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     :Optional[int]			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    if weight_type is not None:
        __UpperCamelCase     :List[Any]			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	).shape
    else:
        __UpperCamelCase     :Optional[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     :List[str]			  =		value
    elif weight_type == "weight_g":
        __UpperCamelCase     :str			  =		value
    elif weight_type == "weight_v":
        __UpperCamelCase     :Dict			  =		value
    elif weight_type == "bias":
        __UpperCamelCase     :Dict			  =		value
    elif weight_type == "running_mean":
        __UpperCamelCase     :Dict			  =		value
    elif weight_type == "running_var":
        __UpperCamelCase     :Union[str, Any]			  =		value
    elif weight_type == "num_batches_tracked":
        __UpperCamelCase     :Any			  =		value
    elif weight_type == "weight_ih_l0":
        __UpperCamelCase     :str			  =		value
    elif weight_type == "weight_hh_l0":
        __UpperCamelCase     :int			  =		value
    elif weight_type == "bias_ih_l0":
        __UpperCamelCase     :List[str]			  =		value
    elif weight_type == "bias_hh_l0":
        __UpperCamelCase     :Tuple			  =		value
    elif weight_type == "weight_ih_l1":
        __UpperCamelCase     :List[Any]			  =		value
    elif weight_type == "weight_hh_l1":
        __UpperCamelCase     :List[str]			  =		value
    elif weight_type == "bias_ih_l1":
        __UpperCamelCase     :Dict			  =		value
    elif weight_type == "bias_hh_l1":
        __UpperCamelCase     :Tuple			  =		value
    else:
        __UpperCamelCase     :int			  =		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     :Dict			  =		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     :Dict			  =		[]

    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     :Optional[Any]			  =		False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                __UpperCamelCase  ,       __UpperCamelCase     :Tuple			  =		key.split('''.*.'''	)
                if prefix in name and suffix in name:
                    __UpperCamelCase     :Union[str, 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     :Tuple			  =		name.split(SCREAMING_SNAKE_CASE	)[0].split('''.'''	)[-2]
                    __UpperCamelCase     :Optional[int]			  =		mapped_key.replace('''*'''  ,			SCREAMING_SNAKE_CASE	)
                if "weight_g" in name:
                    __UpperCamelCase     :Dict			  =		'''weight_g'''
                elif "weight_v" in name:
                    __UpperCamelCase     :List[Any]			  =		'''weight_v'''
                elif "weight_ih_l0" in name:
                    __UpperCamelCase     :Optional[int]			  =		'''weight_ih_l0'''
                elif "weight_hh_l0" in name:
                    __UpperCamelCase     :Optional[Any]			  =		'''weight_hh_l0'''
                elif "bias_ih_l0" in name:
                    __UpperCamelCase     :List[Any]			  =		'''bias_ih_l0'''
                elif "bias_hh_l0" in name:
                    __UpperCamelCase     :Optional[int]			  =		'''bias_hh_l0'''
                elif "weight_ih_l1" in name:
                    __UpperCamelCase     :List[str]			  =		'''weight_ih_l1'''
                elif "weight_hh_l1" in name:
                    __UpperCamelCase     :Any			  =		'''weight_hh_l1'''
                elif "bias_ih_l1" in name:
                    __UpperCamelCase     :List[Any]			  =		'''bias_ih_l1'''
                elif "bias_hh_l1" in name:
                    __UpperCamelCase     :Optional[int]			  =		'''bias_hh_l1'''
                elif "bias" in name:
                    __UpperCamelCase     :int			  =		'''bias'''
                elif "weight" in name:
                    __UpperCamelCase     :int			  =		'''weight'''
                elif "running_mean" in name:
                    __UpperCamelCase     :List[str]			  =		'''running_mean'''
                elif "running_var" in name:
                    __UpperCamelCase     :str			  =		'''running_var'''
                elif "num_batches_tracked" in name:
                    __UpperCamelCase     :List[str]			  =		'''num_batches_tracked'''
                else:
                    __UpperCamelCase     :int			  =		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     :Any			  =		EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :Optional[int]			  =		EncodecConfig()

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

    __UpperCamelCase     :str			  =		EncodecModel(SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :Union[str, 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     :Union[str, Any]			  =		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     :List[str]			  =		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,
    )










'''simple docstring'''






# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCamelCase__:   Optional[int]								=			"3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
						import torch

						print("Torch version:", torch.__version__)
						print("Cuda available:", torch.cuda.is_available())
						print("Cuda version:", torch.version.cuda)
						print("CuDNN version:", torch.backends.cudnn.version())
						print("Number of GPUs available:", torch.cuda.device_count())
						print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
						print("Torch version:", None)

try:
						import deepspeed

						print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
						print("DeepSpeed version:", None)

try:
						import tensorflow as tf

						print("TensorFlow version:", tf.__version__)
						print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
						print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
						print("TensorFlow version:", None)





