
import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput

class __snake_case	(   nn.Module  ):
    def __init__(						self	,snake_case	,snake_case=1.0   ):



           '''simple docstring'''







           super().__init__()

           lowercase							:  List[str] 			=   cut_size
           lowercase							:  Dict 			=   cut_power
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case   ):



           '''simple docstring'''







           lowercase			,    lowercase							:  Optional[int] 			=   pixel_values.shape[2:4]
           lowercase							:  Optional[int] 			=   min(snake_case	,snake_case   )
           lowercase							:  str 			=   min(snake_case	,snake_case	,self.cut_size   )
           lowercase							:  Tuple 			=   []
           for _ in range(snake_case   ):
                  lowercase							:  str 			=   int(torch.rand([]   ) ** self.cut_power * (max_size - min_size) + min_size   )
                  lowercase							:  Optional[int] 			=   torch.randint(0	,sideX - size + 1	,()   )
                  lowercase							:  Dict 			=   torch.randint(0	,sideY - size + 1	,()   )
                  lowercase							:  str 			=   pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
                  cutouts.append(F.adaptive_avg_poolad(snake_case	,self.cut_size   )   )
           return torch.cat(snake_case   )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					str:
       lowercase							:  Optional[Any] 			=   F.normalize(SCREAMING_SNAKE_CASE__      ,			dim=-1       )
       lowercase							:  Union[str, Any] 			=   F.normalize(SCREAMING_SNAKE_CASE__      ,			dim=-1       )
       return (x - y).norm(dim=-1       ).div(2       ).arcsin().pow(2       ).mul(2       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Union[str, Any]:
       for param in model.parameters():
              lowercase							:  Dict 			=   value




class __snake_case	(   lowerCAmelCase  ):
    def __init__(						self	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,):



           '''simple docstring'''







           super().__init__()
           self.register_modules(
               vae=snake_case	,text_encoder=snake_case	,clip_model=snake_case	,tokenizer=snake_case	,unet=snake_case	,scheduler=snake_case	,feature_extractor=snake_case	,)

           lowercase							:  Dict 			=   transforms.Normalize(mean=feature_extractor.image_mean	,std=feature_extractor.image_std   )
           lowercase							:  int 			=   (
               feature_extractor.size
               if isinstance(feature_extractor.size	,snake_case   )
               else feature_extractor.size["""shortest_edge"""]
           )
           lowercase							:  str 			=   MakeCutouts(self.cut_out_size   )

           set_requires_grad(self.text_encoder	,snake_case   )
           set_requires_grad(self.clip_model	,snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case = "auto"   ):



           '''simple docstring'''







           if slice_size == "auto":
                  # half the attention head size is usually a good trade-off between
                  # speed and memory
                  lowercase							:  Optional[Any] 			=   self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.enable_attention_slicing(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           set_requires_grad(self.vae	,snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           set_requires_grad(self.vae	,snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           set_requires_grad(self.unet	,snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           set_requires_grad(self.unet	,snake_case   )
    @torch.enable_grad()
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case=True	,):



           '''simple docstring'''







           lowercase							:  List[str] 			=   latents.detach().requires_grad_()

           lowercase							:  Optional[int] 			=   self.scheduler.scale_model_input(snake_case	,snake_case   )

           # predict the noise residual
           lowercase							:  Tuple 			=   self.unet(snake_case	,snake_case	,encoder_hidden_states=snake_case   ).sample

           if isinstance(self.scheduler	,(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
                  lowercase							:  Tuple 			=   self.scheduler.alphas_cumprod[timestep]
                  lowercase							:  str 			=   1 - alpha_prod_t
                  # compute predicted original sample from predicted noise also called
                  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
                  lowercase							:  Dict 			=   (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

                  lowercase							:  Dict 			=   torch.sqrt(snake_case   )
                  lowercase							:  int 			=   pred_original_sample * (fac) + latents * (1 - fac)
           elif isinstance(self.scheduler	,snake_case   ):
                  lowercase							:  int 			=   self.scheduler.sigmas[index]
                  lowercase							:  str 			=   latents - sigma * noise_pred
           else:
                  raise ValueError(f"scheduler type {type(self.scheduler   )} not supported"   )

