
"""simple docstring"""







from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

lowerCamelCase__   					=		"""
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")
        >>> pipe.to(\"cuda\")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save(\"cat.png\")
        ```
"""
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=8		):
	__lowerCAmelCase			:						Dict									=      h // scale_factor**2
	if h % scale_factor**2 != 0:
		new_h += 1
	__lowerCAmelCase			:						List[str]									=      w // scale_factor**2
	if w % scale_factor**2 != 0:
		new_w += 1
	return new_h * scale_factor, new_w * scale_factor




class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		):
			super().__init__()

			self.register_modules(
			    text_encoder=_SCREAMING_SNAKE_CASE       ,		tokenizer=_SCREAMING_SNAKE_CASE       ,		unet=_SCREAMING_SNAKE_CASE       ,		scheduler=_SCREAMING_SNAKE_CASE       ,		movq=_SCREAMING_SNAKE_CASE       ,		)
			__lowerCAmelCase			:						Dict									=      2 ** (len(self.movq.config.block_out_channels ) - 1)

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			if latents is None:
				__lowerCAmelCase			:						Tuple									=      randn_tensor(_SCREAMING_SNAKE_CASE       ,		generator=_SCREAMING_SNAKE_CASE       ,		device=_SCREAMING_SNAKE_CASE       ,		dtype=_SCREAMING_SNAKE_CASE )
			else:
				if latents.shape != shape:
					raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}" )
				__lowerCAmelCase			:						Any									=      latents.to(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Dict									=      latents * scheduler.init_noise_sigma
			return latents

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=None       ,		):
			__lowerCAmelCase			:						Tuple									=      len(_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ) else 1
			# get prompt text embeddings
			__lowerCAmelCase			:						Dict									=      self.tokenizer(
			    _SCREAMING_SNAKE_CASE       ,		padding='max_length'       ,		truncation=_SCREAMING_SNAKE_CASE       ,		max_length=77       ,		return_attention_mask=_SCREAMING_SNAKE_CASE       ,		add_special_tokens=_SCREAMING_SNAKE_CASE       ,		return_tensors='pt'       ,		)

			__lowerCAmelCase			:						Tuple									=      text_inputs.input_ids
			__lowerCAmelCase			:						Union[str, Any]									=      self.tokenizer(_SCREAMING_SNAKE_CASE       ,		padding='longest'       ,		return_tensors='pt' ).input_ids

			if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						List[Any]									=      self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
				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			:						Dict									=      text_input_ids.to(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Dict									=      text_inputs.attention_mask.to(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			,   __lowerCAmelCase			:						str									=      self.text_encoder(
			    input_ids=_SCREAMING_SNAKE_CASE       ,		attention_mask=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Any									=      prompt_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE       ,		dim=0 )
			__lowerCAmelCase			:						Dict									=      text_encoder_hidden_states.repeat_interleave(_SCREAMING_SNAKE_CASE       ,		dim=0 )
			__lowerCAmelCase			:						Optional[int]									=      text_mask.repeat_interleave(_SCREAMING_SNAKE_CASE       ,		dim=0 )

			if do_classifier_free_guidance:
				__lowerCAmelCase			:						List[str]
				if negative_prompt is None:
					__lowerCAmelCase			:						Union[str, Any]									=      [''] * batch_size
				elif type(_SCREAMING_SNAKE_CASE ) is not type(_SCREAMING_SNAKE_CASE ):
					raise TypeError(
					    f"`negative_prompt` should be the same type to `prompt`, but got {type(_SCREAMING_SNAKE_CASE )} !="
					    f" {type(_SCREAMING_SNAKE_CASE )}." )
				elif isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
					__lowerCAmelCase			:						Optional[int]									=      [negative_prompt]
				elif batch_size != len(_SCREAMING_SNAKE_CASE ):
					raise ValueError(
					    f"`negative_prompt`: {negative_prompt} has batch size {len(_SCREAMING_SNAKE_CASE )}, but `prompt`:"
					    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
					    ' the batch size of `prompt`.' )
				else:
					__lowerCAmelCase			:						Optional[int]									=      negative_prompt

				__lowerCAmelCase			:						Tuple									=      self.tokenizer(
				    _SCREAMING_SNAKE_CASE       ,		padding='max_length'       ,		max_length=77       ,		truncation=_SCREAMING_SNAKE_CASE       ,		return_attention_mask=_SCREAMING_SNAKE_CASE       ,		add_special_tokens=_SCREAMING_SNAKE_CASE       ,		return_tensors='pt'       ,		)
				__lowerCAmelCase			:						Union[str, Any]									=      uncond_input.input_ids.to(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						Optional[int]									=      uncond_input.attention_mask.to(_SCREAMING_SNAKE_CASE )

