






import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__UpperCamelCase      :      Optional[int]     						=						logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if "token" in model_name_or_path:
		return "rag_token"
	if "sequence" in model_name_or_path:
		return "rag_sequence"
	if "bart" in model_name_or_path:
		return "bart"
	return None





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return max(metric_fn(lowerCamelCase				,  lowerCamelCase					) for gt in ground_truths					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  [line.strip() for line in open(lowerCamelCase				,  """r"""					).readlines()]
	__lowercase             =  []

	if args.gold_data_mode == "qa":
		__lowercase             =  pd.read_csv(lowerCamelCase				,  sep="""\t"""				,  header=lowerCamelCase					)
		for answer_list in data[1]:
			__lowercase             =  ast.literal_eval(lowerCamelCase					)
			answers.append(lowerCamelCase					)
	else:
		__lowercase             =  [line.strip() for line in open(lowerCamelCase				,  """r"""					).readlines()]
		__lowercase             =  [[reference] for reference in references]

	__lowercase             =  __lowercase             =  __lowercase             =  0
	for prediction, ground_truths in zip(lowerCamelCase				,  lowerCamelCase					):
		total += 1
		em += metric_max_over_ground_truths(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
		fa += metric_max_over_ground_truths(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)

	__lowercase             =  100.0 * em / total
	__lowercase             =  100.0 * fa / total

	logger.info(F'F1: {fa:.2f}'					)
	logger.info(F'EM: {em:.2f}'					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  args.k
	__lowercase             =  [line.strip() for line in open(lowerCamelCase				,  """r"""					).readlines()]
	__lowercase             =  [line.strip() for line in open(lowerCamelCase				,  """r"""					).readlines()]

	__lowercase             =  __lowercase             =  0
	for hypo, reference in zip(lowerCamelCase				,  lowerCamelCase					):
		__lowercase             =  set(hypo.split("""\t"""					)[:k]					)
		__lowercase             =  set(reference.split("""\t"""					)					)
		total += 1
		em += len(hypo_provenance & ref_provenance					) / k

	__lowercase             =  100.0 * em / total
	logger.info(F'Precision@{k}: {em: .2f}'					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	def strip_title(lowerCamelCase					):
		if title.startswith("""\""""					):
			__lowercase             =  title[1:]
		if title.endswith("""\""""					):
			__lowercase             =  title[:-1]
		return title

	__lowercase             =  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
	    lowerCamelCase				,  return_tensors="""pt"""				,  padding=lowerCamelCase				,  truncation=lowerCamelCase				,  )["""input_ids"""].to(args.device					)

	__lowercase             =  rag_model.rag.question_encoder(lowerCamelCase					)
	__lowercase             =  question_enc_outputs[0]

	__lowercase             =  rag_model.retriever(
	    lowerCamelCase				,  question_enc_pool_output.cpu().detach().to(torch.floataa					).numpy()				,  prefix=rag_model.rag.generator.config.prefix				,  n_docs=rag_model.config.n_docs				,  return_tensors="""pt"""				,  )
	__lowercase             =  rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
	__lowercase             =  []
	for docs in all_docs:
		__lowercase             =  [strip_title(lowerCamelCase					) for title in docs["""title"""]]
		provenance_strings.append("""\t""".join(lowerCamelCase					)					)
	return provenance_strings





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	with torch.no_grad():
		__lowercase             =  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    lowerCamelCase				,  return_tensors="""pt"""				,  padding=lowerCamelCase				,  truncation=lowerCamelCase					)

		__lowercase             =  inputs_dict.input_ids.to(args.device					)
		__lowercase             =  inputs_dict.attention_mask.to(args.device					)
		__lowercase             =  rag_model.generate(  # rag_model overwrites generate
		    lowerCamelCase				,  attention_mask=lowerCamelCase				,  num_beams=args.num_beams				,  min_length=args.min_length				,  max_length=args.max_length				,  early_stopping=lowerCamelCase				,  num_return_sequences=1				,  bad_words_ids=[[0, 0]]				,  )
		__lowercase             =  rag_model.retriever.generator_tokenizer.batch_decode(lowerCamelCase				,  skip_special_tokens=lowerCamelCase					)

