import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
__lowercase											=      logging.get_logger('''transformers.models.encodec''')

__lowercase											=      {
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
__lowercase											=      {
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
__lowercase											=      {
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
__lowercase											=      {
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
__lowercase											=      {
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
__lowercase											=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__lowercase											=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__lowercase											=      []
__lowercase											=      []



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  :Tuple    						=	getattr(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    if weight_type is not None:
        __UpperCamelCase  :Optional[int]    						=	getattr(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			).shape
    else:
        __UpperCamelCase  :Any    						=	hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            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  :Tuple    						=	value
    elif weight_type == "weight_g":
        __UpperCamelCase  :List[str]    						=	value
    elif weight_type == "weight_v":
        __UpperCamelCase  :Union[str, Any]    						=	value
    elif weight_type == "bias":
        __UpperCamelCase  :List[str]    						=	value
    elif weight_type == "running_mean":
        __UpperCamelCase  :List[Any]    						=	value
    elif weight_type == "running_var":
        __UpperCamelCase  :int    						=	value
    elif weight_type == "num_batches_tracked":
        __UpperCamelCase  :List[str]    						=	value
    elif weight_type == "weight_ih_l0":
        __UpperCamelCase  :List[str]    						=	value
    elif weight_type == "weight_hh_l0":
        __UpperCamelCase  :int    						=	value
    elif weight_type == "bias_ih_l0":
        __UpperCamelCase  :List[Any]    						=	value
    elif weight_type == "bias_hh_l0":
        __UpperCamelCase  :List[str]    						=	value
    elif weight_type == "weight_ih_l1":
        __UpperCamelCase  :Optional[int]    						=	value
    elif weight_type == "weight_hh_l1":
        __UpperCamelCase  :List[Any]    						=	value
    elif weight_type == "bias_ih_l1":
        __UpperCamelCase  :int    						=	value
    elif weight_type == "bias_hh_l1":
        __UpperCamelCase  :Dict    						=	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'''







    for key in ignore_keys:
        if key.endswith('''.*'''			):
            if name.startswith(key[:-1]			):
                return True
        elif ".*." in key:
            __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	key.split('''.*.'''			)
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False



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







    __UpperCamelCase  :Any    						=	[]

    if model_name == "encodec_24khz" or "encodec_32khz":
        __UpperCamelCase  :Any    						=	MAPPING_24K
    elif model_name == "encodec_48khz":
        __UpperCamelCase  :Optional[int]    						=	MAPPING_48K
    else:
        raise ValueError(f"""Unsupported model: {model_name}"""			)

    for name, value in orig_dict.items():
        if should_ignore(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
            logger.info(f"""{name} was ignored"""			)
            continue

        __UpperCamelCase  :str    						=	False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                __UpperCamelCase						, __UpperCamelCase  :int    						=	key.split('''.*.'''			)
                if prefix in name and suffix in name:
                    __UpperCamelCase  :Any    						=	suffix

            if key in name:
                # HACK otherwise .embed gets initialized with .embed_avg too
                if key.endswith('''embed'''			) and name.endswith('''embed_avg'''			):
                    continue

                __UpperCamelCase  :Optional[Any]    						=	True
                if "*" in mapped_key:
                    __UpperCamelCase  :Dict    						=	name.split(SCREAMING_SNAKE_CASE			)[0].split('''.'''			)[-2]
                    __UpperCamelCase  :Optional[int]    						=	mapped_key.replace('''*'''						,    SCREAMING_SNAKE_CASE			)
                if "weight_g" in name:
                    __UpperCamelCase  :Any    						=	'''weight_g'''
                elif "weight_v" in name:
                    __UpperCamelCase  :List[Any]    						=	'''weight_v'''
                elif "weight_ih_l0" in name:
                    __UpperCamelCase  :int    						=	'''weight_ih_l0'''
                elif "weight_hh_l0" in name:
                    __UpperCamelCase  :Optional[Any]    						=	'''weight_hh_l0'''
                elif "bias_ih_l0" in name:
                    __UpperCamelCase  :int    						=	'''bias_ih_l0'''
                elif "bias_hh_l0" in name:
                    __UpperCamelCase  :Optional[int]    						=	'''bias_hh_l0'''
                elif "weight_ih_l1" in name:
                    __UpperCamelCase  :Union[str, Any]    						=	'''weight_ih_l1'''
                elif "weight_hh_l1" in name:
                    __UpperCamelCase  :Dict    						=	'''weight_hh_l1'''
                elif "bias_ih_l1" in name:
                    __UpperCamelCase  :Optional[int]    						=	'''bias_ih_l1'''
                elif "bias_hh_l1" in name:
                    __UpperCamelCase  :List[str]    						=	'''bias_hh_l1'''
                elif "bias" in name:
                    __UpperCamelCase  :Optional[Any]    						=	'''bias'''
                elif "weight" in name:
                    __UpperCamelCase  :Optional[int]    						=	'''weight'''
                elif "running_mean" in name:
                    __UpperCamelCase  :List[Any]    						=	'''running_mean'''
                elif "running_var" in name:
                    __UpperCamelCase  :Any    						=	'''running_var'''
                elif "num_batches_tracked" in name:
                    __UpperCamelCase  :Dict    						=	'''num_batches_tracked'''
                else:
                    __UpperCamelCase  :List[str]    						=	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}"""			)



@torch.no_grad()
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    ):
    '''simple docstring'''







    if config_path is not None:
        __UpperCamelCase  :str    						=	EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE			)
    else:
        __UpperCamelCase  :List[Any]    						=	EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        __UpperCamelCase  :Tuple    						=	[8, 5, 4, 4]
        __UpperCamelCase  :int    						=	[2.2]
        __UpperCamelCase  :Optional[Any]    						=	64
        __UpperCamelCase  :Tuple    						=	32_000
        __UpperCamelCase  :Dict    						=	2_048
        __UpperCamelCase  :List[str]    						=	False
        __UpperCamelCase  :List[Any]    						=	False
        __UpperCamelCase  :Dict    						=	False
    elif model_name == "encodec_48khz":
        __UpperCamelCase  :str    						=	[8, 5, 4, 2]
        __UpperCamelCase  :Union[str, Any]    						=	[3.0, 6.0, 12.0, 24.0]
        __UpperCamelCase  :str    						=	48_000
        __UpperCamelCase  :Optional[Any]    						=	2
        __UpperCamelCase  :Optional[Any]    						=	False
        __UpperCamelCase  :str    						=	'''time_group_norm'''
        __UpperCamelCase  :Any    						=	True
        __UpperCamelCase  :str    						=	1.0
        __UpperCamelCase  :Union[str, Any]    						=	0.01
    else:
        raise ValueError(f"""Unknown model name: {model_name}"""			)

    __UpperCamelCase  :List[str]    						=	EncodecModel(SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :List[Any]    						=	EncodecFeatureExtractor(
        feature_size=config.audio_channels						,    sampling_rate=config.sampling_rate						,    chunk_length_s=config.chunk_length_s						,    overlap=config.overlap						,    )
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Optional[int]    						=	torch.load(SCREAMING_SNAKE_CASE			)
    if "best_state" in original_checkpoint:
        # we might have a training state saved, in which case discard the yaml results and just retain the weights
        __UpperCamelCase  :Optional[int]    						=	original_checkpoint['''best_state''']
    recursively_load_weights(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    model.save_pretrained(SCREAMING_SNAKE_CASE			)

    if repo_id:
        print('''Pushing to the hub...'''			)
        feature_extractor.push_to_hub(SCREAMING_SNAKE_CASE			)
        model.push_to_hub(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  __lowercase											=      argparse.ArgumentParser()
  parser.add_argument(
      '''--model''',
      default='''encodec_24khz''',
      type=str,
      help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
  )
  parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  parser.add_argument(
      '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
  )

  __lowercase											=      parser.parse_args()
  convert_checkpoint(
      args.model,
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )






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]))





