



"""simple docstring"""






from sklearn.metrics import roc_auc_score

import datasets


_lowercase					:					Optional[Any] 		=		"\nThis metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.\n\nThis metric has three separate use cases:\n    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.\n    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.\n    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.\n"

_lowercase					:					str 		=		"\nArgs:\n- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples,)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples, n_classes)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:\n    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).\n    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.\n    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.\n- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.\n- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.\n- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:\n    - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.\n    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.\n- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in\n    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in\n    `prediction_scores` is used. Defaults to `None`.\nReturns:\n    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.\nExamples:\n    Example 1:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")\n        >>> refs = [1, 0, 1, 1, 0, 0]\n        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)\n        >>> print(round(results['roc_auc'], 2))\n        0.78\n\n    Example 2:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")\n        >>> refs = [1, 0, 1, 2, 2, 0]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')\n        >>> print(round(results['roc_auc'], 2))\n        0.85\n\n    Example 3:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")\n        >>> refs = [[1, 1, 0],\n        ...         [1, 1, 0],\n        ...         [0, 1, 0],\n        ...         [0, 0, 1],\n        ...         [0, 1, 1],\n        ...         [1, 0, 1]]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)\n        >>> print([round(res, 2) for res in results['roc_auc']])\n        [0.83, 0.38, 0.94]\n"

_lowercase					:					Dict 		=		"\\n@article{doi:10.1177/0272989X8900900307,\nauthor = {Donna Katzman McClish},\ntitle ={Analyzing a Portion of the ROC Curve},\njournal = {Medical Decision Making},\nvolume = {9},\nnumber = {3},\npages = {190-195},\nyear = {1989},\ndoi = {10.1177/0272989X8900900307},\n    note ={PMID: 2668680},\nURL = {https://doi.org/10.1177/0272989X8900900307},\neprint = {https://doi.org/10.1177/0272989X8900900307}\n}\n\n\n@article{10.1023/A:1010920819831,\nauthor = {Hand, David J. and Till, Robert J.},\ntitle = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},\nyear = {2001},\nissue_date = {November 2001},\npublisher = {Kluwer Academic Publishers},\naddress = {USA},\nvolume = {45},\nnumber = {2},\nissn = {0885-6125},\nurl = {https://doi.org/10.1023/A:1010920819831},\ndoi = {10.1023/A:1010920819831},\njournal = {Mach. Learn.},\nmonth = {oct},\npages = {171–186},\nnumpages = {16},\nkeywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}\n}\n\n\n@article{scikit-learn,\ntitle={Scikit-learn: Machine Learning in {P}ython},\nauthor={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\njournal={Journal of Machine Learning Research},\nvolume={12},\npages={2825--2830},\nyear={2011}\n}\n"

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


					'''simple docstring'''





					def 						snake_case			(			self				:    int	)->   Dict:
								return datasets.MetricInfo(
								    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
								        {
								            '''prediction_scores''': datasets.Sequence(datasets.Value('''float'''	)	),
								            '''references''': datasets.Value('''int32'''	),
								        }
								        if self.config_name == '''multiclass'''
								        else {
								            '''references''': datasets.Sequence(datasets.Value('''int32'''	)	),
								            '''prediction_scores''': datasets.Sequence(datasets.Value('''float'''	)	),
								        }
								        if self.config_name == '''multilabel'''
								        else {
								            '''references''': datasets.Value('''int32'''	),
								            '''prediction_scores''': datasets.Value('''float'''	),
								        }	),    reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html'''],    )




					def 						snake_case			(			self				:    str,    lowerCamelCase				:    Any,    lowerCamelCase				:    Union[str, Any],    lowerCamelCase				:    Dict="macro",    lowerCamelCase				:    Tuple=None,    lowerCamelCase				:    List[str]=None,    lowerCamelCase				:    Union[str, Any]="raise",    lowerCamelCase				:    Optional[int]=None,    )->   int:
								return {
								    "roc_auc": roc_auc_score(
								        lowerCamelCase,    lowerCamelCase,    average=lowerCamelCase,    sample_weight=lowerCamelCase,    max_fpr=lowerCamelCase,    multi_class=lowerCamelCase,    labels=lowerCamelCase,    )
								}









"""simple docstring"""






import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
		from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
		raise ImportError("You need to install the version of fairseq from the Mega repo!")

