
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowercase__		:List[Any]   =    argparse.ArgumentParser()

							parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

							parser.add_argument(
							    "--txt2img_unclip",
							    default="kakaobrain/karlo-v1-alpha",
							    type=str,
							    required=False,
							    help="The pretrained txt2img unclip.",
							)

							lowercase__		:List[Any]   =    parser.parse_args()

							lowercase__		:List[str]   =    UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowercase__		:Dict   =    CLIPImageProcessor()
							lowercase__		:Dict   =    CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

							lowercase__		:Any   =    UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)










"""simple docstring"""






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowercase__     						=						"""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

lowercase__     						=						"""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

lowercase__     						=						"""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def      _snake_case			(				lowercase__							,    lowercase__		):
				return float((preds == labels).mean()		)







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__="binary"		):
				_lowerCamelCase     :			str    =    simple_accuracy(lowercase__							,    lowercase__		)
				_lowerCamelCase     :			Any    =    float(fa_score(y_true=lowercase__							,    y_pred=lowercase__							,    average=lowercase__		)		)
				return {
				    "accuracy": acc,
				    "f1": fa,
				}







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Any    =    {}
				for id_pred, label in zip(lowercase__							,    lowercase__		):
								_lowerCamelCase     :			Tuple    =    f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
								_lowerCamelCase     :			Union[str, Any]    =    id_pred['prediction']
								if question_id in question_map:
												question_map[question_id].append((pred, label)		)
								else:
												_lowerCamelCase     :			Optional[Any]    =    [(pred, label)]
				_lowerCamelCase, _lowerCamelCase     :			Optional[int]    =    [], []
				for question, preds_labels in question_map.items():
								_lowerCamelCase, _lowerCamelCase     :			Tuple    =    zip(*lowercase__		)
								_lowerCamelCase     :			List[str]    =    fa_score(y_true=lowercase__							,    y_pred=lowercase__							,    average='macro'		)
								fas.append(lowercase__		)
								_lowerCamelCase     :			int    =    int(sum(pred == label for pred, label in preds_labels		) == len(lowercase__		)		)
								ems.append(lowercase__		)
				_lowerCamelCase     :			Optional[Any]    =    float(sum(lowercase__		) / len(lowercase__		)		)
				_lowerCamelCase     :			Optional[int]    =    sum(lowercase__		) / len(lowercase__		)
				_lowerCamelCase     :			List[Any]    =    float(fa_score(y_true=lowercase__							,    y_pred=[id_pred['prediction'] for id_pred in ids_preds]		)		)
				return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






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






			'''simple docstring'''



			def 					A_    (  self  ):
							if self.config_name not in [
							    "boolq",
							    "cb",
							    "copa",
							    "multirc",
							    "record",
							    "rte",
							    "wic",
							    "wsc",
							    "wsc.fixed",
							    "axb",
							    "axg",
							]:
											raise KeyError(
											    'You should supply a configuration name selected in '
											    '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'  )
							return datasets.MetricInfo(
							    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(self._get_feature_types()  )  ,      codebase_urls=[]  ,      reference_urls=[]  ,      format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None  ,      )
			def 					A_    (  self  ):
							if self.config_name == "record":
											return {
											    "predictions": {
											        "idx": {
											            "passage": datasets.Value('int64'  ),
											            "query": datasets.Value('int64'  ),
											        },
											        "prediction_text": datasets.Value('string'  ),
											    },
											    "references": {
											        "idx": {
											            "passage": datasets.Value('int64'  ),
											            "query": datasets.Value('int64'  ),
											        },
											        "answers": datasets.Sequence(datasets.Value('string'  )  ),
											    },
											}
							elif self.config_name == "multirc":
											return {
											    "predictions": {
											        "idx": {
											            "answer": datasets.Value('int64'  ),
											            "paragraph": datasets.Value('int64'  ),
											            "question": datasets.Value('int64'  ),
											        },
											        "prediction": datasets.Value('int64'  ),
											    },
											    "references": datasets.Value('int64'  ),
											}
							else:
											return {
											    "predictions": datasets.Value('int64'  ),
											    "references": datasets.Value('int64'  ),
											}






			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							if self.config_name == "axb":
											return {"matthews_correlation": matthews_corrcoef(lowercase  ,      lowercase  )}
							elif self.config_name == "cb":
											return acc_and_fa(lowercase  ,      lowercase  ,      fa_avg='macro'  )
							elif self.config_name == "record":
											_lowerCamelCase     :			List[str]    =    [
											    {
											        'qas': [
											            {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
											            for ref in references
											        ]
											    }
											]
											_lowerCamelCase     :			Union[str, Any]    =    {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
											return evaluate_record(lowercase  ,      lowercase  )[0]
							elif self.config_name == "multirc":
											return evaluate_multirc(lowercase  ,      lowercase  )
							elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
											return {"accuracy": simple_accuracy(lowercase  ,      lowercase  )}
							else:
											raise KeyError(
											    'You should supply a configuration name selected in '
											    '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'  )