






"""simple docstring"""


import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE   =   {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
    """self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
    """self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
__SCREAMING_SNAKE_CASE   =   [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						for attribute in key.split('.' ):
												lowerCAmelCase   :str						=			getattr(_UpperCAmelCase     ,      _UpperCAmelCase )

						if weight_type is not None:
												lowerCAmelCase   :Union[str, Any]						=			getattr(_UpperCAmelCase     ,      _UpperCAmelCase ).shape
						else:
												lowerCAmelCase   :Dict						=			hf_pointer.shape

						assert hf_shape == value.shape, (
						    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":
												lowerCAmelCase   :int						=			value
						elif weight_type == "weight_g":
												lowerCAmelCase   :Optional[int]						=			value
						elif weight_type == "weight_v":
												lowerCAmelCase   :Optional[int]						=			value
						elif weight_type == "bias":
												lowerCAmelCase   :str						=			value
						else:
												lowerCAmelCase   :str						=			value

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


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			[]
						lowerCAmelCase   :int						=			fairseq_model.state_dict()

						lowerCAmelCase   :Any						=			hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												lowerCAmelCase   :Union[str, Any]						=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      hf_model.config.feat_extract_norm == 'group'     ,      )
																		lowerCAmelCase   :List[Any]						=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
																														lowerCAmelCase   :Optional[Any]						=			True
																														if "*" in mapped_key:
																																				lowerCAmelCase   :str						=			name.split(_UpperCAmelCase )[0].split('.' )[-2]
																																				lowerCAmelCase   :List[Any]						=			mapped_key.replace('*'     ,      _UpperCAmelCase )
																														if "weight_g" in name:
																																				lowerCAmelCase   :List[str]						=			"weight_g"
																														elif "weight_v" in name:
																																				lowerCAmelCase   :Dict						=			"weight_v"
																														elif "bias" in name and "relative_attention_bias" not in name:
																																				lowerCAmelCase   :Dict						=			"bias"
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				lowerCAmelCase   :List[Any]						=			"weight"
																														else:
																																				lowerCAmelCase   :Optional[int]						=			None

																														set_recursively(_UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase )
																								continue
												if not is_used:
																		unused_weights.append(_UpperCAmelCase )

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


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Dict						=			full_name.split('conv_layers.' )[-1]
						lowerCAmelCase   :str						=			name.split('.' )
						lowerCAmelCase   :Any						=			int(items[0] )
						lowerCAmelCase   :Union[str, Any]						=			int(items[1] )

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																		)
																		lowerCAmelCase   :Union[str, Any]						=			value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																		)
																		lowerCAmelCase   :str						=			value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																		    " found."
																		)
																		lowerCAmelCase   :Dict						=			value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																		)
																		lowerCAmelCase   :int						=			value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
						else:
												unused_weights.append(_UpperCAmelCase )


@torch.no_grad()
def        UpperCAmelCase      (					a__     ,      a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :int						=			torch.load(_UpperCAmelCase )
						lowerCAmelCase   :Any						=			WavLMConfigOrig(checkpoint['cfg'] )
						lowerCAmelCase   :Optional[int]						=			WavLMOrig(_UpperCAmelCase )
						model.load_state_dict(checkpoint['model'] )
						model.eval()

						if config_path is not None:
												lowerCAmelCase   :Any						=			WavLMConfig.from_pretrained(_UpperCAmelCase )
						else:
												lowerCAmelCase   :List[Any]						=			WavLMConfig()

						lowerCAmelCase   :Optional[int]						=			WavLMModel(_UpperCAmelCase )

						recursively_load_weights(_UpperCAmelCase     ,      _UpperCAmelCase )

						hf_wavlm.save_pretrained(_UpperCAmelCase )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCamelCase_						:     List[Any]	       =							3



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			int:






						'''simple docstring'''

						print("Generating primitive root of p"  )
						while True:
												_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )
												if pow(_UpperCAmelCase ,			2 ,			_UpperCAmelCase  ) == 1:
																		continue
												if pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) == 1:
																		continue
												return g



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			tuple[tuple[int, int, int, int], tuple[int, int]]:






						'''simple docstring'''

						print("Generating prime p..."  )
						_lowercase       :	Optional[int]           =   rabin_miller.generate_large_prime(_UpperCAmelCase  )  # select large prime number.
						_lowercase       :	Dict           =   primitive_root(_UpperCAmelCase  )  # one primitive root on modulo p.
						_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )  # private_key -> have to be greater than 2 for safety.
						_lowercase       :	Tuple           =   cryptomath.find_mod_inverse(pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) ,			_UpperCAmelCase  )

						_lowercase       :	List[Any]           =   (key_size, e_a, e_a, p)
						_lowercase       :	Union[str, Any]           =   (key_size, d)

						return public_key, private_key



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   int  )   ->			None:






						'''simple docstring'''

						if os.path.exists(f"""{name}_pubkey.txt"""  ) or os.path.exists(f"""{name}_privkey.txt"""  ):
												print("\nWARNING:"  )
												print(
												    f"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
												    "Use a different name or delete these files and re-run this program."  )
												sys.exit()

						_lowercase      ,    _lowercase       :	Optional[Any]           =   generate_key(_UpperCAmelCase  )
						print(f"""\nWriting public key to file {name}_pubkey.txt..."""  )
						with open(f"""{name}_pubkey.txt""" ,			"w"  ) as fo:
												fo.write(f"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""  )

						print(f"""Writing private key to file {name}_privkey.txt..."""  )
						with open(f"""{name}_privkey.txt""" ,			"w"  ) as fo:
												fo.write(f"""{private_key[0]},{private_key[1]}"""  )



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						print("Making key files..."  )
						make_key_files("elgamal" ,			2048  )
						print("Key files generation successful"  )


if __name__ == "__main__":
							main()




