



"""simple docstring"""

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase__			   =  "\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

lowercase__			   =  "\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


lowercase__			   =  "\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]					):

							def remove_articles(_lowerCamelCase				: List[str]					):
														__a		:		str									=       re.compile(r"""\b(a|an|the)\b"""  ,       re.UNICODE					)
														return re.sub(_lowerCamelCase  ,       """ """  ,       _lowerCamelCase					)

							def white_space_fix(_lowerCamelCase				: List[str]					):
														return " ".join(text.split()					)

							def remove_punc(_lowerCamelCase				: str					):
														__a		:		str									=       set(string.punctuation					)
														return "".join(ch for ch in text if ch not in exclude					)

							def lower(_lowerCamelCase				: Dict					):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase					)					)					)					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: int					):
							return int(normalize_answer(_lowerCamelCase					) == normalize_answer(_lowerCamelCase					)					)





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: Tuple					):
							__a		:		str									=       [any(compute_exact(_lowerCamelCase  ,       _lowerCamelCase					) for ref in refs					) for pred, refs in zip(_lowerCamelCase  ,       _lowerCamelCase					)]
							return (sum(_lowerCamelCase					) / len(_lowerCamelCase					)) * 1_0_0





def       __magic_name__    (   _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: Optional[Any]					):
							__a		:		Tuple									=       [rgram for rgrams in rgramslist for rgram in rgrams]
							__a		:		Union[str, Any]									=       Counter(_lowerCamelCase					)

							__a		:		List[str]									=       Counter(_lowerCamelCase					)
							__a		:		str									=       Counter()
							for sgram, scount in sgramcounter.items():
														__a		:		Optional[int]									=       scount * numref

							__a		:		Optional[Any]									=       Counter(_lowerCamelCase					)
							__a		:		Optional[int]									=       Counter()
							for cgram, ccount in cgramcounter.items():
														__a		:		Dict									=       ccount * numref

							# KEEP
							__a		:		List[str]									=       sgramcounter_rep & cgramcounter_rep
							__a		:		Union[str, Any]									=       keepgramcounter_rep & rgramcounter
							__a		:		Union[str, Any]									=       sgramcounter_rep & rgramcounter

							__a		:		int									=       0
							__a		:		str									=       0
							for keepgram in keepgramcountergood_rep:
														keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
														# Fix an alleged bug [2] in the keep score computation.
														# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
														keeptmpscorea += keepgramcountergood_rep[keepgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							#      a target exactly.
							__a		:		Optional[Any]									=       1
							__a		:		List[str]									=       1
							if len(_lowerCamelCase					) > 0:
														__a		:		List[Any]									=       keeptmpscorea / len(_lowerCamelCase					)
							if len(_lowerCamelCase					) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														__a		:		int									=       keeptmpscorea / sum(keepgramcounterall_rep.values()					)
							__a		:		List[Any]									=       0
							if keepscore_precision > 0 or keepscore_recall > 0:
														__a		:		Dict									=       2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

							# DELETION
							__a		:		Optional[Any]									=       sgramcounter_rep - cgramcounter_rep
							__a		:		Tuple									=       delgramcounter_rep - rgramcounter
							__a		:		int									=       sgramcounter_rep - rgramcounter
							__a		:		Any									=       0
							__a		:		Union[str, Any]									=       0
							for delgram in delgramcountergood_rep:
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							__a		:		Any									=       1
							if len(_lowerCamelCase					) > 0:
														__a		:		List[str]									=       deltmpscorea / len(_lowerCamelCase					)

							# ADDITION
							__a		:		Dict									=       set(_lowerCamelCase					) - set(_lowerCamelCase					)
							__a		:		Optional[int]									=       set(_lowerCamelCase					) & set(_lowerCamelCase					)
							__a		:		List[Any]									=       set(_lowerCamelCase					) - set(_lowerCamelCase					)

							__a		:		Tuple									=       0
							for addgram in addgramcountergood:
														addtmpscore += 1

							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							__a		:		Union[str, Any]									=       1
							__a		:		Optional[Any]									=       1
							if len(_lowerCamelCase					) > 0:
														__a		:		Optional[Any]									=       addtmpscore / len(_lowerCamelCase					)
							if len(_lowerCamelCase					) > 0:
														__a		:		Tuple									=       addtmpscore / len(_lowerCamelCase					)
							__a		:		Tuple									=       0
							if addscore_precision > 0 or addscore_recall > 0:
														__a		:		str									=       2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)





def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Optional[int]					):
							__a		:		List[str]									=       len(_lowerCamelCase					)