           lowercase							:  Dict 			=   1 / self.vae.config.scaling_factor * sample
           lowercase							:  Optional[Any] 			=   self.vae.decode(snake_case   ).sample
           lowercase							:  str 			=   (image / 2 + 0.5).clamp(0	,1   )

           if use_cutouts:
                  lowercase							:  Dict 			=   self.make_cutouts(snake_case	,snake_case   )
           else:
                  lowercase							:  str 			=   transforms.Resize(self.cut_out_size   )(snake_case   )
           lowercase							:  Any 			=   self.normalize(snake_case   ).to(latents.dtype   )

           lowercase							:  List[str] 			=   self.clip_model.get_image_features(snake_case   )
           lowercase							:  Tuple 			=   image_embeddings_clip / image_embeddings_clip.norm(p=2	,dim=-1	,keepdim=snake_case   )

           if use_cutouts:
                  lowercase							:  str 			=   spherical_dist_loss(snake_case	,snake_case   )
                  lowercase							:  int 			=   dists.view([num_cutouts, sample.shape[0], -1]   )
                  lowercase							:  Tuple 			=   dists.sum(2   ).mean(0   ).sum() * clip_guidance_scale
           else:
                  lowercase							:  int 			=   spherical_dist_loss(snake_case	,snake_case   ).mean() * clip_guidance_scale

           lowercase							:  Dict 			=   -torch.autograd.grad(snake_case	,snake_case   )[0]

           if isinstance(self.scheduler	,snake_case   ):
                  lowercase							:  List[str] 			=   latents.detach() + grads * (sigma**2)
                  lowercase							:  List[str] 			=   noise_pred_original
           else:
                  lowercase							:  Union[str, Any] 			=   noise_pred_original - torch.sqrt(snake_case   ) * grads
           return noise_pred, latents
    @torch.no_grad()
    def __call__(						self	,snake_case	,snake_case = 512	,snake_case = 512	,snake_case = 50	,snake_case = 7.5	,snake_case = 1	,snake_case = 0.0	,snake_case = 100	,snake_case = None	,snake_case = 4	,snake_case = True	,snake_case = None	,snake_case = None	,snake_case = "pil"	,snake_case = True	,):



           '''simple docstring'''







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

           if height % 8 != 0 or width % 8 != 0:
                  raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}."   )

           # get prompt text embeddings
           lowercase							:  Optional[int] 			=   self.tokenizer(
               snake_case	,padding="""max_length"""	,max_length=self.tokenizer.model_max_length	,truncation=snake_case	,return_tensors="""pt"""	,)
           lowercase							:  int 			=   self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
           # duplicate text embeddings for each generation per prompt
           lowercase							:  Union[str, Any] 			=   text_embeddings.repeat_interleave(snake_case	,dim=0   )

           if clip_guidance_scale > 0:
                  if clip_prompt is not None:
                         lowercase							:  Optional[Any] 			=   self.tokenizer(
                             snake_case	,padding="""max_length"""	,max_length=self.tokenizer.model_max_length	,truncation=snake_case	,return_tensors="""pt"""	,).input_ids.to(self.device   )
                  else:
                         lowercase							:  Union[str, Any] 			=   text_input.input_ids.to(self.device   )
                  lowercase							:  List[str] 			=   self.clip_model.get_text_features(snake_case   )
                  lowercase							:  Union[str, Any] 			=   text_embeddings_clip / text_embeddings_clip.norm(p=2	,dim=-1	,keepdim=snake_case   )
                  # duplicate text embeddings clip for each generation per prompt
                  lowercase							:  Any 			=   text_embeddings_clip.repeat_interleave(snake_case	,dim=0   )

           # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
           # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
           # corresponds to doing no classifier free guidance.
           lowercase							:  List[Any] 			=   guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
                  lowercase							:  Dict 			=   text_input.input_ids.shape[-1]
                  lowercase							:  Optional[Any] 			=   self.tokenizer([""""""]	,padding="""max_length"""	,max_length=snake_case	,return_tensors="""pt"""   )
                  lowercase							:  Tuple 			=   self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
                  # duplicate unconditional embeddings for each generation per prompt
                  lowercase							:  Optional[int] 			=   uncond_embeddings.repeat_interleave(snake_case	,dim=0   )