				__lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      self.text_encoder(
				    input_ids=_SCREAMING_SNAKE_CASE       ,		attention_mask=_SCREAMING_SNAKE_CASE )

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

				__lowerCAmelCase			:						List[str]									=      negative_prompt_embeds.shape[1]
				__lowerCAmelCase			:						Any									=      negative_prompt_embeds.repeat(1       ,		_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						List[Any]									=      negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,		_SCREAMING_SNAKE_CASE )

				__lowerCAmelCase			:						int									=      uncond_text_encoder_hidden_states.shape[1]
				__lowerCAmelCase			:						List[Any]									=      uncond_text_encoder_hidden_states.repeat(1       ,		_SCREAMING_SNAKE_CASE       ,		1 )
				__lowerCAmelCase			:						Optional[int]									=      uncond_text_encoder_hidden_states.view(
				    batch_size * num_images_per_prompt       ,		_SCREAMING_SNAKE_CASE       ,		-1 )
				__lowerCAmelCase			:						Optional[Any]									=      uncond_text_mask.repeat_interleave(_SCREAMING_SNAKE_CASE       ,		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
				__lowerCAmelCase			:						Tuple									=      torch.cat([negative_prompt_embeds, prompt_embeds] )
				__lowerCAmelCase			:						Tuple									=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )

				__lowerCAmelCase			:						int									=      torch.cat([uncond_text_mask, text_mask] )

			return prompt_embeds, text_encoder_hidden_states, text_mask

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=0 ):
			if is_accelerate_available():
				from accelerate import cpu_offload
			else:
				raise ImportError('Please install accelerate via `pip install accelerate`' )

			__lowerCAmelCase			:						Union[str, Any]									=      torch.device(f"cuda:{gpu_id}" )

			__lowerCAmelCase			:						List[Any]									=      [
			    self.unet,
			    self.text_encoder,
			    self.movq,
			]
			for cpu_offloaded_model in models:
				if cpu_offloaded_model is not None:
					cpu_offload(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=0 ):
			if is_accelerate_available() and is_accelerate_version('>='       ,		'0.17.0.dev0' ):
				from accelerate import cpu_offload_with_hook
			else:
				raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )

			__lowerCAmelCase			:						str									=      torch.device(f"cuda:{gpu_id}" )

			if self.device.type != "cpu":
				self.to('cpu'       ,		silence_dtype_warnings=_SCREAMING_SNAKE_CASE )
				torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

			__lowerCAmelCase			:						Any									=      None
			for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
				__lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      cpu_offload_with_hook(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		prev_module_hook=_SCREAMING_SNAKE_CASE )

			if self.safety_checker is not None:
				__lowerCAmelCase			,   __lowerCAmelCase			:						Dict									=      cpu_offload_with_hook(self.safety_checker       ,		_SCREAMING_SNAKE_CASE       ,		prev_module_hook=_SCREAMING_SNAKE_CASE )

			# We'll offload the last model manually.
			__lowerCAmelCase			:						Optional[Any]									=      hook

		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 							__lowerCamelCase							(      self ):
			if not hasattr(self.unet       ,		'_hf_hook' ):
				return self.device
			for module in self.unet.modules():
				if (
				    hasattr(_SCREAMING_SNAKE_CASE       ,		'_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







		@torch.no_grad()
		@replace_example_docstring(_SCREAMING_SNAKE_CASE )
		def __call__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = 5_12       ,		_SCREAMING_SNAKE_CASE = 5_12       ,		_SCREAMING_SNAKE_CASE = 1_00       ,		_SCREAMING_SNAKE_CASE = 4.0       ,		_SCREAMING_SNAKE_CASE = 1       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = "pil"       ,		_SCREAMING_SNAKE_CASE = True       ,		):

			if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						List[str]									=      1
			elif isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Optional[int]									=      len(_SCREAMING_SNAKE_CASE )
			else:
				raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(_SCREAMING_SNAKE_CASE )}" )

			__lowerCAmelCase			:						Dict									=      self._execution_device

			__lowerCAmelCase			:						Optional[Any]									=      batch_size * num_images_per_prompt
			__lowerCAmelCase			:						Optional[int]									=      guidance_scale > 1.0

