






from __future__ import annotations





class        snake_case__					:
	def __init__(				self			,   lowerCAmelCase__ )							->      Dict:
							__magic_name__			:   List[Any]   						=					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:
													__magic_name__			:   List[Any]   						=					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
													__magic_name__			:   List[Any]   						=					rows
							else:
													__magic_name__			:   int   						=					[]
	def 			__magic_name__				(				self )							->      Tuple:
							return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
	@property
	def 			__magic_name__				(				self )							->      Optional[int]:
							return len(self.rows )
	@property
	def 			__magic_name__				(				self )							->      str:
							return len(self.rows[0] )
	@property
	def 			__magic_name__				(				self )							->      Optional[Any]:
							return (self.num_rows, self.num_columns)
	@property
	def 			__magic_name__				(				self )							->      Optional[Any]:
							return self.order[0] == self.order[1]
	def 			__magic_name__				(				self )							->      List[str]:
							__magic_name__			:   Any   						=					[
							    [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 			__magic_name__				(				self )							->      Dict:

							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 			__magic_name__				(				self )							->      Union[str, Any]:
							return bool(self.determinant() )
	def 			__magic_name__				(				self			,   lowerCAmelCase__			,   lowerCAmelCase__ )							->      List[str]:
							__magic_name__			:   Optional[int]   						=					[
							    [
							        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 			__magic_name__				(				self			,   lowerCAmelCase__			,   lowerCAmelCase__ )							->      Optional[int]:

							if (row + column) % 2 == 0:
													return self.get_minor(a_			,   a_ )
							return -1 * self.get_minor(a_			,   a_ )
	def 			__magic_name__				(				self )							->      str:
							return Matrix(
							    [
							        [self.get_minor(a_			,   a_ ) for column in range(self.num_columns )]
							        for row in range(self.num_rows )
							    ] )
	def 			__magic_name__				(				self )							->      List[Any]:
							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 			__magic_name__				(				self )							->      Any:
							__magic_name__			:   Dict   						=					[
							    [self.cofactors().rows[column][row] for column in range(self.num_columns )]
							    for row in range(self.num_rows )
							]
							return Matrix(a_ )
	def 			__magic_name__				(				self )							->      List[Any]:
							__magic_name__			:   str   						=					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 )							->      Dict:
							return str(self.rows )
	def __str__(				self )							->      Any:

							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 			__magic_name__				(				self			,   lowerCAmelCase__			,   lowerCAmelCase__ = None )							->      Optional[Any]:
							__magic_name__			:   List[str]   						=					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:
													__magic_name__			:   List[Any]   						=					self.rows[0:position] + [row] + self.rows[position:]
	def 			__magic_name__				(				self			,   lowerCAmelCase__			,   lowerCAmelCase__ = None )							->      Dict:
							__magic_name__			:   Union[str, Any]   						=					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:
													__magic_name__			:   str   						=					[self.rows[i] + [column[i]] for i in range(self.num_rows )]
							else:
													__magic_name__			:   Union[str, Any]   						=					[
													    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
													    for i in range(self.num_rows )
													]
	def __eq__(				self			,   lowerCAmelCase__ )							->      Union[str, Any]:

							if not isinstance(a_			,   a_ ):
													return NotImplemented
							return self.rows == other.rows
	def __ne__(				self			,   lowerCAmelCase__ )							->      Optional[Any]:
							return not self == other
	def __neg__(				self )							->      int:
							return self * -1
	def __add__(				self			,   lowerCAmelCase__ )							->      List[str]:

							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			,   lowerCAmelCase__ )							->      Optional[int]:

							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			,   lowerCAmelCase__ )							->      str:

							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			,   lowerCAmelCase__ )							->      Union[str, Any]:

							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""" )
							__magic_name__			:   Dict   						=					self
							for _ in range(other - 1 ):
													result *= self
							return result







	@classmethod
	def 			__magic_name__				(				cls			,   lowerCAmelCase__			,   lowerCAmelCase__ )							->      Tuple:
							return sum(row[i] * column[i] for i in range(len(a_ ) ) )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




"""simple docstring"""
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,
)


SCREAMING_SNAKE_CASE :    List[str]									=				getLogger(__name__)






def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      str     ,   _snake_case						:      str     ,   _snake_case						:      int = 8     ,   _snake_case						:      int = 1_024     ,   _snake_case						:      Any="val"     ,   _snake_case						:      Tuple=None     ,   _snake_case						:      Any=False     ,   _snake_case						:      str="summarization"     ,   _snake_case						:      Dict=None     ,   _snake_case						:      Optional[Any]=1     ,   _snake_case						:      Dict = None     ,   _snake_case						:      List[Any]=""     ,   **_snake_case						:      int     ,   )     ->Dict:
	"""simple docstring"""





	__snake_case	:						int						=				str(_snake_case     )
	assert local_rank is not None
	torch.distributed.init_process_group(backend='''nccl'''     ,   rank=_snake_case     )

