






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_a       =    logging.get_logger(__name__)

_a       =    {
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    List[str]:





						"""simple docstring"""



						for attribute in key.split('.'      ):
												_UpperCAmelCase							   =     getattr(UpperCAmelCase_					,						UpperCAmelCase_      )

						if weight_type is not None:
												_UpperCAmelCase							   =     getattr(UpperCAmelCase_					,						UpperCAmelCase_      ).shape
						else:
												_UpperCAmelCase							   =     hf_pointer.shape

						assert hf_shape == value.shape, (
						    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
						    F""" {value.shape} for {full_name}"""
						)

						if weight_type == "weight":
												_UpperCAmelCase							   =     value
						elif weight_type == "weight_g":
												_UpperCAmelCase							   =     value
						elif weight_type == "weight_v":
												_UpperCAmelCase							   =     value
						elif weight_type == "bias":
												_UpperCAmelCase							   =     value
						else:
												_UpperCAmelCase							   =     value

						logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     []
						_UpperCAmelCase							   =     fairseq_model.state_dict()

						_UpperCAmelCase							   =     hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCAmelCase							   =     False
												if "conv_layers" in name:
																		load_conv_layer(
																		    UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					,						hf_model.config.feat_extract_norm == 'group'					,						)
																		_UpperCAmelCase							   =     True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCAmelCase							   =     'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

																								if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
																														_UpperCAmelCase							   =     True
																														if "*" in mapped_key:
																																				_UpperCAmelCase							   =     name.split(UpperCAmelCase_      )[0].split('.'      )[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				_UpperCAmelCase							   =     mapped_key.replace('*'					,						UpperCAmelCase_      )
																														if "weight_g" in name:
																																				_UpperCAmelCase							   =     'weight_g'
																														elif "weight_v" in name:
																																				_UpperCAmelCase							   =     'weight_v'
																														elif "weight" in name:
																																				_UpperCAmelCase							   =     'weight'
																														elif "bias" in name:
																																				_UpperCAmelCase							   =     'bias'
																														else:
																																				_UpperCAmelCase							   =     None
																														set_recursively(UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_      )
																								continue
												if not is_used:
																		unused_weights.append(UpperCAmelCase_      )

						logger.warning(F"""Unused weights: {unused_weights}"""      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     full_name.split('conv_layers.'      )[-1]
						_UpperCAmelCase							   =     name.split('.'      )
						_UpperCAmelCase							   =     int(items[0]      )
						_UpperCAmelCase							   =     int(items[1]      )

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																		)
																		_UpperCAmelCase							   =     value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																		)
																		_UpperCAmelCase							   =     value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																		    " found."
																		)
																		_UpperCAmelCase							   =     value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																		)
																		_UpperCAmelCase							   =     value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
						else:
												unused_weights.append(UpperCAmelCase_      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Any:





						"""simple docstring"""



						_UpperCAmelCase							   =     SEWDConfig()
						if is_finetuned:
												_UpperCAmelCase							   =     model.wav_encoder.wav_model.cfg
						else:
												_UpperCAmelCase							   =     model.cfg

						_UpperCAmelCase							   =     fs_config.conv_bias
						_UpperCAmelCase							   =     eval(fs_config.conv_feature_layers      )
						_UpperCAmelCase							   =     [x[0] for x in conv_layers]
						_UpperCAmelCase							   =     [x[1] for x in conv_layers]
						_UpperCAmelCase							   =     [x[2] for x in conv_layers]
						_UpperCAmelCase							   =     'gelu'
						_UpperCAmelCase							   =     'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
						_UpperCAmelCase							   =     0.0
						_UpperCAmelCase							   =     fs_config.activation_fn.name
						_UpperCAmelCase							   =     fs_config.encoder_embed_dim
						_UpperCAmelCase							   =     0.02
						_UpperCAmelCase							   =     fs_config.encoder_ffn_embed_dim
						_UpperCAmelCase							   =     1E-5
						_UpperCAmelCase							   =     fs_config.encoder_layerdrop
						_UpperCAmelCase							   =     fs_config.encoder_attention_heads
						_UpperCAmelCase							   =     fs_config.conv_pos_groups
						_UpperCAmelCase							   =     fs_config.conv_pos
						_UpperCAmelCase							   =     len(UpperCAmelCase_      )
						_UpperCAmelCase							   =     fs_config.encoder_layers
						_UpperCAmelCase							   =     fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						_UpperCAmelCase							   =     fs_config.max_position_embeddings
						_UpperCAmelCase							   =     fs_config.position_buckets
						_UpperCAmelCase							   =     fs_config.share_att_key
						_UpperCAmelCase							   =     fs_config.relative_attention
						_UpperCAmelCase							   =     fs_config.position_biased_input
						_UpperCAmelCase							   =     tuple(fs_config.pos_att_type.split('|'      )      )
						_UpperCAmelCase							   =     fs_config.norm_rel_ebd

