


"""simple docstring"""
import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


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







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							snake_case_          =						os.path.abspath(_SCREAMING_SNAKE_CASE			)
							logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""			)
							# Load weights from TF model
							snake_case_          =						tf.train.list_variables(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						[]
							snake_case_          =						[]
							snake_case_          =						[]
							for full_name, shape in init_vars:
														# logger.info(f"Loading TF weight {name} with shape {shape}")
														snake_case_          =						full_name.split("""/"""			)
														if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
																					logger.info(f"""Skipping non-model layer {full_name}"""			)
																					continue
														if "optimizer" in full_name:
																					logger.info(f"""Skipping optimization layer {full_name}"""			)
																					continue
														if name[0] == "model":
																					# ignore initial 'model'
																					snake_case_          =						name[1:]
														# figure out how many levels deep the name is
														snake_case_          =						0
														for _name in name:
																					if _name.startswith("""layer_with_weights"""			):
																												depth += 1
																					else:
																												break
														layer_depth.append(_SCREAMING_SNAKE_CASE			)
														# read data
														snake_case_          =						tf.train.load_variable(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
														names.append("""/""".join(_SCREAMING_SNAKE_CASE			)			)
														arrays.append(_SCREAMING_SNAKE_CASE			)
							logger.info(f"""Read a total of {len(_SCREAMING_SNAKE_CASE			):,} layers"""			)

							# Sanity check
							if len(set(_SCREAMING_SNAKE_CASE			)			) != 1:
														raise ValueError(f"""Found layer names with different depths (layer depth {list(set(_SCREAMING_SNAKE_CASE			)			)})"""			)
							snake_case_          =						list(set(_SCREAMING_SNAKE_CASE			)			)[0]
							if layer_depth != 1:
														raise ValueError(
														    """The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"""
														    """ heads."""			)

							# convert layers
							logger.info("""Converting weights..."""			)
							for full_name, array in zip(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			):
														snake_case_          =						full_name.split("""/"""			)
														snake_case_          =						model
														snake_case_          =						[]
														for i, m_name in enumerate(_SCREAMING_SNAKE_CASE			):
																					if m_name == ".ATTRIBUTES":
																												# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																												break
																					if m_name.startswith("""layer_with_weights"""			):
																												snake_case_          =						int(m_name.split("""-"""			)[-1]			)
																												if layer_num <= 2:
																																			# embedding layers
																																			# layer_num 0: word_embeddings
																																			# layer_num 1: position_embeddings
																																			# layer_num 2: token_type_embeddings
																																			continue
																												elif layer_num == 3:
																																			# embedding LayerNorm
																																			trace.extend(["""embeddings""", """LayerNorm"""]			)
																																			snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""embeddings"""			)
																																			snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""LayerNorm"""			)
																												elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																																			# encoder layers
																																			trace.extend(["""encoder""", """layer""", str(layer_num - 4			)]			)
																																			snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""encoder"""			)
																																			snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""layer"""			)
																																			snake_case_          =						pointer[layer_num - 4]
																												elif layer_num == config.num_hidden_layers + 4:
																																			# pooler layer
																																			trace.extend(["""pooler""", """dense"""]			)
																																			snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""pooler"""			)
																																			snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""dense"""			)
																					elif m_name == "embeddings":
																												trace.append("""embeddings"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""embeddings"""			)
																												if layer_num == 0:
																																			trace.append("""word_embeddings"""			)
																																			snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""word_embeddings"""			)
																												elif layer_num == 1:
																																			trace.append("""position_embeddings"""			)
																																			snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""position_embeddings"""			)
																												elif layer_num == 2:
																																			trace.append("""token_type_embeddings"""			)
																																			snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""token_type_embeddings"""			)
																												else:
																																			raise ValueError(f"""Unknown embedding layer with name {full_name}"""			)
																												trace.append("""weight"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""weight"""			)
																					elif m_name == "_attention_layer":
																												# self-attention layer
																												trace.extend(["""attention""", """self"""]			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""attention"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""self"""			)
																					elif m_name == "_attention_layer_norm":
																												# output attention norm
																												trace.extend(["""attention""", """output""", """LayerNorm"""]			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""attention"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""output"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""LayerNorm"""			)
																					elif m_name == "_attention_output_dense":
																												# output attention dense
																												trace.extend(["""attention""", """output""", """dense"""]			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""attention"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""output"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""dense"""			)
																					elif m_name == "_output_dense":
																												# output dense
																												trace.extend(["""output""", """dense"""]			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""output"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""dense"""			)
																					elif m_name == "_output_layer_norm":
																												# output dense
																												trace.extend(["""output""", """LayerNorm"""]			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""output"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""LayerNorm"""			)
																					elif m_name == "_key_dense":
																												# attention key
																												trace.append("""key"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""key"""			)
																					elif m_name == "_query_dense":
																												# attention query
																												trace.append("""query"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""query"""			)
																					elif m_name == "_value_dense":
																												# attention value
																												trace.append("""value"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""value"""			)
																					elif m_name == "_intermediate_dense":
																												# attention intermediate dense
																												trace.extend(["""intermediate""", """dense"""]			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""intermediate"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""dense"""			)
																					elif m_name == "_output_layer_norm":
																												# output layer norm
																												trace.append("""output"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""output"""			)
																					# weights & biases
																					elif m_name in ["bias", "beta"]:
																												trace.append("""bias"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""bias"""			)
																					elif m_name in ["kernel", "gamma"]:
																												trace.append("""weight"""			)
																												snake_case_          =						getattr(_SCREAMING_SNAKE_CASE			,	"""weight"""			)
																					else:
																												logger.warning(f"""Ignored {m_name}"""			)
        # for certain layers reshape is necessary
														snake_case_          =						""".""".join(_SCREAMING_SNAKE_CASE			)
														if re.match(r"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""			,	_SCREAMING_SNAKE_CASE			) or re.match(
														    r"""(\S+)\.attention\.output\.dense\.weight"""			,	_SCREAMING_SNAKE_CASE			):
																					snake_case_          =						array.reshape(pointer.data.shape			)
														if "kernel" in full_name:
																					snake_case_          =						array.transpose()
														if pointer.shape == array.shape:
																					snake_case_          =						torch.from_numpy(_SCREAMING_SNAKE_CASE			)
														else:
																					raise ValueError(
																					    f"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
																					    f""" {array.shape}"""			)
														logger.info(f"""Successfully set variable {full_name} to PyTorch layer {trace}"""			)
							return model