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

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

	if max_source_length is None:
		__snake_case	:						List[str]						=				tokenizer.model_max_length
	if prefix is None:
		__snake_case	:						List[str]						=				prefix or getattr(model.config     ,   '''prefix'''     ,   ''''''     ) or ''''''
	__snake_case	:						List[str]						=				SeqaSeqDataset(
	    _snake_case     ,   _snake_case     ,   _snake_case     ,   max_target_length=1_024     ,   type_path=_snake_case     ,   n_obs=_snake_case     ,   prefix=_snake_case     ,   **_snake_case     ,   )
	# 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.
	__snake_case	:						Union[str, Any]						=				ds.make_sortish_sampler(_snake_case     ,   distributed=_snake_case     ,   add_extra_examples=_snake_case     ,   shuffle=_snake_case     )
	__snake_case	:						List[Any]						=				DataLoader(_snake_case     ,   sampler=_snake_case     ,   batch_size=_snake_case     ,   collate_fn=ds.collate_fn     )
	__snake_case	:						Union[str, Any]						=				[]
	for batch in tqdm(_snake_case     ):
		__snake_case	:						Tuple						=				model.generate(
		    input_ids=batch['''input_ids'''].to(model.device     )     ,   attention_mask=batch['''attention_mask'''].to(model.device     )     ,   num_return_sequences=_snake_case     ,   num_beams=_snake_case     ,   **_snake_case     ,   )
		__snake_case	:						List[Any]						=				tokenizer.batch_decode(_snake_case     ,   skip_special_tokens=_snake_case     ,   clean_up_tokenization_spaces=_snake_case     )
		__snake_case	:						List[str]						=				batch['''ids''']
		if num_return_sequences > 1:
			__snake_case	:						Dict						=				chunks(_snake_case     ,   _snake_case     )  # batch size chunks, each of size num_return_seq
		for i, pred in enumerate(_snake_case     ):
			results.append({'''pred''': pred, '''id''': ids[i].item()}     )
	save_json(_snake_case     ,   _snake_case     )
	return results, sampler.num_replicas






def 		lowercase							(     )     ->int:
	"""simple docstring"""





	__snake_case	:						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=_snake_case     ,   help='''like cnn_dm/test.source'''     )
	parser.add_argument(
	    '''--model_name'''     ,   type=_snake_case     ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''     ,   default='''sshleifer/distilbart-xsum-12-3'''     ,   )
	parser.add_argument('''--save_dir'''     ,   type=_snake_case     ,   help='''where to save'''     ,   default='''tmp_gen'''     )
	parser.add_argument('''--max_source_length'''     ,   type=_snake_case     ,   default=_snake_case     )
	parser.add_argument(
	    '''--type_path'''     ,   type=_snake_case     ,   default='''test'''     ,   help='''which subset to evaluate typically train/val/test'''     )
	parser.add_argument('''--task'''     ,   type=_snake_case     ,   default='''summarization'''     ,   help='''used for task_specific_params + metrics'''     )
	parser.add_argument('''--bs'''     ,   type=_snake_case     ,   default=8     ,   required=_snake_case     ,   help='''batch size'''     )
	parser.add_argument(
	    '''--local_rank'''     ,   type=_snake_case     ,   default=-1     ,   required=_snake_case     ,   help='''should be passed by distributed.launch'''     )

	parser.add_argument(
	    '''--n_obs'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     ,   help='''How many observations. Defaults to all.'''     )
	parser.add_argument(
	    '''--num_return_sequences'''     ,   type=_snake_case     ,   default=1     ,   required=_snake_case     ,   help='''How many sequences to return'''     )
	parser.add_argument(
	    '''--sync_timeout'''     ,   type=_snake_case     ,   default=600     ,   required=_snake_case     ,   help='''How long should master process wait for other processes to finish.'''     ,   )
	parser.add_argument('''--src_lang'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     )
	parser.add_argument('''--tgt_lang'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     )
	parser.add_argument(
	    '''--prefix'''     ,   type=_snake_case     ,   required=_snake_case     ,   default=_snake_case     ,   help='''will be added to the begininng of src examples'''     )
	parser.add_argument('''--fp16'''     ,   action='''store_true'''     )
	parser.add_argument('''--debug'''     ,   action='''store_true'''     )
	__snake_case	:						str						=				time.time()
	__snake_case	,							__snake_case	:						Any						=				parser.parse_known_args()
	__snake_case	:						List[Any]						=				parse_numeric_n_bool_cl_kwargs(_snake_case     )
	if generate_kwargs and args.local_rank <= 0:
		print(f"""parsed the following generate kwargs: {generate_kwargs}"""     )
	__snake_case	:						List[Any]						=				Path(args.save_dir + '''_tmp'''     )
	Path(_snake_case     ).mkdir(exist_ok=_snake_case     )  # this handles locking.
	__snake_case	:						Optional[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.
	__snake_case	:						Dict						=				{}
	if args.src_lang is not None:
		__snake_case	:						Dict						=				args.src_lang
	if args.tgt_lang is not None:
		__snake_case	:						Dict						=				args.tgt_lang

	Path(args.save_dir     ).mkdir(exist_ok=_snake_case     )
	__snake_case	,							__snake_case	:						List[Any]						=				eval_data_dir(
	    args.data_dir     ,   _snake_case     ,   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=_snake_case     ,   **_snake_case     ,   )

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

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






def 		lowercase							(     _snake_case						:      Union[str, Any]     )     ->List:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				[]
	for partial_result in partial_results:
		records.extend(_snake_case     )
	__snake_case	:						List[str]						=				sorted(_snake_case     ,   key=lambda _snake_case     : x["id"]     )
	__snake_case	:						Tuple						=				[x['''pred'''] for x in records]
	return preds






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      List[str]     ,   _snake_case						:      List[Any]     )     ->List[Dict[str, List]]:
	"""simple docstring"""





	__snake_case	:						List[str]						=				time.time()
	logger.info('''waiting for all nodes to finish'''     )
	__snake_case	:						List[str]						=				None
	while (time.time() - start_wait) < timeout:
		__snake_case	:						Any						=				list(save_dir.glob('''rank_*.json'''     )     )
		if len(_snake_case     ) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			__snake_case	:						Tuple						=				lmap(_snake_case     ,   _snake_case     )
			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()