class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    Any,    lowerCamelCase				:    Tuple,    lowerCamelCase				:    int	)->   str:
								super().__init__()
								lowerCamelCase__						:						Tuple						      =mega_args
								lowerCamelCase__						:						List[Any]						      =nn.Embedding(lowerCamelCase,    self.mega_args.encoder_embed_dim	)
								lowerCamelCase__						:						List[Any]						      =nn.ModuleList([MegaEncoderLayer(self.mega_args	) for _ in range(lowerCamelCase	)]	)
								lowerCamelCase__						:						List[Any]						      =depth




					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    int,    lowerCamelCase				:    List[str],    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=0	)->   Union[str, Any]:

								# Mega expects embeddings to be (time, batch, embedding size), but
								# Hugging Face returns tokens as (batch, time)
								if batch_first:
											lowerCamelCase__						:						Any						      =input_ids.T

								# to make things more confusing, Mega expects the attention mask to
								# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
								# which is the opposite of what HF returns
								if ignore_mask_value == 0:
											lowerCamelCase__						:						Union[str, Any]						      =1 - attention_mask

								# get token embeddings from IDs
								lowerCamelCase__						:						Any						      =self.embedding_layer(lowerCamelCase	)

								# pass through the Mega layers
								# input is (time, batch, encoder dim) and output is the same
								for encoder in self.encoders:
											lowerCamelCase__						:						Union[str, Any]						      =encoder(lowerCamelCase,    lowerCamelCase	)

								# return according to the shape specified
								if batch_first:
											# (T, B, H) --> (B, T, H)
											return torch.transpose(lowerCamelCase,    0,    1	)
								else:
											return embeds





class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    Tuple	)->   Dict:
								super().__init__()
								lowerCamelCase__						:						Dict						      =MegaLM(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								lowerCamelCase__						:						Optional[Any]						      =nn.Linear(mega_args.encoder_embed_dim,    lowerCamelCase	)
								lowerCamelCase__						:						List[Any]						      =nn.Dropout(p=0.1	)




					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    Optional[int],    lowerCamelCase				:    List[str],    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=0	)->   int:
								lowerCamelCase__						:						Optional[int]						      =self.mega(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self.mlm_head(self.dropout(lowerCamelCase	)	)






def 	snake_case__  (						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			with open(os.path.join(__lowerCamelCase				,						'''model_args.pkl'''      )				,						'''rb'''      ) as f:
						lowerCamelCase__						:						Any						      =pkl.load(__lowerCamelCase      )

			# load the original encoder
			lowerCamelCase__						:						Tuple						      =OriginalMegaForMaskedLM(**__lowerCamelCase      ).eval()

			# load its weights
			print(
			    '''Original Mega encoder:'''				,						original_mlm.mega.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''encoder_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)
			print(
			    '''Original Mega MLM layer:'''				,						original_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''mlm_head_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)

			# create a new config from the old one
			lowerCamelCase__						:						Union[str, Any]						      =MegaConfig(
			    num_hidden_layers=mega_original_args['''depth''']				,						vocab_size=mega_original_args['''vocab_size''']				,						hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim				,						shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim				,						intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim				,						ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim				,						dropout_prob=mega_original_args['''mega_args'''].dropout				,						attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout				,						hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout				,						activation=mega_original_args['''mega_args'''].activation_fn				,						attention_activation=mega_original_args['''mega_args'''].attention_activation_fn				,						bidirectional=mega_original_args['''mega_args'''].bidirectional				,						use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0				,						chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size				,						truncation=mega_original_args['''mega_args'''].truncation_length				,						normalization_type=mega_original_args['''mega_args'''].normalization_type				,						normalize_before_mega=__lowerCamelCase				,						norm_affine=__lowerCamelCase				,						use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout				,						relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias				,						max_positions=mega_original_args['''mega_args'''].max_source_positions				,						nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim				,						normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before				,						nffn_activation_dropout_prob=0.0				,						add_token_type_embeddings=__lowerCamelCase				,						add_lm_hidden_dense_layer=__lowerCamelCase				,						)

			lowerCamelCase__						:						Union[str, Any]						      =MegaForMaskedLM(__lowerCamelCase      ).eval()

			# the originl checkpoint just uses nn.Embedding for the word embeddings
			# we use a wrapper module for embeddings to add support for positional embeddings
			lowerCamelCase__						:						str						      =original_mlm.mega.embedding_layer.weight

			# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
			# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
			# also renaming previously confusing parameter names
			lowerCamelCase__						:						int						      =original_mlm.mega.encoders.state_dict()
			lowerCamelCase__						:						List[Any]						      ={}
			for module_name in original_state_dict.keys():
						lowerCamelCase__						:						Union[str, Any]						      =None
						# have to handle gamma, beta, and alpha differently due to their use
						# in multiple modules within the original repository;
						# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
						# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
						if "beta" in module_name:
									# EMA sub-layers were always called "move" in the original repo
									if "move.beta" in module_name:
												lowerCamelCase__						:						Any						      =module_name.replace('''move.beta'''				,						'''ema_gate.ema_expansion_matrix'''      )
									elif "mega_layer.beta" in module_name:
												lowerCamelCase__						:						Tuple						      =module_name.replace('''beta'''				,						'''qk_bias'''      )
									else:
												lowerCamelCase__						:						Dict						      =module_name.replace('''beta'''				,						'''b_param'''      )
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
						elif "gamma" in module_name:
									if "move.gamma" in module_name:
												lowerCamelCase__						:						List[str]						      =module_name.replace('''move.gamma'''				,						'''ema_gate.kernel_projection_matrix'''      )
									elif "mega_layer.gamma" in module_name:
												lowerCamelCase__						:						Tuple						      =module_name.replace('''gamma'''				,						'''qk_weight'''      )
									else:
												lowerCamelCase__						:						int						      =module_name.replace('''gamma'''				,						'''g_param'''      )
        # alpha is used in EMA and positional bias; renaming to improve readability
						elif "move.alpha" in module_name:
									lowerCamelCase__						:						int						      =module_name.replace('''move.alpha'''				,						'''ema_gate.decay_factor'''      )
						# delta is only used in EMA; renaming to improve readability
						elif "move.delta" in module_name:
									lowerCamelCase__						:						Union[str, Any]						      =module_name.replace('''move.delta'''				,						'''ema_gate.damping_factor'''      )
						# omega is only used in EMA; renaming to improve readability
						elif "omega" in module_name:
									lowerCamelCase__						:						Any						      =module_name.replace('''move.omega'''				,						'''ema_gate.residual_weight'''      )

						if new_module_name:
									lowerCamelCase__						:						Union[str, Any]						      =new_module_name

			if len(__lowerCamelCase      ) != 0:
						print(f'''Renaming these keys: {updated_keys.keys()}'''      )
			else:
						print('''No need to rename state dict entries'''      )
			for old, new in updated_keys.items():
						lowerCamelCase__						:						Optional[int]						      =original_state_dict.pop(__lowerCamelCase      )

			# now attempt to load the state dictionary with updated names
			# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
			print('''HF Mega encoder:'''				,						hf_mlm.mega.layers.load_state_dict(__lowerCamelCase      )      )

			# load the MLM head weights directly
			print(
			    '''HF Mega MLM layer:'''				,						hf_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''mlm_head_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)

			# test on a randomly generated input sequence
			lowerCamelCase__						:						str						      =torch.randint(0				,						hf_config.vocab_size				,						size=(4, 256)      )
			lowerCamelCase__						:						List[Any]						      =torch.ones_like(__lowerCamelCase      )
			# mask a few tokens to make sure masking is applied appropriately :)
			lowerCamelCase__						:						List[str]						      =0

			# run forward passes
			lowerCamelCase__						:						Optional[int]						      =original_mlm(__lowerCamelCase				,						__lowerCamelCase				,						batch_first=__lowerCamelCase				,						ignore_mask_value=0      )
			lowerCamelCase__						:						Optional[int]						      =hf_mlm(__lowerCamelCase				,						__lowerCamelCase      )[0]

			# print shapes and diff
			print(f'''original output {original_output.shape}'''      )
			print(f'''hf output {hf_output.shape}'''      )
			print(f'''max diff: {(original_output - hf_output).max()}'''      )  # 0.0
			lowerCamelCase__						:						List[Any]						      =torch.allclose(__lowerCamelCase				,						__lowerCamelCase				,						atol=1e-3      )

			if success:
						print('''Yay!'''      )
						hf_mlm.save_pretrained(__lowerCamelCase      )
			else:
						raise RuntimeError(f'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'''      )

			if includes_tokenizer:
						print('''Transferring tokenizer'''      )
						lowerCamelCase__						:						Any						      =AutoTokenizer.from_pretrained(__lowerCamelCase      )
						tokenizer.save_pretrained(__lowerCamelCase      )


if __name__ == "__main__":
		_lowercase					:					Dict 		=		argparse.ArgumentParser()

		parser.add_argument(
		    "--pretrained_checkpoint_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Point to the directory containing your model weights using the official Mega repo",
		)

		parser.add_argument(
		    "--output_path", default=None, type=str, required=True, help="Location to save the Hugging Face version"
		)

		parser.add_argument(
		    "--includes_tokenizer",
		    action="store_true",
		    help="Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo",
		)

		_lowercase					:					Dict 		=		parser.parse_args()

		convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)




