


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_rembert import RemBertTokenizer
else:
	A			:						Optional[int]        =  None

A			:						Dict        =  logging.get_logger(__name__)
A			:						Optional[Any]        =  {'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

A			:						List[str]        =  {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

A			:						Optional[Any]        =  {
    'google/rembert': 2_5_6,
}

A			:						Tuple        =  '▁'



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					PRETRAINED_VOCAB_FILES_MAP
					A__     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					RemBertTokenizer

					def __init__(self       :		List[Any] ,   _UpperCAmelCase       :		int=None ,   _UpperCAmelCase       :		List[Any]=None ,   _UpperCAmelCase       :		Union[str, Any]=True ,   _UpperCAmelCase       :		Any=True ,   _UpperCAmelCase       :		Any=False ,   _UpperCAmelCase       :		int="[CLS]" ,   _UpperCAmelCase       :		Dict="[SEP]" ,   _UpperCAmelCase       :		Optional[int]="<unk>" ,   _UpperCAmelCase       :		int="[SEP]" ,   _UpperCAmelCase       :		int="<pad>" ,   _UpperCAmelCase       :		str="[CLS]" ,   _UpperCAmelCase       :		Union[str, Any]="[MASK]" ,   **_UpperCAmelCase       :		int ,   )		->  Dict:




							"""simple docstring"""


							lowercase__	     =						AddedToken(_UpperCAmelCase ,   lstrip=_UpperCAmelCase ,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else mask_token

							super().__init__(
							    _UpperCAmelCase ,   tokenizer_file=_UpperCAmelCase ,   do_lower_case=_UpperCAmelCase ,   remove_space=_UpperCAmelCase ,   keep_accents=_UpperCAmelCase ,   bos_token=_UpperCAmelCase ,   eos_token=_UpperCAmelCase ,   unk_token=_UpperCAmelCase ,   sep_token=_UpperCAmelCase ,   pad_token=_UpperCAmelCase ,   cls_token=_UpperCAmelCase ,   mask_token=_UpperCAmelCase ,   **_UpperCAmelCase ,   )

							lowercase__	     =						do_lower_case
							lowercase__	     =						remove_space
							lowercase__	     =						keep_accents
							lowercase__	     =						vocab_file
							lowercase__	     =						False if not self.vocab_file else True

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		Optional[List[int]] = None						)		->  List[int]:




							"""simple docstring"""


							lowercase__	     =						[self.sep_token_id]
							lowercase__	     =						[self.cls_token_id]
							if token_ids_a is None:
									return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep

					def     lowerCamelCase__	(self       :		Dict ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		Optional[List[int]] = None ,   _UpperCAmelCase       :		bool = False						)		->  List[int]:




							"""simple docstring"""


							if already_has_special_tokens:
									if token_ids_a is not None:
											raise ValueError(
											    """You should not supply a second sequence if the provided sequence of """
											    """ids is already formatted with special tokens for the model."""						)
									return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

							if token_ids_a is not None:
									return [1] + ([0] * len(_UpperCAmelCase						)) + [1] + ([0] * len(_UpperCAmelCase						)) + [1]
							return [1] + ([0] * len(_UpperCAmelCase						)) + [1]

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		Optional[List[int]] = None						)		->  List[int]:




							"""simple docstring"""


							lowercase__	     =						[self.sep_token_id]
							lowercase__	     =						[self.cls_token_id]

							if token_ids_a is None:
									return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]


					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = None						)		->  Tuple[str]:




							"""simple docstring"""


							if not os.path.isdir(_UpperCAmelCase						):
									logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCAmelCase						)						)
									return
							lowercase__	     =						os.path.join(
							    _UpperCAmelCase ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						):
									copyfile(self.vocab_file ,   _UpperCAmelCase						)

							return (out_vocab_file,)




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()
SCREAMING_SNAKE_CASE		:List[str]						=     logging.get_logger("""transformers.models.encodec""")

SCREAMING_SNAKE_CASE		:Tuple						=     {
    """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""",
}
SCREAMING_SNAKE_CASE		:List[str]						=     {
    """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""",
}
SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """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""",
}
SCREAMING_SNAKE_CASE		:Optional[Any]						=     {
    """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""",
}
SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """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""",
}
SCREAMING_SNAKE_CASE		:List[Any]						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
SCREAMING_SNAKE_CASE		:int						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
SCREAMING_SNAKE_CASE		:Dict						=     []
SCREAMING_SNAKE_CASE		:int						=     []




