





"""simple docstring"""




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


a						: List[str]     	=			[
    # tf -> hf
    ('''/''', '''.'''),
    ('''layer_''', '''layers.'''),
    ('''kernel''', '''weight'''),
    ('''beta''', '''bias'''),
    ('''gamma''', '''weight'''),
    ('''pegasus''', '''model'''),
]
a						: Any     	=			[
    ('''.output.dense''', '''.fc2'''),
    ('''intermediate.LayerNorm''', '''final_layer_norm'''),
    ('''intermediate.dense''', '''fc1'''),
]

a						: List[str]     	=			(
    INIT_COMMON
    + [
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.out_proj'''),
        ('''attention.self''', '''self_attn'''),
        ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
        ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
        ('''attention.encdec''', '''encoder_attn'''),
        ('''key''', '''k_proj'''),
        ('''value''', '''v_proj'''),
        ('''query''', '''q_proj'''),
        ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

a						: str     	=			(
    INIT_COMMON
    + [
        ('''embeddings.word_embeddings''', '''shared.weight'''),
        ('''embeddings.position_embeddings''', '''embed_positions.weight'''),
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.output'''),
        ('''attention.self''', '''self_attn.self'''),
        ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

a						: Any     	=			[
    '''encdec/key/bias''',
    '''encdec/query/bias''',
    '''encdec/value/bias''',
    '''self/key/bias''',
    '''self/query/bias''',
    '''self/value/bias''',
    '''encdec_output/dense/bias''',
    '''attention/output/dense/bias''',
]






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






	'''simple docstring'''






	for tf_name, hf_name in patterns:
		a      :		List[Any]	=				k.replace(_lowercase    ,       _lowercase					)
	return k






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    dict    ,       _lowercase						:    dict					) ->BigBirdPegasusForConditionalGeneration:






	'''simple docstring'''






	a      :		List[Any]	=				BigBirdPegasusConfig(**_lowercase					)
	a      :		Any	=				BigBirdPegasusForConditionalGeneration(_lowercase					)
	a      :		Optional[Any]	=				torch_model.state_dict()
	a      :		Tuple	=				{}

	# separating decoder weights
	a      :		int	=				{k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"					)}
	a      :		Tuple	=				{k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder"					)}

	for k, v in tqdm(decoder_weights.items()    ,       "tf -> hf conversion"					):
		a      :		str	=				[k.endswith(_lowercase					) for ending in KEYS_TO_IGNORE]
		if any(_lowercase					):
			continue
		a      :		Union[str, Any]	=				DECODER_PATTERNS
		a      :		List[Any]	=				rename_state_dict_key(_lowercase    ,       _lowercase					)
		if new_k not in state_dict:
			raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""					)
		if any(True if i in k else False for i in ["dense", "query", "key", "value"]					):
			a      :		Optional[Any]	=				v.T
		a      :		List[str]	=				torch.from_numpy(_lowercase					)
		assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

	for k, v in tqdm(remaining_weights.items()    ,       "tf -> hf conversion"					):
		a      :		Dict	=				[k.endswith(_lowercase					) for ending in KEYS_TO_IGNORE]
		if any(_lowercase					):
			continue
		a      :		Any	=				REMAINING_PATTERNS
		a      :		Optional[Any]	=				rename_state_dict_key(_lowercase    ,       _lowercase					)
		if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
			raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""					)
		if any(True if i in k else False for i in ["dense", "query", "key", "value"]					):
			a      :		Dict	=				v.T
		a      :		Optional[Any]	=				torch.from_numpy(_lowercase					)
		if k != "pegasus/embeddings/position_embeddings":
			assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

	a      :		str	=				mapping["model.embed_positions.weight"]
	a      :		Union[str, Any]	=				mapping.pop("model.embed_positions.weight"					)
	a,       a      :		Optional[Any]	=				torch_model.load_state_dict(_lowercase    ,       strict=_lowercase					)
	a      :		Optional[Any]	=				[
	    k
	    for k in missing
	    if k
	    not in [
	        "final_logits_bias",
	        "model.encoder.embed_tokens.weight",
	        "model.decoder.embed_tokens.weight",
	        "lm_head.weight",
	    ]
	]
	assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
	assert extra == [], F"""no matches found for the following tf keys {extra}"""
	return torch_model






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






	'''simple docstring'''






	a      :		int	=				tf.train.list_variables(_lowercase					)
	a      :		str	=				{}
	a      :		Optional[Any]	=				["global_step"]
	for name, shape in tqdm(_lowercase    ,       desc="converting tf checkpoint to dict"					):
		a      :		List[Any]	=				any(pat in name for pat in ignore_name					)
		if skip_key:
			continue
		a      :		Optional[int]	=				tf.train.load_variable(_lowercase    ,       _lowercase					)
		a      :		Optional[Any]	=				array
	return tf_weights






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






	'''simple docstring'''






	a      :		List[Any]	=				get_tf_weights_as_numpy(_lowercase					)
	a      :		Union[str, Any]	=				convert_bigbird_pegasus(_lowercase    ,       _lowercase					)
	torch_model.save_pretrained(_lowercase					)


if __name__ == "__main__":
		a						: List[Any]     	=			argparse.ArgumentParser()
		parser.add_argument('''--tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
		parser.add_argument('''--save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
		a						: Optional[Any]     	=			parser.parse_args()
		a						: Optional[Any]     	=			{}
		convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)









"""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()