                  # 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
                  lowercase							:  Tuple 			=   torch.cat([uncond_embeddings, text_embeddings]   )

           # get the initial random noise unless the user supplied it

           # Unlike in other pipelines, latents need to be generated in the target device
           # for 1-to-1 results reproducibility with the CompVis implementation.
           # However this currently doesn't work in `mps`.
           lowercase							:  str 			=   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
           lowercase							:  Tuple 			=   text_embeddings.dtype
           if latents is None:
                  if self.device.type == "mps":
                         # randn does not work reproducibly on mps
                         lowercase							:  Tuple 			=   torch.randn(snake_case	,generator=snake_case	,device="""cpu"""	,dtype=snake_case   ).to(
                             self.device   )
                  else:
                         lowercase							:  Tuple 			=   torch.randn(snake_case	,generator=snake_case	,device=self.device	,dtype=snake_case   )
           else:
                  if latents.shape != latents_shape:
                         raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"   )
                  lowercase							:  Optional[Any] 			=   latents.to(self.device   )

           # set timesteps
           lowercase							:  Optional[Any] 			=   """offset""" in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
           lowercase							:  str 			=   {}
           if accepts_offset:
                  lowercase							:  Union[str, Any] 			=   1

           self.scheduler.set_timesteps(snake_case	,**snake_case   )

           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           lowercase							:  str 			=   self.scheduler.timesteps.to(self.device   )

           # scale the initial noise by the standard deviation required by the scheduler
           lowercase							:  Dict 			=   latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           lowercase							:  List[str] 			=   """eta""" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
           lowercase							:  Optional[Any] 			=   {}
           if accepts_eta:
                  lowercase							:  Dict 			=   eta

           # check if the scheduler accepts generator
           lowercase							:  Dict 			=   """generator""" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
           if accepts_generator:
                  lowercase							:  List[Any] 			=   generator

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

                  # predict the noise residual
                  lowercase							:  List[Any] 			=   self.unet(snake_case	,snake_case	,encoder_hidden_states=snake_case   ).sample

                  # perform classifier free guidance
                  if do_classifier_free_guidance:
                         lowercase			,    lowercase							:  List[Any] 			=   noise_pred.chunk(2   )
                         lowercase							:  Dict 			=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                  # perform clip guidance
                  if clip_guidance_scale > 0:
                         lowercase							:  Optional[int] 			=   (
                             text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
                         )
                         lowercase			,    lowercase							:  Any 			=   self.cond_fn(
                             snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,)

                  # compute the previous noisy sample x_t -> x_t-1
                  lowercase							:  Union[str, Any] 			=   self.scheduler.step(snake_case	,snake_case	,snake_case	,**snake_case   ).prev_sample

           # scale and decode the image latents with vae
           lowercase							:  int 			=   1 / self.vae.config.scaling_factor * latents
           lowercase							:  Optional[int] 			=   self.vae.decode(snake_case   ).sample

           lowercase							:  Optional[int] 			=   (image / 2 + 0.5).clamp(0	,1   )
           lowercase							:  List[Any] 			=   image.cpu().permute(0	,2	,3	,1   ).numpy()

           if output_type == "pil":
                  lowercase							:  List[str] 			=   self.numpy_to_pil(snake_case   )

           if not return_dict:
                  return (image, None)

           return StableDiffusionPipelineOutput(images=snake_case	,nsfw_content_detected=snake_case   )


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
  import torch

  from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer

@require_torch
class __snake_case	(   unittest.TestCase  ):
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  int 			=   XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""   )
           model.to(snake_case   )

           # encoder-decoder outputs
           lowercase							:  List[str] 			=   torch.tensor([[17, 96208, 103471, 2]]   ).to(snake_case   )
           lowercase							:  List[str] 			=   torch.tensor(
               [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]   ).to(snake_case   )
           lowercase							:  str 			=   model(
               input_ids=snake_case	,attention_mask=snake_case	,encoder_outputs=snake_case	,decoder_input_ids=snake_case   )
           lowercase							:  Tuple 			=   output[0]
           lowercase							:  int 			=   torch.Size((1, 14, 250012)   )
           self.assertEqual(output_predited_logis.shape	,snake_case   )
           lowercase							:  Union[str, Any] 			=   torch.tensor(
               [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]   ).to(snake_case   )
           self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	,snake_case	,atol=1e-4   )   )

