



'''simple docstring'''





import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_lowerCAmelCase      						=	datasets.logging.get_logger(__name__)


_lowerCAmelCase      						=	'''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

_lowerCAmelCase      						=	'''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

_lowerCAmelCase      						=	'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

_lowerCAmelCase      						=	{
    '''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
    '''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
    '''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
    '''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
    '''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
    '''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
    '''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
    '''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
    '''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
    '''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						, _KWARGS_DESCRIPTION     )
class        lowerCAmelCase_(				datasets.Metric     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,citation=_CITATION  ,homepage="""https://github.com/google-research/bleurt"""  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
              {
                  """predictions""": datasets.Value("""string"""  ,id="""sequence"""      ),
                  """references""": datasets.Value("""string"""  ,id="""sequence"""      ),
              }      )  ,codebase_urls=["""https://github.com/google-research/bleurt"""]  ,reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""]  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Tuple:
      # check that config name specifies a valid BLEURT model
      if self.config_name == "default":
          logger.warning(
              """Using default BLEURT-Base checkpoint for sequence maximum length 128. """
              """You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."""      )
          lowerCAmelCase__ :  str							      =					"""bleurt-base-128"""

      if self.config_name.lower() in CHECKPOINT_URLS:
          lowerCAmelCase__ :  Union[str, Any]							      =					self.config_name.lower()

      elif self.config_name.upper() in CHECKPOINT_URLS:
          lowerCAmelCase__ :  List[Any]							      =					self.config_name.upper()

      else:
          raise KeyError(
              F"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"""      )

      # download the model checkpoint specified by self.config_name and set up the scorer
      lowerCAmelCase__ :  int							      =					dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]      )
      lowerCAmelCase__ :  Dict							      =					score.BleurtScorer(os.path.join(__UpperCAmelCase  ,__UpperCAmelCase      )      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Union[str, Any]							      =					self.scorer.score(references=__UpperCAmelCase  ,candidates=__UpperCAmelCase      )
      return {"scores": scores}







'''simple docstring'''




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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

class 			UpperCamelCase_ (							__magic_name__			):





	def __init__(  self		,		A		,		A		,		A		,		A		,		A		,		A		,		A		,		)			->			Optional[Any]:
								super().__init__()
								self.register_modules(
								    vae=A		,		text_encoder=A		,		tokenizer=A		,		unet=A		,		scheduler=A		,		safety_checker=A		,		feature_extractor=A		,		)





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





	def  _lowercase(  self    )			->			Dict:
								self.enable_attention_slicing(A    )






	@torch.no_grad()
	def __call__(  self		,		A		,		A = 512		,		A = 512		,		A = 50		,		A = 7.5		,		A = None		,		A = 1		,		A = 0.0		,		A = None		,		A = None		,		A = "pil"		,		A = True		,		A = None		,		A = 1		,		A = None		,		**A		,		)			->			List[Any]:

								if isinstance(A		,		A    ):
															UpperCAmelCase     :		List[str]							     =		1
								elif isinstance(A		,		A    ):
															UpperCAmelCase     :		Dict							     =		len(A    )
								else:
															raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(A    )}'''    )

								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}.'''    )

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(A		,		A    ) or callback_steps <= 0)
								):
															raise ValueError(
															    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
															    f''' {type(A    )}.'''    )

								# get prompt text embeddings
								UpperCAmelCase     :		List[str]							     =		self.tokenizer(
								    A		,		padding="""max_length"""		,		max_length=self.tokenizer.model_max_length		,		return_tensors="""pt"""		,		)
								UpperCAmelCase     :		List[Any]							     =		text_inputs.input_ids

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

								if text_embeddings is None:
															UpperCAmelCase     :		Union[str, Any]							     =		self.text_encoder(text_input_ids.to(self.device    )    )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		text_embeddings.shape
								UpperCAmelCase     :		List[str]							     =		text_embeddings.repeat(1		,		A		,		1    )
								UpperCAmelCase     :		List[Any]							     =		text_embeddings.view(bs_embed * num_images_per_prompt		,		A		,		-1    )

								# 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.
								UpperCAmelCase     :		Optional[int]							     =		guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
															UpperCAmelCase     :		List[str]
															if negative_prompt is None:
																						UpperCAmelCase     :		Any							     =		[""""""]
															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    ):
																						UpperCAmelCase     :		Optional[int]							     =		[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:
																						UpperCAmelCase     :		Any							     =		negative_prompt

