



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 argparse
import struct
import unittest


class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       List[str]				,					_snake_case					:       bytes					):






								"""simple docstring"""


								A__       =    data

								# Initialize hash values
								A__       =    [
								    0x6A09E667,
								    0xBB67AE85,
								    0x3C6EF372,
								    0xA54FF53A,
								    0x510E527F,
								    0x9B05688C,
								    0x1F83D9AB,
								    0x5BE0CD19,
								]

								# Initialize round constants
								A__       =    [
								    0x428A2F98,
								    0x71374491,
								    0xB5C0FBCF,
								    0xE9B5DBA5,
								    0x3956C25B,
								    0x59F111F1,
								    0x923F82A4,
								    0xAB1C5ED5,
								    0xD807AA98,
								    0x12835B01,
								    0x243185BE,
								    0x550C7DC3,
								    0x72BE5D74,
								    0x80DEB1FE,
								    0x9BDC06A7,
								    0xC19BF174,
								    0xE49B69C1,
								    0xEFBE4786,
								    0x0FC19DC6,
								    0x240CA1CC,
								    0x2DE92C6F,
								    0x4A7484AA,
								    0x5CB0A9DC,
								    0x76F988DA,
								    0x983E5152,
								    0xA831C66D,
								    0xB00327C8,
								    0xBF597FC7,
								    0xC6E00BF3,
								    0xD5A79147,
								    0x06CA6351,
								    0x14292967,
								    0x27B70A85,
								    0x2E1B2138,
								    0x4D2C6DFC,
								    0x53380D13,
								    0x650A7354,
								    0x766A0ABB,
								    0x81C2C92E,
								    0x92722C85,
								    0xA2BFE8A1,
								    0xA81A664B,
								    0xC24B8B70,
								    0xC76C51A3,
								    0xD192E819,
								    0xD6990624,
								    0xF40E3585,
								    0x106AA070,
								    0x19A4C116,
								    0x1E376C08,
								    0x2748774C,
								    0x34B0BCB5,
								    0x391C0CB3,
								    0x4ED8AA4A,
								    0x5B9CCA4F,
								    0x682E6FF3,
								    0x748F82EE,
								    0x78A5636F,
								    0x84C87814,
								    0x8CC70208,
								    0x90BEFFFA,
								    0xA4506CEB,
								    0xBEF9A3F7,
								    0xC67178F2,
								]

								A__       =    self.preprocessing(self.data					)
								self.final_hash()



					@staticmethod
					def     _a							(     _snake_case					:       bytes					):






								"""simple docstring"""


								A__       =    B'\x80' + (B'\x00' * (63 - (len(_snake_case					) + 8) % 64))
								A__       =    struct.pack('>Q'				,					(len(_snake_case					) * 8)					)
								return data + padding + big_endian_integer



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    [
								    self.preprocessed_data[x : x + 64]
								    for x in range(0				,					len(self.preprocessed_data					)				,					64					)
								]

								for block in self.blocks:
											# Convert the given block into a list of 4 byte integers
											A__       =    list(struct.unpack('>16L'				,					_snake_case					)					)
											# add 48 0-ed integers
											words += [0] * 48

											A__     , A__     , A__     , A__     , A__     , A__     , A__     , A__       =    self.hashes

											for index in range(0				,					64					):
														if index > 15:
																	# modify the zero-ed indexes at the end of the array
																	A__       =    (
																	    self.ror(words[index - 15]				,					7					)
																	    ^ self.ror(words[index - 15]				,					18					)
																	    ^ (words[index - 15] >> 3)
																	)
																	A__       =    (
																	    self.ror(words[index - 2]				,					17					)
																	    ^ self.ror(words[index - 2]				,					19					)
																	    ^ (words[index - 2] >> 10)
																	)

																	A__       =    (
																	    words[index - 16] + sa + words[index - 7] + sa
																	) % 0x100000000

														# Compression
														A__       =    self.ror(_snake_case				,					6					) ^ self.ror(_snake_case				,					11					) ^ self.ror(_snake_case				,					25					)
														A__       =    (e & f) ^ ((~e & 0xFFFFFFFF) & g)
														A__       =    (
														    h + sa + ch + self.round_constants[index] + words[index]
														) % 0x100000000
														A__       =    self.ror(_snake_case				,					2					) ^ self.ror(_snake_case				,					13					) ^ self.ror(_snake_case				,					22					)
														A__       =    (a & b) ^ (a & c) ^ (b & c)
														A__       =    (sa + maj) % 0x100000000

														A__     , A__     , A__     , A__     , A__     , A__     , A__     , A__       =    (
														    g,
														    f,
														    e,
														    ((d + tempa) % 0x100000000),
														    c,
														    b,
														    a,
														    ((tempa + tempa) % 0x100000000),
														)

											A__       =    [a, b, c, d, e, f, g, h]

											# Modify final values
											A__       =    [
											    ((element + mutated_hash_values[index]) % 0x100000000)
											    for index, element in enumerate(self.hashes					)
											]

								A__       =    ''.join([hex(_snake_case					)[2:].zfill(8					) for value in self.hashes]					)



					def     _a							(     self					:       Dict				,					_snake_case					:       int				,					_snake_case					:       int					):






								"""simple docstring"""


								return 0xFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)







class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					def     _a							(     self					:       str					):






								"""simple docstring"""


								import hashlib

								A__       =    bytes('Test String'				,					'utf-8'					)
								self.assertEqual(SHAaaa(_snake_case					).hash				,					hashlib.shaaaa(_snake_case					).hexdigest()					)







def A						(       )						->      None:
			import doctest

			doctest.testmod()

			A__       =    argparse.ArgumentParser()
			parser.add_argument(
			    '-s' ,	'--string' ,	dest='input_string' ,	default='Hello World!! Welcome to Cryptography' ,	help='Hash the string' ,	)
			parser.add_argument(
			    '-f' ,	'--file' ,	dest='input_file' ,	help='Hash contents of a file'					)

			A__       =    parser.parse_args()

			A__       =    args.input_string

			# hash input should be a bytestring
			if args.input_file:
						with open(args.input_file ,	'rb'					) as f:
									A__       =    f.read()
			else:
						A__       =    bytes(__UpperCamelCase ,	'utf-8'					)

			print(SHAaaa(__UpperCamelCase					).hash					)


if __name__ == "__main__":
							main()




