:	int							=      negative_prompt_embeds.repeat(1    ,  __magic_name__    ,  1      )
        lowerCamelCase		:	Tuple							=      negative_prompt_embeds.view(batch_size * num_images_per_prompt    ,  __magic_name__    ,  -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
        lowerCamelCase		:	List[str]							=      torch.cat([negative_prompt_embeds, prompt_embeds]      )

       return prompt_embeds
      @torch.no_grad()
      def __call__(      self    ,  __magic_name__    ,  __magic_name__ = 1_0_0    ,  __magic_name__ = 5.0    ,  __magic_name__ = 1.0    ,  __magic_name__ = 1    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = "pil"    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = 1    ,  ):
       if isinstance(__magic_name__    ,  __magic_name__      ):
        lowerCamelCase		:	List[str]							=      1
       elif isinstance(__magic_name__    ,  __magic_name__      ):
        lowerCamelCase		:	Dict							=      len(__magic_name__      )
       else:
        raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__magic_name__      )}'''      )

       lowerCamelCase		:	Tuple							=      batch_size * num_images_per_prompt

       lowerCamelCase		:	Dict							=      guidance_scale > 1.0

       lowerCamelCase		:	Tuple							=      self._encode_prompt(__magic_name__    ,  __magic_name__    ,  __magic_name__      )

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

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

       lowerCamelCase		:	List[Any]							=      (batch_size, self.transformer.num_latent_pixels)
       if latents is None:
        lowerCamelCase		:	int							=      self.transformer.num_vector_embeds - 1
        lowerCamelCase		:	Any							=      torch.full(__magic_name__    ,  __magic_name__      ).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).'''      )
        lowerCamelCase		:	Union[str, Any]							=      latents.to(self.device      )

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

       lowerCamelCase		:	Tuple							=      self.scheduler.timesteps.to(self.device      )

       lowerCamelCase		:	Any							=      latents

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

        # predict the un-noised image
        # model_output == `log_p_x_0`
        lowerCamelCase		:	Tuple							=      self.transformer(__magic_name__    ,  encoder_hidden_states=__magic_name__    ,  timestep=__magic_name__      ).sample

        if do_classifier_free_guidance:
         lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      model_output.chunk(2      )
         lowerCamelCase		:	Optional[int]							=      model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
         model_output -= torch.logsumexp(__magic_name__    ,  dim=1    ,  keepdim=__magic_name__      )

        lowerCamelCase		:	List[str]							=      self.truncate(__magic_name__    ,  __magic_name__      )

        # remove `log(0)`'s (`-inf`s)
        lowerCamelCase		:	List[Any]							=      model_output.clamp(-7_0      )

        # compute the previous noisy sample x_t -> x_t-1
        lowerCamelCase		:	Dict							=      self.scheduler.step(__magic_name__    ,  timestep=__magic_name__    ,  sample=__magic_name__    ,  generator=__magic_name__      ).prev_sample

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

       lowerCamelCase		:	int							=      self.vqvae.config.vq_embed_dim
       lowerCamelCase		:	Optional[Any]							=      (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
       lowerCamelCase		:	Optional[int]							=      self.vqvae.quantize.get_codebook_entry(__magic_name__    ,  shape=__magic_name__      )
       lowerCamelCase		:	Any							=      self.vqvae.decode(__magic_name__    ,  force_not_quantize=__magic_name__      ).sample

       lowerCamelCase		:	Any							=      (image / 2 + 0.5).clamp(0    ,  1      )
       lowerCamelCase		:	int							=      image.cpu().permute(0    ,  2    ,  3    ,  1      ).numpy()

       if output_type == "pil":
        lowerCamelCase		:	List[Any]							=      self.numpy_to_pil(__magic_name__      )

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=__magic_name__      )






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      torch.sort(__magic_name__    ,  1    ,  descending=__magic_name__      )
       lowerCamelCase		:	List[Any]							=      torch.exp(__magic_name__      )
       lowerCamelCase		:	Dict							=      sorted_p_x_0.cumsum(dim=1      ) < truncation_rate

       # Ensure that at least the largest probability is not zeroed out
       lowerCamelCase		:	Optional[Any]							=      torch.full_like(keep_mask[:, 0:1, :]    ,  __magic_name__      )
       lowerCamelCase		:	Union[str, Any]							=      torch.cat((all_true, keep_mask)    ,  dim=1      )
       lowerCamelCase		:	Optional[int]							=      keep_mask[:, :-1, :]

       lowerCamelCase		:	str							=      keep_mask.gather(1    ,  indices.argsort(1      )      )

       lowerCamelCase		:	Optional[Any]							=      log_p_x_0.clone()

       lowerCamelCase		:	str							=      -torch.inf  # -inf = log(0)

       return rv






from __future__ import annotations

import collections
import pprint
from pathlib import Path





def     _a					(						lowerCamelCase							):
 return "".join(sorted(lowerCamelCase							)							)





def     _a					(						lowerCamelCase							):
 return word_by_signature[signature(lowerCamelCase							)]


_lowerCamelCase					   =Path(__file__).parent.joinpath("""words.txt""").read_text(encoding="""utf-8""")
_lowerCamelCase					   =sorted({word.strip().lower() for word in data.splitlines()})

_lowerCamelCase					   =collections.defaultdict(list)
for word in word_list:
      word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
      _lowerCamelCase					   ={word: anagram(word) for word in word_list if len(anagram(word)) > 1}

      with open("""anagrams.txt""", """w""") as file:
            file.write("""all_anagrams = \n """)
            file.write(pprint.pformat(all_anagrams))




