



'''simple docstring'''

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

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Any:
		for attribute in key.split('.'  ):
				_a     :      Optional[int]        =							getattr(lowerCAmelCase_						,			lowerCAmelCase_  )

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

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Union[str, Any]:
		for key in ignore_keys:
				if key.endswith('.*'  ):
						if name.startswith(key[:-1]  ):
								return True
				elif ".*." in key:
						_a       ,					_a     :      Union[str, Any]        =							key.split('.*.'  )
						if prefix in name and suffix in name:
								return True
				elif key in name:
						return True
		return False



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Dict:
		_a     :      Union[str, Any]        =							[]

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

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

				_a     :      List[Any]        =							False
				for key, mapped_key in MAPPING.items():
						if "*" in key:
								_a       ,					_a     :      Optional[Any]        =							key.split('.*.'  )
								if prefix in name and suffix in name:
										_a     :      Tuple        =							suffix

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

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

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



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			)			->      Union[str, Any]:
		if config_path is not None:
				_a     :      Optional[Any]        =							EncodecConfig.from_pretrained(lowerCAmelCase_  )
		else:
				_a     :      int        =							EncodecConfig()

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

		_a     :      Optional[int]        =							EncodecModel(lowerCAmelCase_  )

		_a     :      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(lowerCAmelCase_  )

		_a     :      Tuple        =							torch.load(lowerCAmelCase_  )
		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
				_a     :      Union[str, Any]        =							original_checkpoint['best_state']
		recursively_load_weights(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
		model.save_pretrained(lowerCAmelCase_  )

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


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

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










def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       "list[int]":



       """simple docstring"""

       if upper_limit < 0:
              raise ValueError('''Limit for the Catalan sequence must be ≥ 0'''						)

       A__       =    [0] * (upper_limit + 1)

       # Base case: C(0) = C(1) = 1
       A__       =    1
       if upper_limit > 0:
              A__       =    1

       # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
       for i in range(2 ,				upper_limit + 1						):
              for j in range(lowercase_						):
                     catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

       return catalan_list


if __name__ == "__main__":
  print("""\n********* Catalan Numbers Using Dynamic Programming ************\n""")
  print("""\n*** Enter -1 at any time to quit ***""")
  print("""\nEnter the upper limit (≥ 0) for the Catalan number sequence: """, end="""""")
  try:
    while True:
      _lowerCamelCase	:		List[Any]							       =   int(input().strip())
      if N < 0:
        print("""\n********* Goodbye!! ************""")
        break
      else:
        print(F'''The Catalan numbers from 0 through {N} are:''')
        print(catalan_numbers(N))
        print("""Try another upper limit for the sequence: """, end="""""")
  except (NameError, ValueError):
    print("""\n********* Invalid input, goodbye! ************\n""")

  import doctest

  doctest.testmod()


