






"""simple docstring"""
from __future__ import annotations

from typing import Any






def        lowerCamelCase_  (						*_lowerCamelCase				):
 if all(_check_not_integer(_lowerCamelCase				) for m in matrix_s				):
  for i in matrix_s[1:]:
   _verify_matrix_sizes(matrix_s[0]			,   _lowerCamelCase				)
  return [[sum(_lowerCamelCase				) for t in zip(*_lowerCamelCase				)] for m in zip(*_lowerCamelCase				)]
 raise TypeError('Expected a matrix, got int/list instead'				)






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 if (
     _check_not_integer(_lowerCamelCase				)
     and _check_not_integer(_lowerCamelCase				)
     and _verify_matrix_sizes(_lowerCamelCase			,   _lowerCamelCase				)
 ):
  return [[i - j for i, j in zip(*_lowerCamelCase				)] for m in zip(_lowerCamelCase			,   _lowerCamelCase				)]
 raise TypeError('Expected a matrix, got int/list instead'				)






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 return [[x * n for x in row] for row in matrix]






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 if _check_not_integer(_lowerCamelCase				) and _check_not_integer(_lowerCamelCase				):
  lowerCamelCase__				,     lowerCamelCase__     :			Dict     				=						_verify_matrix_sizes(_lowerCamelCase			,   _lowerCamelCase				)

 if cols[0] != rows[1]:
  lowerCamelCase__     :			Any     				=						(
      'Cannot multiply matrix of dimensions '
      f'''({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})'''
  )
  raise ValueError(_lowerCamelCase				)
 return [
     [sum(m * n for m, n in zip(_lowerCamelCase			,   _lowerCamelCase				)				) for j in zip(*_lowerCamelCase				)] for i in matrix_a
 ]






def        lowerCamelCase_  (						_lowerCamelCase				):
 lowerCamelCase__     :			Union[str, Any]     				=						int(_lowerCamelCase				)
 return [[int(row == column				) for column in range(_lowerCamelCase				)] for row in range(_lowerCamelCase				)]






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase = True				):
 if _check_not_integer(_lowerCamelCase				):
  if return_map:
   return map(_lowerCamelCase			,   zip(*_lowerCamelCase				)				)
  else:
   return list(map(_lowerCamelCase			,   zip(*_lowerCamelCase				)				)				)
 raise TypeError('Expected a matrix, got int/list instead'				)






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			str     				=						matrix[:row] + matrix[row + 1 :]
 return [row[:column] + row[column + 1 :] for row in minor]






def        lowerCamelCase_  (						_lowerCamelCase				):
 if len(_lowerCamelCase				) == 1:
  return matrix[0][0]

 return sum(
     x * determinant(minor(_lowerCamelCase			,   0			,   _lowerCamelCase				)				) * (-1) ** i
     for i, x in enumerate(matrix[0]				)				)






def        lowerCamelCase_  (						_lowerCamelCase				):
 lowerCamelCase__     :			Dict     				=						determinant(_lowerCamelCase				)
 if det == 0:
  return None

 lowerCamelCase__     :			Dict     				=						[
     [determinant(minor(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				)				) for j in range(len(_lowerCamelCase				)				)]
     for i in range(len(_lowerCamelCase				)				)
 ]

 lowerCamelCase__     :			List[str]     				=						[
     [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]				)]
     for row in range(len(_lowerCamelCase				)				)
 ]
 lowerCamelCase__     :			List[str]     				=						list(transpose(_lowerCamelCase				)				)
 return scalar_multiply(_lowerCamelCase			,   1 / det				)






def        lowerCamelCase_  (						_lowerCamelCase				):
 return not isinstance(_lowerCamelCase			,   _lowerCamelCase				) and not isinstance(matrix[0]			,   _lowerCamelCase				)






def        lowerCamelCase_  (						_lowerCamelCase				):
 return len(_lowerCamelCase				), len(matrix[0]				)






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Tuple     				=						_shape(_lowerCamelCase				) + _shape(_lowerCamelCase				)
 if shape[0] != shape[3] or shape[1] != shape[2]:
  lowerCamelCase__     :			List[Any]     				=						(
      'operands could not be broadcast together with shape '
      f'''({shape[0], shape[1]}), ({shape[2], shape[3]})'''
  )
  raise ValueError(_lowerCamelCase				)
 return (shape[0], shape[2]), (shape[1], shape[3])






def        lowerCamelCase_  (						):
 lowerCamelCase__     :			Optional[Any]     				=						[[12, 10], [3, 9]]
 lowerCamelCase__     :			Optional[Any]     				=						[[3, 4], [7, 4]]
 lowerCamelCase__     :			Any     				=						[[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
 lowerCamelCase__     :			List[str]     				=						[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
 print(f'''Add Operation, {add(_lowerCamelCase			,   _lowerCamelCase				) = } \n'''				)
 print(
     f'''Multiply Operation, {multiply(_lowerCamelCase			,   _lowerCamelCase				) = } \n'''			,   )
 print(f'''Identity: {identity(5				)}\n'''				)
 print(f'''Minor of {matrix_c} = {minor(_lowerCamelCase			,   1			,   2				)} \n'''				)
 print(f'''Determinant of {matrix_b} = {determinant(_lowerCamelCase				)} \n'''				)
 print(f'''Inverse of {matrix_d} = {inverse(_lowerCamelCase				)}\n'''				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()



