


"""simple docstring"""







from __future__ import annotations

from typing import Any




class  UpperCamelCase_	:

	"""simple docstring"""





	def __init__( self			:				Tuple			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				float = 0   )       ->	None:
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				row, column
						__SCREAMING_SNAKE_CASE						   =				[[default_value for c in range(UpperCAmelCase__   )] for r in range(UpperCAmelCase__   )]




	def __str__( self			:				str   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

						# Make string identifier
						__SCREAMING_SNAKE_CASE						   =				0
						for row_vector in self.array:
											for obj in row_vector:
																__SCREAMING_SNAKE_CASE						   =				max(UpperCAmelCase__			,			len(str(UpperCAmelCase__   )   )   )
						__SCREAMING_SNAKE_CASE						   =				F"""%{max_element_length}s"""

						# Make string and return
						def single_line(UpperCAmelCase__			:				list[float]   ) -> str:
											nonlocal string_format_identifier
											__SCREAMING_SNAKE_CASE						   =				"["
											line += ", ".join(string_format_identifier % (obj,) for obj in row_vector   )
											line += "]"
											return line

						s += "\n".join(single_line(UpperCAmelCase__   ) for row_vector in self.array   )
						return s




	def __repr__( self			:				Union[str, Any]   )       ->	str:
						return str(self   )




	def   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				tuple[int, int]   )       ->	bool:
						if not (isinstance(UpperCAmelCase__			,			(list, tuple)   ) and len(UpperCAmelCase__   ) == 2):
											return False
						elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
											return False
						else:
											return True




	def __getitem__( self			:				Tuple			,			UpperCAmelCase__			:				tuple[int, int]   )       ->	Any:
						assert self.validate_indicies(UpperCAmelCase__   )
						return self.array[loc[0]][loc[1]]




	def __setitem__( self			:				Optional[int]			,			UpperCAmelCase__			:				tuple[int, int]			,			UpperCAmelCase__			:				float   )       ->	None:
						assert self.validate_indicies(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				value




	def __add__( self			:				Any			,			UpperCAmelCase__			:				Matrix   )       ->	Matrix:
						assert isinstance(UpperCAmelCase__			,			UpperCAmelCase__   )
						assert self.row == another.row and self.column == another.column

						# Add
						__SCREAMING_SNAKE_CASE						   =				Matrix(self.row			,			self.column   )
						for r in range(self.row   ):
											for c in range(self.column   ):
																__SCREAMING_SNAKE_CASE						   =				self[r, c] + another[r, c]
						return result




	def __neg__( self			:				List[str]   )       ->	Matrix:
						__SCREAMING_SNAKE_CASE						   =				Matrix(self.row			,			self.column   )
						for r in range(self.row   ):
											for c in range(self.column   ):
																__SCREAMING_SNAKE_CASE						   =				-self[r, c]
						return result




	def __sub__( self			:				Optional[int]			,			UpperCAmelCase__			:				Matrix   )       ->	Matrix:
						return self + (-another)




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

						if isinstance(UpperCAmelCase__			,			(int, float)   ):  # Scalar multiplication
											__SCREAMING_SNAKE_CASE						   =				Matrix(self.row			,			self.column   )
											for r in range(self.row   ):
																for c in range(self.column   ):
																					__SCREAMING_SNAKE_CASE						   =				self[r, c] * another
											return result
						elif isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):  # Matrix multiplication
											assert self.column == another.row
											__SCREAMING_SNAKE_CASE						   =				Matrix(self.row			,			another.column   )
											for r in range(self.row   ):
																for c in range(another.column   ):
																					for i in range(self.column   ):
																										result[r, c] += self[r, i] * another[i, c]
											return result
						else:
											__SCREAMING_SNAKE_CASE						   =				F"""Unsupported type given for another ({type(UpperCAmelCase__   )})"""
											raise TypeError(UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Matrix:
						__SCREAMING_SNAKE_CASE						   =				Matrix(self.column			,			self.row   )
						for r in range(self.row   ):
											for c in range(self.column   ):
																__SCREAMING_SNAKE_CASE						   =				self[r, c]
						return result




	def   UpperCAmelCase_      ( self			:				Dict			,			UpperCAmelCase__			:				Matrix			,			UpperCAmelCase__			:				Matrix   )       ->	Any:
						assert isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ) and isinstance(UpperCAmelCase__			,			UpperCAmelCase__   )
						assert self.row == self.column == u.row == v.row  # u, v should be column vector
						assert u.column == v.column == 1  # u, v should be column vector