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												_UpperCAmelCase							   =     model.cfg
												_UpperCAmelCase							   =     fs_config.final_dropout
												_UpperCAmelCase							   =     fs_config.layerdrop
						_UpperCAmelCase							   =     fs_config.activation_dropout
						_UpperCAmelCase							   =     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						_UpperCAmelCase							   =     fs_config.attention_dropout
						_UpperCAmelCase							   =     fs_config.dropout_input
						_UpperCAmelCase							   =     fs_config.dropout
						_UpperCAmelCase							   =     fs_config.mask_channel_length
						_UpperCAmelCase							   =     fs_config.mask_channel_prob
						_UpperCAmelCase							   =     fs_config.mask_length
						_UpperCAmelCase							   =     fs_config.mask_prob

						_UpperCAmelCase							   =     'Wav2Vec2FeatureExtractor'
						_UpperCAmelCase							   =     'Wav2Vec2CTCTokenizer'

						return config


@torch.no_grad()
def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=None					,						__lowerCAmelCase=None					,						__lowerCAmelCase=True      )->    Tuple:





						"""simple docstring"""



						if is_finetuned:
												_UpperCAmelCase							   =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]					,						arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
						else:
												_UpperCAmelCase							   =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

						if config_path is not None:
												_UpperCAmelCase							   =     SEWDConfig.from_pretrained(UpperCAmelCase_      )
						else:
												_UpperCAmelCase							   =     convert_config(model[0]					,						UpperCAmelCase_      )
						_UpperCAmelCase							   =     model[0].eval()

						_UpperCAmelCase							   =     True if config.feat_extract_norm == 'layer' else False
						_UpperCAmelCase							   =     WavaVecaFeatureExtractor(
						    feature_size=1					,						sampling_rate=16_000					,						padding_value=0					,						do_normalize=UpperCAmelCase_					,						return_attention_mask=UpperCAmelCase_					,						)

						if is_finetuned:
												if dict_path:
																		_UpperCAmelCase							   =     Dictionary.load(UpperCAmelCase_      )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		_UpperCAmelCase							   =     target_dict.pad_index
																		_UpperCAmelCase							   =     target_dict.bos_index
																		_UpperCAmelCase							   =     target_dict.pad_index
																		_UpperCAmelCase							   =     target_dict.bos_index
																		_UpperCAmelCase							   =     target_dict.eos_index
																		_UpperCAmelCase							   =     len(target_dict.symbols      )
																		_UpperCAmelCase							   =     os.path.join(UpperCAmelCase_					,						'vocab.json'      )
																		if not os.path.isdir(UpperCAmelCase_      ):
																								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCAmelCase_      )      )
																								return
																		os.makedirs(UpperCAmelCase_					,						exist_ok=UpperCAmelCase_      )
																		with open(UpperCAmelCase_					,						'w'					,						encoding='utf-8'      ) as vocab_handle:
																								json.dump(target_dict.indices					,						UpperCAmelCase_      )
																		_UpperCAmelCase							   =     WavaVecaCTCTokenizer(
																		    UpperCAmelCase_					,						unk_token=target_dict.unk_word					,						pad_token=target_dict.pad_word					,						bos_token=target_dict.bos_word					,						eos_token=target_dict.eos_word					,						word_delimiter_token='|'					,						do_lower_case=UpperCAmelCase_					,						)
																		_UpperCAmelCase							   =     WavaVecaProcessor(feature_extractor=UpperCAmelCase_					,						tokenizer=UpperCAmelCase_      )
																		processor.save_pretrained(UpperCAmelCase_      )

												_UpperCAmelCase							   =     SEWDForCTC(UpperCAmelCase_      )
						else:
												_UpperCAmelCase							   =     SEWDModel(UpperCAmelCase_      )
												feature_extractor.save_pretrained(UpperCAmelCase_      )

						recursively_load_weights(UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_      )

						hf_model.save_pretrained(UpperCAmelCase_      )


if __name__ == "__main__":
		_a       =    argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
		)
		_a       =    parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)



'''simple docstring'''

from sklearn.metrics import roc_auc_score

import datasets


lowercase__            =     "\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__            =     "\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__            =     "\\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 			A_					(							datasets.Metric				):






     '''simple docstring'''



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