							__a		:		str									=       ssent.split(""" """					)
							__a		:		str									=       csent.split(""" """					)
							__a		:		Optional[int]									=       []
							__a		:		Any									=       []
							__a		:		Dict									=       []
							__a		:		Tuple									=       []
							__a		:		str									=       []
							__a		:		Union[str, Any]									=       []

							__a		:		List[Any]									=       []
							__a		:		Dict									=       []
							__a		:		Union[str, Any]									=       []
							__a		:		Tuple									=       []
							for rsent in rsents:
														__a		:		str									=       rsent.split(""" """					)
														__a		:		Optional[int]									=       []
														__a		:		Optional[int]									=       []
														__a		:		Union[str, Any]									=       []
														ragramslist.append(_lowerCamelCase					)
														for i in range(0  ,       len(_lowerCamelCase					) - 1					):
																					if i < len(_lowerCamelCase					) - 1:
																												__a		:		str									=       ragrams[i] + """ """ + ragrams[i + 1]
																												ragrams.append(_lowerCamelCase					)
																					if i < len(_lowerCamelCase					) - 2:
																												__a		:		Dict									=       ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
																												ragrams.append(_lowerCamelCase					)
																					if i < len(_lowerCamelCase					) - 3:
																												__a		:		List[str]									=       ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
																												ragrams.append(_lowerCamelCase					)
														ragramslist.append(_lowerCamelCase					)
														ragramslist.append(_lowerCamelCase					)
														ragramslist.append(_lowerCamelCase					)

							for i in range(0  ,       len(_lowerCamelCase					) - 1					):
														if i < len(_lowerCamelCase					) - 1:
																					__a		:		List[Any]									=       sagrams[i] + """ """ + sagrams[i + 1]
																					sagrams.append(_lowerCamelCase					)
														if i < len(_lowerCamelCase					) - 2:
																					__a		:		Optional[Any]									=       sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
																					sagrams.append(_lowerCamelCase					)
														if i < len(_lowerCamelCase					) - 3:
																					__a		:		Tuple									=       sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
																					sagrams.append(_lowerCamelCase					)

							for i in range(0  ,       len(_lowerCamelCase					) - 1					):
														if i < len(_lowerCamelCase					) - 1:
																					__a		:		Dict									=       cagrams[i] + """ """ + cagrams[i + 1]
																					cagrams.append(_lowerCamelCase					)
														if i < len(_lowerCamelCase					) - 2:
																					__a		:		Optional[Any]									=       cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
																					cagrams.append(_lowerCamelCase					)
														if i < len(_lowerCamelCase					) - 3:
																					__a		:		Optional[int]									=       cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
																					cagrams.append(_lowerCamelCase					)

							(__a)		:		str									=       SARIngram(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
							(__a)		:		Optional[Any]									=       SARIngram(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
							(__a)		:		str									=       SARIngram(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
							(__a)		:		str									=       SARIngram(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
							__a		:		Optional[Any]									=       sum([keepascore, keepascore, keepascore, keepascore]					) / 4
							__a		:		List[str]									=       sum([delascore, delascore, delascore, delascore]					) / 4
							__a		:		Union[str, Any]									=       sum([addascore, addascore, addascore, addascore]					) / 4
							__a		:		int									=       (avgkeepscore + avgdelscore + avgaddscore) / 3
							return finalscore





def       __magic_name__    (   _lowerCamelCase				: Tuple  ,       _lowerCamelCase				: bool = True  ,       _lowerCamelCase				: str = "13a"  ,       _lowerCamelCase				: bool = True					):
							# Normalization is requried for the ASSET dataset (one of the primary
							# datasets in sentence simplification) to allow using space
							# to split the sentence. Even though Wiki-Auto and TURK datasets,
							# do not require normalization, we do it for consistency.
							# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
							# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

							if lowercase:
														__a		:		Optional[Any]									=       sentence.lower()

							if tokenizer in ["13a", "intl"]:
														if version.parse(sacrebleu.__version__					).major >= 2:
																					__a		:		Tuple									=       sacrebleu.metrics.bleu._get_tokenizer(_lowerCamelCase					)()(_lowerCamelCase					)
														else:
																					__a		:		List[Any]									=       sacrebleu.TOKENIZERS[tokenizer]()(_lowerCamelCase					)
							elif tokenizer == "moses":
														__a		:		Tuple									=       sacremoses.MosesTokenizer().tokenize(_lowerCamelCase  ,       return_str=_lowerCamelCase  ,       escape=_lowerCamelCase					)
							elif tokenizer == "penn":
														__a		:		Tuple									=       sacremoses.MosesTokenizer().penn_tokenize(_lowerCamelCase  ,       return_str=_lowerCamelCase					)
							else:
														__a		:		str									=       sentence

