


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


__snake_case   							=						logging.getLogger(__name__)



@dataclass
class 	__lowerCamelCase			:
       _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 	__lowerCamelCase			:
       _lowercase											=     field(
           metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}						)
       _lowercase											=     field(
           default=_a				,      metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}				,      )
       _lowercase											=     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."""
               )
           }				,      )
       _lowercase											=     field(
           default=_a				,      metadata={"""help""": """Overwrite the cached training and evaluation sets"""}						)






def 		_A		(		)					-> str:


     """simple docstring"""


     __UpperCamelCase =			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.
          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
     else:
          __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(_lowercase					,  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 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'					,  _lowercase )

     # Set seed
     set_seed(training_args.seed )

     # Prepare CONLL-2003 task
     __UpperCamelCase =			token_classification_task.get_labels(data_args.labels )
     __UpperCamelCase =			dict(enumerate(_lowercase ) )
     __UpperCamelCase =			len(_lowercase )

     # 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=_lowercase					,  idalabel=_lowercase					,  labelaid={label: i for i, label in enumerate(_lowercase )}					,  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					,  )
     __UpperCamelCase =			AutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path					,  from_tf=bool('.ckpt' in model_args.model_name_or_path )					,  config=_lowercase					,  cache_dir=model_args.cache_dir					,  )

     # Get datasets
     __UpperCamelCase =			(
         TokenClassificationDataset(
             token_classification_task=_lowercase					,  data_dir=data_args.data_dir					,  tokenizer=_lowercase					,  labels=_lowercase					,  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 =			(
         TokenClassificationDataset(
             token_classification_task=_lowercase					,  data_dir=data_args.data_dir					,  tokenizer=_lowercase					,  labels=_lowercase					,  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(_lowercase					,  _lowercase ) -> Tuple[List[int], List[int]]:
          __UpperCamelCase =			np.argmax(_lowercase					,  axis=2 )

          __UpperCamelCase,    __UpperCamelCase =			preds.shape

          __UpperCamelCase =			[[] for _ in range(_lowercase )]
          __UpperCamelCase =			[[] for _ in range(_lowercase )]

          for i in range(_lowercase ):
               for j in range(_lowercase ):
                    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(_lowercase ) -> Dict:
          __UpperCamelCase,    __UpperCamelCase =			align_predictions(p.predictions					,  p.label_ids )
          return {
              "accuracy_score": accuracy_score(_lowercase					,  _lowercase ),
              "precision": precision_score(_lowercase					,  _lowercase ),
              "recall": recall_score(_lowercase					,  _lowercase ),
              "f1": fa_score(_lowercase					,  _lowercase ),
          }

     # Data collator
     __UpperCamelCase =			DataCollatorWithPadding(_lowercase					,  pad_to_multiple_of=8 ) if training_args.fpaa else None

     # Initialize our Trainer
     __UpperCamelCase =			Trainer(
         model=_lowercase					,  args=_lowercase					,  train_dataset=_lowercase					,  eval_dataset=_lowercase					,  compute_metrics=_lowercase					,  data_collator=_lowercase					,  )

     # 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
     __UpperCamelCase =			{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***' )

          __UpperCamelCase =			trainer.evaluate()

          __UpperCamelCase =			os.path.join(training_args.output_dir					,  'eval_results.txt' )
          if trainer.is_world_process_zero():
               with open(_lowercase					,  'w' ) as writer:
                    logger.info('***** Eval results *****' )
                    for key, value in result.items():
                         logger.info('  %s = %s'					,  _lowercase					,  _lowercase )
                         writer.write('%s = %s\n' % (key, value) )

               results.update(_lowercase )

    # Predict
     if training_args.do_predict:
          __UpperCamelCase =			TokenClassificationDataset(
              token_classification_task=_lowercase					,  data_dir=data_args.data_dir					,  tokenizer=_lowercase					,  labels=_lowercase					,  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(_lowercase )
          __UpperCamelCase,    __UpperCamelCase =			align_predictions(_lowercase					,  _lowercase )

