




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()
_lowerCamelCase      :  List[str]      						=						logging.get_logger(__name__)

_lowerCamelCase      :  str      						=						{
    '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',
}
_lowerCamelCase      :  Union[str, Any]      						=						[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def 							_UpperCAmelCase  (UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : Dict      ,    UpperCamelCase_ : Any      ,    UpperCamelCase_ : Any      ,    UpperCamelCase_ : Optional[int]       ):
    '''simple docstring'''






    for attribute in key.split("""."""       ):
        _lowerCAmelCase    :    Any										=      getattr(UpperCamelCase_      ,    UpperCamelCase_       )

    if weight_type is not None:
        _lowerCAmelCase    :    int										=      getattr(UpperCamelCase_      ,    UpperCamelCase_       ).shape
    else:
        _lowerCAmelCase    :    List[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":
        _lowerCAmelCase    :    Any										=      value
    elif weight_type == "weight_g":
        _lowerCAmelCase    :    List[Any]										=      value
    elif weight_type == "weight_v":
        _lowerCAmelCase    :    List[Any]										=      value
    elif weight_type == "bias":
        _lowerCAmelCase    :    Optional[Any]										=      value
    else:
        _lowerCAmelCase    :    Any										=      value

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




def 							_UpperCAmelCase  (UpperCamelCase_ : int      ,    UpperCamelCase_ : Optional[Any]       ):
    '''simple docstring'''
    _lowerCAmelCase    :    List[Any]										=      []
    _lowerCAmelCase    :    Dict										=      fairseq_model.state_dict()

    _lowerCAmelCase    :    List[str]										=      hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        _lowerCAmelCase    :    List[Any]										=      False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    hf_model.config.feat_extract_norm == """group"""      ,    )
            _lowerCAmelCase    :    Any										=      True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("""w2v_model."""       )[-1] == name.split("""."""       )[0]:
                    _lowerCAmelCase    :    str										=      True
                    if "*" in mapped_key:
                        _lowerCAmelCase    :    str										=      name.split(UpperCamelCase_       )[0].split("""."""       )[-2]
                        _lowerCAmelCase    :    Optional[Any]										=      mapped_key.replace("""*"""      ,    UpperCamelCase_       )
                    if "weight_g" in name:
                        _lowerCAmelCase    :    List[Any]										=      '''weight_g'''
                    elif "weight_v" in name:
                        _lowerCAmelCase    :    str										=      '''weight_v'''
                    elif "bias" in name and "relative_attention_bias" not in name:
                        _lowerCAmelCase    :    Union[str, Any]										=      '''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        _lowerCAmelCase    :    List[Any]										=      '''weight'''
                    else:
                        _lowerCAmelCase    :    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 							_UpperCAmelCase  (UpperCamelCase_ : int      ,    UpperCamelCase_ : int      ,    UpperCamelCase_ : str      ,    UpperCamelCase_ : Dict      ,    UpperCamelCase_ : Optional[int]       ):
    '''simple docstring'''
    _lowerCAmelCase    :    Any										=      full_name.split("""conv_layers."""       )[-1]
    _lowerCAmelCase    :    Tuple										=      name.split("""."""       )
    _lowerCAmelCase    :    Tuple										=      int(items[0]       )
    _lowerCAmelCase    :    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."
            )
            _lowerCAmelCase    :    Optional[int]										=      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."
            )
            _lowerCAmelCase    :    Union[str, Any]										=      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."
            )
            _lowerCAmelCase    :    Optional[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."
            )
            _lowerCAmelCase    :    str										=      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 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : str=None       ):
    '''simple docstring'''
    _lowerCAmelCase    :    Dict										=      torch.load(UpperCamelCase_       )
    _lowerCAmelCase    :    Union[str, Any]										=      WavLMConfigOrig(checkpoint["""cfg"""]       )
    _lowerCAmelCase    :    List[str]										=      WavLMOrig(UpperCamelCase_       )
    model.load_state_dict(checkpoint["""model"""]       )
    model.eval()

    if config_path is not None:
        _lowerCAmelCase    :    str										=      WavLMConfig.from_pretrained(UpperCamelCase_       )
    else:
        _lowerCAmelCase    :    Optional[Any]										=      WavLMConfig()

    _lowerCAmelCase    :    str										=      WavLMModel(UpperCamelCase_       )

    recursively_load_weights(UpperCamelCase_      ,    UpperCamelCase_       )

    hf_wavlm.save_pretrained(UpperCamelCase_       )


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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 _lowerCamelCase      :  Dict      						=						parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_lowerCamelCase      :  Dict      						=						pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
_lowerCamelCase      :  Union[str, Any]      						=						dataset.iloc[:, 1:2].values
_lowerCamelCase      :  Any      						=						dataset.iloc[:, 2].values


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


_lowerCamelCase      :  Optional[Any]      						=						PolynomialFeatures(degree=4)
_lowerCamelCase      :  Optional[Any]      						=						poly_reg.fit_transform(X)
_lowerCamelCase      :  Dict      						=						LinearRegression()
pol_reg.fit(X_poly, y)




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				plt.scatter(UpperCamelCase_      ,    UpperCamelCase_      ,    color="""red"""       )
				plt.plot(UpperCamelCase_      ,    pol_reg.predict(poly_reg.fit_transform(UpperCamelCase_       )       )      ,    color="""blue"""       )
				plt.title("""Truth or Bluff (Linear Regression)"""       )
				plt.xlabel("""Position level"""       )
				plt.ylabel("""Salary"""       )
				plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003