			__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						int									=      self._encode_prompt(
			    _SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

			if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						int									=      torch.cat(_SCREAMING_SNAKE_CASE       ,		dim=0 )
			if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Dict									=      torch.cat(_SCREAMING_SNAKE_CASE       ,		dim=0 )

			if do_classifier_free_guidance:
				__lowerCAmelCase			:						Optional[Any]									=      image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE       ,		dim=0 )
				__lowerCAmelCase			:						int									=      negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE       ,		dim=0 )

			__lowerCAmelCase			:						Any									=      torch.cat([negative_image_embeds, image_embeds]       ,		dim=0 ).to(
			    dtype=prompt_embeds.dtype       ,		device=_SCREAMING_SNAKE_CASE )

			self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE       ,		device=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[int]									=      self.scheduler.timesteps

			__lowerCAmelCase			:						int									=      self.unet.config.in_channels

			__lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      get_new_h_w(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		self.movq_scale_factor )

			# create initial latent
			__lowerCAmelCase			:						str									=      self.prepare_latents(
			    (batch_size, num_channels_latents, height, width)       ,		text_encoder_hidden_states.dtype       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		self.scheduler       ,		)

			for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE ) ):
				# expand the latents if we are doing classifier free guidance
				__lowerCAmelCase			:						List[str]									=      torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

				__lowerCAmelCase			:						Union[str, Any]									=      {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
				__lowerCAmelCase			:						Optional[Any]									=      self.unet(
				    sample=_SCREAMING_SNAKE_CASE       ,		timestep=_SCREAMING_SNAKE_CASE       ,		encoder_hidden_states=_SCREAMING_SNAKE_CASE       ,		added_cond_kwargs=_SCREAMING_SNAKE_CASE       ,		return_dict=_SCREAMING_SNAKE_CASE       ,		)[0]

				if do_classifier_free_guidance:
					__lowerCAmelCase			,   __lowerCAmelCase			:						Dict									=      noise_pred.split(latents.shape[1]       ,		dim=1 )
					__lowerCAmelCase			,   __lowerCAmelCase			:						Optional[Any]									=      noise_pred.chunk(2 )
					__lowerCAmelCase			,   __lowerCAmelCase			:						int									=      variance_pred.chunk(2 )
					__lowerCAmelCase			:						Dict									=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
					__lowerCAmelCase			:						Any									=      torch.cat([noise_pred, variance_pred_text]       ,		dim=1 )

				if not (
				    hasattr(self.scheduler.config       ,		'variance_type' )
				    and self.scheduler.config.variance_type in ["learned", "learned_range"]
				):
					__lowerCAmelCase			,   __lowerCAmelCase			:						Union[str, Any]									=      noise_pred.split(latents.shape[1]       ,		dim=1 )

				# compute the previous noisy sample x_t -> x_t-1
				__lowerCAmelCase			:						List[str]									=      self.scheduler.step(
				    _SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		generator=_SCREAMING_SNAKE_CASE       ,		).prev_sample
			# post-processing
			__lowerCAmelCase			:						Tuple									=      self.movq.decode(_SCREAMING_SNAKE_CASE       ,		force_not_quantize=_SCREAMING_SNAKE_CASE )['sample']

			if output_type not in ["pt", "np", "pil"]:
				raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )

			if output_type in ["np", "pil"]:
				__lowerCAmelCase			:						List[str]									=      image * 0.5 + 0.5
				__lowerCAmelCase			:						Dict									=      image.clamp(0       ,		1 )
				__lowerCAmelCase			:						Dict									=      image.cpu().permute(0       ,		2       ,		3       ,		1 ).float().numpy()

			if output_type == "pil":
				__lowerCAmelCase			:						Union[str, Any]									=      self.numpy_to_pil(_SCREAMING_SNAKE_CASE )

			if not return_dict:
				return (image,)

			return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE )


import os
def      a      (			)    ->      Any:





      """simple docstring"""



      with open(os.path.dirname(A__     ) + '/p022_names.txt'     ) as file:
            _lowercase	       =str(file.readlines()[0]     )
            _lowercase	       =names.replace('"'      ,   ''     ).split(','     )

      names.sort()

      _lowercase	       =0
      _lowercase	       =0

      for i, name in enumerate(A__     ):
            for letter in name:
                  name_score += ord(A__     ) - 64

            total_score += (i + 1) * name_score
            _lowercase	       =0
      return total_score


if __name__ == "__main__":
  print(solution())


