
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    _SCREAMING_SNAKE_CASE					( nn.Module	):
      def __init__(     self						,		lowerCamelCase						,		lowerCamelCase=1.0	):
          super().__init__()

          snake_case__  =			cut_size
          snake_case__  =			cut_power



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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			F.normalize(__lowerCAmelCase      ,						dim=-1							)
    snake_case__  =			F.normalize(__lowerCAmelCase      ,						dim=-1							)
    return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    for param in model.parameters():
        snake_case__  =			value





class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      def __init__(     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		):
          super().__init__()
          self.register_modules(
              vae=lowerCamelCase						,		text_encoder=lowerCamelCase						,		clip_model=lowerCamelCase						,		tokenizer=lowerCamelCase						,		unet=lowerCamelCase						,		scheduler=lowerCamelCase						,		feature_extractor=lowerCamelCase						,		)

          snake_case__  =			transforms.Normalize(mean=feature_extractor.image_mean						,		std=feature_extractor.image_std	)
          snake_case__  =			(
              feature_extractor.size
              if isinstance(feature_extractor.size						,		lowerCamelCase	)
              else feature_extractor.size["shortest_edge"]
          )
          snake_case__  =			MakeCutouts(self.cut_out_size	)

          set_requires_grad(self.text_encoder						,		lowerCamelCase	)
          set_requires_grad(self.clip_model						,		lowerCamelCase	)
      def A_     (     self						,		lowerCamelCase = "auto"	):
          if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              snake_case__  =			self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(lowerCamelCase	)
      def A_     (     self	):
          self.enable_attention_slicing(lowerCamelCase	)
      def A_     (     self	):
          set_requires_grad(self.vae						,		lowerCamelCase	)
      def A_     (     self	):
          set_requires_grad(self.vae						,		lowerCamelCase	)
      def A_     (     self	):
          set_requires_grad(self.unet						,		lowerCamelCase	)
      def A_     (     self	):
          set_requires_grad(self.unet						,		lowerCamelCase	)
      @torch.enable_grad()
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase=True						,		):
          snake_case__  =			latents.detach().requires_grad_()

          snake_case__  =			self.scheduler.scale_model_input(lowerCamelCase						,		lowerCamelCase	)

          # predict the noise residual
          snake_case__  =			self.unet(lowerCamelCase						,		lowerCamelCase						,		encoder_hidden_states=lowerCamelCase	).sample

          if isinstance(self.scheduler						,		(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)	):
              snake_case__  =			self.scheduler.alphas_cumprod[timestep]
              snake_case__  =			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
              snake_case__  =			(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

              snake_case__  =			torch.sqrt(lowerCamelCase	)
              snake_case__  =			pred_original_sample * (fac) + latents * (1 - fac)
          elif isinstance(self.scheduler						,		lowerCamelCase	):
              snake_case__  =			self.scheduler.sigmas[index]
              snake_case__  =			latents - sigma * noise_pred
          else:
              raise ValueError(F"""scheduler type {type(self.scheduler	)} not supported"""	)

          snake_case__  =			1 / self.vae.config.scaling_factor * sample
          snake_case__  =			self.vae.decode(lowerCamelCase	).sample
          snake_case__  =			(image / 2 + 0.5).clamp(0						,		1	)

          if use_cutouts:
              snake_case__  =			self.make_cutouts(lowerCamelCase						,		lowerCamelCase	)
          else:
              snake_case__  =			transforms.Resize(self.cut_out_size	)(lowerCamelCase	)
          snake_case__  =			self.normalize(lowerCamelCase	).to(latents.dtype	)

          snake_case__  =			self.clip_model.get_image_features(lowerCamelCase	)
          snake_case__  =			image_embeddings_clip / image_embeddings_clip.norm(p=2						,		dim=-1						,		keepdim=lowerCamelCase	)

          if use_cutouts:
              snake_case__  =			spherical_dist_loss(lowerCamelCase						,		lowerCamelCase	)
              snake_case__  =			dists.view([num_cutouts, sample.shape[0], -1]	)
              snake_case__  =			dists.sum(2	).mean(0	).sum() * clip_guidance_scale
          else:
              snake_case__  =			spherical_dist_loss(lowerCamelCase						,		lowerCamelCase	).mean() * clip_guidance_scale

          snake_case__  =			-torch.autograd.grad(lowerCamelCase						,		lowerCamelCase	)[0]

          if isinstance(self.scheduler						,		lowerCamelCase	):
              snake_case__  =			latents.detach() + grads * (sigma**2)
              snake_case__  =			noise_pred_original
          else:
              snake_case__  =			noise_pred_original - torch.sqrt(lowerCamelCase	) * grads
          return noise_pred, latents