		if args.print_predictions:
			for q, a in zip(lowerCamelCase				,  lowerCamelCase					):
				logger.info("""Q: {} - A: {}""".format(lowerCamelCase				,  lowerCamelCase					)					)

		return answers





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  argparse.ArgumentParser()
	parser.add_argument(
	    """--model_type"""				,  choices=["""rag_sequence""", """rag_token""", """bart"""]				,  type=lowerCamelCase				,  help=(
	        """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
	        """ model_name_or_path"""
	    )				,  )
	parser.add_argument(
	    """--index_name"""				,  default=lowerCamelCase				,  choices=["""exact""", """compressed""", """legacy"""]				,  type=lowerCamelCase				,  help="""RAG model retriever type"""				,  )
	parser.add_argument(
	    """--index_path"""				,  default=lowerCamelCase				,  type=lowerCamelCase				,  help="""Path to the retrieval index"""				,  )
	parser.add_argument("""--n_docs"""				,  default=5				,  type=lowerCamelCase				,  help="""Number of retrieved docs"""					)
	parser.add_argument(
	    """--model_name_or_path"""				,  default=lowerCamelCase				,  type=lowerCamelCase				,  required=lowerCamelCase				,  help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""				,  )
	parser.add_argument(
	    """--eval_mode"""				,  choices=["""e2e""", """retrieval"""]				,  default="""e2e"""				,  type=lowerCamelCase				,  help=(
	        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
	        """ precision@k."""
	    )				,  )
	parser.add_argument("""--k"""				,  default=1				,  type=lowerCamelCase				,  help="""k for the precision@k calculation"""					)
	parser.add_argument(
	    """--evaluation_set"""				,  default=lowerCamelCase				,  type=lowerCamelCase				,  required=lowerCamelCase				,  help="""Path to a file containing evaluation samples"""				,  )
	parser.add_argument(
	    """--gold_data_path"""				,  default=lowerCamelCase				,  type=lowerCamelCase				,  required=lowerCamelCase				,  help="""Path to a tab-separated file with gold samples"""				,  )
	parser.add_argument(
	    """--gold_data_mode"""				,  default="""qa"""				,  type=lowerCamelCase				,  choices=["""qa""", """ans"""]				,  help=(
	        """Format of the gold data file"""
	        """qa - a single line in the following format: question [tab] answer_list"""
	        """ans - a single line of the gold file contains the expected answer string"""
	    )				,  )
	parser.add_argument(
	    """--predictions_path"""				,  type=lowerCamelCase				,  default="""predictions.txt"""				,  help="""Name of the predictions file, to be stored in the checkpoints directory"""				,  )
	parser.add_argument(
	    """--eval_all_checkpoints"""				,  action="""store_true"""				,  help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"""				,  )
	parser.add_argument(
	    """--eval_batch_size"""				,  default=8				,  type=lowerCamelCase				,  help="""Batch size per GPU/CPU for evaluation."""				,  )
	parser.add_argument(
	    """--recalculate"""				,  help="""Recalculate predictions even if the prediction file exists"""				,  action="""store_true"""				,  )
	parser.add_argument(
	    """--num_beams"""				,  default=4				,  type=lowerCamelCase				,  help="""Number of beams to be used when generating answers"""				,  )
	parser.add_argument("""--min_length"""				,  default=1				,  type=lowerCamelCase				,  help="""Min length of the generated answers"""					)
	parser.add_argument("""--max_length"""				,  default=50				,  type=lowerCamelCase				,  help="""Max length of the generated answers"""					)

	parser.add_argument(
	    """--print_predictions"""				,  action="""store_true"""				,  help="""If True, prints predictions while evaluating."""				,  )
	parser.add_argument(
	    """--print_docs"""				,  action="""store_true"""				,  help="""If True, prints docs retried while generating."""				,  )
	__lowercase             =  parser.parse_args()
	__lowercase             =  torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""					)
	return args





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  {}
	if args.model_type is None:
		__lowercase             =  infer_model_type(args.model_name_or_path					)
		assert args.model_type is not None
	if args.model_type.startswith("""rag"""					):
		__lowercase             =  RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
		__lowercase             =  args.n_docs
		if args.index_name is not None:
			__lowercase             =  args.index_name
		if args.index_path is not None:
			__lowercase             =  args.index_path
	else:
		__lowercase             =  BartForConditionalGeneration

