


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

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


a					:				List[str]       =      logging.get_logger(__name__)  # pylint: disable=invalid-name






def  _UpperCAmelCase						( self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->str:


 '''simple docstring'''
 snake_case :			Dict							=   image.to(device=__a							)
 snake_case :			Union[str, Any]							=   image  # take image as image_embeddings
 snake_case :			Dict							=   image_embeddings.unsqueeze(1							)

 # duplicate image embeddings for each generation per prompt, using mps friendly method
 snake_case :			str							=   image_embeddings.shape
 snake_case :			int							=   image_embeddings.repeat(1			,  __a			,  1							)
 snake_case :			int							=   image_embeddings.view(bs_embed * num_images_per_prompt			,  __a			,  -1							)

 if do_classifier_free_guidance:
  snake_case :			str							=   torch.zeros_like(__a							)

  # 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
  snake_case :			List[Any]							=   torch.cat([uncond_embeddings, image_embeddings]							)

 return image_embeddings





class   lowerCamelCase						(	__snake_case  ):

       def __init__(      self						,    a__						,    a__						,    a__						,    a__						,    a__ = None						,    ):






        '''simple docstring'''






        super().__init__()

        snake_case :			Any							=   {"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        snake_case :			List[Any]							=   decoder_pipe_kwargs.get('''torch_dtype'''						,    lowerCAmelCase__       ) or prior.dtype

        snake_case :			Union[str, Any]							=   StableDiffusionImageVariationPipeline.from_pretrained(
            '''lambdalabs/sd-image-variations-diffusers'''						,    **lowerCAmelCase__       )

        # replace `_encode_image` method
        snake_case :			Tuple							=   types.MethodType(_encode_image						,    self.decoder_pipe       )

        self.register_modules(
            prior=lowerCAmelCase__						,    tokenizer=lowerCAmelCase__						,    text_encoder=lowerCAmelCase__						,    prior_scheduler=lowerCAmelCase__						,    )

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__						,    a__ = None						,    a__ = None						,    ):






        '''simple docstring'''






        if text_model_output is None:
         snake_case :			Optional[int]							=   len(lowerCAmelCase__       ) if isinstance(lowerCAmelCase__						,    lowerCAmelCase__       ) else 1
         # get prompt text embeddings
         snake_case :			Any							=   self.tokenizer(
             lowerCAmelCase__						,    padding='''max_length'''						,    max_length=self.tokenizer.model_max_length						,    return_tensors='''pt'''						,    )
         snake_case :			List[str]							=   text_inputs.input_ids
         snake_case :			Tuple							=   text_inputs.attention_mask.bool().to(lowerCAmelCase__       )

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          snake_case :			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}'''       )
          snake_case :			Tuple							=   text_input_ids[:, : self.tokenizer.model_max_length]

         snake_case :			str							=   self.text_encoder(text_input_ids.to(lowerCAmelCase__       )       )

         snake_case :			str							=   text_encoder_output.text_embeds
         snake_case :			Any							=   text_encoder_output.last_hidden_state

        else:
         snake_case :			Dict							=   text_model_output[0].shape[0]
         snake_case :			Dict							=   text_model_output[0], text_model_output[1]
         snake_case :			Tuple							=   text_attention_mask

        snake_case :			Optional[Any]							=   text_embeddings.repeat_interleave(lowerCAmelCase__						,    dim=0       )
        snake_case :			Optional[int]							=   text_encoder_hidden_states.repeat_interleave(lowerCAmelCase__						,    dim=0       )
        snake_case :			List[Any]							=   text_mask.repeat_interleave(lowerCAmelCase__						,    dim=0       )

        if do_classifier_free_guidance:
         snake_case :			Optional[int]							=   [""""""] * batch_size

         snake_case :			Dict							=   self.tokenizer(
             lowerCAmelCase__						,    padding='''max_length'''						,    max_length=self.tokenizer.model_max_length						,    truncation=lowerCAmelCase__						,    return_tensors='''pt'''						,    )
         snake_case :			Any							=   uncond_input.attention_mask.bool().to(lowerCAmelCase__       )
         snake_case :			Union[str, Any]							=   self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase__       )       )

         snake_case :			Optional[int]							=   uncond_embeddings_text_encoder_output.text_embeds
         snake_case :			Optional[Any]							=   uncond_embeddings_text_encoder_output.last_hidden_state

