



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

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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Any:
			for attribute in key.split('.'					):
						A__       =    getattr(__UpperCamelCase ,	__UpperCamelCase					)

			if weight_type is not None:
						A__       =    getattr(__UpperCamelCase ,	__UpperCamelCase					).shape
			else:
						A__       =    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__       =    value
			elif weight_type == "weight_g":
						A__       =    value
			elif weight_type == "weight_v":
						A__       =    value
			elif weight_type == "bias":
						A__       =    value
			elif weight_type == "running_mean":
						A__       =    value
			elif weight_type == "running_var":
						A__       =    value
			elif weight_type == "num_batches_tracked":
						A__       =    value
			elif weight_type == "weight_ih_l0":
						A__       =    value
			elif weight_type == "weight_hh_l0":
						A__       =    value
			elif weight_type == "bias_ih_l0":
						A__       =    value
			elif weight_type == "bias_hh_l0":
						A__       =    value
			elif weight_type == "weight_ih_l1":
						A__       =    value
			elif weight_type == "weight_hh_l1":
						A__       =    value
			elif weight_type == "bias_ih_l1":
						A__       =    value
			elif weight_type == "bias_hh_l1":
						A__       =    value
			else:
						A__       =    value

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


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


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Any:
			A__       =    []

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

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

						A__       =    False
						for key, mapped_key in MAPPING.items():
									if "*" in key:
												A__     , A__       =    key.split('.*.'					)
												if prefix in name and suffix in name:
															A__       =    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__       =    True
												if "*" in mapped_key:
															A__       =    name.split(__UpperCamelCase					)[0].split('.'					)[-2]
															A__       =    mapped_key.replace('*' ,	__UpperCamelCase					)
												if "weight_g" in name:
															A__       =    'weight_g'
												elif "weight_v" in name:
															A__       =    'weight_v'
												elif "weight_ih_l0" in name:
															A__       =    'weight_ih_l0'
												elif "weight_hh_l0" in name:
															A__       =    'weight_hh_l0'
												elif "bias_ih_l0" in name:
															A__       =    'bias_ih_l0'
												elif "bias_hh_l0" in name:
															A__       =    'bias_hh_l0'
												elif "weight_ih_l1" in name:
															A__       =    'weight_ih_l1'
												elif "weight_hh_l1" in name:
															A__       =    'weight_hh_l1'
												elif "bias_ih_l1" in name:
															A__       =    'bias_ih_l1'
												elif "bias_hh_l1" in name:
															A__       =    'bias_hh_l1'
												elif "bias" in name:
															A__       =    'bias'
												elif "weight" in name:
															A__       =    'weight'
												elif "running_mean" in name:
															A__       =    'running_mean'
												elif "running_var" in name:
															A__       =    'running_var'
												elif "num_batches_tracked" in name:
															A__       =    'num_batches_tracked'
												else:
															A__       =    None
												set_recursively(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)
									continue
						if not is_used:
									unused_weights.append(__UpperCamelCase					)

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


@torch.no_grad()
def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	)						->      int:
			if config_path is not None:
						A__       =    EncodecConfig.from_pretrained(__UpperCamelCase					)
			else:
						A__       =    EncodecConfig()

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

			A__       =    EncodecModel(__UpperCamelCase					)

			A__       =    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(__UpperCamelCase					)

			A__       =    torch.load(__UpperCamelCase					)
			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__       =    original_checkpoint['best_state']
			recursively_load_weights(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)
			model.save_pretrained(__UpperCamelCase					)

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      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__       =      parser.parse_args()
							convert_checkpoint(
							    args.model,
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.push_to_hub,
							)









import math
import random


def A						(       __UpperCamelCase ,	__UpperCamelCase = False					)						->      float:
			if deriv:
						return value * (1 - value)
			return 1 / (1 + math.exp(-value					))


# Initial Value
SCREAMING_SNAKE_CASE__       =      0.02


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      float:
			A__       =    float(2 * (random.randint(1 ,	100					)) - 1					)

			for _ in range(__UpperCamelCase					):
						# Forward propagation
						A__       =    sigmoid_function(INITIAL_VALUE * weight					)
						# How much did we miss?
						A__       =    (expected / 100) - layer_a
						# Error delta
						A__       =    layer_1_error * sigmoid_function(__UpperCamelCase ,	__UpperCamelCase					)
						# Update weight
						weight += INITIAL_VALUE * layer_1_delta

			return layer_a * 100


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE__       =      int(input('''Expected value: '''))
							SCREAMING_SNAKE_CASE__       =      int(input('''Number of propagations: '''))
							print(forward_propagation(expected, number_propagations))