							if not return_str:
														__a		:		Tuple									=       normalized_sent.split()

							return normalized_sent





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: List[str]					):
							if not (len(_lowerCamelCase					) == len(_lowerCamelCase					) == len(_lowerCamelCase					)):
														raise ValueError("""Sources length must match predictions and references lengths."""					)
							__a		:		Optional[Any]									=       0
							for src, pred, refs in zip(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					):
														sari_score += SARIsent(normalize(_lowerCamelCase					)  ,       normalize(_lowerCamelCase					)  ,       [normalize(_lowerCamelCase					) for sent in refs]					)
							__a		:		Optional[int]									=       sari_score / len(_lowerCamelCase					)
							return 1_0_0 * sari_score





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: Dict="exp"  ,       _lowerCamelCase				: Optional[Any]=None  ,       _lowerCamelCase				: Optional[Any]=False  ,       _lowerCamelCase				: Union[str, Any]=False  ,       _lowerCamelCase				: List[str]=False  ,       ):
							__a		:		Any									=       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"""					)
							__a		:		int									=       [[refs[i] for refs in references] for i in range(_lowerCamelCase					)]
							__a		:		Any									=       sacrebleu.corpus_bleu(
							    _lowerCamelCase  ,       _lowerCamelCase  ,       smooth_method=_lowerCamelCase  ,       smooth_value=_lowerCamelCase  ,       force=_lowerCamelCase  ,       lowercase=_lowerCamelCase  ,       use_effective_order=_lowerCamelCase  ,       )
							return output.score







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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return datasets.MetricInfo(
													    description=_DESCRIPTION						,     citation=_CITATION						,     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/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py""",
													        """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
													        """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
													        """https://github.com/mjpost/sacreBLEU""",
													    ]						,     reference_urls=[
													        """https://www.aclweb.org/anthology/Q16-1029.pdf""",
													        """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     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Any									=       {}
													result.update({"""sari""": compute_sari(sources=_lowercase						,     predictions=_lowercase						,     references=_lowercase							)}							)
													result.update({"""sacrebleu""": compute_sacrebleu(predictions=_lowercase						,     references=_lowercase							)}							)
													result.update({"""exact""": compute_em(predictions=_lowercase						,     references=_lowercase							)}							)
													return result




"""simple docstring"""

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	lowercase__			   =  argparse.ArgumentParser()

	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
	)
	parser.add_argument(
	    "--original_config_file",
	    type=str,
	    required=True,
	    help="The YAML config file corresponding to the original architecture.",
	)
	parser.add_argument(
	    "--num_in_channels",
	    default=None,
	    type=int,
	    help="The number of input channels. If `None` number of input channels will be automatically inferred.",
	)
	parser.add_argument(
	    "--image_size",
	    default=512,
	    type=int,
	    help=(
	        "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
	        " Base. Use 768 for Stable Diffusion v2."
	    ),
	)
	parser.add_argument(
	    "--extract_ema",
	    action="store_true",
	    help=(
	        "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
	        " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
	        " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
	    ),
	)
	parser.add_argument(
	    "--upcast_attention",
	    action="store_true",
	    help=(
	        "Whether the attention computation should always be upcasted. This is necessary when running stable"
	        " diffusion 2.1."
	    ),
	)
	parser.add_argument(
	    "--from_safetensors",
	    action="store_true",
	    help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
	)
	parser.add_argument(
	    "--to_safetensors",
	    action="store_true",
	    help="Whether to store pipeline in safetensors format or not.",
	)
	parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
	parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")





	def       __magic_name__    (   _lowerCamelCase				: Optional[Any]					):
								if string == "True":
															return True
								elif string == "False":
															return False
								else:
															raise ValueError(F'''could not parse string as bool {string}'''					)

	parser.add_argument(
	    "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
	)

	parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

	lowercase__			   =  parser.parse_args()

	lowercase__			   =  download_controlnet_from_original_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    extract_ema=args.extract_ema,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    use_linear_projection=args.use_linear_projection,
	    cross_attention_dim=args.cross_attention_dim,
	)







	controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