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






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

    if weight_type is not None:
        UpperCamelCase_			  =						getattr(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			).shape
    else:
        UpperCamelCase_			  =						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_			  =						value
    elif weight_type == "weight_g":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_v":
        UpperCamelCase_			  =						value
    elif weight_type == "bias":
        UpperCamelCase_			  =						value
    elif weight_type == "running_mean":
        UpperCamelCase_			  =						value
    elif weight_type == "running_var":
        UpperCamelCase_			  =						value
    elif weight_type == "num_batches_tracked":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_ih_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_hh_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_ih_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_hh_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_ih_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_hh_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_ih_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_hh_l1":
        UpperCamelCase_			  =						value
    else:
        UpperCamelCase_			  =						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_			)->						int:



    """simple docstring"""






    for key in ignore_keys:
        if key.endswith(".*"			):
            if name.startswith(key[:-1]			):
                return True
        elif ".*." in key:
            UpperCamelCase_  ,       UpperCamelCase_			  =						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_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[]

    if model_name == "encodec_24khz" or "encodec_32khz":
        UpperCamelCase_			  =						MAPPING_24K
    elif model_name == "encodec_48khz":
        UpperCamelCase_			  =						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_			  =						False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                UpperCamelCase_  ,       UpperCamelCase_			  =						key.split(".*."			)
                if prefix in name and suffix in name:
                    UpperCamelCase_			  =						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_			  =						True
                if "*" in mapped_key:
                    UpperCamelCase_			  =						name.split(SCREAMING_SNAKE_CASE_			)[0].split("."			)[-2]
                    UpperCamelCase_			  =						mapped_key.replace("*"			,						SCREAMING_SNAKE_CASE_			)
                if "weight_g" in name:
                    UpperCamelCase_			  =						"weight_g"
                elif "weight_v" in name:
                    UpperCamelCase_			  =						"weight_v"
                elif "weight_ih_l0" in name:
                    UpperCamelCase_			  =						"weight_ih_l0"
                elif "weight_hh_l0" in name:
                    UpperCamelCase_			  =						"weight_hh_l0"
                elif "bias_ih_l0" in name:
                    UpperCamelCase_			  =						"bias_ih_l0"
                elif "bias_hh_l0" in name:
                    UpperCamelCase_			  =						"bias_hh_l0"
                elif "weight_ih_l1" in name:
                    UpperCamelCase_			  =						"weight_ih_l1"
                elif "weight_hh_l1" in name:
                    UpperCamelCase_			  =						"weight_hh_l1"
                elif "bias_ih_l1" in name:
                    UpperCamelCase_			  =						"bias_ih_l1"
                elif "bias_hh_l1" in name:
                    UpperCamelCase_			  =						"bias_hh_l1"
                elif "bias" in name:
                    UpperCamelCase_			  =						"bias"
                elif "weight" in name:
                    UpperCamelCase_			  =						"weight"
                elif "running_mean" in name:
                    UpperCamelCase_			  =						"running_mean"
                elif "running_var" in name:
                    UpperCamelCase_			  =						"running_var"
                elif "num_batches_tracked" in name:
                    UpperCamelCase_			  =						"num_batches_tracked"
                else:
                    UpperCamelCase_			  =						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			,						)->						Union[str, Any]:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_			  =						EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
    else:
        UpperCamelCase_			  =						EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        UpperCamelCase_			  =						[8, 5, 4, 4]
        UpperCamelCase_			  =						[2.2]
        UpperCamelCase_			  =						6_4
        UpperCamelCase_			  =						3_2_0_0_0
        UpperCamelCase_			  =						2_0_4_8
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						False
    elif model_name == "encodec_48khz":
        UpperCamelCase_			  =						[8, 5, 4, 2]
        UpperCamelCase_			  =						[3.0, 6.0, 12.0, 24.0]
        UpperCamelCase_			  =						4_8_0_0_0
        UpperCamelCase_			  =						2
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						"time_group_norm"
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						1.0
        UpperCamelCase_			  =						0.01
    else:
        raise ValueError(f"Unknown model name: {model_name}"			)

    UpperCamelCase_			  =						EncodecModel(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						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_			  =						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_			  =						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__":
 SCREAMING_SNAKE_CASE		:Tuple						=     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."""
 )

 SCREAMING_SNAKE_CASE		:Dict						=     parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )
