import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

__lowercase											=      {
    '''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''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__lowercase											=      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    for attribute in key.split('''.'''			):
        __UpperCamelCase  :Dict    						=	getattr(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    if weight_type is not None:
        __UpperCamelCase  :Union[str, Any]    						=	getattr(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			).shape
    else:
        __UpperCamelCase  :int    						=	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  :Any    						=	value
    elif weight_type == "weight_g":
        __UpperCamelCase  :int    						=	value
    elif weight_type == "weight_v":
        __UpperCamelCase  :Optional[Any]    						=	value
    elif weight_type == "bias":
        __UpperCamelCase  :int    						=	value
    else:
        __UpperCamelCase  :Tuple    						=	value

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	[]
    __UpperCamelCase  :List[Any]    						=	fairseq_model.state_dict()

    __UpperCamelCase  :Union[str, Any]    						=	hf_model.feature_extractor
    __UpperCamelCase  :Union[str, Any]    						=	hf_model.adapter

    for name, value in fairseq_dict.items():
        __UpperCamelCase  :List[Any]    						=	False
        if "conv_layers" in name:
            load_conv_layer(
                SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    hf_model.config.feat_extract_norm == '''group'''						,    )
            __UpperCamelCase  :Any    						=	True
        elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']			):
            load_adapter(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Tuple    						=	True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split('''w2v_model.'''			)[-1] == name.split('''.'''			)[0]:
                    __UpperCamelCase  :int    						=	True
                    if "*" in mapped_key:
                        __UpperCamelCase  :List[str]    						=	name.split(SCREAMING_SNAKE_CASE			)[0].split('''.'''			)[-2]
                        __UpperCamelCase  :int    						=	mapped_key.replace('''*'''						,    SCREAMING_SNAKE_CASE			)
                    if "weight_g" in name:
                        __UpperCamelCase  :Dict    						=	'''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase  :Tuple    						=	'''weight_v'''
                    elif "bias" in name:
                        __UpperCamelCase  :str    						=	'''bias'''
                    elif "weight" in name:
                        __UpperCamelCase  :Any    						=	'''weight'''
                    else:
                        __UpperCamelCase  :Any    						=	None
                    set_recursively(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
                continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE			)

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	full_name.split('''adaptor.'''			)[-1]
    __UpperCamelCase  :Any    						=	name.split('''.'''			)

    if items[1].isdigit():
        __UpperCamelCase  :Optional[int]    						=	int(items[1]			)
    else:
        __UpperCamelCase  :Optional[Any]    						=	None

    if "adaptor" not in full_name:
        if "proj_ln" in full_name:
            # has to be layer norm
            if "bias" in name:
                assert (
                    value.shape == adapter.proj_layer_norm.bias.data.shape
                ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
                __UpperCamelCase  :Optional[Any]    						=	value
                logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""			)
            if "weight" in name:
                assert (
                    value.shape == adapter.proj_layer_norm.weight.data.shape
                ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
                __UpperCamelCase  :Dict    						=	value
        else:
            # has to be projection layer
            if "bias" in name:
                assert (
                    value.shape == adapter.proj.bias.data.shape
                ), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
                __UpperCamelCase  :List[str]    						=	value
                logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""			)
            if "weight" in name:
                assert (
                    value.shape == adapter.proj.weight.data.shape
                ), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
                __UpperCamelCase  :Optional[int]    						=	value
                logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""			)
    elif isinstance(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
        if "bias" in name:
            assert (
                value.shape == adapter.layers[layer_id].conv.bias.data.shape
            ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
            __UpperCamelCase  :Union[str, Any]    						=	value
            logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""			)
        elif "weight" in name:
            assert (
                value.shape == adapter.layers[layer_id].conv.weight.data.shape
            ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
            __UpperCamelCase  :Any    						=	value
            logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""			)
    else:
        unused_weights.append(SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	emb.weight.shape
    __UpperCamelCase  :Any    						=	nn.Linear(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    bias=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Dict    						=	emb.weight.data
    return lin_layer



@torch.no_grad()
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    ):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	WavaVecaConfig.from_pretrained(
        SCREAMING_SNAKE_CASE						,    add_adapter=SCREAMING_SNAKE_CASE						,    adapter_stride=SCREAMING_SNAKE_CASE						,    adapter_kernel_size=SCREAMING_SNAKE_CASE						,    use_auth_token=SCREAMING_SNAKE_CASE						,    output_hidden_size=SCREAMING_SNAKE_CASE						,    )
    __UpperCamelCase  :List[Any]    						=	MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE			)

