
"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase__      :    Union[str, Any]    							= logging.getLogger(__name__)



@dataclass
class 	lowerCAmelCase_						:


				"""simple docstring"""



				__UpperCamelCase      :						str					       =   field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  )
				__UpperCamelCase      :						Optional[str]					       =   field(
				    default=a__ ,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
				__UpperCamelCase      :						Optional[str]					       =   field(
				    default='''NER''' ,					metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}  )
				__UpperCamelCase      :						Optional[str]					       =   field(
				    default=a__ ,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
				__UpperCamelCase      :						bool					       =   field(default=a__ ,					metadata={'''help''': '''Set this flag to use fast tokenization.'''}  )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				__UpperCamelCase      :						Optional[str]					       =   field(
				    default=a__ ,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,					)






@dataclass
class 	lowerCAmelCase_						:


				"""simple docstring"""



				__UpperCamelCase      :						str					       =   field(
				    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}  )
				__UpperCamelCase      :						Optional[str]					       =   field(
				    default=a__ ,					metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''} ,					)
				__UpperCamelCase      :						int					       =   field(
				    default=128 ,					metadata={
				        '''help''': (
				            '''The maximum total input sequence length after tokenization. Sequences longer '''
				            '''than this will be truncated, sequences shorter will be padded.'''
				        )
				    } ,					)
				__UpperCamelCase      :						bool					       =   field(
				    default=a__ ,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}  )






def lowercase_  (   ):
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
							if len(sys.argv						) == 2 and sys.argv[1].endswith(""".json"""						):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
							else:
														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir						)
							    and os.listdir(training_args.output_dir						)
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
														    """ --overwrite_output_dir to overcome."""						)

							SCREAMING_SNAKE_CASE__						:       Any           =   import_module("""tasks"""						)
							try:
														SCREAMING_SNAKE_CASE__						:       int           =   getattr(_snake_case   ,model_args.task_type						)
														SCREAMING_SNAKE_CASE__						:       TokenClassificationTask           =   token_classification_task_clazz()
							except AttributeError:
														raise ValueError(
														    f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
														    f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''						)

							# Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""   ,datefmt="""%m/%d/%Y %H:%M:%S"""   ,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,)
							logger.warning(
							    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""   ,training_args.local_rank   ,training_args.device   ,training_args.n_gpu   ,bool(training_args.local_rank != -1						)   ,training_args.fpaa   ,)
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank						):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info("""Training/evaluation parameters %s"""   ,_snake_case						)

							# Set seed
							set_seed(training_args.seed						)

							# Prepare CONLL-2003 task
							SCREAMING_SNAKE_CASE__						:       Dict           =   token_classification_task.get_labels(data_args.labels						)
							SCREAMING_SNAKE_CASE__						:       Dict[int, str]           =   dict(enumerate(_snake_case						)						)
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   len(_snake_case						)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,num_labels=_snake_case   ,idalabel=_snake_case   ,labelaid={label: i for i, label in enumerate(_snake_case						)}   ,cache_dir=model_args.cache_dir   ,)
							SCREAMING_SNAKE_CASE__						:       str           =   AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,cache_dir=model_args.cache_dir   ,use_fast=model_args.use_fast   ,)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   AutoModelForTokenClassification.from_pretrained(
							    model_args.model_name_or_path   ,from_tf=bool(""".ckpt""" in model_args.model_name_or_path						)   ,config=_snake_case   ,cache_dir=model_args.cache_dir   ,)