      @torch.no_grad()
      def __call__(     self						,		lowerCamelCase						,		lowerCamelCase = 5_12						,		lowerCamelCase = 5_12						,		lowerCamelCase = 50						,		lowerCamelCase = 7.5						,		lowerCamelCase = 1						,		lowerCamelCase = 0.0						,		lowerCamelCase = 1_00						,		lowerCamelCase = None						,		lowerCamelCase = 4						,		lowerCamelCase = True						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = "pil"						,		lowerCamelCase = True						,		):
          if isinstance(lowerCamelCase						,		lowerCamelCase	):
              snake_case__  =			1
          elif isinstance(lowerCamelCase						,		lowerCamelCase	):
              snake_case__  =			len(lowerCamelCase	)
          else:
              raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase	)}"""	)

          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
          snake_case__  =			self.tokenizer(
              lowerCamelCase						,		padding="max_length"						,		max_length=self.tokenizer.model_max_length						,		truncation=lowerCamelCase						,		return_tensors="pt"						,		)
          snake_case__  =			self.text_encoder(text_input.input_ids.to(self.device	)	)[0]
          # duplicate text embeddings for each generation per prompt
          snake_case__  =			text_embeddings.repeat_interleave(lowerCamelCase						,		dim=0	)

          if clip_guidance_scale > 0:
              if clip_prompt is not None:
                  snake_case__  =			self.tokenizer(
                      lowerCamelCase						,		padding="max_length"						,		max_length=self.tokenizer.model_max_length						,		truncation=lowerCamelCase						,		return_tensors="pt"						,		).input_ids.to(self.device	)
              else:
                  snake_case__  =			text_input.input_ids.to(self.device	)
              snake_case__  =			self.clip_model.get_text_features(lowerCamelCase	)
              snake_case__  =			text_embeddings_clip / text_embeddings_clip.norm(p=2						,		dim=-1						,		keepdim=lowerCamelCase	)
              # duplicate text embeddings clip for each generation per prompt
              snake_case__  =			text_embeddings_clip.repeat_interleave(lowerCamelCase						,		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.
          snake_case__  =			guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
              snake_case__  =			text_input.input_ids.shape[-1]
              snake_case__  =			self.tokenizer([""]						,		padding="max_length"						,		max_length=lowerCamelCase						,		return_tensors="pt"	)
              snake_case__  =			self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]
              # duplicate unconditional embeddings for each generation per prompt
              snake_case__  =			uncond_embeddings.repeat_interleave(lowerCamelCase						,		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
              snake_case__  =			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`.
          snake_case__  =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
          snake_case__  =			text_embeddings.dtype
          if latents is None:
              if self.device.type == "mps":
                  # randn does not work reproducibly on mps
                  snake_case__  =			torch.randn(lowerCamelCase						,		generator=lowerCamelCase						,		device="cpu"						,		dtype=lowerCamelCase	).to(
                      self.device	)
              else:
                  snake_case__  =			torch.randn(lowerCamelCase						,		generator=lowerCamelCase						,		device=self.device						,		dtype=lowerCamelCase	)
          else:
              if latents.shape != latents_shape:
                  raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
              snake_case__  =			latents.to(self.device	)

          # set timesteps
          snake_case__  =			"offset" in set(inspect.signature(self.scheduler.set_timesteps	).parameters.keys()	)
          snake_case__  =			{}
          if accepts_offset:
              snake_case__  =			1

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

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

          # scale the initial noise by the standard deviation required by the scheduler
          snake_case__  =			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]
          snake_case__  =			"eta" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
          snake_case__  =			{}
          if accepts_eta:
              snake_case__  =			eta

          # check if the scheduler accepts generator
          snake_case__  =			"generator" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
          if accepts_generator:
              snake_case__  =			generator

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

              # predict the noise residual
              snake_case__  =			self.unet(lowerCamelCase						,		lowerCamelCase						,		encoder_hidden_states=lowerCamelCase	).sample

              # perform classifier free guidance
              if do_classifier_free_guidance:
                  snake_case__  ,   snake_case__  =			noise_pred.chunk(2	)
                  snake_case__  =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              # perform clip guidance
              if clip_guidance_scale > 0:
                  snake_case__  =			(
                      text_embeddings.chunk(2	)[1] if do_classifier_free_guidance else text_embeddings
                  )
                  snake_case__  ,   snake_case__  =			self.cond_fn(
                      lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		)

              # compute the previous noisy sample x_t -> x_t-1
              snake_case__  =			self.scheduler.step(lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		**lowerCamelCase	).prev_sample

