

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


_a:      List[Any]								=     getLogger(__name__)


_a:      Dict								=     """cuda""" if torch.cuda.is_available() else """cpu"""



def  __lowerCAmelCase (  A    ,       A    ,       A    ,       A = 8    ,       A = DEFAULT_DEVICE    ,       A=False    ,       A="summarization"    ,       A=None    ,       **A    ,       ):
	UpperCAmelCase_    			=     Path(A      ).open("w"    ,       encoding="utf-8"      )
	UpperCAmelCase_    			=     str(A      )
	UpperCAmelCase_    			=     AutoModelForSeqaSeqLM.from_pretrained(A      ).to(A      )
	if fpaa:
		UpperCAmelCase_    			=     model.half()

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

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



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



def  __lowerCAmelCase (  A=True      ):
	UpperCAmelCase_    			=     argparse.ArgumentParser()
	parser.add_argument("model_name"    ,       type=A    ,       help="like facebook/bart-large-cnn,t5-base, etc."      )
	parser.add_argument("input_path"    ,       type=A    ,       help="like cnn_dm/test.source"      )
	parser.add_argument("save_path"    ,       type=A    ,       help="where to save summaries"      )
	parser.add_argument("--reference_path"    ,       type=A    ,       required=A    ,       help="like cnn_dm/test.target"      )
	parser.add_argument("--score_path"    ,       type=A    ,       required=A    ,       default="metrics.json"    ,       help="where to save metrics"      )
	parser.add_argument("--device"    ,       type=A    ,       required=A    ,       default=A    ,       help="cuda, cuda:1, cpu etc."      )
	parser.add_argument(
	    "--prefix"    ,       type=A    ,       required=A    ,       default=A    ,       help="will be added to the begininng of src examples"      )
	parser.add_argument("--task"    ,       type=A    ,       default="summarization"    ,       help="used for task_specific_params + metrics"      )
	parser.add_argument("--bs"    ,       type=A    ,       default=8    ,       required=A    ,       help="batch size"      )
	parser.add_argument(
	    "--n_obs"    ,       type=A    ,       default=-1    ,       required=A    ,       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=A    ,       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
	UpperCAmelCase_			,				UpperCAmelCase_    			=     parser.parse_known_args()
	UpperCAmelCase_    			=     parse_numeric_n_bool_cl_kwargs(A      )
	if parsed_args and verbose:
		print(F"parsed the following generate kwargs: {parsed_args}"      )
	with open(args.input_path      ) as f:
		UpperCAmelCase_    			=     [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
	if args.n_obs > 0:
		UpperCAmelCase_    			=     examples[: args.n_obs]
	Path(args.save_path      ).parent.mkdir(exist_ok=A      )
	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."      )
	UpperCAmelCase_    			=     generate_summaries_or_translations(
	    A    ,       args.save_path    ,       args.model_name    ,       batch_size=args.bs    ,       device=args.device    ,       fpaa=args.fpaa    ,       task=args.task    ,       prefix=args.prefix    ,       **A    ,       )

	if args.reference_path is None:
		return {}

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

	if args.dump_args:
		scores.update(A      )
	if args.info:
		UpperCAmelCase_    			=     args.info

	if verbose:
		print(A      )

	if args.score_path is not None:
		json.dump(A    ,       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)


# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint

class   __UpperCamelCase      :




	def __init__(						self		:  Optional[Any]						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     key
		UpperCAmelCase_    			=     freq




	def __str__(						self		:  List[str]							):


		'''simple docstring'''







		return F"Node(key={self.key}, freq={self.freq})"



def  __lowerCAmelCase (  A    ,       A    ,       A    ,       A    ,       A    ,       A      ):
	if i > j or i < 0 or j > len(A      ) - 1:
		return

	UpperCAmelCase_    			=     root[i][j]
	if parent == -1:  # root does not have a parent
		print(F"{key[node]} is the root of the binary search tree."      )
	elif is_left:
		print(F"{key[node]} is the left child of key {parent}."      )
	else:
		print(F"{key[node]} is the right child of key {parent}."      )

	print_binary_search_tree(A    ,       A    ,       A    ,       node - 1    ,       key[node]    ,       A      )
	print_binary_search_tree(A    ,       A    ,       node + 1    ,       A    ,       key[node]    ,       A      )



def  __lowerCAmelCase (  A      ):
	nodes.sort(key=lambda A      : node.key      )

	UpperCAmelCase_    			=     len(A      )

	UpperCAmelCase_    			=     [nodes[i].key for i in range(A      )]
	UpperCAmelCase_    			=     [nodes[i].freq for i in range(A      )]

	# This 2D array stores the overall tree cost (which's as minimized as possible);
	# for a single key, cost is equal to frequency of the key.
	UpperCAmelCase_    			=     [[freqs[i] if i == j else 0 for j in range(A      )] for i in range(A      )]
	# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
	# array
	UpperCAmelCase_    			=     [[freqs[i] if i == j else 0 for j in range(A      )] for i in range(A      )]
	# stores tree roots that will be used later for constructing binary search tree
	UpperCAmelCase_    			=     [[i if i == j else 0 for j in range(A      )] for i in range(A      )]

	for interval_length in range(2    ,       n + 1      ):
		for i in range(n - interval_length + 1      ):
			UpperCAmelCase_    			=     i + interval_length - 1

			UpperCAmelCase_    			=     sys.maxsize  # set the value to "infinity"
			UpperCAmelCase_    			=     total[i][j - 1] + freqs[j]

			# Apply Knuth's optimization
			# Loop without optimization: for r in range(i, j + 1):
			for r in range(root[i][j - 1]    ,       root[i + 1][j] + 1      ):  # r is a temporal root
				UpperCAmelCase_    			=     dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
				UpperCAmelCase_    			=     dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
				UpperCAmelCase_    			=     left + total[i][j] + right

				if dp[i][j] > cost:
					UpperCAmelCase_    			=     cost
					UpperCAmelCase_    			=     r

	print("Binary search tree nodes:"      )
	for node in nodes:
		print(A      )

	print(F"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."      )
	print_binary_search_tree(A    ,       A    ,       0    ,       n - 1    ,       -1    ,       A      )



def  __lowerCAmelCase (  ):
	# A sample binary search tree
	UpperCAmelCase_    			=     [Node(A    ,       randint(1    ,       50      )      ) for i in range(10    ,       0    ,       -1      )]
	find_optimal_binary_search_tree(A      )


if __name__ == "__main__":
				main()