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

         snake_case :			List[Any]							=   uncond_embeddings.shape[1]
         snake_case :			Dict							=   uncond_embeddings.repeat(1						,    lowerCAmelCase__       )
         snake_case :			Optional[int]							=   uncond_embeddings.view(batch_size * num_images_per_prompt						,    lowerCAmelCase__       )

         snake_case :			Optional[Any]							=   uncond_text_encoder_hidden_states.shape[1]
         snake_case :			List[Any]							=   uncond_text_encoder_hidden_states.repeat(1						,    lowerCAmelCase__						,    1       )
         snake_case :			Optional[Any]							=   uncond_text_encoder_hidden_states.view(
             batch_size * num_images_per_prompt						,    lowerCAmelCase__						,    -1       )
         snake_case :			Tuple							=   uncond_text_mask.repeat_interleave(lowerCAmelCase__						,    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
         snake_case :			Any							=   torch.cat([uncond_embeddings, text_embeddings]       )
         snake_case :			Any							=   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]       )

         snake_case :			Dict							=   torch.cat([uncond_text_mask, text_mask]       )

        return text_embeddings, text_encoder_hidden_states, text_mask

       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        if self.device != torch.device('''meta'''       ) or not hasattr(self.prior						,    '''_hf_hook'''       ):
         return self.device
        for module in self.prior.modules():
         if (
             hasattr(lowerCAmelCase__						,    '''_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

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






        if latents is None:
         snake_case :			Optional[int]							=   randn_tensor(lowerCAmelCase__						,    generator=lowerCAmelCase__						,    device=lowerCAmelCase__						,    dtype=lowerCAmelCase__       )
        else:
         if latents.shape != shape:
          raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''       )
         snake_case :			List[str]							=   latents.to(lowerCAmelCase__       )

        snake_case :			Any							=   latents * scheduler.init_noise_sigma
        return latents

       def 					UpperCAmelCase					(      self						,    a__ = None       ):






        '''simple docstring'''






        self.decoder_pipe.to(lowerCAmelCase__       )
        super().to(lowerCAmelCase__       )



       @torch.no_grad()
       def __call__(      self						,    a__ = None						,    a__ = None						,    a__ = None						,    a__ = 1						,    a__ = 25						,    a__ = None						,    a__ = None						,    a__ = None						,    a__ = None						,    a__ = 4.0						,    a__ = 8.0						,    a__ = 50						,    a__ = 1						,    a__ = 0.0						,    a__ = "pil"						,    a__ = True						,    ):






        '''simple docstring'''






        if prompt is not None:
         if isinstance(lowerCAmelCase__						,    lowerCAmelCase__       ):
          snake_case :			List[Any]							=   1
         elif isinstance(lowerCAmelCase__						,    lowerCAmelCase__       ):
          snake_case :			List[Any]							=   len(lowerCAmelCase__       )
         else:
          raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__       )}'''       )
        else:
         snake_case :			int							=   text_model_output[0].shape[0]

        snake_case :			List[str]							=   self._execution_device

        snake_case :			List[str]							=   batch_size * num_images_per_prompt

        snake_case :			Optional[Any]							=   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        snake_case :			Optional[Any]							=   self._encode_prompt(
            lowerCAmelCase__						,    lowerCAmelCase__						,    lowerCAmelCase__						,    lowerCAmelCase__						,    lowerCAmelCase__						,    lowerCAmelCase__       )