           # encoder outputs
           lowercase							:  Tuple 			=   model.prophetnet.encoder(snake_case   )[0]
           lowercase							:  Any 			=   torch.tensor(
               [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]   ).to(snake_case   )
           lowercase							:  Optional[Any] 			=   torch.Size((1, 4, 1024)   )
           self.assertEqual(encoder_outputs.shape	,snake_case   )
           self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]	,snake_case	,atol=1e-4   )   )

           # decoder outputs
           lowercase							:  Tuple 			=   model.prophetnet.decoder(
               snake_case	,encoder_hidden_states=snake_case	,)
           lowercase							:  Optional[int] 			=   decoder_outputs[1].view(1	,model.config.ngram	,14	,-1   )
           lowercase							:  Optional[int] 			=   model.lm_head(snake_case   )
           lowercase							:  List[Any] 			=   predicting_streams_logits[:, 0]
           self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]	,snake_case	,atol=1e-4   )   )
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   XLMProphetNetForConditionalGeneration.from_pretrained(
               """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""   )
           model.to(snake_case   )

           lowercase							:  Optional[int] 			=   torch.tensor([[17, 96208, 103471, 2]]   ).to(snake_case   )
           lowercase							:  Dict 			=   torch.tensor(
               [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]   ).to(snake_case   )
           lowercase							:  Any 			=   model(
               input_ids=snake_case	,attention_mask=snake_case	,encoder_outputs=snake_case	,decoder_input_ids=snake_case   )
           lowercase							:  Optional[int] 			=   output[0]
           lowercase							:  Dict 			=   torch.Size((1, 14, 250012)   )
           self.assertEqual(output_predited_logis.shape	,snake_case   )
           # compare the actual values for a slice.
           lowercase							:  int 			=   torch.tensor(
               [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]   ).to(snake_case   )

           self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	,snake_case	,atol=1e-4   )   )
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   XLMProphetNetForConditionalGeneration.from_pretrained(
               """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""   )
           model.to(snake_case   )
           lowercase							:  Optional[int] 			=   512

           lowercase							:  List[Any] 			=   XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""   )

           lowercase							:  int 			=   (
               """Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"""
               """ January 14, 2020, according to the official portal of the organization. From that day, users of this"""
               """ system will not be able to receive security updates, which could make their computers vulnerable to"""
               """ cyber attacks."""
           )
           lowercase							:  List[str] 			=   (
               """орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"""
               """ после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"""
               """ этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"""
               """ к кибератакам."""
           )
           lowercase							:  str 			=   (
               """根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"""
           )

           lowercase							:  Tuple 			=   tokenizer(
               [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]	,padding=snake_case	,max_length=255	,return_tensors="""pt"""   ).input_ids
           lowercase							:  Union[str, Any] 			=   input_ids.to(snake_case   )

           lowercase							:  Dict 			=   model.generate(
               snake_case	,num_beams=10	,length_penalty=1.0	,no_repeat_ngram_size=3	,early_stopping=snake_case   )
           lowercase							:  Any 			=   [tokenizer.decode(snake_case	,skip_special_tokens=snake_case   ) for g in summary_ids]
           lowercase							:  Union[str, Any] 			=   """Microsoft to end Windows 7 free support after January 14, 2020"""
           lowercase							:  Tuple 			=   """Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
           lowercase							:  Dict 			=   """微软打算终止对Windows 7操作系统的免费支持"""
           self.assertListEqual(
               [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]	,snake_case	,)

           lowercase							:  int 			=   model.generate(
               snake_case	,num_beams=1	,length_penalty=1.0	,no_repeat_ngram_size=3	,early_stopping=snake_case   )
           lowercase							:  List[Any] 			=   [
               tokenizer.convert_ids_to_tokens(snake_case	,skip_special_tokens=snake_case   ) for g in summary_ids_beama
           ]
           lowercase							:  List[str] 			=   """▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """   )
           lowercase							:  Optional[Any] 			=   """▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
               """ """   )
           lowercase							:  Optional[Any] 			=   """微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持""".split(""" """   )
           self.assertListEqual(
               [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]	,snake_case	,)
