
'''simple docstring'''


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def 					__magic_name__(       ):
						def filter_log(UpperCAmelCase__ :       str	):
												return False if "This IS expected if you are initializing" in record.msg else True

						UpperCamelCase_     :	List[Any]					     =     datasets.utils.logging.get_logger("""transformers.modeling_utils"""	)
						logger.addFilter(UpperCAmelCase__	)
						try:
												yield
						finally:
												logger.removeFilter(UpperCAmelCase__	)


__SCREAMING_SNAKE_CASE						=      "\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n"

__SCREAMING_SNAKE_CASE						=      "\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

__SCREAMING_SNAKE_CASE						=      "\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,			_KWARGS_DESCRIPTION	)
class     __snake_case			(    datasets.Metric	):




		def 	_snake_case	(  self					:List[Any])					->			List[str]:






								'''simple docstring'''






								return datasets.MetricInfo(
								    description=_DESCRIPTION			,    citation=_CITATION			,    homepage="""https://github.com/Tiiiger/bert_score"""			,    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/Tiiiger/bert_score"""]			,    reference_urls=[
								        """https://github.com/Tiiiger/bert_score""",
								        """https://arxiv.org/abs/1904.09675""",
								    ]			,    )




		def 	_snake_case	(  self					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Any			,    _SCREAMING_SNAKE_CASE					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Optional[Any]=None			,    _SCREAMING_SNAKE_CASE					:Optional[Any]=None			,    _SCREAMING_SNAKE_CASE					:int=None			,    _SCREAMING_SNAKE_CASE					:str=False			,    _SCREAMING_SNAKE_CASE					:Optional[Any]=False			,    _SCREAMING_SNAKE_CASE					:Optional[int]=None			,    _SCREAMING_SNAKE_CASE					:Optional[int]=6_4			,    _SCREAMING_SNAKE_CASE					:int=4			,    _SCREAMING_SNAKE_CASE					:Optional[int]=False			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=False			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:Dict=False			,    )					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[Any]					     =     bert_score.utils.get_hash
								UpperCamelCase_     :	Tuple					     =     bert_score.BERTScorer

								if version.parse(bert_score.__version__) >= version.parse("""0.3.10"""):
														UpperCamelCase_     :	str					     =     functools.partial(_SCREAMING_SNAKE_CASE			,    use_fast_tokenizer=_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Optional[int]					     =     functools.partial(_SCREAMING_SNAKE_CASE			,    use_fast_tokenizer=_SCREAMING_SNAKE_CASE)
								elif use_fast_tokenizer:
														raise ImportWarning(
														    """To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"""
														    """You can install it with `pip install \"bert-score>=0.3.10\"`.""")

								if model_type is None:
														assert lang is not None, "either lang or model_type should be specified"
														UpperCamelCase_     :	Dict					     =     bert_score.utils.langamodel[lang.lower()]

								if num_layers is None:
														UpperCamelCase_     :	int					     =     bert_score.utils.modelalayers[model_type]

								UpperCamelCase_     :	Optional[int]					     =     get_hash(
								    model=_SCREAMING_SNAKE_CASE			,    num_layers=_SCREAMING_SNAKE_CASE			,    idf=_SCREAMING_SNAKE_CASE			,    rescale_with_baseline=_SCREAMING_SNAKE_CASE			,    use_custom_baseline=baseline_path is not None			,    )

								with filter_logging_context():
														if not hasattr(self			,    """cached_bertscorer""") or self.cached_bertscorer.hash != hashcode:
																				UpperCamelCase_     :	Optional[int]					     =     scorer(
																				    model_type=_SCREAMING_SNAKE_CASE			,    num_layers=_SCREAMING_SNAKE_CASE			,    batch_size=_SCREAMING_SNAKE_CASE			,    nthreads=_SCREAMING_SNAKE_CASE			,    all_layers=_SCREAMING_SNAKE_CASE			,    idf=_SCREAMING_SNAKE_CASE			,    device=_SCREAMING_SNAKE_CASE			,    lang=_SCREAMING_SNAKE_CASE			,    rescale_with_baseline=_SCREAMING_SNAKE_CASE			,    baseline_path=_SCREAMING_SNAKE_CASE			,    )

