



"""simple docstring"""






def        snake_case_  (      A_					:		list[list]						):


       '''simple docstring'''





       _lowerCamelCase						:				Optional[int]    			= current_set.copy()
       for row_index, row in enumerate(A_						):
              _lowerCamelCase						:				Tuple    			= row[0]
              for column_index, column in enumerate(A_						):
                     if magnitude == 0:
                            _lowerCamelCase						:				List[Any]    			= column
                            continue
                     _lowerCamelCase						:				List[Any]    			= column / magnitude
    # Subtract to cancel term
       _lowerCamelCase						:				Union[str, Any]    			= current_set[0]
       _lowerCamelCase						:				Dict    			= [first_row]
       _lowerCamelCase						:				str    			= current_set[1::]
       for row in current_set:
              _lowerCamelCase						:				Union[str, Any]    			= []
              # If first term is 0, it is already in form we want, so we preserve it
              if row[0] == 0:
                     final_set.append(A_						)
                     continue
              for column_index in range(len(A_						)						):
                     temp_row.append(first_row[column_index] - row[column_index]						)
              final_set.append(A_						)
       # Create next recursion iteration set
       if len(final_set[0]						) != 3:
              _lowerCamelCase						:				Any    			= final_set[0]
              _lowerCamelCase						:				Any    			= []
              _lowerCamelCase						:				Optional[int]    			= []
              for row in final_set[1::]:
                     current_first_column.append(row[0]						)
                     next_iteration.append(row[1::]						)
              _lowerCamelCase						:				Dict    			= simplify(A_						)
              for i in range(len(A_						)						):
                     resultant[i].insert(0,				current_first_column[i]						)
              resultant.insert(0,				A_						)
              _lowerCamelCase						:				Tuple    			= resultant
       return final_set






def        snake_case_  (      A_					:		list[list]						):


       '''simple docstring'''





       if len(A_						) == 0:
              raise IndexError('''solve_simultaneous() requires n lists of length n+1'''						)
       _lowerCamelCase						:				Dict    			= len(A_						) + 1
       if any(len(A_						) != _length for item in equations						):
              raise IndexError('''solve_simultaneous() requires n lists of length n+1'''						)
       for row in equations:
              if any(not isinstance(A_,				(int, float)						) for column in row						):
                     raise ValueError('''solve_simultaneous() requires lists of integers'''						)
       if len(A_						) == 1:
              return [equations[0][-1] / equations[0][0]]
       _lowerCamelCase						:				Optional[Any]    			= equations.copy()
       if any(0 in row for row in data_set						):
              _lowerCamelCase						:				str    			= data_set.copy()
              _lowerCamelCase						:				List[Any]    			= []
              for row_index, row in enumerate(A_						):
                     if 0 not in row:
                            _lowerCamelCase						:				Union[str, Any]    			= data_set.pop(A_						)
                            break
              if not full_row:
                     raise ValueError('''solve_simultaneous() requires at least 1 full equation'''						)
              data_set.insert(0,				A_						)
       _lowerCamelCase						:				List[str]    			= data_set.copy()
       _lowerCamelCase						:				int    			= simplify(A_						)
       _lowerCamelCase						:				int    			= simplified[::-1]
       _lowerCamelCase						:				list    			= []
       for row in simplified:
              _lowerCamelCase						:				Tuple    			= row[-1]
              if not solutions:
                     if row[-2] == 0:
                            solutions.append(0						)
                            continue
                     solutions.append(current_solution / row[-2]						)
                     continue
              _lowerCamelCase						:				Optional[Any]    			= row.copy()[: len(A_						) - 1 :]
              while temp_row[0] == 0:
                     temp_row.pop(0						)
              if len(A_						) == 0:
                     solutions.append(0						)
                     continue
              _lowerCamelCase						:				Tuple    			= temp_row[1::]
              _lowerCamelCase						:				Tuple    			= temp_row[::-1]
              for column_index, column in enumerate(A_						):
                     current_solution -= column * solutions[column_index]
              solutions.append(A_						)
       _lowerCamelCase						:				Optional[int]    			= []
       for item in solutions:
              final.append(float(round(A_,				5						)						)						)
       return final[::-1]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   lowerCAmelCase__        =				[
       [2, 1, 1, 1, 1, 4],
       [1, 2, 1, 1, 1, 5],
       [1, 1, 2, 1, 1, 6],
       [1, 1, 1, 2, 1, 7],
       [1, 1, 1, 1, 2, 8],
   ]
   print(solve_simultaneous(eq))
   print(solve_simultaneous([[4, 2]]))