          __UpperCamelCase =			os.path.join(training_args.output_dir					,  'test_results.txt' )
          if trainer.is_world_process_zero():
               with open(_lowercase					,  'w' ) as writer:
                    for key, value in metrics.items():
                         logger.info('  %s = %s'					,  _lowercase					,  _lowercase )
                         writer.write('%s = %s\n' % (key, value) )

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

     return results






def 		_A		(		_lowercase )					-> Dict:


     """simple docstring"""


     main()


if __name__ == "__main__":
    main()








'''simple docstring'''

import heapq
import sys

import numpy as np

a    :							Dict										=    tuple[int, int]






class 		a							:


				def __init__(						self       :   Dict   ):
									snake_case_		     =							[]
									snake_case_		     =							set()


				def     A_					(						self       :   int   ):
									if not self.empty():
														return self.elements[0][0]
									else:
														return float('''inf'''   )


				def     A_					(						self       :   List[str]   ):
									return len(self.elements   ) == 0


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   str			,							lowercase_       :   List[str]   ):
									if item not in self.set:
														heapq.heappush(self.elements			,							(priority, item)   )
														self.set.add(lowercase_   )
									else:
														# update
														# print("update", item)
														snake_case_		     =							[]
														((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														while x != item:
																			temp.append((pri, x)   )
																			((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														temp.append((priority, item)   )
														for pro, xxx in temp:
																			heapq.heappush(self.elements			,							(pro, xxx)   )


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   List[Any]   ):
									if item in self.set:
														self.set.remove(lowercase_   )
														snake_case_		     =							[]
														((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														while x != item:
																			temp.append((pro, x)   )
																			((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														for prito, yyy in temp:
																			heapq.heappush(self.elements			,							(prito, yyy)   )


				def     A_					(						self       :   Optional[int]   ):
									return self.elements[0][1]


				def     A_					(						self       :   Tuple   ):
									((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
									self.set.remove(lowercase_   )
									return (priority, item)







def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Any:

					'''simple docstring'''

					snake_case_		     =							np.array(__UpperCAmelCase      )
					snake_case_		     =							np.array(__UpperCAmelCase      )
					return np.linalg.norm(a - b      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					return consistent_heuristic(__UpperCAmelCase, __UpperCAmelCase      ) // t


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					return abs(p[0] - goal[0]      ) + abs(p[1] - goal[1]      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

					snake_case_		     =							g_function[start] + Wa * heuristics[i](__UpperCAmelCase, __UpperCAmelCase      )
					return ans


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							np.chararray((n, n)      )
					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															snake_case_		     =							'''*'''

					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															if (j, (n - 1) - i) in blocks:
																				snake_case_		     =							'''#'''

					snake_case_		     =							'''-'''
					snake_case_		     =							back_pointer[goal]
					while x != start:
										((snake_case_)     ,(snake_case_))		     =							x
										# print(x)
										snake_case_		     =							'''-'''
										snake_case_		     =							back_pointer[x]
					snake_case_		     =							'''-'''

					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															if (i, j) == (0, n - 1):
																				print(grid[i][j], end=''' '''      )
																				print('''<-- End position''', end=''' '''      )
															else:
																				print(grid[i][j], end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )
					print('''PATH TAKEN BY THE ALGORITHM IS:-'''      )
					snake_case_		     =							back_pointer[goal]
					while x != start:
										print(__UpperCAmelCase, end=''' '''      )
										snake_case_		     =							back_pointer[x]
					print(__UpperCAmelCase      )
					sys.exit()


def 						__magic_name__       (      __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					if p[0] < 0 or p[0] > n - 1:
										return False
					if p[1] < 0 or p[1] > n - 1:
										return False
					return True


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )						->   str:

