
"""simple docstring"""



from __future__ import annotations
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				def __init__(					self	:			Tuple				,     UpperCAmelCase_	:			list[list[int]]  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			TypeError(
											    'Matrices must be formed from a list of zero or more lists containing at '
											    'least one and the same number of values, each of which must be of type '
											    'int or float.'  )
											if len(__A  ) != 0:
																		_lowerCAmelCase  	=			len(rows[0]  )
																		if cols == 0:
																									raise error
																		for row in rows:
																									if len(__A  ) != cols:
																																raise error
																									for value in row:
																																if not isinstance(__A				,     (int, float)  ):
																																							raise error
																		_lowerCAmelCase  	=			rows
											else:
																		_lowerCAmelCase  	=			[]


				def 				__lowerCamelCase	(					self	:			Any  )		->  list[list[int]]:







											"""simple docstring"""




											return [[row[i] for row in self.rows] for i in range(len(self.rows[0]  )  )]


				@property
				def 				__lowerCamelCase	(					self	:			int  )		->  int:







											"""simple docstring"""




											return len(self.rows  )


				@property
				def 				__lowerCamelCase	(					self	:			int  )		->  int:







											"""simple docstring"""




											return len(self.rows[0]  )


				@property
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  tuple[int, int]:







											"""simple docstring"""




											return (self.num_rows, self.num_columns)


				@property
				def 				__lowerCamelCase	(					self	:			Any  )		->  bool:







											"""simple docstring"""




											return self.order[0] == self.order[1]


				def 				__lowerCamelCase	(					self	:			int  )		->  Matrix:







											"""simple docstring"""




											_lowerCAmelCase  	=			[
											    [0 if column_num != row_num else 1 for column_num in range(self.num_rows  )]
											    for row_num in range(self.num_rows  )
											]
											return Matrix(__A  )


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  int:







											"""simple docstring"""




											if not self.is_square:
																		return 0
											if self.order == (0, 0):
																		return 1
											if self.order == (1, 1):
																		return int(self.rows[0][0]  )
											if self.order == (2, 2):
																		return int(
																		    (self.rows[0][0] * self.rows[1][1])
																		    - (self.rows[0][1] * self.rows[1][0])  )
											else:
																		return sum(
																		    self.rows[0][column] * self.cofactors().rows[0][column]
																		    for column in range(self.num_columns  )  )


				def 				__lowerCamelCase	(					self	:			str  )		->  bool:







											"""simple docstring"""




											return bool(self.determinant()  )


				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			[
											    [
											        self.rows[other_row][other_column]
											        for other_column in range(self.num_columns  )
											        if other_column != column
											    ]
											    for other_row in range(self.num_rows  )
											    if other_row != row
											]
											return Matrix(__A  ).determinant()


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int  )		->  int:







											"""simple docstring"""




											if (row + column) % 2 == 0:
																		return self.get_minor(__A				,     __A  )
											return -1 * self.get_minor(__A				,     __A  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Matrix:







											"""simple docstring"""




											return Matrix(
											    [
											        [self.get_minor(__A				,     __A  ) for column in range(self.num_columns  )]
											        for row in range(self.num_rows  )
											    ]  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Matrix:







											"""simple docstring"""




											return Matrix(
											    [
											        [
											            self.minors().rows[row][column]
											            if (row + column) % 2 == 0
											            else self.minors().rows[row][column] * -1
											            for column in range(self.minors().num_columns  )
											        ]
											        for row in range(self.minors().num_rows  )
											    ]  )


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Matrix:







											"""simple docstring"""




											_lowerCAmelCase  	=			[
											    [self.cofactors().rows[column][row] for column in range(self.num_columns  )]
											    for row in range(self.num_rows  )
											]
											return Matrix(__A  )


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Matrix:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.determinant()
											if not determinant:
																		raise TypeError('Only matrices with a non-zero determinant have an inverse'  )
											return self.adjugate() * (1 / determinant)


				def __repr__(					self	:			List[str]  )		->  str:







											"""simple docstring"""




											return str(self.rows  )


				def __str__(					self	:			Any  )		->  str:







											"""simple docstring"""




											if self.num_rows == 0:
																		return "[]"
											if self.num_rows == 1:
																		return "[[" + ". ".join(str(self.rows[0]  )  ) + "]]"
											return (
											    "["
											    + "\n ".join(
											        [
											            '[' + '. '.join([str(__A  ) for value in row]  ) + '.]'
											            for row in self.rows
											        ]  )
											    + "]"
											)


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			list[int]				,     UpperCAmelCase_	:			int | None = None  )		->  None:







											"""simple docstring"""




