
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase__    :		int          =   logging.get_logger(__name__)

lowerCamelCase__    :		Dict          =   {
    '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_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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	int:


		'''simple docstring'''






		for attribute in key.split("""."""   ):
				lowercase__  :   Dict    		=      getattr(A_ , A_   )

		if weight_type is not None:
				lowercase__  :   Union[str, Any]    		=      getattr(A_ , A_   ).shape
		else:
				lowercase__  :   Dict    		=      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":
				lowercase__  :   Any    		=      value
		elif weight_type == "weight_g":
				lowercase__  :   int    		=      value
		elif weight_type == "weight_v":
				lowercase__  :   Tuple    		=      value
		elif weight_type == "bias":
				lowercase__  :   Union[str, Any]    		=      value
		else:
				lowercase__  :   List[str]    		=      value

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

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Union[str, Any]:


		'''simple docstring'''






		lowercase__  :   Tuple    		=      []
		lowercase__  :   List[str]    		=      fairseq_model.state_dict()

		lowercase__  :   List[Any]    		=      hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				lowercase__  :   List[str]    		=      False
				if "conv_layers" in name:
						load_conv_layer(
						    A_ , A_ , A_ , A_ , hf_model.config.feat_extract_norm == """group""" , )
						lowercase__  :   Tuple    		=      True
				else:
						for key, mapped_key in MAPPING.items():
								lowercase__  :   Union[str, Any]    		=      """hubert.""" + 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] and not is_finetuned):
										lowercase__  :   Optional[Any]    		=      True
										if "*" in mapped_key:
												lowercase__  :   List[str]    		=      name.split(A_   )[0].split("""."""   )[-2]
												lowercase__  :   int    		=      mapped_key.replace("""*""" , A_   )
										if "weight_g" in name:
												lowercase__  :   int    		=      """weight_g"""
										elif "weight_v" in name:
												lowercase__  :   int    		=      """weight_v"""
										elif "weight" in name:
												lowercase__  :   Any    		=      """weight"""
										elif "bias" in name:
												lowercase__  :   Union[str, Any]    		=      """bias"""
										else:
												lowercase__  :   int    		=      None
										set_recursively(A_ , A_ , A_ , A_ , A_   )
								continue
				if not is_used:
						unused_weights.append(A_   )

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

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	List[str]:


		'''simple docstring'''






		lowercase__  :   Any    		=      full_name.split("""conv_layers."""   )[-1]
		lowercase__  :   Optional[Any]    		=      name.split("""."""   )
		lowercase__  :   Any    		=      int(items[0]   )
		lowercase__  :   Tuple    		=      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.'
						)
						lowercase__  :   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.'
						)
						lowercase__  :   Optional[int]    		=      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."
						)
						lowercase__  :   Union[str, Any]    		=      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.'
						)
						lowercase__  :   Any    		=      value
						logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
		else:
				unused_weights.append(A_   )

@torch.no_grad()
def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_=None , lowercase_=None , lowercase_=True   )		->	str:


		'''simple docstring'''






		if config_path is not None:
				lowercase__  :   Any    		=      HubertConfig.from_pretrained(A_   )
		else:
				lowercase__  :   Optional[Any]    		=      HubertConfig()

		if is_finetuned:
				if dict_path:
						lowercase__  :   Union[str, Any]    		=      Dictionary.load(A_   )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						lowercase__  :   str    		=      target_dict.pad_index
						lowercase__  :   List[str]    		=      target_dict.bos_index
						lowercase__  :   List[str]    		=      target_dict.eos_index
						lowercase__  :   str    		=      len(target_dict.symbols   )
						lowercase__  :   Any    		=      os.path.join(A_ , """vocab.json"""   )
						if not os.path.isdir(A_   ):
								logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(A_   )   )
								return
						os.makedirs(A_ , exist_ok=A_   )
						with open(A_ , """w""" , encoding="""utf-8"""   ) as vocab_handle:
								json.dump(target_dict.indices , A_   )
						lowercase__  :   Optional[Any]    		=      WavaVecaCTCTokenizer(
						    A_ , 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=A_ , )
						lowercase__  :   Union[str, Any]    		=      True if config.feat_extract_norm == """layer""" else False
						lowercase__  :   Optional[Any]    		=      WavaVecaFeatureExtractor(
						    feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=A_ , return_attention_mask=A_ , )
						lowercase__  :   List[str]    		=      WavaVecaProcessor(feature_extractor=A_ , tokenizer=A_   )
						processor.save_pretrained(A_   )

				lowercase__  :   Any    		=      HubertForCTC(A_   )
		else:
				lowercase__  :   List[str]    		=      HubertModel(A_   )

		if is_finetuned:
				lowercase__		,    lowercase__		,    lowercase__  :   Optional[Any]    		=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
		else:
				lowercase__		,    lowercase__		,    lowercase__  :   Dict    		=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

		lowercase__  :   Tuple    		=      model[0].eval()

		recursively_load_weights(A_ , A_ , A_   )

		hf_wavavec.save_pretrained(A_   )


if __name__ == "__main__":
					lowerCamelCase__    :		Union[str, Any]          =   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(
					    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					lowerCamelCase__    :		Dict          =   parser.parse_args()
					convert_hubert_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)








from scipy.stats import spearmanr

import datasets


lowerCamelCase__    :		Union[str, Any]          =   """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""

lowerCamelCase__    :		int          =   """
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {'spearmanr': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results['spearmanr'])
        -0.7
        >>> print(round(results['spearmanr_pvalue'], 2))
        0.19
"""

lowerCamelCase__    :		str          =   R"""\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
"""



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


 def      lowercase__     (					self):


   '''simple docstring'''




   return datasets.MetricInfo(
       description=_DESCRIPTION					,  citation=_CITATION					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
           {
               """predictions""": datasets.Value("""float"""),
               """references""": datasets.Value("""float"""),
           })					,  reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""]					,  )



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=False):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      spearmanr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   if return_pvalue:
     return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
   else:
     return {"spearmanr": results[0]}






