

"""simple docstring"""

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def 				__UpperCamelCase   (    )				->			int:




					"""simple docstring"""



					def filter_log(a__: List[Any]			):
										return False if "This IS expected if you are initializing" in record.msg else True

					__lowercase												=			datasets.utils.logging.get_logger('''transformers.modeling_utils'''			)
					logger.addFilter(a__			)
					try:
										yield
					finally:
										logger.removeFilter(a__			)


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

a__ :	Optional[int]						= """\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project's README at https://github.com/Tiiiger/bert_score#readme for more information.
"""

a__ :	Tuple						= """
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. 'en').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bertscore = datasets.load_metric(\"bertscore\")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")
    >>> print([round(v, 2) for v in results[\"f1\"]])
    [1.0, 1.0]
"""


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







						def        _UpperCamelCase    (self							)			->						Union[str, Any]:
											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        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=False     ,							UpperCAmelCase_=False     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=6_4     ,							UpperCAmelCase_=4     ,							UpperCAmelCase_=False     ,							UpperCAmelCase_=False     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=False     ,							)			->						Optional[int]:
											__lowercase												=			bert_score.utils.get_hash
											__lowercase												=			bert_score.BERTScorer

											if version.parse(bert_score.__version__							) >= version.parse('''0.3.10'''							):
																__lowercase												=			functools.partial(UpperCAmelCase_     ,							use_fast_tokenizer=UpperCAmelCase_							)
																__lowercase												=			functools.partial(UpperCAmelCase_     ,							use_fast_tokenizer=UpperCAmelCase_							)
											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"
																__lowercase												=			bert_score.utils.langamodel[lang.lower()]

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

											__lowercase												=			get_hash(
											    model=UpperCAmelCase_     ,							num_layers=UpperCAmelCase_     ,							idf=UpperCAmelCase_     ,							rescale_with_baseline=UpperCAmelCase_     ,							use_custom_baseline=baseline_path is not None     ,							)

											with filter_logging_context():
																if not hasattr(self     ,							'''cached_bertscorer'''							) or self.cached_bertscorer.hash != hashcode:
																					__lowercase												=			scorer(
																					    model_type=UpperCAmelCase_     ,							num_layers=UpperCAmelCase_     ,							batch_size=UpperCAmelCase_     ,							nthreads=UpperCAmelCase_     ,							all_layers=UpperCAmelCase_     ,							idf=UpperCAmelCase_     ,							device=UpperCAmelCase_     ,							lang=UpperCAmelCase_     ,							rescale_with_baseline=UpperCAmelCase_     ,							baseline_path=UpperCAmelCase_     ,							)

											((__lowercase)						, (__lowercase)						, (__lowercase))												=			self.cached_bertscorer.score(
											    cands=UpperCAmelCase_     ,							refs=UpperCAmelCase_     ,							verbose=UpperCAmelCase_     ,							batch_size=UpperCAmelCase_     ,							)
											__lowercase												=			{
											    '''precision''': P.tolist(),
											    '''recall''': R.tolist(),
											    '''f1''': F.tolist(),
											    '''hashcode''': hashcode,
											}
											return output_dict







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							**UpperCAmelCase_							)			->						int:
											# References can be strings or lists of strings
											# Let's change strings to lists of strings with one element
											if references is not None:
																__lowercase												=			[[ref] if isinstance(UpperCAmelCase_     ,							UpperCAmelCase_							) else ref for ref in references]
											super().add_batch(predictions=UpperCAmelCase_     ,							references=UpperCAmelCase_     ,							**UpperCAmelCase_							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=None     ,							**UpperCAmelCase_							)			->						Optional[Any]:
											# References can be strings or lists of strings
											# Let's change strings to lists of strings with one element
											if isinstance(UpperCAmelCase_     ,							UpperCAmelCase_							):
																__lowercase												=			[reference]
											super().add(prediction=UpperCAmelCase_     ,							reference=UpperCAmelCase_     ,							**UpperCAmelCase_							)





"""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


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


class 			_snake_case     (						UpperCamelCase       ):
						__snake_case 			=     ['safety_checker', 'feature_extractor']







						def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = True     ,							)			->						Optional[int]:
											super().__init__()
											if hasattr(scheduler.config     ,							'''steps_offset'''							) and scheduler.config.steps_offset != 1:
																__lowercase												=			(
																    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'''     ,							UpperCAmelCase_     ,							standard_warn=UpperCAmelCase_							)
																__lowercase												=			dict(scheduler.config							)
																__lowercase												=			1
																__lowercase												=			FrozenDict(UpperCAmelCase_							)

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







						@property
						def        _UpperCamelCase    (self							)			->						Dict[str, Any]:
											return {k: getattr(self     ,							UpperCAmelCase_							) for k in self.config.keys() if not k.startswith('''_'''							)}







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_ = "auto"							)			->						str:
											if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																__lowercase												=			self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						int:
											self.enable_attention_slicing(UpperCAmelCase_							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.8     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							)			->						str:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionInpaintPipelineLegacy(**self.components							)(
											    prompt=UpperCAmelCase_     ,							image=UpperCAmelCase_     ,							mask_image=UpperCAmelCase_     ,							strength=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.8     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							**UpperCAmelCase_     ,							)			->						List[str]:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionImgaImgPipeline(**self.components							)(
											    prompt=UpperCAmelCase_     ,							image=UpperCAmelCase_     ,							strength=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							callback_steps=UpperCAmelCase_     ,							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 5_1_2     ,							UpperCAmelCase_ = 5_1_2     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							)			->						Optional[int]:
											# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
											return StableDiffusionPipeline(**self.components							)(
											    prompt=UpperCAmelCase_     ,							height=UpperCAmelCase_     ,							width=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							latents=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							callback_steps=UpperCAmelCase_     ,							)


