



import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


UpperCAmelCase :				Tuple							=logging.getLogger(__name__)


@dataclass
class    _lowercase :
				'''simple docstring'''







				lowercase__     =    field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
				lowercase__     =    field(
				    default=a_   , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
				lowercase__     =    field(
				    default="""NER"""   , metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}   )
				lowercase__     =    field(
				    default=a_   , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
				lowercase__     =    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.
				lowercase__     =    field(
				    default=a_   , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}   , )




@dataclass
class    _lowercase :
				'''simple docstring'''







				lowercase__     =    field(
				    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}   )
				lowercase__     =    field(
				    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}   )
				lowercase__     =    field(
				    default=1_28   , metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }   , )
				lowercase__     =    field(
				    default=a_   , metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )




def  _lowerCAmelCase     ():
					# 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.
					UpperCamelCase_			= HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
					UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= 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.")

					UpperCamelCase_			= import_module("tasks")

					try:
										UpperCamelCase_			= getattr(_lowerCAmelCase ,						model_args.task_type)
										UpperCamelCase_			= 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 ,						)
					logger.info(
					    "n_replicas: %s, distributed training: %s, 16-bits training: %s" ,						training_args.n_replicas ,						bool(training_args.n_replicas > 1) ,						training_args.fpaa ,						)
					logger.info("Training/evaluation parameters %s" ,						_lowerCAmelCase)

					# Prepare Token Classification task
					UpperCamelCase_			= token_classification_task.get_labels(data_args.labels)
					UpperCamelCase_			= dict(enumerate(_lowerCAmelCase))
					UpperCamelCase_			= len(_lowerCAmelCase)

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

					UpperCamelCase_			= AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,						num_labels=_lowerCAmelCase ,						idalabel=_lowerCAmelCase ,						labelaid={label: i for i, label in enumerate(_lowerCAmelCase)} ,						cache_dir=model_args.cache_dir ,						)
					UpperCamelCase_			= 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 ,						)

					with training_args.strategy.scope():
										UpperCamelCase_			= TFAutoModelForTokenClassification.from_pretrained(
										    model_args.model_name_or_path ,						from_pt=bool(".bin" in model_args.model_name_or_path) ,						config=_lowerCAmelCase ,						cache_dir=model_args.cache_dir ,						)

					# Get datasets
					UpperCamelCase_			= (
					    TFTokenClassificationDataset(
					        token_classification_task=_lowerCAmelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCAmelCase ,						labels=_lowerCAmelCase ,						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
					)
					UpperCamelCase_			= (
					    TFTokenClassificationDataset(
					        token_classification_task=_lowerCAmelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCAmelCase ,						labels=_lowerCAmelCase ,						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(_lowerCAmelCase ,						_lowerCAmelCase) -> Tuple[List[int], List[int]]:
										UpperCamelCase_			= np.argmax(_lowerCAmelCase ,						axis=2)
										UpperCamelCase_	,     UpperCamelCase_			= preds.shape
										UpperCamelCase_			= [[] for _ in range(_lowerCAmelCase)]
										UpperCamelCase_			= [[] for _ in range(_lowerCAmelCase)]

										for i in range(_lowerCAmelCase):
															for j in range(_lowerCAmelCase):
																				if label_ids[i, j] != -1_00:
																									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(_lowerCAmelCase) -> Dict:
										UpperCamelCase_	,     UpperCamelCase_			= align_predictions(p.predictions ,						p.label_ids)

										return {
										    "precision": precision_score(_lowerCAmelCase ,						_lowerCAmelCase),
										    "recall": recall_score(_lowerCAmelCase ,						_lowerCAmelCase),
										    "f1": fa_score(_lowerCAmelCase ,						_lowerCAmelCase),
										}

					# Initialize our Trainer
					UpperCamelCase_			= TFTrainer(
					    model=_lowerCAmelCase ,						args=_lowerCAmelCase ,						train_dataset=train_dataset.get_dataset() if train_dataset else None ,						eval_dataset=eval_dataset.get_dataset() if eval_dataset else None ,						compute_metrics=_lowerCAmelCase ,						)

					# Training
					if training_args.do_train:
										trainer.train()
										trainer.save_model()
										tokenizer.save_pretrained(training_args.output_dir)

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

										UpperCamelCase_			= trainer.evaluate()
										UpperCamelCase_			= os.path.join(training_args.output_dir ,						"eval_results.txt")

										with open(_lowerCAmelCase ,						"w") as writer:
															logger.info("***** Eval results *****")

															for key, value in result.items():
																				logger.info("  %s = %s" ,						_lowerCAmelCase ,						_lowerCAmelCase)
																				writer.write("%s = %s\n" % (key, value))

															results.update(_lowerCAmelCase)