import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

				lowercase__    =			(
				    ('''layer.''', '''layer_'''),
				    ('''word_embeddings.weight''', '''word_embeddings'''),
				    ('''position_embeddings.weight''', '''position_embeddings'''),
				    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
				    ('''.''', '''/'''),
				    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
				    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
				    ('''weight''', '''kernel'''),
				)

				if not os.path.isdir(lowerCamelCase_				):
								os.makedirs(lowerCamelCase_				)

				lowercase__    =			model.state_dict()

				def to_tf_var_name(lowerCamelCase_				):
								for patt, repl in iter(lowerCamelCase_				):
												lowercase__    =			name.replace(lowerCamelCase_		,   lowerCamelCase_				)
								return F"""bert/{name}"""

				def create_tf_var(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):
								lowercase__    =			tf.dtypes.as_dtype(tensor.dtype				)
								lowercase__    =			tf.get_variable(dtype=lowerCamelCase_		,   shape=tensor.shape		,   name=lowerCamelCase_		,   initializer=tf.zeros_initializer()				)
								session.run(tf.variables_initializer([tf_var]				)				)
								session.run(lowerCamelCase_				)
								return tf_var

				tf.reset_default_graph()
				with tf.Session() as session:
								for var_name in state_dict:
												lowercase__    =			to_tf_var_name(lowerCamelCase_				)
												lowercase__    =			state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose				):
																lowercase__    =			torch_tensor.T
												lowercase__    =			create_tf_var(tensor=lowerCamelCase_		,   name=lowerCamelCase_		,   session=lowerCamelCase_				)
												tf.keras.backend.set_value(lowerCamelCase_		,   lowerCamelCase_				)
												lowercase__    =			session.run(lowerCamelCase_				)
												print(F"""Successfully created {tf_name}: {np.allclose(lowerCamelCase_		,   lowerCamelCase_				)}"""				)

								lowercase__    =			tf.train.Saver(tf.trainable_variables()				)
								saver.save(lowerCamelCase_		,   os.path.join(lowerCamelCase_		,   model_name.replace('''-'''		,   '''_'''				) + '''.ckpt'''				)				)






def   a			(						lowerCamelCase_=None				):






				'''simple docstring'''
				lowercase__    =			argparse.ArgumentParser()
				parser.add_argument('''--model_name'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''model name e.g. bert-base-uncased'''				)
				parser.add_argument(
				    '''--cache_dir'''		,   type=lowerCamelCase_		,   default=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''Directory containing pytorch model'''				)
				parser.add_argument('''--pytorch_model_path'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''/path/to/<pytorch-model-name>.bin'''				)
				parser.add_argument('''--tf_cache_dir'''		,   type=lowerCamelCase_		,   required=lowerCamelCase_		,   help='''Directory in which to save tensorflow model'''				)
				lowercase__    =			parser.parse_args(lowerCamelCase_				)

				lowercase__    =			BertModel.from_pretrained(
				    pretrained_model_name_or_path=args.model_name		,   state_dict=torch.load(args.pytorch_model_path				)		,   cache_dir=args.cache_dir		,   )

				convert_pytorch_checkpoint_to_tf(model=lowerCamelCase_		,   ckpt_dir=args.tf_cache_dir		,   model_name=args.model_name				)


if __name__ == "__main__":
							main()