							# Get datasets
							SCREAMING_SNAKE_CASE__						:       Tuple           =   (
							    TokenClassificationDataset(
							        token_classification_task=_snake_case   ,data_dir=data_args.data_dir   ,tokenizer=_snake_case   ,labels=_snake_case   ,model_type=config.model_type   ,max_seq_length=data_args.max_seq_length   ,overwrite_cache=data_args.overwrite_cache   ,mode=Split.train   ,)
							    if training_args.do_train
							    else None
							)
							SCREAMING_SNAKE_CASE__						:       Any           =   (
							    TokenClassificationDataset(
							        token_classification_task=_snake_case   ,data_dir=data_args.data_dir   ,tokenizer=_snake_case   ,labels=_snake_case   ,model_type=config.model_type   ,max_seq_length=data_args.max_seq_length   ,overwrite_cache=data_args.overwrite_cache   ,mode=Split.dev   ,)
							    if training_args.do_eval
							    else None
							)

							def align_predictions(_snake_case   ,_snake_case						) -> Tuple[List[int], List[int]]:
														SCREAMING_SNAKE_CASE__						:       Dict           =   np.argmax(_snake_case   ,axis=2						)

														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Any           =   preds.shape

														SCREAMING_SNAKE_CASE__						:       int           =   [[] for _ in range(_snake_case						)]
														SCREAMING_SNAKE_CASE__						:       List[str]           =   [[] for _ in range(_snake_case						)]

														for i in range(_snake_case						):
																					for j in range(_snake_case						):
																												if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																																			out_label_list[i].append(label_map[label_ids[i][j]]						)
																																			preds_list[i].append(label_map[preds[i][j]]						)

														return preds_list, out_label_list

							def compute_metrics(_snake_case						) -> Dict:
														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   align_predictions(p.predictions   ,p.label_ids						)
														return {
														    "accuracy_score": accuracy_score(_snake_case   ,_snake_case						),
														    "precision": precision_score(_snake_case   ,_snake_case						),
														    "recall": recall_score(_snake_case   ,_snake_case						),
														    "f1": fa_score(_snake_case   ,_snake_case						),
														}

							# Data collator
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   DataCollatorWithPadding(_snake_case   ,pad_to_multiple_of=8						) if training_args.fpaa else None

							# Initialize our Trainer
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   Trainer(
							    model=_snake_case   ,args=_snake_case   ,train_dataset=_snake_case   ,eval_dataset=_snake_case   ,compute_metrics=_snake_case   ,data_collator=_snake_case   ,)

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_process_zero():
																					tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
							SCREAMING_SNAKE_CASE__						:       Tuple           =   {}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""						)

														SCREAMING_SNAKE_CASE__						:       Dict           =   trainer.evaluate()

														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   os.path.join(training_args.output_dir   ,"""eval_results.txt"""						)
														if trainer.is_world_process_zero():
																					with open(_snake_case   ,"""w"""						) as writer:
																												logger.info("""***** Eval results *****"""						)
																												for key, value in result.items():
																																			logger.info("""  %s = %s"""   ,_snake_case   ,_snake_case						)
																																			writer.write("""%s = %s\n""" % (key, value)						)

																					results.update(_snake_case						)

    # Predict
							if training_args.do_predict:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   TokenClassificationDataset(
														    token_classification_task=_snake_case   ,data_dir=data_args.data_dir   ,tokenizer=_snake_case   ,labels=_snake_case   ,model_type=config.model_type   ,max_seq_length=data_args.max_seq_length   ,overwrite_cache=data_args.overwrite_cache   ,mode=Split.test   ,)

														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Dict           =   trainer.predict(_snake_case						)
														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[int]           =   align_predictions(_snake_case   ,_snake_case						)

														SCREAMING_SNAKE_CASE__						:       List[Any]           =   os.path.join(training_args.output_dir   ,"""test_results.txt"""						)
														if trainer.is_world_process_zero():
																					with open(_snake_case   ,"""w"""						) as writer:
																												for key, value in metrics.items():
																																			logger.info("""  %s = %s"""   ,_snake_case   ,_snake_case						)
																																			writer.write("""%s = %s\n""" % (key, value)						)

        # Save predictions
														SCREAMING_SNAKE_CASE__						:       Dict           =   os.path.join(training_args.output_dir   ,"""test_predictions.txt"""						)
														if trainer.is_world_process_zero():
																					with open(_snake_case   ,"""w"""						) as writer:
																												with open(os.path.join(data_args.data_dir   ,"""test.txt"""						)   ,"""r"""						) as f:
																																			token_classification_task.write_predictions_to_file(_snake_case   ,_snake_case   ,_snake_case						)

							return results

def lowercase_  (   _snake_case						):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
					main()






