





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


def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list]    ):





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

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_lowerCamelCase   				=			data_utils.TransfoXLTokenizer
_lowerCamelCase   				=			data_utils.TransfoXLCorpus
_lowerCamelCase   				=			data_utils
_lowerCamelCase   				=			data_utils






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Dict					,   UpperCamelCase__:							Any					,   UpperCamelCase__:							Any					,   UpperCamelCase__:							Tuple    ):
		if transfo_xl_dataset_file:
				# Convert a pre-processed corpus (see original TensorFlow repo)
				with open(UpperCamelCase__					,   """rb"""    ) as fp:
						SCREAMING_SNAKE_CASE__			=      pickle.load(UpperCamelCase__					,   encoding="""latin1"""    )
				# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
				SCREAMING_SNAKE_CASE__			=      pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
				print(f'''Save vocabulary to {pytorch_vocab_dump_path}'''    )
				SCREAMING_SNAKE_CASE__			=      corpus.vocab.__dict__
				torch.save(UpperCamelCase__					,   UpperCamelCase__    )

				SCREAMING_SNAKE_CASE__			=      corpus.__dict__
				corpus_dict_no_vocab.pop("""vocab"""					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      pytorch_dump_folder_path + """/""" + CORPUS_NAME
				print(f'''Save dataset to {pytorch_dataset_dump_path}'''    )
				torch.save(UpperCamelCase__					,   UpperCamelCase__    )

		if tf_checkpoint_path:
				# Convert a pre-trained TensorFlow model
				SCREAMING_SNAKE_CASE__			=      os.path.abspath(UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      os.path.abspath(UpperCamelCase__    )

				print(f'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''    )
				# Initialise PyTorch model
				if transfo_xl_config_file == "":
						SCREAMING_SNAKE_CASE__			=      TransfoXLConfig()
				else:
						SCREAMING_SNAKE_CASE__			=      TransfoXLConfig.from_json_file(UpperCamelCase__    )
				print(f'''Building PyTorch model from configuration: {config}'''    )
				SCREAMING_SNAKE_CASE__			=      TransfoXLLMHeadModel(UpperCamelCase__    )

				SCREAMING_SNAKE_CASE__			=      load_tf_weights_in_transfo_xl(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
				# Save pytorch-model
				SCREAMING_SNAKE_CASE__			=      os.path.join(UpperCamelCase__					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      os.path.join(UpperCamelCase__					,   UpperCamelCase__    )
				print(f'''Save PyTorch model to {os.path.abspath(UpperCamelCase__    )}'''    )
				torch.save(model.state_dict()					,   UpperCamelCase__    )
				print(f'''Save configuration file to {os.path.abspath(UpperCamelCase__    )}'''    )
				with open(UpperCamelCase__					,   """w"""					,   encoding="""utf-8"""    ) as f:
						f.write(config.to_json_string()    )


if __name__ == "__main__":
	_lowerCamelCase   				=			argparse.ArgumentParser()
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to the folder to store the PyTorch model or dataset/vocab.',
	)
	parser.add_argument(
	    '--tf_checkpoint_path',
	    default='',
	    type=str,
	    help='An optional path to a TensorFlow checkpoint path to be converted.',
	)
	parser.add_argument(
	    '--transfo_xl_config_file',
	    default='',
	    type=str,
	    help=(
	        'An optional config json file corresponding to the pre-trained BERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--transfo_xl_dataset_file',
	    default='',
	    type=str,
	    help='An optional dataset file to be converted in a vocabulary.',
	)
	_lowerCamelCase   				=			parser.parse_args()
	convert_transfo_xl_checkpoint_to_pytorch(
	    args.tf_checkpoint_path,
	    args.transfo_xl_config_file,
	    args.pytorch_dump_folder_path,
	    args.transfo_xl_dataset_file,
	)