    # load model
    __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]						,    arg_overrides={
            '''config_yaml''': config_yaml_path,
            '''data''': '''/'''.join(dict_path.split('''/'''			)[:-1]			),
            '''w2v_path''': checkpoint_path,
            '''load_pretrained_decoder_from''': None,
        }						,    )
    __UpperCamelCase  :List[Any]    						=	model[0].eval()

    # load feature extractor
    __UpperCamelCase  :List[Any]    						=	WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE						,    use_auth_token=SCREAMING_SNAKE_CASE			)

    # set weights for wav2vec2 encoder
    __UpperCamelCase  :Optional[int]    						=	WavaVecaModel(SCREAMING_SNAKE_CASE			)

    recursively_load_weights_wavaveca(model.encoder						,    SCREAMING_SNAKE_CASE			)

    # load decoder weights
    __UpperCamelCase  :Any    						=	MBartForCausalLM(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase						, __UpperCamelCase  :int    						=	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,    strict=SCREAMING_SNAKE_CASE			)
    logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""			)
    logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""			)

    __UpperCamelCase  :Dict    						=	SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE						,    decoder=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Any    						=	False

    __UpperCamelCase  :List[Any]    						=	MBartaaTokenizer(SCREAMING_SNAKE_CASE			)
    tokenizer.save_pretrained(SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :int    						=	hf_wavavec.config.to_dict()
    __UpperCamelCase  :Union[str, Any]    						=	tokenizer.pad_token_id
    __UpperCamelCase  :List[Any]    						=	tokenizer.bos_token_id
    __UpperCamelCase  :List[str]    						=	tokenizer.eos_token_id
    __UpperCamelCase  :Tuple    						=	'''mbart50'''
    __UpperCamelCase  :str    						=	'''wav2vec2'''

    __UpperCamelCase  :List[str]    						=	tokenizer.eos_token_id
    __UpperCamelCase  :Tuple    						=	250_004
    __UpperCamelCase  :Union[str, Any]    						=	tokenizer.eos_token_id

    __UpperCamelCase  :Tuple    						=	SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE			)

    hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE			)
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    __lowercase											=      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_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
    parser.add_argument(
        '''--encoder_config_path''',
        default='''facebook/wav2vec2-xls-r-1b''',
        type=str,
        help='''Path to hf encoder wav2vec2 checkpoint config''',
    )
    parser.add_argument(
        '''--decoder_config_path''',
        default='''facebook/mbart-large-50-one-to-many-mmt''',
        type=str,
        help='''Path to hf decoder checkpoint config''',
    )
    parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
    parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
    parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
    parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
    parser.add_argument('''--start_token_id''', default=25_0004, type=int, help='''`decoder_start_token_id` of model config''')

    __lowercase											=      parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.dict_path,
        args.config_yaml_path,
        encoder_config_path=args.encoder_config_path,
        decoder_config_path=args.decoder_config_path,
        add_adapter=args.add_adapter,
        adapter_kernel_size=args.adapter_kernel_size,
        adapter_stride=args.adapter_stride,
        decoder_start_token_id=args.start_token_id,
        encoder_output_dim=args.encoder_output_dim,
    )






from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

__lowercase											=      datasets.load_iris()

__lowercase											=      np.array(data['''data'''])
__lowercase											=      np.array(data['''target'''])
__lowercase											=      data['''target_names''']

__lowercase				, __lowercase				, __lowercase				, __lowercase											=      train_test_split(X, y)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return np.linalg.norm(np.array(SCREAMING_SNAKE_CASE			) - np.array(SCREAMING_SNAKE_CASE			)			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=5			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	zip(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    # List of distances of all points from the point to be classified
    __UpperCamelCase  :List[str]    						=	[]
    for data_point in data:
        __UpperCamelCase  :Optional[int]    						=	euclidean_distance(data_point[0]						,    SCREAMING_SNAKE_CASE			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    __UpperCamelCase  :Any    						=	[i[1] for i in sorted(SCREAMING_SNAKE_CASE			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    __UpperCamelCase  :Union[str, Any]    						=	Counter(SCREAMING_SNAKE_CASE			).most_common(1			)[0][0]
    return classes[result]


if __name__ == "__main__":
    print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))