					'''simple docstring'''

					for itera in range(__UpperCAmelCase      ):
										open_list[itera].remove_element(__UpperCAmelCase      )
					# print("s", s)
					# print("j", j)
					((snake_case_)     ,(snake_case_))		     =							s
					snake_case_		     =							(x - 1, y)
					snake_case_		     =							(x + 1, y)
					snake_case_		     =							(x, y + 1)
					snake_case_		     =							(x, y - 1)

					for neighbours in [left, right, up, down]:
										if neighbours not in blocks:
															if valid(__UpperCAmelCase      ) and neighbours not in visited:
																				# print("neighbour", neighbours)
																				visited.add(__UpperCAmelCase      )
																				snake_case_		     =							-1
																				snake_case_		     =							float('''inf'''      )

															if valid(__UpperCAmelCase      ) and g_function[neighbours] > g_function[s] + 1:
																				snake_case_		     =							g_function[s] + 1
																				snake_case_		     =							s
																				if neighbours not in close_list_anchor:
																									open_list[0].put(__UpperCAmelCase, key(__UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase      )      )
																									if neighbours not in close_list_inad:
																														for var in range(1, __UpperCAmelCase      ):
																																			if key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      ) <= Wa * key(
																																			    __UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase      ):
																																								open_list[j].put(
																																								    __UpperCAmelCase, key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )      )


def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					snake_case_		     =							[]
					for x in range(1, 5      ):
										for y in range(1, 6      ):
															some_list.append((x, y)      )

					for x in range(15, 20      ):
										some_list.append((x, 17)      )

					for x in range(10, 19      ):
										for y in range(1, 15      ):
															some_list.append((x, y)      )

    # L block
					for x in range(1, 4      ):
										for y in range(12, 19      ):
															some_list.append((x, y)      )
					for x in range(3, 13      ):
										for y in range(16, 19      ):
															some_list.append((x, y)      )
					return some_list


a    :							Tuple										=    {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a    :							Dict										=    [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
a    :							List[str]										=    make_common_ground()


a    :							Optional[int]										=    blocks_blk
# hyper parameters
a    :							Union[str, Any]										=    1
a    :							str										=    1
a    :							int										=    20
a    :							Optional[int]										=    3  # one consistent and two other inconsistent

# start and end destination
a    :							List[str]										=    (0, 0)
a    :							str										=    (n - 1, n - 1)

a    :							str										=    1


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					snake_case_		     =							{start: 0, goal: float('''inf'''      )}
					snake_case_		     =							{start: -1, goal: -1}
					snake_case_		     =							[]
					snake_case_		     =							set()

					for i in range(__UpperCAmelCase      ):
										open_list.append(PriorityQueue()      )
										open_list[i].put(__UpperCAmelCase, key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )      )

					snake_case_		     =							[]
					snake_case_		     =							[]
					while open_list[0].minkey() < float('''inf'''      ):
										for i in range(1, __UpperCAmelCase      ):
															# print(open_list[0].minkey(), open_list[i].minkey())
															if open_list[i].minkey() <= Wa * open_list[0].minkey():
																				global t
																				t += 1
																				if g_function[goal] <= open_list[i].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
																				else:
																									snake_case_     ,snake_case_		     =							open_list[i].top_show()
																									visited.add(__UpperCAmelCase      )
																									expand_state(
																									    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )
																									close_list_inad.append(__UpperCAmelCase      )
															else:
																				if g_function[goal] <= open_list[0].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
																				else:
																									snake_case_		     =							open_list[0].top_show()
																									visited.add(__UpperCAmelCase      )
																									expand_state(
																									    __UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )
																									close_list_anchor.append(__UpperCAmelCase      )
					print('''No path found to goal'''      )
					print()
					for i in range(n - 1, -1, -1      ):
										for j in range(__UpperCAmelCase      ):
															if (j, i) in blocks:
																				print('''#''', end=''' '''      )
															elif (j, i) in back_pointer:
																				if (j, i) == (n - 1, n - 1):
																									print('''*''', end=''' '''      )
																				else:
																									print('''-''', end=''' '''      )
															else:
																				print('''*''', end=''' '''      )
															if (j, i) == (n - 1, n - 1):
																				print('''<-- End position''', end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )


if __name__ == "__main__":
							multi_a_star(start, goal, n_heuristic)

