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


SCREAMING_SNAKE_CASE__		:					Optional[Any]						=					logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE__		:					Optional[int]						=					"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n        >>> pipe.to(\"cuda\")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save(\"cat.png\")\n        ```\n"







def     _a				(      lowercase__  :			Dict     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			Tuple=8 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		h // scale_factor**2
     if h % scale_factor**2 != 0:
          new_h += 1
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		w // scale_factor**2
     if w % scale_factor**2 != 0:
          new_w += 1
     return new_h * scale_factor, new_w * scale_factor







class       snake_case					(      UpperCamelCase_						):




  def __init__(      self     :	Optional[Any]     , a_     :	MultilingualCLIP     , a_     :	XLMRobertaTokenizer     , a_     :	UNetaDConditionModel     , a_     :	Union[DDIMScheduler, DDPMScheduler]     , a_     :	VQModel     , )->       Dict:




       """simple docstring"""
       super().__init__()

       self.register_modules(
           text_encoder=a_     , tokenizer=a_     , unet=a_     , scheduler=a_     , movq=a_     , )
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		2 ** (len(self.movq.config.block_out_channels				) - 1)




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	int     , a_     :	Any     , a_     :	int     , a_     :	Union[str, Any]     , a_     :	Optional[int]     , a_     :	Union[str, Any]				)->       Tuple:




       """simple docstring"""
       if latents is None:
            SCREAMING_SNAKE_CASE__				:				Tuple			=		randn_tensor(a_     , generator=a_     , device=a_     , dtype=a_				)
       else:
            if latents.shape != shape:
                 raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''				)
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		latents.to(a_				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		latents * scheduler.init_noise_sigma
       return latents




  def 	__lowercase(      self     :	List[Any]     , a_     :	List[Any]     , a_     :	Tuple     , a_     :	List[str]     , a_     :	Dict     , a_     :	str=None     , )->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		len(a_				) if isinstance(a_     , a_				) else 1
       # get prompt text embeddings
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.tokenizer(
           a_     , padding='max_length'     , truncation=a_     , max_length=77     , return_attention_mask=a_     , add_special_tokens=a_     , return_tensors='pt'     , )

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		text_inputs.input_ids
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.tokenizer(a_     , padding='longest'     , return_tensors='pt'				).input_ids

       if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(a_     , a_				):
            SCREAMING_SNAKE_CASE__				:				Union[str, 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}'''				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		text_input_ids.to(a_				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		text_inputs.attention_mask.to(a_				)

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.text_encoder(
           input_ids=a_     , attention_mask=a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		prompt_embeds.repeat_interleave(a_     , dim=0				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		text_encoder_hidden_states.repeat_interleave(a_     , dim=0				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		text_mask.repeat_interleave(a_     , dim=0				)

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

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

            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Tuple			=		self.text_encoder(
                input_ids=a_     , attention_mask=a_				)

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

            SCREAMING_SNAKE_CASE__				:				List[str]			=		negative_prompt_embeds.shape[1]
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		negative_prompt_embeds.repeat(1     , a_				)
            SCREAMING_SNAKE_CASE__				:				Dict			=		negative_prompt_embeds.view(batch_size * num_images_per_prompt     , a_				)

            SCREAMING_SNAKE_CASE__				:				Dict			=		uncond_text_encoder_hidden_states.shape[1]
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		uncond_text_encoder_hidden_states.repeat(1     , a_     , 1				)
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		uncond_text_encoder_hidden_states.view(
                batch_size * num_images_per_prompt     , a_     , -1				)
            SCREAMING_SNAKE_CASE__				:				int			=		uncond_text_mask.repeat_interleave(a_     , 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
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.cat([negative_prompt_embeds, prompt_embeds]				)
            SCREAMING_SNAKE_CASE__				:				int			=		torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]				)

            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.cat([uncond_text_mask, text_mask]				)

       return prompt_embeds, text_encoder_hidden_states, text_mask




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	Tuple=0				)->       Optional[Any]:




       """simple docstring"""
       if is_accelerate_available():
            from accelerate import cpu_offload
       else:
            raise ImportError('Please install accelerate via `pip install accelerate`'				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.device(F'''cuda:{gpu_id}'''				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[
           self.unet,
           self.text_encoder,
           self.movq,
       ]
       for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
                 cpu_offload(a_     , a_				)




  def 	__lowercase(      self     :	Any     , a_     :	Union[str, Any]=0				)->       Union[str, Any]:




       """simple docstring"""
       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.'				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.device(F'''cuda:{gpu_id}'''				)

