


'''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


_SCREAMING_SNAKE_CASE       :   int        = getLogger(__name__)


_SCREAMING_SNAKE_CASE       :   str        = "cuda" if torch.cuda.is_available() else "cpu"



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = 8     ,   UpperCamelCase__ = DEFAULT_DEVICE     ,   UpperCamelCase__=False     ,   UpperCamelCase__="summarization"     ,   UpperCamelCase__=None     ,   **UpperCamelCase__     ,   ):






				"""simple docstring"""



				__magic_name__				:					Tuple						       =       Path(snake_case_		).open("w"     ,   encoding="utf-8"		)
				__magic_name__				:					List[str]						       =       str(snake_case_		)
				__magic_name__				:					Tuple						       =       AutoModelForSeqaSeqLM.from_pretrained(snake_case_		).to(snake_case_		)
				if fpaa:
								__magic_name__				:					Union[str, Any]						       =       model.half()

				__magic_name__				:					List[str]						       =       AutoTokenizer.from_pretrained(snake_case_		)
				logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""		)  # if this is wrong, check config.model_type.

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



def 						_UpperCamelCase							( ):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__=True		):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       argparse.ArgumentParser()
				parser.add_argument("model_name"     ,   type=snake_case_     ,   help="like facebook/bart-large-cnn,t5-base, etc."		)
				parser.add_argument("input_path"     ,   type=snake_case_     ,   help="like cnn_dm/test.source"		)
				parser.add_argument("save_path"     ,   type=snake_case_     ,   help="where to save summaries"		)
				parser.add_argument("--reference_path"     ,   type=snake_case_     ,   required=snake_case_     ,   help="like cnn_dm/test.target"		)
				parser.add_argument("--score_path"     ,   type=snake_case_     ,   required=snake_case_     ,   default="metrics.json"     ,   help="where to save metrics"		)
				parser.add_argument("--device"     ,   type=snake_case_     ,   required=snake_case_     ,   default=snake_case_     ,   help="cuda, cuda:1, cpu etc."		)
				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("--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(
				    "--n_obs"     ,   type=snake_case_     ,   default=-1     ,   required=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=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
				__magic_name__				:					Tuple						       =       parser.parse_known_args()
				__magic_name__				:					List[str]						       =       parse_numeric_n_bool_cl_kwargs(snake_case_		)
				if parsed_args and verbose:
								print(F"""parsed the following generate kwargs: {parsed_args}"""		)
				with open(args.input_path		) as f:
								__magic_name__				:					Optional[int]						       =       [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
				if args.n_obs > 0:
								__magic_name__				:					Tuple						       =       examples[: args.n_obs]
				Path(args.save_path		).parent.mkdir(exist_ok=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."""		)
				__magic_name__				:					List[Any]						       =       generate_summaries_or_translations(
				    snake_case_     ,   args.save_path     ,   args.model_name     ,   batch_size=args.bs     ,   device=args.device     ,   fpaa=args.fpaa     ,   task=args.task     ,   prefix=args.prefix     ,   **snake_case_     ,   )

				if args.reference_path is None:
								return {}

				# Compute scores
				__magic_name__				:					List[Any]						       =       calculate_bleu if '''translation''' in args.task else calculate_rouge
				__magic_name__				:					Optional[int]						       =       [x.rstrip() for x in open(args.save_path		).readlines()]
				__magic_name__				:					Any						       =       [x.rstrip() for x in open(args.reference_path		).readlines()][: len(snake_case_		)]
				__magic_name__				:					dict						       =       score_fn(snake_case_     ,   snake_case_		)
				scores.update(snake_case_		)

				if args.dump_args:
								scores.update(snake_case_		)
				if args.info:
								__magic_name__				:					int						       =       args.info

				if verbose:
								print(snake_case_		)

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



'''simple docstring'''







import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 		_snake_case    (				snake_case_  ,  unittest.TestCase		):






						'''simple docstring'''







						__snake_case   =      GPTNeoXJapaneseTokenizer
						__snake_case   =      False
						__snake_case   =      {"do_clean_text": False, "add_prefix_space": False}







						def lowerCAmelCase__ (  self:	Any     )							->	List[str]:
										super().setUp()

