



'''simple docstring'''




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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

a     :    Optional[Any]      						=					{
    """post_extract_proj""": """feature_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.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    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    List[str]:
							for attribute in key.split("""."""	):
														UpperCAmelCase  :	Tuple							  =	getattr(_lowercase			,       _lowercase	)

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

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Any:
							UpperCAmelCase  :	int							  =	[]
							UpperCAmelCase  :	List[Any]							  =	fairseq_model.state_dict()

							UpperCAmelCase  :	Optional[int]							  =	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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



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



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    List[Any]:
							UpperCAmelCase  :	int							  =	SEWConfig()
							if is_finetuned:
														UpperCAmelCase  :	Union[str, Any]							  =	model.wav_encoder.wav_model.cfg
							else:
														UpperCAmelCase  :	Tuple							  =	model.cfg

							UpperCAmelCase  :	Tuple							  =	fs_config.conv_bias
							UpperCAmelCase  :	Dict							  =	eval(fs_config.conv_feature_layers	)
							UpperCAmelCase  :	Any							  =	[x[0] for x in conv_layers]
							UpperCAmelCase  :	List[Any]							  =	[x[1] for x in conv_layers]
							UpperCAmelCase  :	Union[str, Any]							  =	[x[2] for x in conv_layers]
							UpperCAmelCase  :	Tuple							  =	"""gelu"""
							UpperCAmelCase  :	Optional[Any]							  =	"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
							UpperCAmelCase  :	List[str]							  =	0.0
							UpperCAmelCase  :	List[Any]							  =	fs_config.activation_fn.name
							UpperCAmelCase  :	Optional[Any]							  =	fs_config.encoder_embed_dim
							UpperCAmelCase  :	Optional[Any]							  =	0.02
							UpperCAmelCase  :	str							  =	fs_config.encoder_ffn_embed_dim
							UpperCAmelCase  :	Union[str, Any]							  =	1e-5
							UpperCAmelCase  :	Union[str, Any]							  =	fs_config.encoder_layerdrop
							UpperCAmelCase  :	Union[str, Any]							  =	fs_config.encoder_attention_heads
							UpperCAmelCase  :	str							  =	fs_config.conv_pos_groups
							UpperCAmelCase  :	Dict							  =	fs_config.conv_pos
							UpperCAmelCase  :	Optional[Any]							  =	len(_lowercase	)
							UpperCAmelCase  :	List[str]							  =	fs_config.encoder_layers
							UpperCAmelCase  :	Tuple							  =	fs_config.squeeze_factor

							# take care of any params that are overridden by the Wav2VecCtc model
							if is_finetuned:
														UpperCAmelCase  :	List[Any]							  =	model.cfg
														UpperCAmelCase  :	int							  =	fs_config.final_dropout
														UpperCAmelCase  :	Union[str, Any]							  =	fs_config.layerdrop
							UpperCAmelCase  :	Any							  =	fs_config.activation_dropout
							UpperCAmelCase  :	List[str]							  =	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
							UpperCAmelCase  :	Tuple							  =	fs_config.attention_dropout
							UpperCAmelCase  :	Optional[int]							  =	fs_config.dropout_input
							UpperCAmelCase  :	Any							  =	fs_config.dropout
							UpperCAmelCase  :	Tuple							  =	fs_config.mask_channel_length
							UpperCAmelCase  :	int							  =	fs_config.mask_channel_prob
							UpperCAmelCase  :	Union[str, Any]							  =	fs_config.mask_length
							UpperCAmelCase  :	str							  =	fs_config.mask_prob

							UpperCAmelCase  :	Any							  =	"""Wav2Vec2FeatureExtractor"""
							UpperCAmelCase  :	Any							  =	"""Wav2Vec2CTCTokenizer"""

							return config



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=True	)   ->    Dict:

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

							if config_path is not None:
														UpperCAmelCase  :	str							  =	SEWConfig.from_pretrained(_lowercase	)
							else:
														UpperCAmelCase  :	str							  =	convert_config(model[0]			,       _lowercase	)
							UpperCAmelCase  :	Optional[int]							  =	model[0].eval()

							UpperCAmelCase  :	List[str]							  =	True if config.feat_extract_norm == """layer""" else False
							UpperCAmelCase  :	Optional[int]							  =	WavaVecaFeatureExtractor(
							    feature_size=1			,       sampling_rate=1_6_0_0_0			,       padding_value=0			,       do_normalize=_lowercase			,       return_attention_mask=_lowercase			,       )

							if is_finetuned:
														if dict_path:
																					UpperCAmelCase  :	Optional[Any]							  =	Dictionary.load(_lowercase	)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					UpperCAmelCase  :	List[Any]							  =	target_dict.pad_index
																					UpperCAmelCase  :	int							  =	target_dict.bos_index
																					UpperCAmelCase  :	Dict							  =	target_dict.pad_index
																					UpperCAmelCase  :	List[str]							  =	target_dict.bos_index
																					UpperCAmelCase  :	Tuple							  =	target_dict.eos_index
																					UpperCAmelCase  :	Optional[int]							  =	len(target_dict.symbols	)
																					UpperCAmelCase  :	int							  =	os.path.join(_lowercase			,       """vocab.json"""	)
																					if not os.path.isdir(_lowercase	):
																												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowercase	)	)
																												return
																					os.makedirs(_lowercase			,       exist_ok=_lowercase	)
																					with open(_lowercase			,       """w"""			,       encoding="""utf-8"""	) as vocab_handle:
																												json.dump(target_dict.indices			,       _lowercase	)
																					UpperCAmelCase  :	List[str]							  =	WavaVecaCTCTokenizer(
																					    _lowercase			,       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=_lowercase			,       )
																					UpperCAmelCase  :	Tuple							  =	WavaVecaProcessor(feature_extractor=_lowercase			,       tokenizer=_lowercase	)
																					processor.save_pretrained(_lowercase	)

														UpperCAmelCase  :	List[Any]							  =	SEWForCTC(_lowercase	)
							else:
														UpperCAmelCase  :	List[Any]							  =	SEWModel(_lowercase	)
														feature_extractor.save_pretrained(_lowercase	)

							recursively_load_weights(_lowercase			,       _lowercase			,       _lowercase	)

							hf_model.save_pretrained(_lowercase	)


if __name__ == "__main__":
							a     :    Dict      						=					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     :    Any      						=					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 mean_squared_error

import datasets


a     :    Union[str, Any]      						=					"""\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={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.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

a     :    int      						=					"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


a     :    int      						=					"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""

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





	def 				_lowercase(						self					)    ->    List[str]:
								return datasets.MetricInfo(
								    description=_DESCRIPTION	,	citation=_CITATION	,	inputs_description=_KWARGS_DESCRIPTION	,	features=datasets.Features(self._get_feature_types()					)	,	reference_urls=[
								        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
								    ]	,	)





	def 				_lowercase(						self					)    ->    List[Any]:
								if self.config_name == "multilist":
															return {
															    "predictions": datasets.Sequence(datasets.Value("""float"""					)					),
															    "references": datasets.Sequence(datasets.Value("""float"""					)					),
															}
								else:
															return {
															    "predictions": datasets.Value("""float"""					),
															    "references": datasets.Value("""float"""					),
															}






	def 				_lowercase(						self	,	A	,	A	,	A=None	,	A="uniform_average"	,	A=True					)    ->    List[Any]:
								UpperCAmelCase  :	List[Any]							  =	mean_squared_error(
								    A	,	A	,	sample_weight=A	,	multioutput=A	,	squared=A					)

								return {"mse": mse}