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

       SCREAMING_SNAKE_CASE__				:				Any			=		None
       for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		cpu_offload_with_hook(a_     , a_     , prev_module_hook=a_				)

       if self.safety_checker is not None:
            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				int			=		cpu_offload_with_hook(self.safety_checker     , a_     , prev_module_hook=a_				)

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




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 	__lowercase(      self     :	Optional[int]				)->       Dict:




       """simple docstring"""
       if not hasattr(self.unet     , '_hf_hook'				):
            return self.device
       for module in self.unet.modules():
            if (
                hasattr(a_     , '_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(a_				)
  def __call__(      self     :	List[Any]     , a_     :	Union[str, List[str]]     , a_     :	Union[torch.FloatTensor, List[torch.FloatTensor]]     , a_     :	Union[torch.FloatTensor, List[torch.FloatTensor]]     , a_     :	Optional[Union[str, List[str]]] = None     , a_     :	int = 512     , a_     :	int = 512     , a_     :	int = 100     , a_     :	float = 4.0     , a_     :	int = 1     , a_     :	Optional[Union[torch.Generator, List[torch.Generator]]] = None     , a_     :	Optional[torch.FloatTensor] = None     , a_     :	Optional[str] = "pil"     , a_     :	bool = True     , )->       List[Any]:




       """simple docstring"""

       if isinstance(a_     , a_				):
            SCREAMING_SNAKE_CASE__				:				Tuple			=		1
       elif isinstance(a_     , a_				):
            SCREAMING_SNAKE_CASE__				:				List[str]			=		len(a_				)
       else:
            raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(a_				)}'''				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self._execution_device

       SCREAMING_SNAKE_CASE__				:				str			=		batch_size * num_images_per_prompt
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		guidance_scale > 1.0

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				str			=		self._encode_prompt(
           a_     , a_     , a_     , a_     , a_				)

       if isinstance(a_     , a_				):
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.cat(a_     , dim=0				)
       if isinstance(a_     , a_				):
            SCREAMING_SNAKE_CASE__				:				int			=		torch.cat(a_     , dim=0				)

       if do_classifier_free_guidance:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_embeds.repeat_interleave(a_     , dim=0				)
            SCREAMING_SNAKE_CASE__				:				int			=		negative_image_embeds.repeat_interleave(a_     , dim=0				)

       SCREAMING_SNAKE_CASE__				:				str			=		torch.cat([negative_image_embeds, image_embeds]     , dim=0				).to(
           dtype=prompt_embeds.dtype     , device=a_				)

       self.scheduler.set_timesteps(a_     , device=a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.scheduler.timesteps

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.unet.config.in_channels

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[str]			=		get_new_h_w(a_     , a_     , self.movq_scale_factor				)

       # create initial latent
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.prepare_latents(
           (batch_size, num_channels_latents, height, width)     , text_encoder_hidden_states.dtype     , a_     , a_     , a_     , self.scheduler     , )

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

            SCREAMING_SNAKE_CASE__				:				str			=		{'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
            SCREAMING_SNAKE_CASE__				:				int			=		self.unet(
                sample=a_     , timestep=a_     , encoder_hidden_states=a_     , added_cond_kwargs=a_     , return_dict=a_     , )[0]

            if do_classifier_free_guidance:
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		noise_pred.split(latents.shape[1]     , dim=1				)
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		noise_pred.chunk(2				)
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				int			=		variance_pred.chunk(2				)
                 SCREAMING_SNAKE_CASE__				:				Dict			=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		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"]
            ):
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		noise_pred.split(latents.shape[1]     , dim=1				)

            # compute the previous noisy sample x_t -> x_t-1
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.scheduler.step(
                a_     , a_     , a_     , generator=a_     , ).prev_sample
       # post-processing
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.movq.decode(a_     , force_not_quantize=a_				)['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"]:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image * 0.5 + 0.5
            SCREAMING_SNAKE_CASE__				:				List[str]			=		image.clamp(0     , 1				)
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		image.cpu().permute(0     , 2     , 3     , 1				).float().numpy()

       if output_type == "pil":
            SCREAMING_SNAKE_CASE__				:				Dict			=		self.numpy_to_pil(a_				)

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=a_				)




import requests

SCREAMING_SNAKE_CASE__		:					int						=					"https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		requests.get(_NEWS_API + bbc_news_api_key ).json()
     # each article in the list is a dict
     for i, article in enumerate(bbc_news_page['articles']     ,  1 ):
          print(f'''{i}.) {article['title']}''' )


if __name__ == "__main__":
      fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")



