'''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


UpperCamelCase_      	=	getLogger(__name__)


UpperCamelCase_      	=	'''cuda''' if torch.cuda.is_available() else '''cpu'''


def    lowerCamelCase       (			UpperCAmelCase__				:  List[str]				,						UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  int = 8				,						UpperCAmelCase__				:  str = DEFAULT_DEVICE				,						UpperCAmelCase__				:  List[str]=False				,						UpperCAmelCase__				:  Any="summarization"				,						UpperCAmelCase__				:  Dict=None				,						**UpperCAmelCase__				:  Optional[Any]				,						)			->	Dict:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Any						    = Path(UpperCAmelCase__		).open('w'				,						encoding='utf-8'		)
							SCREAMING_SNAKE_CASE__     :str						    = str(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase__		).to(UpperCAmelCase__		)
							if fpaa:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = model.half()

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = AutoTokenizer.from_pretrained(UpperCAmelCase__		)
							logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''		)  # if this is wrong, check config.model_type.

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


def    lowerCamelCase       (			)			->	List[str]:


							'''simple docstring'''






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


def    lowerCamelCase       (			UpperCAmelCase__				:  Union[str, Any]=True		)			->	Any:


							'''simple docstring'''






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

							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'		)

							SCREAMING_SNAKE_CASE__     :Dict						    = generate_summaries_or_translations(
							    UpperCAmelCase__				,						args.save_path				,						args.model_name				,						batch_size=args.bs				,						device=args.device				,						fpaa=args.fpaa				,						task=args.task				,						prefix=args.prefix				,						**UpperCAmelCase__				,						)

							if args.reference_path is None:
														return {}

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

							if args.dump_args:
														scores.update(UpperCAmelCase__		)
							if args.info:
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = args.info

							if verbose:
														print(UpperCAmelCase__		)

							if args.score_path is not None:
														json.dump(UpperCAmelCase__				,						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 unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase_      	=	get_tests_dir('''fixtures/test_sentencepiece.model''')
UpperCamelCase_      	=	get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

UpperCamelCase_      	=	'''pt''' if is_torch_available() else '''tf'''




@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Tuple     		= CamembertTokenizer
						A_			:							Dict     		= CamembertTokenizerFast
						A_			:							Any     		= True
						A_			:							List[Any]     		= True




						def      __lowerCamelCase       (    self :       int      )     ->				Tuple:
													super().setUp()

													# We have a SentencePiece fixture for testing
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = CamembertTokenizer(UpperCamelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )




						def      __lowerCamelCase       (    self :       int      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = '<pad>'
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 1

													self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_      )							,							UpperCamelCase_      )
													self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_      )							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       int      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Any						    = list(self.get_tokenizer().get_vocab().keys()      )

													self.assertEqual(vocab_keys[0]							,							'<s>NOTUSED'      )
													self.assertEqual(vocab_keys[1]							,							'<pad>'      )
													self.assertEqual(vocab_keys[-1]							,							'<mask>'      )
													self.assertEqual(len(UpperCamelCase_      )							,							10_04      )




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[str]:
													self.assertEqual(self.get_tokenizer().vocab_size							,							10_05      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Any						    = CamembertTokenizer(UpperCamelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )
													SCREAMING_SNAKE_CASE__     :Dict						    = CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 'I was born in 92000, and this is falsé.'

													SCREAMING_SNAKE_CASE__     :List[str]						    = tokenizer.encode(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = rust_tokenizer.encode(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :int						    = tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = rust_tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													# <unk> tokens are not the same for `rust` than for `slow`.
													# Because spm gives back raw token instead of `unk` in EncodeAsPieces
													# tokens = tokenizer.tokenize(sequence)
													SCREAMING_SNAKE_CASE__     :int						    = tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = rust_tokenizer.tokenize(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				List[str]:
													if not self.test_rust_tokenizer:
																				return

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.get_tokenizer()
													SCREAMING_SNAKE_CASE__     :Dict						    = self.get_rust_tokenizer()

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'I was born in 92000, and this is falsé.'

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer.tokenize(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = rust_tokenizer.tokenize(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = rust_tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :str						    = self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tokenizer.encode(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rust_tokenizer.encode(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )




						@slow
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Any:
													# fmt: off
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
													# fmt: on

													# camembert is a french model. So we also use french texts.
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [
													    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
													    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
													    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
													    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
													    'telles que la traduction et la synthèse de texte.',
													]

													self.tokenizer_integration_test_util(
													    expected_encoding=UpperCamelCase_							,							model_name='camembert-base'							,							revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'							,							sequences=UpperCamelCase_							,							)