											_lowerCAmelCase  	=			TypeError('Row must be a list containing all ints and/or floats'  )
											if not isinstance(__A				,     __A  ):
																		raise type_error
											for value in row:
																		if not isinstance(__A				,     (int, float)  ):
																									raise type_error
											if len(__A  ) != self.num_columns:
																		raise ValueError(
																		    'Row must be equal in length to the other rows in the matrix'  )
											if position is None:
																		self.rows.append(__A  )
											else:
																		_lowerCAmelCase  	=			self.rows[0:position] + [row] + self.rows[position:]


				def 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			list[int]				,     UpperCAmelCase_	:			int | None = None  )		->  None:







											"""simple docstring"""




											_lowerCAmelCase  	=			TypeError(
											    'Column must be a list containing all ints and/or floats'  )
											if not isinstance(__A				,     __A  ):
																		raise type_error
											for value in column:
																		if not isinstance(__A				,     (int, float)  ):
																									raise type_error
											if len(__A  ) != self.num_rows:
																		raise ValueError(
																		    'Column must be equal in length to the other columns in the matrix'  )
											if position is None:
																		_lowerCAmelCase  	=			[self.rows[i] + [column[i]] for i in range(self.num_rows  )]
											else:
																		_lowerCAmelCase  	=			[
																		    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
																		    for i in range(self.num_rows  )
																		]


				def __eq__(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			object  )		->  bool:







											"""simple docstring"""




											if not isinstance(__A				,     __A  ):
																		return NotImplemented
											return self.rows == other.rows


				def __ne__(					self	:			Optional[int]				,     UpperCAmelCase_	:			object  )		->  bool:







											"""simple docstring"""




											return not self == other


				def __neg__(					self	:			Dict  )		->  Matrix:







											"""simple docstring"""




											return self * -1


				def __add__(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			Matrix  )		->  Matrix:







											"""simple docstring"""




											if self.order != other.order:
																		raise ValueError('Addition requires matrices of the same order'  )
											return Matrix(
											    [
											        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns  )]
											        for i in range(self.num_rows  )
											    ]  )


				def __sub__(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			Matrix  )		->  Matrix:







											"""simple docstring"""




											if self.order != other.order:
																		raise ValueError('Subtraction requires matrices of the same order'  )
											return Matrix(
											    [
											        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns  )]
											        for i in range(self.num_rows  )
											    ]  )


				def __mul__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			Matrix | int | float  )		->  Matrix:







											"""simple docstring"""




											if isinstance(__A				,     (int, float)  ):
																		return Matrix(
																		    [[int(element * other  ) for element in row] for row in self.rows]  )
											elif isinstance(__A				,     __A  ):
																		if self.num_columns != other.num_rows:
																									raise ValueError(
																									    'The number of columns in the first matrix must '
																									    'be equal to the number of rows in the second'  )
																		return Matrix(
																		    [
																		        [Matrix.dot_product(__A				,     __A  ) for column in other.columns()]
																		        for row in self.rows
																		    ]  )
											else:
																		raise TypeError(
																		    'A Matrix can only be multiplied by an int, float, or another matrix'  )


				def __pow__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			int  )		->  Matrix:







											"""simple docstring"""




											if not isinstance(__A				,     __A  ):
																		raise TypeError('A Matrix can only be raised to the power of an int'  )
											if not self.is_square:
																		raise ValueError('Only square matrices can be raised to a power'  )
											if other == 0:
																		return self.identity()
											if other < 0:
																		if self.is_invertable():
																									return self.inverse() ** (-other)
																		raise ValueError(
																		    'Only invertable matrices can be raised to a negative power'  )
											_lowerCAmelCase  	=			self
											for _ in range(other - 1  ):
																		result *= self
											return result







				@classmethod
				def 				__lowerCamelCase	(					cls	:			Dict				,     UpperCAmelCase_	:			list[int]				,     UpperCAmelCase_	:			list[int]  )		->  int:







											"""simple docstring"""




											return sum(row[i] * column[i] for i in range(len(__A  )  )  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()








"""simple docstring"""



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_snake_case          =						getLogger(__name__)


_snake_case          =						'''cuda''' if torch.cuda.is_available() else '''cpu'''






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					int = 8     ,  SCREAMING_SNAKE_CASE:					str = DEFAULT_DEVICE     ,  SCREAMING_SNAKE_CASE:					Optional[int]=False     ,  SCREAMING_SNAKE_CASE:					int="summarization"     ,  SCREAMING_SNAKE_CASE:					List[Any]=None     ,  **SCREAMING_SNAKE_CASE:					Tuple     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			Path(SCREAMING_SNAKE_CASE						).open('w'     ,  encoding='utf-8'						)
							_lowerCAmelCase  	=			str(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)
							if fpaa:
														_lowerCAmelCase  	=			model.half()

							_lowerCAmelCase  	=			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE						)
							logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