										__magic_name__				:					Union[str, Any]						       =       [
										    "こん",
										    "こんに",
										    "にちは",
										    "ばんは",
										    "世界,㔺界",
										    "、",
										    "。",
										    "<BR>",
										    "<SP>",
										    "<TAB>",
										    "<URL>",
										    "<EMAIL>",
										    "<TEL>",
										    "<DATE>",
										    "<PRICE>",
										    "<BLOCK>",
										    "<KIGOU>",
										    "<U2000U2BFF>",
										    "<|emoji1|>",
										    "<unk>",
										    "<|startoftext|>",
										    "<|endoftext|>",
										]
										__magic_name__				:					List[str]						       =       {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
										__magic_name__				:					str						       =       {"unk_token": "<unk>"}

										__magic_name__				:					Dict						       =       os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES["vocab_file"]     )
										__magic_name__				:					Dict						       =       os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES["emoji_file"]     )
										with open(self.vocab_file      ,							"w"      ,							encoding="utf-8"     ) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in vocab_tokens]     )     )
										with open(self.emoji_file      ,							"w"     ) as emoji_writer:
														emoji_writer.write(json.dumps(__UpperCamelCase     )     )







						def lowerCAmelCase__ (  self:	Tuple      ,							**__UpperCamelCase:	str     )							->	Union[str, Any]:
										kwargs.update(self.special_tokens_map     )
										return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	List[str]      ,							__UpperCamelCase:	str     )							->	List[str]:
										__magic_name__				:					str						       =       "こんにちは、世界。 \nこんばんは、㔺界。😀"
										__magic_name__				:					str						       =       "こんにちは、世界。 \nこんばんは、世界。😀"
										return input_text, output_text







						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							__UpperCamelCase:	List[Any]     )							->	Dict:
										__magic_name__       ,     __magic_name__				:					Union[str, Any]						       =       self.get_input_output_texts(__UpperCamelCase     )
										__magic_name__				:					List[Any]						       =       tokenizer.encode(__UpperCamelCase      ,							add_special_tokens=__UpperCamelCase     )
										__magic_name__				:					Optional[Any]						       =       tokenizer.decode(__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )
										return text, ids







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Union[str, Any]:
										pass  # TODO add if relevant







						def lowerCAmelCase__ (  self:	str     )							->	List[str]:
										pass  # TODO add if relevant







						def lowerCAmelCase__ (  self:	str     )							->	str:
										pass  # TODO add if relevant







						def lowerCAmelCase__ (  self:	Union[str, Any]     )							->	Dict:
										__magic_name__				:					List[str]						       =       self.get_tokenizer()

										# Testing tokenization
										__magic_name__				:					Any						       =       "こんにちは、世界。　こんばんは、㔺界。"
										__magic_name__				:					Optional[int]						       =       ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
										__magic_name__				:					List[Any]						       =       tokenizer.tokenize(__UpperCamelCase     )
										self.assertListEqual(__UpperCamelCase      ,							__UpperCamelCase     )

										# Testing conversion to ids without special tokens
										__magic_name__				:					int						       =       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
										__magic_name__				:					str						       =       tokenizer.convert_tokens_to_ids(__UpperCamelCase     )
										self.assertListEqual(__UpperCamelCase      ,							__UpperCamelCase     )

										# Testing conversion to ids with special tokens
										__magic_name__				:					str						       =       tokens + [tokenizer.unk_token]
										__magic_name__				:					Tuple						       =       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
										__magic_name__				:					Dict						       =       tokenizer.convert_tokens_to_ids(__UpperCamelCase     )
										self.assertListEqual(__UpperCamelCase      ,							__UpperCamelCase     )







						@slow
						def lowerCAmelCase__ (  self:	List[Any]     )							->	Union[str, Any]:
										__magic_name__				:					List[str]						       =       self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"     )

										__magic_name__				:					List[Any]						       =       tokenizer.encode("ありがとう。"      ,							add_special_tokens=__UpperCamelCase     )
										__magic_name__				:					Optional[Any]						       =       tokenizer.encode("どういたしまして。"      ,							add_special_tokens=__UpperCamelCase     )

										__magic_name__				:					List[Any]						       =       tokenizer.build_inputs_with_special_tokens(__UpperCamelCase     )
										__magic_name__				:					str						       =       tokenizer.build_inputs_with_special_tokens(__UpperCamelCase      ,							__UpperCamelCase     )

										assert encoded_sentence == ids_a
										assert encoded_pair == ids_a + ids_a







						def lowerCAmelCase__ (  self:	Tuple     )							->	Dict:
										# Intentionally convert some words to accommodate character fluctuations unique to Japanese
										pass







						def lowerCAmelCase__ (  self:	Dict     )							->	Optional[int]:
										# tokenizer has no padding token
										pass