						# Calculate
						__SCREAMING_SNAKE_CASE						   =				v.transpose()
						__SCREAMING_SNAKE_CASE						   =				(v_t * self * u)[0, 0] + 1
						if numerator_factor == 0:
											return None  # It's not invertable
						return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":




		def    UpperCAmelCase__       ():




							'''simple docstring'''


							__SCREAMING_SNAKE_CASE						   =				Matrix(3  ,						3  ,						0   )
							for i in range(3   ):
												__SCREAMING_SNAKE_CASE						   =				1
							print(f"""a^(-1) is {ainv}"""   )
							# u, v
							__SCREAMING_SNAKE_CASE						   =				Matrix(3  ,						1  ,						0   )
							__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				1, 2, -3
							__SCREAMING_SNAKE_CASE						   =				Matrix(3  ,						1  ,						0   )
							__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				4, -2, 5
							print(f"""u is {u}"""   )
							print(f"""v is {v}"""   )
							print(f"""uv^T is {u * v.transpose()}"""   )
							# Sherman Morrison
							print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(lowerCAmelCase_  ,						lowerCAmelCase_   )}"""   )




		def    UpperCAmelCase__       ():




							'''simple docstring'''


							import doctest

							doctest.testmod()



		testa()





import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__A   			=getLogger(__name__)

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int = 8 ,   _UpperCAmelCase							:					int = 10_24 ,   _UpperCAmelCase							:					Any="val" ,   _UpperCAmelCase							:					Dict=None ,   _UpperCAmelCase							:					Any=False ,   _UpperCAmelCase							:					Union[str, Any]="summarization" ,   _UpperCAmelCase							:					Optional[Any]=None ,   _UpperCAmelCase							:					Union[str, Any]=1 ,   _UpperCAmelCase							:					Dict = None ,   _UpperCAmelCase							:					Dict="" ,   **_UpperCAmelCase							:					List[str] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	str(_UpperCAmelCase						)
		assert local_rank is not None
		torch.distributed.init_process_group(backend='''nccl''' ,   rank=_UpperCAmelCase						)

		__UpperCAmelCase    :     Union[str, Any]													=	Path(_UpperCAmelCase						)
		__UpperCAmelCase    :     Union[str, Any]													=	save_dir.joinpath(f'rank_{local_rank}_output.json'						)
		torch.cuda.set_device(_UpperCAmelCase						)
		__UpperCAmelCase    :     int													=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase						).cuda()
		if fpaa:
				__UpperCAmelCase    :     Any													=	model.half()
		# determine if we need to increase num_beams
		use_task_specific_params(_UpperCAmelCase ,   _UpperCAmelCase						)  # update config with task specific params
		__UpperCAmelCase    :     List[str]													=	generate_kwargs.pop('''num_beams''' ,   model.config.num_beams						)  # AttributeError risk?
		if num_return_sequences > num_beams:
				__UpperCAmelCase    :     Any													=	num_return_sequences

		__UpperCAmelCase    :     Tuple													=	AutoTokenizer.from_pretrained(_UpperCAmelCase						)
		logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'						)  # if this is wrong, check config.model_type.