															UpperCAmelCase     :		Dict							     =		text_input_ids.shape[-1]
															UpperCAmelCase     :		List[Any]							     =		self.tokenizer(
															    A		,		padding="""max_length"""		,		max_length=A		,		truncation=A		,		return_tensors="""pt"""		,		)
															UpperCAmelCase     :		Tuple							     =		self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															UpperCAmelCase     :		int							     =		uncond_embeddings.shape[1]
															UpperCAmelCase     :		List[Any]							     =		uncond_embeddings.repeat(A		,		A		,		1    )
															UpperCAmelCase     :		List[str]							     =		uncond_embeddings.view(batch_size * num_images_per_prompt		,		A		,		-1    )

															# 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
															UpperCAmelCase     :		List[str]							     =		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`.
								UpperCAmelCase     :		Tuple							     =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								UpperCAmelCase     :		Optional[int]							     =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
								UpperCAmelCase     :		str							     =		text_embeddings.dtype
								if latents is None:
															if self.device.type == "mps":
																						# randn does not exist on mps
																						UpperCAmelCase     :		Dict							     =		torch.randn(
																						    A		,		generator=A		,		device="""cpu"""		,		dtype=A    ).to(self.device    )
																						UpperCAmelCase     :		int							     =		torch.randn(A		,		generator=A		,		device="""cpu"""		,		dtype=A    ).to(
																						    self.device    )
															else:
																						UpperCAmelCase     :		int							     =		torch.randn(
																						    A		,		generator=A		,		device=self.device		,		dtype=A    )
																						UpperCAmelCase     :		int							     =		torch.randn(A		,		generator=A		,		device=self.device		,		dtype=A    )
								else:
															if latents_reference.shape != latents_shape:
																						raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''    )
															UpperCAmelCase     :		Optional[Any]							     =		latents_reference.to(self.device    )
															UpperCAmelCase     :		Tuple							     =		latents.to(self.device    )

								# This is the key part of the pipeline where we
								# try to ensure that the generated images w/ the same seed
								# but different sizes actually result in similar images
								UpperCAmelCase     :		int							     =		(latents_shape[3] - latents_shape_reference[3]) // 2
								UpperCAmelCase     :		List[str]							     =		(latents_shape[2] - latents_shape_reference[2]) // 2
								UpperCAmelCase     :		Union[str, Any]							     =		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
								UpperCAmelCase     :		Union[str, Any]							     =		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
								UpperCAmelCase     :		Optional[int]							     =		0 if dx < 0 else dx
								UpperCAmelCase     :		List[str]							     =		0 if dy < 0 else dy
								UpperCAmelCase     :		Union[str, Any]							     =		max(-dx		,		0    )
								UpperCAmelCase     :		List[Any]							     =		max(-dy		,		0    )
								# import pdb
								# pdb.set_trace()
								UpperCAmelCase     :		str							     =		latents_reference[:, :, dy : dy + h, dx : dx + w]

								# set timesteps
								self.scheduler.set_timesteps(A    )

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

								# scale the initial noise by the standard deviation required by the scheduler
								UpperCAmelCase     :		Optional[int]							     =		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]
								UpperCAmelCase     :		int							     =		"""eta""" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
								UpperCAmelCase     :		Optional[Any]							     =		{}
								if accepts_eta:
															UpperCAmelCase     :		List[str]							     =		eta

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

															# predict the noise residual
															UpperCAmelCase     :		Any							     =		self.unet(A		,		A		,		encoder_hidden_states=A    ).sample

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

															# compute the previous noisy sample x_t -> x_t-1
															UpperCAmelCase     :		Dict							     =		self.scheduler.step(A		,		A		,		A		,		**A    ).prev_sample

															# call the callback, if provided
															if callback is not None and i % callback_steps == 0:
																						callback(A		,		A		,		A    )

								UpperCAmelCase     :		Union[str, Any]							     =		1 / 0.1_8_2_1_5 * latents
								UpperCAmelCase     :		Tuple							     =		self.vae.decode(A    ).sample

								UpperCAmelCase     :		Union[str, Any]							     =		(image / 2 + 0.5).clamp(0		,		1    )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								UpperCAmelCase     :		Union[str, Any]							     =		image.cpu().permute(0		,		2		,		3		,		1    ).float().numpy()

								if self.safety_checker is not None:
															UpperCAmelCase     :		int							     =		self.feature_extractor(self.numpy_to_pil(A    )		,		return_tensors="""pt"""    ).to(
															    self.device    )
															UpperCAmelCase     ,     UpperCAmelCase     :		int							     =		self.safety_checker(
															    images=A		,		clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
								else:
															UpperCAmelCase     :		Any							     =		None

								if output_type == "pil":
															UpperCAmelCase     :		int							     =		self.numpy_to_pil(A    )

								if not return_dict:
															return (image, has_nsfw_concept)

								return StableDiffusionPipelineOutput(images=A		,		nsfw_content_detected=A    )