        # prior

        self.prior_scheduler.set_timesteps(lowerCAmelCase__						,    device=lowerCAmelCase__       )
        snake_case :			Optional[Any]							=   self.prior_scheduler.timesteps

        snake_case :			int							=   self.prior.config.embedding_dim

        snake_case :			Union[str, Any]							=   self.prepare_latents(
            (batch_size, embedding_dim)						,    text_embeddings.dtype						,    lowerCAmelCase__						,    lowerCAmelCase__						,    lowerCAmelCase__						,    self.prior_scheduler						,    )

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

         snake_case :			Dict							=   self.prior(
             lowerCAmelCase__						,    timestep=lowerCAmelCase__						,    proj_embedding=lowerCAmelCase__						,    encoder_hidden_states=lowerCAmelCase__						,    attention_mask=lowerCAmelCase__						,    ).predicted_image_embedding

         if do_classifier_free_guidance:
          snake_case :			Tuple							=   predicted_image_embedding.chunk(2       )
          snake_case :			Dict							=   predicted_image_embedding_uncond + prior_guidance_scale * (
              predicted_image_embedding_text - predicted_image_embedding_uncond
          )

         if i + 1 == prior_timesteps_tensor.shape[0]:
          snake_case :			Tuple							=   None
         else:
          snake_case :			Optional[int]							=   prior_timesteps_tensor[i + 1]

         snake_case :			Tuple							=   self.prior_scheduler.step(
             lowerCAmelCase__						,    timestep=lowerCAmelCase__						,    sample=lowerCAmelCase__						,    generator=lowerCAmelCase__						,    prev_timestep=lowerCAmelCase__						,    ).prev_sample

        snake_case :			Any							=   self.prior.post_process_latents(lowerCAmelCase__       )

        snake_case :			Tuple							=   prior_latents

        snake_case :			Tuple							=   self.decoder_pipe(
            image=lowerCAmelCase__						,    height=lowerCAmelCase__						,    width=lowerCAmelCase__						,    num_inference_steps=lowerCAmelCase__						,    guidance_scale=lowerCAmelCase__						,    generator=lowerCAmelCase__						,    output_type=lowerCAmelCase__						,    return_dict=lowerCAmelCase__						,    num_images_per_prompt=lowerCAmelCase__						,    eta=lowerCAmelCase__						,    )
        return output







'''simple docstring'''

import numpy as np

_lowerCamelCase :				int														= [
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]




class    lowerCamelCase__  :



   def __init__(    self     )				->     None:
        """simple docstring"""





        _UpperCamelCase  :Any       =np.array(lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     np.ndarray:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :str       =np.where(letter == self.SQUARE     )
        _UpperCamelCase  :Union[str, Any]       =np.concatenate([indexa + 1, indexa + 1]     )
        return indexes



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        return self.SQUARE[indexa - 1, indexa - 1]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :str       =message.lower()
        _UpperCamelCase  :List[Any]       =message.replace("""j"""					,      """i"""     )

        _UpperCamelCase  :List[str]       =""""""
        for letter_index in range(len(lowerCAmelCase__     )     ):
             if message[letter_index] != " ":
                  _UpperCamelCase  :Dict       =self.letter_to_numbers(message[letter_index]     )
                  _UpperCamelCase  :List[Any]       =encoded_message + str(numbers[0]     ) + str(numbers[1]     )
             elif message[letter_index] == " ":
                  _UpperCamelCase  :List[Any]       =encoded_message + """ """

        return encoded_message




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =message.replace(""" """					,      """  """     )
        _UpperCamelCase  :Dict       =""""""
        for numbers_index in range(int(len(lowerCAmelCase__     ) / 2     )     ):
             if message[numbers_index * 2] != " ":
                  _UpperCamelCase  :Tuple       =message[numbers_index * 2]
                  _UpperCamelCase  :List[str]       =message[numbers_index * 2 + 1]

                  _UpperCamelCase  :Dict       =self.numbers_to_letter(int(lowerCAmelCase__     )					,      int(lowerCAmelCase__     )     )
                  _UpperCamelCase  :Union[str, Any]       =decoded_message + letter
             elif message[numbers_index * 2] == " ":
                  _UpperCamelCase  :Dict       =decoded_message + """ """

        return decoded_message