		if max_source_length is None:
				__UpperCAmelCase    :     Optional[Any]													=	tokenizer.model_max_length
		if prefix is None:
				__UpperCAmelCase    :     str													=	prefix or getattr(model.config ,   '''prefix''' ,   ''''''						) or ''''''
		__UpperCAmelCase    :     Union[str, Any]													=	SeqaSeqDataset(
		    _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   max_target_length=10_24 ,   type_path=_UpperCAmelCase ,   n_obs=_UpperCAmelCase ,   prefix=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
		# I set shuffle=True for a more accurate progress bar.
		# If all the longest samples are first, the prog bar estimate is too high at the beginning.
		__UpperCAmelCase    :     str													=	ds.make_sortish_sampler(_UpperCAmelCase ,   distributed=_UpperCAmelCase ,   add_extra_examples=_UpperCAmelCase ,   shuffle=_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	DataLoader(_UpperCAmelCase ,   sampler=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   collate_fn=ds.collate_fn						)
		__UpperCAmelCase    :     List[Any]													=	[]
		for batch in tqdm(_UpperCAmelCase						):
				__UpperCAmelCase    :     str													=	model.generate(
				    input_ids=batch['''input_ids'''].to(model.device						) ,   attention_mask=batch['''attention_mask'''].to(model.device						) ,   num_return_sequences=_UpperCAmelCase ,   num_beams=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
				__UpperCAmelCase    :     Optional[int]													=	tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase						)
				__UpperCAmelCase    :     List[str]													=	batch['''ids''']
				if num_return_sequences > 1:
						__UpperCAmelCase    :     Any													=	chunks(_UpperCAmelCase ,   _UpperCAmelCase						)  # batch size chunks, each of size num_return_seq
				for i, pred in enumerate(_UpperCAmelCase						):
						results.append({'''pred''': pred, '''id''': ids[i].item()}						)
		save_json(_UpperCAmelCase ,   _UpperCAmelCase						)
		return results, sampler.num_replicas

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	argparse.ArgumentParser(
		    epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''						)
		parser.add_argument('''--data_dir''' ,   type=_UpperCAmelCase ,   help='''like cnn_dm/test.source'''						)
		parser.add_argument(
		    '''--model_name''' ,   type=_UpperCAmelCase ,   help='''like facebook/bart-large-cnn,t5-base, etc.''' ,   default='''sshleifer/distilbart-xsum-12-3''' ,   )
		parser.add_argument('''--save_dir''' ,   type=_UpperCAmelCase ,   help='''where to save''' ,   default='''tmp_gen'''						)
		parser.add_argument('''--max_source_length''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase						)
		parser.add_argument(
		    '''--type_path''' ,   type=_UpperCAmelCase ,   default='''test''' ,   help='''which subset to evaluate typically train/val/test'''						)
		parser.add_argument('''--task''' ,   type=_UpperCAmelCase ,   default='''summarization''' ,   help='''used for task_specific_params + metrics'''						)
		parser.add_argument('''--bs''' ,   type=_UpperCAmelCase ,   default=8 ,   required=_UpperCAmelCase ,   help='''batch size'''						)
		parser.add_argument(
		    '''--local_rank''' ,   type=_UpperCAmelCase ,   default=-1 ,   required=_UpperCAmelCase ,   help='''should be passed by distributed.launch'''						)

		parser.add_argument(
		    '''--n_obs''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''How many observations. Defaults to all.'''						)
		parser.add_argument(
		    '''--num_return_sequences''' ,   type=_UpperCAmelCase ,   default=1 ,   required=_UpperCAmelCase ,   help='''How many sequences to return'''						)
		parser.add_argument(
		    '''--sync_timeout''' ,   type=_UpperCAmelCase ,   default=6_00 ,   required=_UpperCAmelCase ,   help='''How long should master process wait for other processes to finish.''' ,   )
		parser.add_argument('''--src_lang''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase						)
		parser.add_argument('''--tgt_lang''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase						)
		parser.add_argument(
		    '''--prefix''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''will be added to the begininng of src examples'''						)
		parser.add_argument('''--fp16''' ,   action='''store_true'''						)
		parser.add_argument('''--debug''' ,   action='''store_true'''						)
		__UpperCAmelCase    :     Any													=	time.time()
		__UpperCAmelCase							,					__UpperCAmelCase    :     Any													=	parser.parse_known_args()
		__UpperCAmelCase    :     List[Any]													=	parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase						)
		if generate_kwargs and args.local_rank <= 0:
				print(f'parsed the following generate kwargs: {generate_kwargs}'						)
		__UpperCAmelCase    :     Union[str, Any]													=	Path(args.save_dir + '''_tmp'''						)
		Path(_UpperCAmelCase						).mkdir(exist_ok=_UpperCAmelCase						)  # this handles locking.
		__UpperCAmelCase    :     int													=	list(json_save_dir.glob('''rank_*.json'''						)						)
		if intermediate_files:
				raise ValueError(f'Found files at {json_save_dir} please move or remove them.'						)
				# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
		__UpperCAmelCase    :     List[Any]													=	{}
		if args.src_lang is not None:
				__UpperCAmelCase    :     List[str]													=	args.src_lang
		if args.tgt_lang is not None:
				__UpperCAmelCase    :     List[Any]													=	args.tgt_lang

