





'''simple docstring'''

import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)

os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				if hor == 128:
								lowercase         = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
								lowercase         = (32, 128, 256)
								lowercase         = ("UpResnetBlock1D", "UpResnetBlock1D")

				elif hor == 32:
								lowercase         = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
								lowercase         = (32, 64, 128, 256)
								lowercase         = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
				lowercase         = torch.load(f'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'							)
				lowercase         = model.state_dict()
				lowercase         = {
				    "down_block_types": down_block_types,
				    "block_out_channels": block_out_channels,
				    "up_block_types": up_block_types,
				    "layers_per_block": 1,
				    "use_timestep_embedding": True,
				    "out_block_type": "OutConv1DBlock",
				    "norm_num_groups": 8,
				    "downsample_each_block": False,
				    "in_channels": 14,
				    "out_channels": 14,
				    "extra_in_channels": 0,
				    "time_embedding_type": "positional",
				    "flip_sin_to_cos": False,
				    "freq_shift": 1,
				    "sample_size": 6_5536,
				    "mid_block_type": "MidResTemporalBlock1D",
				    "act_fn": "mish",
				}
				lowercase         = UNetaDModel(**lowerCAmelCase_							)
				print(f'length of state dict: {len(state_dict.keys()							)}'							)
				print(f'length of value function dict: {len(hf_value_function.state_dict().keys()							)}'							)
				lowercase         = dict(zip(model.state_dict().keys()		,							hf_value_function.state_dict().keys()							)							)
				for k, v in mapping.items():
								lowercase         = state_dict.pop(lowerCAmelCase_							)
				hf_value_function.load_state_dict(lowerCAmelCase_							)

				torch.save(hf_value_function.state_dict()		,							f'hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'							)
				with open(f'hub/hopper-medium-v2/unet/hor{hor}/config.json'		,							"w"							) as f:
								json.dump(lowerCAmelCase_		,							lowerCAmelCase_							)







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = {
				    "in_channels": 14,
				    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
				    "up_block_types": (),
				    "out_block_type": "ValueFunction",
				    "mid_block_type": "ValueFunctionMidBlock1D",
				    "block_out_channels": (32, 64, 128, 256),
				    "layers_per_block": 1,
				    "downsample_each_block": True,
				    "sample_size": 6_5536,
				    "out_channels": 14,
				    "extra_in_channels": 0,
				    "time_embedding_type": "positional",
				    "use_timestep_embedding": True,
				    "flip_sin_to_cos": False,
				    "freq_shift": 1,
				    "norm_num_groups": 8,
				    "act_fn": "mish",
				}

				lowercase         = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"							)
				lowercase         = model
				lowercase         = UNetaDModel(**lowerCAmelCase_							)
				print(f'length of state dict: {len(state_dict.keys()							)}'							)
				print(f'length of value function dict: {len(hf_value_function.state_dict().keys()							)}'							)

				lowercase         = dict(zip(state_dict.keys()		,							hf_value_function.state_dict().keys()							)							)
				for k, v in mapping.items():
								lowercase         = state_dict.pop(lowerCAmelCase_							)

				hf_value_function.load_state_dict(lowerCAmelCase_							)

				torch.save(hf_value_function.state_dict()		,							"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"							)
				with open("hub/hopper-medium-v2/value_function/config.json"		,							"w"							) as f:
								json.dump(lowerCAmelCase_		,							lowerCAmelCase_							)


if __name__ == "__main__":
			unet(32)
			# unet(128)
			value_function()











'''simple docstring'''

from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__lowerCamelCase : str											=			"\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  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},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

__lowerCamelCase : Optional[int]											=			"\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

__lowerCamelCase : int											=			"\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				return float((preds == labels).mean()							)







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_="binary"							):

				"""simple docstring"""






				lowercase         = simple_accuracy(lowerCAmelCase_		,							lowerCAmelCase_							)
				lowercase         = float(fa_score(y_true=lowerCAmelCase_		,							y_pred=lowerCAmelCase_		,							average=lowerCAmelCase_							)							)
				return {
				    "accuracy": acc,
				    "f1": fa,
				}







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = {}
				for id_pred, label in zip(lowerCAmelCase_		,							lowerCAmelCase_							):
								lowercase         = f'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
								lowercase         = id_pred["prediction"]
								if question_id in question_map:
												question_map[question_id].append((pred, label)							)
								else:
												lowercase         = [(pred, label)]
				lowercase   ,							lowercase         = [], []
				for question, preds_labels in question_map.items():
								lowercase   ,							lowercase         = zip(*lowerCAmelCase_							)
								lowercase         = fa_score(y_true=lowerCAmelCase_		,							y_pred=lowerCAmelCase_		,							average="macro"							)
								fas.append(lowerCAmelCase_							)
								lowercase         = int(sum(pred == label for pred, label in preds_labels							) == len(lowerCAmelCase_							)							)
								ems.append(lowerCAmelCase_							)
				lowercase         = float(sum(lowerCAmelCase_							) / len(lowerCAmelCase_							)							)
				lowercase         = sum(lowerCAmelCase_							) / len(lowerCAmelCase_							)
				lowercase         = float(fa_score(y_true=lowerCAmelCase_		,							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       UpperCAmelCase (						datasets.Metric  ):




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




