






from sklearn.metrics import fa_score

import datasets


UpperCamelCase 				=	"\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"


UpperCamelCase 				=	"\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\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 (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'f1': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['f1'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results['f1'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n        >>> print(round(results['f1'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'f1': array([0.8, 0. , 0. ])}\n"


UpperCamelCase 				=	"\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n"



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







			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									return datasets.MetricInfo(
									    description=_DESCRIPTION      ,	citation=_CITATION      ,	inputs_description=_KWARGS_DESCRIPTION      ,	features=datasets.Features(
									        {
									            """predictions""": datasets.Sequence(datasets.Value("""int32"""     )     ),
									            """references""": datasets.Sequence(datasets.Value("""int32"""     )     ),
									        }
									        if self.config_name == """multilabel"""
									        else {
									            """predictions""": datasets.Value("""int32"""     ),
									            """references""": datasets.Value("""int32"""     ),
									        }     )      ,	reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""]      ,	)







			def 							UpperCamelCase      (	self:  Union[str, Any]      ,	UpperCAmelCase_:  List[str]      ,	UpperCAmelCase_:  Optional[Any]      ,	UpperCAmelCase_:  List[Any]=None      ,	UpperCAmelCase_:  int=1      ,	UpperCAmelCase_:  Optional[Any]="binary"      ,	UpperCAmelCase_:  Optional[Any]=None     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  fa_score(
									    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	labels=_SCREAMING_SNAKE_CASE      ,	pos_label=_SCREAMING_SNAKE_CASE      ,	average=_SCREAMING_SNAKE_CASE      ,	sample_weight=_SCREAMING_SNAKE_CASE     )
									return {"f1": float(_SCREAMING_SNAKE_CASE     ) if score.size == 1 else score}






"""simple docstring"""
import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
A:   Optional[int]								=		logging.get_logger(__name__)

A:   Tuple								=		{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
A:   List[str]								=		[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Any						):
 for attribute in key.split("""."""						):
  UpperCAmelCase	:					Optional[Any]	=	getattr(UpperCamelCase			,		UpperCamelCase						)

 if weight_type is not None:
  UpperCAmelCase	:					List[Any]	=	getattr(UpperCamelCase			,		UpperCamelCase						).shape
 else:
  UpperCAmelCase	:					str	=	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	:					Optional[Any]	=	value
 elif weight_type == "weight_g":
  UpperCAmelCase	:					str	=	value
 elif weight_type == "weight_v":
  UpperCAmelCase	:					Union[str, Any]	=	value
 elif weight_type == "bias":
  UpperCAmelCase	:					str	=	value
 else:
  UpperCAmelCase	:					Union[str, Any]	=	value

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

def       _snake_case						(						UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		Optional[Any]						):
 UpperCAmelCase	:					Tuple	=	[]
 UpperCAmelCase	:					Any	=	fairseq_model.state_dict()

 UpperCAmelCase	:					Tuple	=	hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCAmelCase	:					str	=	False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		hf_model.config.feat_extract_norm == """group"""			,		)
   UpperCAmelCase	:					Union[str, Any]	=	True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split("""w2v_model."""						)[-1] == name.split("""."""						)[0]:
     UpperCAmelCase	:					Dict	=	True
     if "*" in mapped_key:
      UpperCAmelCase	:					str	=	name.split(UpperCamelCase						)[0].split("""."""						)[-2]
      UpperCAmelCase	:					Tuple	=	mapped_key.replace("""*"""			,		UpperCamelCase						)
     if "weight_g" in name:
      UpperCAmelCase	:					Any	=	"""weight_g"""
     elif "weight_v" in name:
      UpperCAmelCase	:					Optional[Any]	=	"""weight_v"""
     elif "bias" in name and "relative_attention_bias" not in name:
      UpperCAmelCase	:					Union[str, Any]	=	"""bias"""
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      UpperCAmelCase	:					str	=	"""weight"""
     else:
      UpperCAmelCase	:					Optional[Any]	=	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       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Any						):
 UpperCAmelCase	:					str	=	full_name.split("""conv_layers."""						)[-1]
 UpperCAmelCase	:					Dict	=	name.split("""."""						)
 UpperCAmelCase	:					List[str]	=	int(items[0]						)
 UpperCAmelCase	:					Any	=	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	:					Optional[Any]	=	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	:					Tuple	=	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	:					str	=	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	:					Optional[Any]	=	value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
 else:
  unused_weights.append(UpperCamelCase						)

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		List[Any]=None						):
 # load the pre-trained checkpoints
 UpperCAmelCase	:					List[Any]	=	torch.load(UpperCamelCase						)
 UpperCAmelCase	:					List[str]	=	WavLMConfigOrig(checkpoint["""cfg"""]						)
 UpperCAmelCase	:					Optional[int]	=	WavLMOrig(UpperCamelCase						)
 model.load_state_dict(checkpoint["""model"""]						)
 model.eval()

 if config_path is not None:
  UpperCAmelCase	:					List[str]	=	WavLMConfig.from_pretrained(UpperCamelCase						)
 else:
  UpperCAmelCase	:					List[Any]	=	WavLMConfig()

 UpperCAmelCase	:					Any	=	WavLMModel(UpperCamelCase						)

 recursively_load_weights(UpperCamelCase			,		UpperCamelCase						)

 hf_wavlm.save_pretrained(UpperCamelCase						)


if __name__ == "__main__":
      A:   int								=		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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      A:   Tuple								=		parser.parse_args()
      convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