								((UpperCamelCase_)     ,(UpperCamelCase_)     ,(UpperCamelCase_))     :	List[Any]					     =     self.cached_bertscorer.score(
								    cands=_SCREAMING_SNAKE_CASE			,    refs=_SCREAMING_SNAKE_CASE			,    verbose=_SCREAMING_SNAKE_CASE			,    batch_size=_SCREAMING_SNAKE_CASE			,    )
								UpperCamelCase_     :	List[Any]					     =     {
								    """precision""": P.tolist(),
								    """recall""": R.tolist(),
								    """f1""": F.tolist(),
								    """hashcode""": hashcode,
								}
								return output_dict




		def 	_snake_case	(  self					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:str=None			,    _SCREAMING_SNAKE_CASE					:List[str]=None			,    **_SCREAMING_SNAKE_CASE					:Any)					->			Any:






								'''simple docstring'''






								if references is not None:
														UpperCamelCase_     :	List[Any]					     =     [[ref] if isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE) else ref for ref in references]
								super().add_batch(predictions=_SCREAMING_SNAKE_CASE			,    references=_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)





		def 	_snake_case	(  self					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:List[Any]=None			,    **_SCREAMING_SNAKE_CASE					:List[Any])					->			Optional[int]:






								'''simple docstring'''






								if isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE):
														UpperCamelCase_     :	Dict					     =     [reference]
								super().add(prediction=_SCREAMING_SNAKE_CASE			,    reference=_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)






'''simple docstring'''


from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


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




class     __snake_case			(    _a	):
		a_          =					["safety_checker", "feature_extractor"]




		def __init__(  self					:str			,    _SCREAMING_SNAKE_CASE					:AutoencoderKL			,    _SCREAMING_SNAKE_CASE					:CLIPTextModel			,    _SCREAMING_SNAKE_CASE					:CLIPTokenizer			,    _SCREAMING_SNAKE_CASE					:UNetaDConditionModel			,    _SCREAMING_SNAKE_CASE					:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,    _SCREAMING_SNAKE_CASE					:StableDiffusionSafetyChecker			,    _SCREAMING_SNAKE_CASE					:CLIPImageProcessor			,    _SCREAMING_SNAKE_CASE					:bool = True			,    )					->			int:






								'''simple docstring'''






								super().__init__()
								if hasattr(scheduler.config			,    """steps_offset""") and scheduler.config.steps_offset != 1:
														UpperCamelCase_     :	Optional[Any]					     =     (
														    F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
														    F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
														    """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
														    """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
														    """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
														    """ file"""
														)
														deprecate("""steps_offset!=1"""			,    """1.0.0"""			,    _SCREAMING_SNAKE_CASE			,    standard_warn=_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Union[str, Any]					     =     dict(scheduler.config)
														UpperCamelCase_     :	Optional[Any]					     =     1
														UpperCamelCase_     :	int					     =     FrozenDict(_SCREAMING_SNAKE_CASE)

								self.register_modules(
								    vae=_SCREAMING_SNAKE_CASE			,    text_encoder=_SCREAMING_SNAKE_CASE			,    tokenizer=_SCREAMING_SNAKE_CASE			,    unet=_SCREAMING_SNAKE_CASE			,    scheduler=_SCREAMING_SNAKE_CASE			,    safety_checker=_SCREAMING_SNAKE_CASE			,    feature_extractor=_SCREAMING_SNAKE_CASE			,    )
								self.register_to_config(requires_safety_checker=_SCREAMING_SNAKE_CASE)




		@property
		def 	_snake_case	(  self					:Tuple)					->			Dict[str, Any]:






								'''simple docstring'''






								return {k: getattr(self			,    _SCREAMING_SNAKE_CASE) for k in self.config.keys() if not k.startswith("""_""")}




		def 	_snake_case	(  self					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, int]] = "auto")					->			Union[str, Any]:






								'''simple docstring'''






								if slice_size == "auto":
														# half the attention head size is usually a good trade-off between
														# speed and memory
														UpperCamelCase_     :	int					     =     self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Union[str, Any])					->			Dict:






								'''simple docstring'''






								self.enable_attention_slicing(_SCREAMING_SNAKE_CASE)




		@torch.no_grad()
		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Union[str, List[str]]			,    _SCREAMING_SNAKE_CASE					:Union[torch.FloatTensor, PIL.Image.Image]			,    _SCREAMING_SNAKE_CASE					:Union[torch.FloatTensor, PIL.Image.Image]			,    _SCREAMING_SNAKE_CASE					:float = 0.8			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 5_0			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 7.5			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, List[str]]] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 1			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 0.0			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Generator] = None			,    _SCREAMING_SNAKE_CASE					:Optional[str] = "pil"			,    _SCREAMING_SNAKE_CASE					:bool = True			,    _SCREAMING_SNAKE_CASE					:Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,    _SCREAMING_SNAKE_CASE					:int = 1			,    )					->			List[str]:






								'''simple docstring'''






								return StableDiffusionInpaintPipelineLegacy(**self.components)(
								    prompt=_SCREAMING_SNAKE_CASE			,    image=_SCREAMING_SNAKE_CASE			,    mask_image=_SCREAMING_SNAKE_CASE			,    strength=_SCREAMING_SNAKE_CASE			,    num_inference_steps=_SCREAMING_SNAKE_CASE			,    guidance_scale=_SCREAMING_SNAKE_CASE			,    negative_prompt=_SCREAMING_SNAKE_CASE			,    num_images_per_prompt=_SCREAMING_SNAKE_CASE			,    eta=_SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE			,    output_type=_SCREAMING_SNAKE_CASE			,    return_dict=_SCREAMING_SNAKE_CASE			,    callback=_SCREAMING_SNAKE_CASE			,    )




		@torch.no_grad()
		def 	_snake_case	(  self					:List[str]			,    _SCREAMING_SNAKE_CASE					:Union[str, List[str]]			,    _SCREAMING_SNAKE_CASE					:Union[torch.FloatTensor, PIL.Image.Image]			,    _SCREAMING_SNAKE_CASE					:float = 0.8			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 5_0			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 7.5			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, List[str]]] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 1			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 0.0			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Generator] = None			,    _SCREAMING_SNAKE_CASE					:Optional[str] = "pil"			,    _SCREAMING_SNAKE_CASE					:bool = True			,    _SCREAMING_SNAKE_CASE					:Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,    _SCREAMING_SNAKE_CASE					:int = 1			,    **_SCREAMING_SNAKE_CASE					:int			,    )					->			Any:






								'''simple docstring'''






								return StableDiffusionImgaImgPipeline(**self.components)(
								    prompt=_SCREAMING_SNAKE_CASE			,    image=_SCREAMING_SNAKE_CASE			,    strength=_SCREAMING_SNAKE_CASE			,    num_inference_steps=_SCREAMING_SNAKE_CASE			,    guidance_scale=_SCREAMING_SNAKE_CASE			,    negative_prompt=_SCREAMING_SNAKE_CASE			,    num_images_per_prompt=_SCREAMING_SNAKE_CASE			,    eta=_SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE			,    output_type=_SCREAMING_SNAKE_CASE			,    return_dict=_SCREAMING_SNAKE_CASE			,    callback=_SCREAMING_SNAKE_CASE			,    callback_steps=_SCREAMING_SNAKE_CASE			,    )





		@torch.no_grad()
		def 	_snake_case	(  self					:Any			,    _SCREAMING_SNAKE_CASE					:Union[str, List[str]]			,    _SCREAMING_SNAKE_CASE					:int = 5_1_2			,    _SCREAMING_SNAKE_CASE					:int = 5_1_2			,    _SCREAMING_SNAKE_CASE					:int = 5_0			,    _SCREAMING_SNAKE_CASE					:float = 7.5			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, List[str]]] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 1			,    _SCREAMING_SNAKE_CASE					:float = 0.0			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Generator] = None			,    _SCREAMING_SNAKE_CASE					:Optional[torch.FloatTensor] = None			,    _SCREAMING_SNAKE_CASE					:Optional[str] = "pil"			,    _SCREAMING_SNAKE_CASE					:bool = True			,    _SCREAMING_SNAKE_CASE					:Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,    _SCREAMING_SNAKE_CASE					:int = 1			,    )					->			List[Any]:






								'''simple docstring'''






								return StableDiffusionPipeline(**self.components)(
								    prompt=_SCREAMING_SNAKE_CASE			,    height=_SCREAMING_SNAKE_CASE			,    width=_SCREAMING_SNAKE_CASE			,    num_inference_steps=_SCREAMING_SNAKE_CASE			,    guidance_scale=_SCREAMING_SNAKE_CASE			,    negative_prompt=_SCREAMING_SNAKE_CASE			,    num_images_per_prompt=_SCREAMING_SNAKE_CASE			,    eta=_SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE			,    latents=_SCREAMING_SNAKE_CASE			,    output_type=_SCREAMING_SNAKE_CASE			,    return_dict=_SCREAMING_SNAKE_CASE			,    callback=_SCREAMING_SNAKE_CASE			,    callback_steps=_SCREAMING_SNAKE_CASE			,    )