'''simple docstring'''
import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
lowerCAmelCase :  Optional[Any]      =      logging.getLogger(__name__)







def 						A_(			):
							UpperCamelCase					       =			argparse.ArgumentParser(description='PyTorch Transformer Language Model')
							parser.add_argument('--model_name'						,			type=A						,			default='transfo-xl-wt103'						,			help='pretrained model name')
							parser.add_argument(
							    '--split'						,			type=A						,			default='test'						,			choices=['all', 'valid', 'test']						,			help='which split to evaluate')
							parser.add_argument('--batch_size'						,			type=A						,			default=10						,			help='batch size')
							parser.add_argument('--tgt_len'						,			type=A						,			default=128						,			help='number of tokens to predict')
							parser.add_argument('--ext_len'						,			type=A						,			default=0						,			help='length of the extended context')
							parser.add_argument('--mem_len'						,			type=A						,			default=1600						,			help='length of the retained previous heads')
							parser.add_argument('--clamp_len'						,			type=A						,			default=1000						,			help='max positional embedding index')
							parser.add_argument('--no_cuda'						,			action='store_true'						,			help='Do not use CUDA even though CUA is available')
							parser.add_argument('--work_dir'						,			type=A						,			required=A						,			help='path to the work_dir')
							parser.add_argument('--no_log'						,			action='store_true'						,			help='do not log the eval result')
							parser.add_argument('--same_length'						,			action='store_true'						,			help='set same length attention with masking')
							parser.add_argument('--server_ip'						,			type=A						,			default=''						,			help='Can be used for distant debugging.')
							parser.add_argument('--server_port'						,			type=A						,			default=''						,			help='Can be used for distant debugging.')
							UpperCamelCase					       =			parser.parse_args()
							assert args.ext_len >= 0, "extended context length must be non-negative"

							if args.server_ip and args.server_port:
														# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
														import ptvsd

														print('Waiting for debugger attach')
														ptvsd.enable_attach(address=(args.server_ip, args.server_port)						,			redirect_output=A)
														ptvsd.wait_for_attach()

							UpperCamelCase					       =			torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu')
							logger.info('device: {}'.format(A))

							# Load a pre-processed dataset
							# You can also build the corpus yourself using TransfoXLCorpus methods
							# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
							# and tokenizing the dataset
							# The pre-processed corpus is a convertion (using the conversion script )
							UpperCamelCase					       =			TransfoXLCorpus.from_pretrained(args.model_name)

							UpperCamelCase					       =			corpus.get_iterator('valid'						,			args.batch_size						,			args.tgt_len						,			device=A						,			ext_len=args.ext_len)
							UpperCamelCase					       =			corpus.get_iterator('test'						,			args.batch_size						,			args.tgt_len						,			device=A						,			ext_len=args.ext_len)

							# Load a pre-trained model
							UpperCamelCase					       =			TransfoXLLMHeadModel.from_pretrained(args.model_name)
							model.to(A)

							logger.info(
							    'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
							        args.batch_size						,			args.tgt_len						,			args.ext_len						,			args.mem_len						,			args.clamp_len))

							model.reset_memory_length(args.mem_len)
							if args.clamp_len > 0:
														UpperCamelCase					       =			args.clamp_len
							if args.same_length:
														UpperCamelCase					       =			True

							###############################################################################
							# Evaluation code
							###############################################################################
							def evaluate(A		:						Dict):
														# Turn on evaluation mode which disables dropout.
														model.eval()
														UpperCamelCase       ,     UpperCamelCase					       =			0, 0.0
														UpperCamelCase					       =			time.time()
														with torch.no_grad():
																					UpperCamelCase					       =			None
																					for idx, (data, target, seq_len) in enumerate(A):
																												UpperCamelCase					       =			model(A						,			lm_labels=A						,			mems=A)
																												UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			ret
																												UpperCamelCase					       =			loss.mean()
																												total_loss += seq_len * loss.item()
																												total_len += seq_len
																					UpperCamelCase					       =			time.time() - start_time
														logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(A						,			1000 * total_time / (idx + 1)))
														return total_loss / total_len

							# Run on test data.
							if args.split == "all":
														UpperCamelCase					       =			evaluate(A)
														UpperCamelCase					       =			evaluate(A)
							elif args.split == "valid":
														UpperCamelCase					       =			evaluate(A)
														UpperCamelCase					       =			None
							elif args.split == "test":
														UpperCamelCase					       =			evaluate(A)
														UpperCamelCase					       =			None

							def format_log(A		:						Any						,			A		:						Optional[Any]):
														UpperCamelCase					       =			'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(A						,			A						,			math.exp(A))
														return log_str

							UpperCamelCase					       =			''
							if valid_loss is not None:
														log_str += format_log(A						,			'valid')
							if test_loss is not None:
														log_str += format_log(A						,			'test')

							logger.info('=' * 100)
							logger.info(A)
							logger.info('=' * 100)


if __name__ == "__main__":
						main()