							_lowerCAmelCase  	=			time.time()
							# update config with task specific params
							use_task_specific_params(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							if prefix is None:
														_lowerCAmelCase  	=			prefix or getattr(model.config     ,  'prefix'     ,  ''						) or ''
							for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)						)						):
														_lowerCAmelCase  	=			[prefix + text for text in examples_chunk]
														_lowerCAmelCase  	=			tokenizer(SCREAMING_SNAKE_CASE     ,  return_tensors='pt'     ,  truncation=SCREAMING_SNAKE_CASE     ,  padding='longest'						).to(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			model.generate(
														    input_ids=batch.input_ids     ,  attention_mask=batch.attention_mask     ,  **SCREAMING_SNAKE_CASE     ,  )
														_lowerCAmelCase  	=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE     ,  skip_special_tokens=SCREAMING_SNAKE_CASE     ,  clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE						)
														for hypothesis in dec:
																					fout.write(hypothesis + '\n'						)
																					fout.flush()
							fout.close()
							_lowerCAmelCase  	=			int(time.time() - start_time						)  # seconds
							_lowerCAmelCase  	=			len(SCREAMING_SNAKE_CASE						)
							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs     ,  4						)}






def 	__snake_case  (    ):






							"""simple docstring"""



							return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any=True						):






							"""simple docstring"""



							_lowerCAmelCase  	=			argparse.ArgumentParser()
							parser.add_argument('model_name'     ,  type=SCREAMING_SNAKE_CASE     ,  help='like facebook/bart-large-cnn,t5-base, etc.'						)
							parser.add_argument('input_path'     ,  type=SCREAMING_SNAKE_CASE     ,  help='like cnn_dm/test.source'						)
							parser.add_argument('save_path'     ,  type=SCREAMING_SNAKE_CASE     ,  help='where to save summaries'						)
							parser.add_argument('--reference_path'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  help='like cnn_dm/test.target'						)
							parser.add_argument('--score_path'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default='metrics.json'     ,  help='where to save metrics'						)
							parser.add_argument('--device'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  help='cuda, cuda:1, cpu etc.'						)
							parser.add_argument(
							    '--prefix'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  help='will be added to the begininng of src examples'						)
							parser.add_argument('--task'     ,  type=SCREAMING_SNAKE_CASE     ,  default='summarization'     ,  help='used for task_specific_params + metrics'						)
							parser.add_argument('--bs'     ,  type=SCREAMING_SNAKE_CASE     ,  default=8     ,  required=SCREAMING_SNAKE_CASE     ,  help='batch size'						)
							parser.add_argument(
							    '--n_obs'     ,  type=SCREAMING_SNAKE_CASE     ,  default=-1     ,  required=SCREAMING_SNAKE_CASE     ,  help='How many observations. Defaults to all.'						)
							parser.add_argument('--fp16'     ,  action='store_true'						)
							parser.add_argument('--dump-args'     ,  action='store_true'     ,  help='print the custom hparams with the results'						)
							parser.add_argument(
							    '--info'     ,  nargs='?'     ,  type=SCREAMING_SNAKE_CASE     ,  const=datetime_now()     ,  help=(
							        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
							        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
							    )     ,  )
							# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
							_lowerCAmelCase	,		_lowerCAmelCase  	=			parser.parse_known_args()
							_lowerCAmelCase  	=			parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE						)
							if parsed_args and verbose:
														print(f"""parsed the following generate kwargs: {parsed_args}"""						)
							_lowerCAmelCase  	=			[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
							if args.n_obs > 0:
														_lowerCAmelCase  	=			examples[: args.n_obs]
							Path(args.save_path						).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE						)

							if args.reference_path is None and Path(args.score_path						).exists():
														warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""						)

							if args.device == "cpu" and args.fpaa:
														# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
														raise ValueError('Can\'t mix --fp16 and --device cpu'						)

							_lowerCAmelCase  	=			generate_summaries_or_translations(
							    SCREAMING_SNAKE_CASE     ,  args.save_path     ,  args.model_name     ,  batch_size=args.bs     ,  device=args.device     ,  fpaa=args.fpaa     ,  task=args.task     ,  prefix=args.prefix     ,  **SCREAMING_SNAKE_CASE     ,  )

							if args.reference_path is None:
														return {}

							# Compute scores
							_lowerCAmelCase  	=			calculate_bleu if 'translation' in args.task else calculate_rouge
							_lowerCAmelCase  	=			[x.rstrip() for x in open(args.save_path						).readlines()]
							_lowerCAmelCase  	=			[x.rstrip() for x in open(args.reference_path						).readlines()][: len(SCREAMING_SNAKE_CASE						)]
							_lowerCAmelCase  	=			score_fn(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							scores.update(SCREAMING_SNAKE_CASE						)

							if args.dump_args:
														scores.update(SCREAMING_SNAKE_CASE						)
							if args.info:
														_lowerCAmelCase  	=			args.info

							if verbose:
														print(SCREAMING_SNAKE_CASE						)

							if args.score_path is not None:
														json.dump(SCREAMING_SNAKE_CASE     ,  open(args.score_path     ,  'w'						)						)

							return scores


if __name__ == "__main__":
	# Usage for MT:
	# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
	run_generate(verbose=True)






