
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


SCREAMING_SNAKE_CASE							:int							 =				getLogger(__name__)


SCREAMING_SNAKE_CASE							:Optional[int]							 =				'cuda' if torch.cuda.is_available() else 'cpu'


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_ = 8		,   a_ = DEFAULT_DEVICE		,   a_=False		,   a_="summarization"		,   a_=None		,   **a_		,   )		->	Optional[Any]:







				"""simple docstring"""



				__A   			= Path(a_						).open("w"		,   encoding="utf-8"						)
				__A   			= str(a_						)
				__A   			= AutoModelForSeqaSeqLM.from_pretrained(a_						).to(a_						)
				if fpaa:
								__A   			= model.half()

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

				__A   			= time.time()
				# update config with task specific params
				use_task_specific_params(a_		,   a_						)
				if prefix is None:
								__A   			= prefix or getattr(model.config		,   "prefix"		,   ""						) or ""
				for examples_chunk in tqdm(list(chunks(a_		,   a_						)						)						):
								__A   			= [prefix + text for text in examples_chunk]
								__A   			= tokenizer(a_		,   return_tensors="pt"		,   truncation=a_		,   padding="longest"						).to(a_						)
								__A   			= model.generate(
								    input_ids=batch.input_ids		,   attention_mask=batch.attention_mask		,   **a_		,   )
								__A   			= 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()
				__A   			= int(time.time() - start_time						)  # seconds
				__A   			= len(a_						)
				return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,   4						)}


def 						UpperCAmelCase			(	)		->	str:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_=True						)		->	str:







				"""simple docstring"""



				__A   			= 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
				__A   			= parser.parse_known_args()
				__A   			= parse_numeric_n_bool_cl_kwargs(a_						)
				if parsed_args and verbose:
								print(F'''parsed the following generate kwargs: {parsed_args}'''						)
				__A   			= [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
				if args.n_obs > 0:
								__A   			= 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.'''						)

				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"						)

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

				if args.dump_args:
								scores.update(a_						)
				if args.info:
								__A   			= 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)




"""simple docstring"""







from __future__ import annotations

import math


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							size
         # approximate the overall size of segment tree with given value
         lowerCAmelCase     :     Optional[int]								=							[0 for i in range(0		,     4 * size						)]
         # create array to store lazy update
         lowerCAmelCase     :     List[str]								=							[0 for i in range(0		,     4 * size						)]
         lowerCAmelCase     :     Dict								=							[0 for i in range(0		,     4 * size						)]  # flag for lazy update

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         return idx * 2

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         return idx * 2 + 1

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if left_element == right_element:
           lowerCAmelCase     :     List[str]								=							a[left_element - 1]
         else:
           lowerCAmelCase     :     Tuple								=							(left_element + right_element) // 2
           self.build(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__						)
           self.build(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__						)
           lowerCAmelCase     :     Tuple								=							max(
               self.segment_tree[self.left(snake_case__						)]		,     self.segment_tree[self.right(snake_case__						)]						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if self.flag[idx] is True:
           lowerCAmelCase     :     Optional[int]								=							self.lazy[idx]
           lowerCAmelCase     :     List[str]								=							False
           if left_element != right_element:
             lowerCAmelCase     :     Optional[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							True
             lowerCAmelCase     :     Optional[Any]								=							True

         if right_element < a or left_element > b:
           return True
         if left_element >= a and right_element <= b:
           lowerCAmelCase     :     str								=							val
           if left_element != right_element:
             lowerCAmelCase     :     Optional[Any]								=							val
             lowerCAmelCase     :     Union[str, Any]								=							val
             lowerCAmelCase     :     int								=							True
             lowerCAmelCase     :     int								=							True
           return True
         lowerCAmelCase     :     List[str]								=							(left_element + right_element) // 2
         self.update(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         self.update(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							max(
             self.segment_tree[self.left(snake_case__						)]		,     self.segment_tree[self.right(snake_case__						)]						)
         return True

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if self.flag[idx] is True:
           lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
           lowerCAmelCase     :     str								=							False
           if left_element != right_element:
             lowerCAmelCase     :     Tuple								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     Optional[int]								=							True
             lowerCAmelCase     :     str								=							True
         if right_element < a or left_element > b:
           return -math.inf
         if left_element >= a and right_element <= b:
           return self.segment_tree[idx]
         lowerCAmelCase     :     Any								=							(left_element + right_element) // 2
         lowerCAmelCase     :     Optional[int]								=							self.query(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         lowerCAmelCase     :     Dict								=							self.query(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__		,     snake_case__						)
         return max(snake_case__		,     snake_case__						)







       def __str__(						self						):






         """simple docstring"""





         return str([self.query(1		,     1		,     self.size		,     snake_case__		,     snake_case__						) for i in range(1		,     self.size + 1						)]						)


if __name__ == "__main__":
     lowerCAmelCase__      				=   [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
     lowerCAmelCase__      				=   15
     lowerCAmelCase__      				=   SegmentTree(size)
     segt.build(1, 1, size, A)
     print(segt.query(1, 1, size, 4, 6))
     print(segt.query(1, 1, size, 7, 11))
     print(segt.query(1, 1, size, 7, 12))
     segt.update(1, 1, size, 1, 3, 111)
     print(segt.query(1, 1, size, 1, 15))
     segt.update(1, 1, size, 7, 8, 235)
     print(segt)