    # Predict
					if training_args.do_predict:
										UpperCamelCase_			= TFTokenClassificationDataset(
										    token_classification_task=_lowerCAmelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCAmelCase ,						labels=_lowerCAmelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.test ,						)

										UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= trainer.predict(test_dataset.get_dataset())
										UpperCamelCase_	,     UpperCamelCase_			= align_predictions(_lowerCAmelCase ,						_lowerCAmelCase)
										UpperCamelCase_			= classification_report(_lowerCAmelCase ,						_lowerCAmelCase)

										logger.info("\n%s" ,						_lowerCAmelCase)

										UpperCamelCase_			= os.path.join(training_args.output_dir ,						"test_results.txt")

										with open(_lowerCAmelCase ,						"w") as writer:
															writer.write("%s\n" % report)

										# Save predictions
										UpperCamelCase_			= os.path.join(training_args.output_dir ,						"test_predictions.txt")

										with open(_lowerCAmelCase ,						"w") as writer:
															with open(os.path.join(data_args.data_dir ,						"test.txt") ,						"r") as f:
																				UpperCamelCase_			= 0

																				for line in f:
																									if line.startswith("-DOCSTART-") or line == "" or line == "\n":
																														writer.write(_lowerCAmelCase)

																														if not preds_list[example_id]:
																																			example_id += 1
																									elif preds_list[example_id]:
																														UpperCamelCase_			= line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"

																														writer.write(_lowerCAmelCase)
																									else:
																														logger.warning("Maximum sequence length exceeded: No prediction for '%s'." ,						line.split()[0])

					return results


if __name__ == "__main__":
	main()







"""simple docstring"""




import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

lowerCamelCase_	:		int         =   pathlib.Path.cwd().joinpath('project_euler')
lowerCamelCase_	:		str         =   pathlib.Path.cwd().joinpath(
    'scripts', 'project_euler_answers.json'
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
   lowerCamelCase_	:		dict[str, str]         =   json.load(file_handle)





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[str]									=    importlib.util.spec_from_file_location(file_path.name					,   str(_UpperCAmelCase   )   )
  A_    :			int									=    importlib.util.module_from_spec(_UpperCAmelCase   )  # type: ignore
  spec.loader.exec_module(_UpperCAmelCase   )  # type: ignore
  return module





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Tuple									=    []
  for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
    if problem_dir_path.is_file() or problem_dir_path.name.startswith('_'   ):
      continue
    for file_path in problem_dir_path.iterdir():
      if file_path.suffix != ".py" or file_path.name.startswith(('_', 'test')   ):
        continue
      solution_file_paths.append(_UpperCAmelCase   )
  return solution_file_paths





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  with open(os.environ['GITHUB_EVENT_PATH']   ) as file:
    A_    :			str									=    json.load(_UpperCAmelCase   )
  return event["pull_request"]["url"] + "/files"





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Any									=    []
  A_    :			Optional[Any]									=    {
      'Accept': 'application/vnd.github.v3+json',
      'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
  }
  A_    :			Union[str, Any]									=    requests.get(get_files_url()					,   headers=_UpperCAmelCase   ).json()
  for file in files:
    A_    :			Optional[int]									=    pathlib.Path.cwd().joinpath(file['filename']   )
    if (
        filepath.suffix != ".py"
        or filepath.name.startswith(('_', 'test')   )
        or not filepath.name.startswith('sol'   )
    ):
      continue
    solution_file_paths.append(_UpperCAmelCase   )
  return solution_file_paths





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  if os.environ.get('CI'   ) and os.environ.get('GITHUB_EVENT_NAME'   ) == "pull_request":
    # Return only if there are any, otherwise default to all solutions
    if filepaths := added_solution_file_path():
      return filepaths
  return all_solution_file_paths()





@pytest.mark.parametrize(
    'solution_path'					,   collect_solution_file_paths()					,   ids=lambda _UpperCAmelCase   : f"""{path.parent.name}/{path.name}"""					,   )
def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			str									=    solution_path.parent.name[8:].zfill(3   )
  A_    :			str									=    PROBLEM_ANSWERS[problem_number]
  A_    :			List[Any]									=    convert_path_to_module(_UpperCAmelCase   )
  A_    :			Optional[int]									=    str(solution_module.solution()   )  # type: ignore
  A_    :			Any									=    hashlib.shaaaa(answer.encode()   ).hexdigest()
  assert (
      answer == expected
  ), f"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""