




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()
__a							     =					logging.get_logger('transformers.models.encodec')

__a							     =					{
    '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',
}
__a							     =					{
    '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',
}
__a							     =					{
    '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',
}
__a							     =					{
    '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',
}
__a							     =					{
    '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',
}
__a							     =					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__a							     =					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__a							     =					[]
__a							     =					[]







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     for attribute in key.split('''.''' ):
          UpperCAmelCase_     :				Optional[Any]  =     getattr(_lowercase					,		_lowercase )

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

     '''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__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Tuple  =     []

     if model_name == "encodec_24khz" or "encodec_32khz":
          UpperCAmelCase_     :				Tuple  =     MAPPING_24K
     elif model_name == "encodec_48khz":
          UpperCAmelCase_     :				str  =     MAPPING_48K
     else:
          raise ValueError(f'''Unsupported model: {model_name}''' )

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

          UpperCAmelCase_     :				int  =     False
          for key, mapped_key in MAPPING.items():
               if "*" in key:
                    UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     key.split('''.*.''' )
                    if prefix in name and suffix in name:
                         UpperCAmelCase_     :				Optional[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_     :				int  =     True
                    if "*" in mapped_key:
                         UpperCAmelCase_     :				Optional[int]  =     name.split(_lowercase )[0].split('''.''' )[-2]
                         UpperCAmelCase_     :				Optional[int]  =     mapped_key.replace('''*'''					,		_lowercase )
                    if "weight_g" in name:
                         UpperCAmelCase_     :				List[Any]  =     '''weight_g'''
                    elif "weight_v" in name:
                         UpperCAmelCase_     :				Tuple  =     '''weight_v'''
                    elif "weight_ih_l0" in name:
                         UpperCAmelCase_     :				Optional[int]  =     '''weight_ih_l0'''
                    elif "weight_hh_l0" in name:
                         UpperCAmelCase_     :				Optional[int]  =     '''weight_hh_l0'''
                    elif "bias_ih_l0" in name:
                         UpperCAmelCase_     :				Dict  =     '''bias_ih_l0'''
                    elif "bias_hh_l0" in name:
                         UpperCAmelCase_     :				Any  =     '''bias_hh_l0'''
                    elif "weight_ih_l1" in name:
                         UpperCAmelCase_     :				int  =     '''weight_ih_l1'''
                    elif "weight_hh_l1" in name:
                         UpperCAmelCase_     :				int  =     '''weight_hh_l1'''
                    elif "bias_ih_l1" in name:
                         UpperCAmelCase_     :				Dict  =     '''bias_ih_l1'''
                    elif "bias_hh_l1" in name:
                         UpperCAmelCase_     :				Union[str, Any]  =     '''bias_hh_l1'''
                    elif "bias" in name:
                         UpperCAmelCase_     :				List[str]  =     '''bias'''
                    elif "weight" in name:
                         UpperCAmelCase_     :				int  =     '''weight'''
                    elif "running_mean" in name:
                         UpperCAmelCase_     :				Tuple  =     '''running_mean'''
                    elif "running_var" in name:
                         UpperCAmelCase_     :				Dict  =     '''running_var'''
                    elif "num_batches_tracked" in name:
                         UpperCAmelCase_     :				Tuple  =     '''num_batches_tracked'''
                    else:
                         UpperCAmelCase_     :				Dict  =     None
                    set_recursively(_lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase )
               continue
          if not is_used:
               unused_weights.append(_lowercase )

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







@torch.no_grad()
def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase=None					,		_lowercase=None					,		):

     '''simple docstring'''


     if config_path is not None:
          UpperCAmelCase_     :				int  =     EncodecConfig.from_pretrained(_lowercase )
     else:
          UpperCAmelCase_     :				List[str]  =     EncodecConfig()

     if model_name == "encodec_24khz":
          pass  # config is already correct
     elif model_name == "encodec_32khz":
          UpperCAmelCase_     :				Union[str, Any]  =     [8, 5, 4, 4]
          UpperCAmelCase_     :				str  =     [2.2]
          UpperCAmelCase_     :				Optional[Any]  =     64
          UpperCAmelCase_     :				str  =     32000
          UpperCAmelCase_     :				Tuple  =     2048
          UpperCAmelCase_     :				str  =     False
          UpperCAmelCase_     :				List[Any]  =     False
          UpperCAmelCase_     :				int  =     False
     elif model_name == "encodec_48khz":
          UpperCAmelCase_     :				List[str]  =     [8, 5, 4, 2]
          UpperCAmelCase_     :				Optional[int]  =     [3.0, 6.0, 12.0, 24.0]
          UpperCAmelCase_     :				Optional[int]  =     48000
          UpperCAmelCase_     :				List[str]  =     2
          UpperCAmelCase_     :				Optional[Any]  =     False
          UpperCAmelCase_     :				Any  =     '''time_group_norm'''
          UpperCAmelCase_     :				Union[str, Any]  =     True
          UpperCAmelCase_     :				Union[str, Any]  =     1.0
          UpperCAmelCase_     :				List[Any]  =     0.01
     else:
          raise ValueError(f'''Unknown model name: {model_name}''' )

     UpperCAmelCase_     :				int  =     EncodecModel(_lowercase )

     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(_lowercase )

     UpperCAmelCase_     :				int  =     torch.load(_lowercase )
     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(_lowercase					,		_lowercase					,		_lowercase )
     model.save_pretrained(_lowercase )

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


if __name__ == "__main__":
     __a							     =					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.'
     )

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






"""simple docstring"""




from __future__ import annotations

from statistics import mean






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]       ,			_lowercase		:       list[int]       ,			_lowercase		:       int   )					->list[int]:






	'''simple docstring'''






	a :						Union[str, Any]      =							[0] * no_of_processes
	a :						int      =							[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(_lowercase   ):
		a :						List[Any]      =							burst_time[i]
	a :						list[int]      =							[]

	a :						List[Any]      =							0
	a :						Any      =							0

	# When processes are not completed,
	# A process whose arrival time has passed \
	# and has remaining execution time is put into the ready_process.
	# The shortest process in the ready_process, target_process is executed.

	while completed != no_of_processes:
		a :						str      =							[]
		a :						Tuple      =							-1

		for i in range(_lowercase   ):
			if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
				ready_process.append(_lowercase   )

		if len(_lowercase   ) > 0:
			a :						List[Any]      =							ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					a :						Optional[Any]      =							i
			total_time += burst_time[target_process]
			completed += 1
			a :						str      =							0
			a :						Union[str, Any]      =							(
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]       ,			_lowercase		:       int       ,			_lowercase		:       list[int]   )					->list[int]:






	'''simple docstring'''






	a :						List[str]      =							[0] * no_of_processes
	for i in range(_lowercase   ):
		a :						Optional[int]      =							burst_time[i] + waiting_time[i]
	return turn_around_time


if __name__ == "__main__":
		print('''[TEST CASE 01]''')

		a		:					Dict	      =     4
		a		:					Tuple	      =     [2, 5, 3, 7]
		a		:					List[Any]	      =     [0, 0, 0, 0]
		a		:					int	      =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)
		a		:					List[Any]	      =     calculate_turnaroundtime(
		    burst_time, no_of_processes, waiting_time
		)

		# Printing the Result
		print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''')
		for i, process_id in enumerate(list(range(1, 5))):
				print(
				    F'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
				    F'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
				)
		print(F'''\nAverage waiting time = {mean(waiting_time):.5f}''')
		print(F'''Average turnaround time = {mean(turn_around_time):.5f}''')