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[Any]:
							# Instantiate model
							logger.info(f"""Loading model based on config from {config_path}..."""			)
							snake_case_          =						BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						BertModel(_SCREAMING_SNAKE_CASE			)

							# Load weights from checkpoint
							logger.info(f"""Loading weights from checkpoint {tf_checkpoint_path}..."""			)
							load_tfa_weights_in_bert(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							# Save pytorch-model
							logger.info(f"""Saving PyTorch model to {pytorch_dump_path}..."""			)
							torch.save(model.state_dict()			,	_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      argparse.ArgumentParser()
				parser.add_argument(
				    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
				)
				parser.add_argument(
				    '--bert_config_file',
				    type=str,
				    required=True,
				    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
				)
				parser.add_argument(
				    '--pytorch_dump_path',
				    type=str,
				    required=True,
				    help='Path to the output PyTorch model (must include filename).',
				)
				__SCREAMING_SNAKE_CASE							:      Any						=      parser.parse_args()
				convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







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







def   _a			( _SCREAMING_SNAKE_CASE = 8			)      ->			str:
							snake_case_          =						ascii_letters + digits + punctuation
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE			) for _ in range(_SCREAMING_SNAKE_CASE			)			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						i // 3
							snake_case_          =						i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							snake_case_          =						(
							    chars_incl
							    + random(_SCREAMING_SNAKE_CASE			,	quotient + remainder			)
							    + random(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							    + random(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							)
							snake_case_          =						list(_SCREAMING_SNAKE_CASE			)
							shuffle(_SCREAMING_SNAKE_CASE			)
							return "".join(_SCREAMING_SNAKE_CASE			)

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







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE			) for _ in range(_SCREAMING_SNAKE_CASE			)			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Any:
							pass  # Put your code here...







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[int]:
							pass  # Put your code here...







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							pass  # Put your code here...







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 8			)      ->			bool:

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

							snake_case_          =						any(char in ascii_uppercase for char in password			)
							snake_case_          =						any(char in ascii_lowercase for char in password			)
							snake_case_          =						any(char in digits for char in password			)
							snake_case_          =						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   _a			( )      ->			Union[str, Any]:
							snake_case_          =						int(input("""Please indicate the max length of your password: """			).strip()			)
							snake_case_          =						input(
							    """Please indicate the characters that must be in your password: """			).strip()
							print("""Password generated:"""			,	password_generator(_SCREAMING_SNAKE_CASE			)			)
							print(
							    """Alternative Password generated:"""			,	alternative_password_generator(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)			,	)
							print("""[If you are thinking of using this passsword, You better save it.]"""			)


if __name__ == "__main__":
				main()