		Path(args.save_dir						).mkdir(exist_ok=_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	eval_data_dir(
		    args.data_dir ,   _UpperCAmelCase ,   args.model_name ,   type_path=args.type_path ,   bs=args.bs ,   fpaa=args.fpaa ,   task=args.task ,   local_rank=args.local_rank ,   n_obs=args.n_obs ,   max_source_length=args.max_source_length ,   num_return_sequences=args.num_return_sequences ,   prefix=args.prefix ,   dataset_kwargs=_UpperCAmelCase ,   **_UpperCAmelCase ,   )

		if args.local_rank <= 0:
				__UpperCAmelCase    :     int													=	Path(args.save_dir						)
				save_dir.mkdir(exist_ok=_UpperCAmelCase						)
				__UpperCAmelCase    :     List[str]													=	gather_results_from_each_node(_UpperCAmelCase ,   _UpperCAmelCase ,   args.sync_timeout						)
				__UpperCAmelCase    :     List[Any]													=	combine_partial_results(_UpperCAmelCase						)
				if args.num_return_sequences > 1:
						__UpperCAmelCase    :     int													=	save_dir.joinpath('''pseudolabel_results.json'''						)
						print(f'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'						)
						save_json(_UpperCAmelCase ,   _UpperCAmelCase						)
						return
				__UpperCAmelCase    :     str													=	Path(args.data_dir						).joinpath(args.type_path + '''.target'''						)
				with open(_UpperCAmelCase						) as f:
						__UpperCAmelCase    :     int													=	[x.rstrip() for x in f.readlines()][: len(_UpperCAmelCase						)]

				# Calculate metrics, save metrics,  and save _generations.txt
				__UpperCAmelCase    :     Optional[Any]													=	'''translation''' in args.task
				__UpperCAmelCase    :     Optional[int]													=	calculate_bleu if calc_bleu else calculate_rouge
				__UpperCAmelCase    :     Union[str, Any]													=	'''bleu''' if calc_bleu else '''rouge'''
				__UpperCAmelCase    :     Dict													=	score_fn(_UpperCAmelCase ,   _UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	len(_UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	time.time() - start_time
				__UpperCAmelCase    :     List[str]													=	round(runtime / metrics['''n_obs'''] ,   4						)
				__UpperCAmelCase    :     List[str]													=	num_replicas
				# TODO(@stas00): add whatever metadata to metrics
				__UpperCAmelCase    :     List[Any]													=	save_dir.joinpath(f'{args.type_path}_{metric_name}.json'						)
				save_json(_UpperCAmelCase ,   _UpperCAmelCase ,   indent=_UpperCAmelCase						)
				print(_UpperCAmelCase						)
				write_txt_file(_UpperCAmelCase ,   save_dir.joinpath(f'{args.type_path}_generations.txt'						)						)
				if args.debug:
						write_txt_file(_UpperCAmelCase ,   save_dir.joinpath(f'{args.type_path}.target'						)						)
				else:
						shutil.rmtree(_UpperCAmelCase						)

def        a						(      _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	[]
		for partial_result in partial_results:
				records.extend(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[str]													=	sorted(_UpperCAmelCase ,   key=lambda _UpperCAmelCase						: x["id"]						)
		__UpperCAmelCase    :     Union[str, Any]													=	[x['''pred'''] for x in records]
		return preds

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	time.time()
		logger.info('''waiting for all nodes to finish'''						)
		__UpperCAmelCase    :     Any													=	None
		while (time.time() - start_wait) < timeout:
				__UpperCAmelCase    :     List[Any]													=	list(save_dir.glob('''rank_*.json'''						)						)
				if len(_UpperCAmelCase						) < num_replicas:
						continue
				try:
						# make sure all json files are fully saved
						__UpperCAmelCase    :     Union[str, Any]													=	lmap(_UpperCAmelCase ,   _UpperCAmelCase						)
						return json_data
				except JSONDecodeError:
						continue
		else:
				raise TimeoutError('''Rank 0 gave up on waiting for other processes'''						)
		# Unreachable


if __name__ == "__main__":
				# Usage for MT:
				run_generate()