          # scale and decode the image latents with vae
          snake_case__  =			1 / self.vae.config.scaling_factor * latents
          snake_case__  =			self.vae.decode(lowerCamelCase	).sample

          snake_case__  =			(image / 2 + 0.5).clamp(0						,		1	)
          snake_case__  =			image.cpu().permute(0						,		2						,		3						,		1	).numpy()

          if output_type == "pil":
              snake_case__  =			self.numpy_to_pil(lowerCamelCase	)

          if not return_dict:
              return (image, None)

          return StableDiffusionPipelineOutput(images=lowerCamelCase						,		nsfw_content_detected=lowerCamelCase	)





import sacrebleu as scb
from packaging import version

import datasets


__magic_name__           =					'''\
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

__magic_name__           =					'''\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich\'s `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
'''

__magic_name__           =					'''
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `\'exp\'`. Possible values are:
        - `\'none\'`: no smoothing
        - `\'floor\'`: increment zero counts
        - `\'add-k\'`: increment num/denom by k for n>1
        - `\'exp\'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method=\'floor\'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method=\'add-k\'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `\'zh\'` for Chinese, `\'ja-mecab\'` for Japanese and `\'13a\'` (mteval) otherwise. Possible values are:
        - `\'none\'`: No tokenization.
        - `\'zh\'`: Chinese tokenization.
        - `\'13a\'`: mimics the `mteval-v13a` script from Moses.
        - `\'intl\'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `\'char\'`: Language-agnostic character-level tokenization.
        - `\'ja-mecab\'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    \'score\': BLEU score,
    \'counts\': Counts,
    \'totals\': Totals,
    \'precisions\': Precisions,
    \'bp\': Brevity penalty,
    \'sys_len\': predictions length,
    \'ref_len\': reference length,

Examples:

    Example 1:
        >>> predictions = ["hello there general kenobi", "foo bar foobar"]
        >>> references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]
        >>> sacrebleu = datasets.load_metric("sacrebleu")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']
        >>> print(round(results["score"], 1))
        100.0

    Example 2:
        >>> predictions = ["hello there general kenobi",
        ...                 "on our way to ankh morpork"]
        >>> references = [["hello there general kenobi", "hello there !"],
        ...                 ["goodbye ankh morpork", "ankh morpork"]]
        >>> sacrebleu = datasets.load_metric("sacrebleu")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']
        >>> print(round(results["score"], 1))
        39.8
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,   _KWARGS_DESCRIPTION	)
class    _SCREAMING_SNAKE_CASE					( datasets.Metric	):
      def A_     (     self	):
          if version.parse(scb.__version__	) < version.parse("1.4.12"	):
              raise ImportWarning(
                  "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
                  "You can install it with `pip install \"sacrebleu>=1.4.12\"`."	)
          return datasets.MetricInfo(
              description=_DESCRIPTION						,		citation=_CITATION						,		homepage="https://github.com/mjpost/sacreBLEU"						,		inputs_description=_KWARGS_DESCRIPTION						,		features=datasets.Features(
                  {
                      "predictions": datasets.Value("string"						,		id="sequence"	),
                      "references": datasets.Sequence(datasets.Value("string"						,		id="sequence"	)						,		id="references"	),
                  }	)						,		codebase_urls=["https://github.com/mjpost/sacreBLEU"]						,		reference_urls=[
                  "https://github.com/mjpost/sacreBLEU",
                  "https://en.wikipedia.org/wiki/BLEU",
                  "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
              ]						,		)



      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase="exp"						,		lowerCamelCase=None						,		lowerCamelCase=False						,		lowerCamelCase=False						,		lowerCamelCase=None						,		lowerCamelCase=False						,		):
          snake_case__  =			len(references[0]	)
          if any(len(lowerCamelCase	) != references_per_prediction for refs in references	):
              raise ValueError("Sacrebleu requires the same number of references for each prediction"	)
          snake_case__  =			[[refs[i] for refs in references] for i in range(lowerCamelCase	)]
          snake_case__  =			scb.corpus_bleu(
              lowerCamelCase						,		lowerCamelCase						,		smooth_method=lowerCamelCase						,		smooth_value=lowerCamelCase						,		force=lowerCamelCase						,		lowercase=lowerCamelCase						,		use_effective_order=lowerCamelCase						,		**({"tokenize": tokenize} if tokenize else {})						,		)
          snake_case__  =			{
              "score": output.score,
              "counts": output.counts,
              "totals": output.totals,
              "precisions": output.precisions,
              "bp": output.bp,
              "sys_len": output.sys_len,
              "ref_len": output.ref_len,
          }
          return output_dict



