


"""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 :    str									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    int									=				{
    """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 :    List[str]									=				[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 		lowercase							(     _snake_case						:      Optional[Any]     ,   _snake_case						:      List[str]     ,   _snake_case						:      List[str]     ,   _snake_case						:      Any     ,   _snake_case						:      Any     )     ->Tuple:
	"""simple docstring"""





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

	if weight_type is not None:
		__snake_case	:						Tuple						=				getattr(_snake_case     ,   _snake_case     ).shape
	else:
		__snake_case	:						List[str]						=				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":
		__snake_case	:						int						=				value
	elif weight_type == "weight_g":
		__snake_case	:						List[str]						=				value
	elif weight_type == "weight_v":
		__snake_case	:						str						=				value
	elif weight_type == "bias":
		__snake_case	:						Any						=				value
	else:
		__snake_case	:						Dict						=				value

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






def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      Optional[int]     )     ->Tuple:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				[]
	__snake_case	:						str						=				fairseq_model.state_dict()

	__snake_case	:						Optional[Any]						=				hf_model.feature_extractor

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

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

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






def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      Tuple     ,   _snake_case						:      Tuple     ,   _snake_case						:      Union[str, Any]     ,   _snake_case						:      Union[str, Any]     )     ->Tuple:
	"""simple docstring"""





	__snake_case	:						str						=				full_name.split('''conv_layers.'''     )[-1]
	__snake_case	:						Union[str, Any]						=				name.split('''.'''     )
	__snake_case	:						Optional[Any]						=				int(items[0]     )
	__snake_case	:						List[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."""
			)
			__snake_case	:						Optional[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."""
			)
			__snake_case	:						Union[str, Any]						=				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."
			)
			__snake_case	:						Optional[int]						=				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."""
			)
			__snake_case	:						Union[str, Any]						=				value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
	else:
		unused_weights.append(_snake_case     )






@torch.no_grad()
def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      str=None     )     ->str:
	"""simple docstring"""





	__snake_case	:						Optional[Any]						=				torch.load(_snake_case     )
	__snake_case	:						Any						=				WavLMConfigOrig(checkpoint['''cfg''']     )
	__snake_case	:						Tuple						=				WavLMOrig(_snake_case     )
	model.load_state_dict(checkpoint['''model''']     )
	model.eval()

	if config_path is not None:
		__snake_case	:						List[str]						=				WavLMConfig.from_pretrained(_snake_case     )
	else:
		__snake_case	:						Optional[Any]						=				WavLMConfig()

	__snake_case	:						int						=				WavLMModel(_snake_case     )

	recursively_load_weights(_snake_case     ,   _snake_case     )

	hf_wavlm.save_pretrained(_snake_case     )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Union[str, Any]									=				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 :    Dict									=				parser.parse_args()
			convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)












"""simple docstring"""




import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int = 8					) ->str:






	'''simple docstring'''






	a      :		List[Any]	=				ascii_letters + digits + punctuation
	return "".join(secrets.choice(_lowercase					) for _ in range(_lowercase					)					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	i -= len(_lowercase					)
	a      :		List[str]	=				i // 3
	a      :		Any	=				i % 3
	# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
	#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
	a      :		int	=				(
	    chars_incl
	    + random(_lowercase    ,       quotient + remainder					)
	    + random(_lowercase    ,       _lowercase					)
	    + random(_lowercase    ,       _lowercase					)
	)
	a      :		List[str]	=				list(_lowercase					)
	shuffle(_lowercase					)
	return "".join(_lowercase					)

	# random is a generalised function for letters, characters and numbers






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	return "".join(secrets.choice(_lowercase					) for _ in range(_lowercase					)					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]    ,       _lowercase						:    int					) ->List[str]:






	'''simple docstring'''






	pass  # Put your code here...






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






	'''simple docstring'''






	pass  # Put your code here...






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






	'''simple docstring'''






	pass  # Put your code here...






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int = 8					) ->bool:






	'''simple docstring'''






	if len(_lowercase					) < min_length:
		# Your Password must be at least 8 characters long
		return False

	a      :		List[str]	=				any(char in ascii_uppercase for char in password					)
	a      :		Optional[int]	=				any(char in ascii_lowercase for char in password					)
	a      :		List[str]	=				any(char in digits for char in password					)
	a      :		int	=				any(char in punctuation for char in password					)

	return upper and lower and num and spec_char
	# Passwords should contain UPPERCASE, lowerase
	# numbers, and special characters






def       _SCREAMING_SNAKE_CASE		(	) ->Union[str, Any]:






	'''simple docstring'''






	a      :		Dict	=				int(input("Please indicate the max length of your password: "					).strip()					)
	a      :		str	=				input(
	    "Please indicate the characters that must be in your password: "					).strip()
	print("Password generated:"    ,       password_generator(_lowercase					)					)
	print(
	    "Alternative Password generated:"    ,       alternative_password_generator(_lowercase    ,       _lowercase					)    ,       )
	print("[If you are thinking of using this passsword, You better save it.]"					)


if __name__ == "__main__":
		main()