"""simple docstring"""




import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase__      :    List[Any]    							= logging.getLogger()

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """\n""".join(_snake_case						)
							Path(_snake_case						).open("""w"""						).writelines(_snake_case						)


UpperCAmelCase__      :    Union[str, Any]    							= 'patrickvonplaten/t5-tiny-random'
UpperCAmelCase__      :    Optional[int]    							= 'sshleifer/bart-tiny-random'
UpperCAmelCase__      :    Dict    							= 'sshleifer/tiny-mbart'

UpperCAmelCase__      :    int    							= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   Path(self.get_auto_remove_tmp_dir()    ) / """utest_input.source"""
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   input_file_name.parent / """utest_output.txt"""
											assert not output_file_name.exists()
											SCREAMING_SNAKE_CASE__						:       str           =   [""" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."""]
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Any           =   str(Path(self.get_auto_remove_tmp_dir()    ) / """scores.json"""    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """translation_en_to_de""" if model == T5_TINY else """summarization"""
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   F'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

											with patch.object(SCREAMING_SNAKE_CASE__   ,       """argv"""   ,       SCREAMING_SNAKE_CASE__    ):
																		run_generate()
																		assert Path(SCREAMING_SNAKE_CASE__    ).exists()
																		# os.remove(Path(output_file_name))



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



				@parameterized.expand([BART_TINY, MBART_TINY]    )
				@slow
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



				@parameterized.expand([T5_TINY, MBART_TINY]    )
				@slow
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   Path(self.get_auto_remove_tmp_dir()    ) / """utest_input.source"""
											SCREAMING_SNAKE_CASE__						:       int           =   input_file_name.parent / """utest_output.txt"""
											assert not output_file_name.exists()

											SCREAMING_SNAKE_CASE__						:       Any           =   {
											    """en""": ["""Machine learning is great, isn't it?""", """I like to eat bananas""", """Tomorrow is another great day!"""],
											    """de""": [
											        """Maschinelles Lernen ist großartig, oder?""",
											        """Ich esse gerne Bananen""",
											        """Morgen ist wieder ein toller Tag!""",
											    ],
											}

											SCREAMING_SNAKE_CASE__						:       List[str]           =   Path(self.get_auto_remove_tmp_dir()    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   str(tmp_dir / """scores.json"""    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   str(tmp_dir / """val.target"""    )
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       text["""en"""]    )
											_dump_articles(SCREAMING_SNAKE_CASE__   ,       text["""de"""]    )
											SCREAMING_SNAKE_CASE__						:       str           =   """translation_en_to_de""" if model == T5_TINY else """summarization"""
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   F'''
            run_eval_search.py
            {model}
            {str(SCREAMING_SNAKE_CASE__    )}
            {str(SCREAMING_SNAKE_CASE__    )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
											testargs.extend(["""--search""", """num_beams=1:2 length_penalty=0.9:1.0"""]    )

											with patch.object(SCREAMING_SNAKE_CASE__   ,       """argv"""   ,       SCREAMING_SNAKE_CASE__    ):
																		with CaptureStdout() as cs:
																									run_search()
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [""" num_beams | length_penalty""", model, """Best score args"""]
																		SCREAMING_SNAKE_CASE__						:       Any           =   ["""Info"""]
																		if "translation" in task:
																									expected_strings.append("""bleu"""    )
																		else:
																									expected_strings.extend(SCREAMING_SNAKE_CASE__    )
																		for w in expected_strings:
																									assert w in cs.out
																		for w in un_expected_strings:
																									assert w not in cs.out
																		assert Path(SCREAMING_SNAKE_CASE__    ).exists()
																		os.remove(Path(SCREAMING_SNAKE_CASE__    )    )