	__lowercase             =  (
	    [f.path for f in os.scandir(args.model_name_or_path					) if f.is_dir()]
	    if args.eval_all_checkpoints
	    else [args.model_name_or_path]
	)

	logger.info("""Evaluate the following checkpoints: %s"""				,  lowerCamelCase					)

	__lowercase             =  get_scores if args.eval_mode == """e2e""" else get_precision_at_k
	__lowercase             =  evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval

	for checkpoint in checkpoints:
		if os.path.exists(args.predictions_path					) and (not args.recalculate):
			logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path					)					)
			score_fn(lowerCamelCase				,  args.predictions_path				,  args.gold_data_path					)
			continue

		logger.info("""***** Running evaluation for {} *****""".format(lowerCamelCase					)					)
		logger.info("""  Batch size = %d"""				,  args.eval_batch_size					)
		logger.info("""  Predictions will be stored under {}""".format(args.predictions_path					)					)

		if args.model_type.startswith("""rag"""					):
			__lowercase             =  RagRetriever.from_pretrained(lowerCamelCase				,  **lowerCamelCase					)
			__lowercase             =  model_class.from_pretrained(lowerCamelCase				,  retriever=lowerCamelCase				,  **lowerCamelCase					)
			model.retriever.init_retrieval()
		else:
			__lowercase             =  model_class.from_pretrained(lowerCamelCase				,  **lowerCamelCase					)
		model.to(args.device					)

		with open(args.evaluation_set				,  """r"""					) as eval_file, open(args.predictions_path				,  """w"""					) as preds_file:
			__lowercase             =  []
			for line in tqdm(lowerCamelCase					):
				questions.append(line.strip()					)
				if len(lowerCamelCase					) == args.eval_batch_size:
					__lowercase             =  evaluate_batch_fn(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
					preds_file.write("""\n""".join(lowerCamelCase					) + """\n"""					)
					preds_file.flush()
					__lowercase             =  []
			if len(lowerCamelCase					) > 0:
				__lowercase             =  evaluate_batch_fn(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
				preds_file.write("""\n""".join(lowerCamelCase					)					)
				preds_file.flush()

			score_fn(lowerCamelCase				,  args.predictions_path				,  args.gold_data_path					)


if __name__ == "__main__":
						__UpperCamelCase      :      Dict     						=						get_args()
						main(args)









import heapq
import sys

import numpy as np

__UpperCamelCase      :      List[str]     						=						tuple[int, int]


class        __UpperCamelCase    :







	def __init__(  self      :							Optional[int]			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  []
		__lowercase             =  set()







	def  _a   (  self      :							int			)		-> List[Any]:
		"""simple docstring"""







		if not self.empty():
			return self.elements[0][0]
		else:
			return float("""inf"""			)







	def  _a   (  self      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		return len(self.elements			) == 0







	def  _a   (  self      :							str       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Dict:
		"""simple docstring"""







		if item not in self.set:
			heapq.heappush(self.elements       ,				(priority, item)			)
			self.set.add(_lowerCAmelCase			)
		else:
			# update
			# print("update", item)
			__lowercase             =  []
			((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			while x != item:
				temp.append((pri, x)			)
				((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			temp.append((priority, item)			)
			for pro, xxx in temp:
				heapq.heappush(self.elements       ,				(pro, xxx)			)







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Dict:
		"""simple docstring"""







		if item in self.set:
			self.set.remove(_lowerCAmelCase			)
			__lowercase             =  []
			((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			while x != item:
				temp.append((pro, x)			)
				((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			for prito, yyy in temp:
				heapq.heappush(self.elements       ,				(prito, yyy)			)







	def  _a   (  self      :							Any			)		-> List[Any]:
		"""simple docstring"""







		return self.elements[0][1]







	def  _a   (  self      :							Optional[int]			)		-> List[Any]:
		"""simple docstring"""







		((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
		self.set.remove(_lowerCAmelCase			)
		return (priority, item)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  np.array(lowerCamelCase					)
	__lowercase             =  np.array(lowerCamelCase					)
	return np.linalg.norm(a - b					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return consistent_heuristic(lowerCamelCase				,  lowerCamelCase					) // t





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  g_function[start] + Wa * heuristics[i](lowerCamelCase				,  lowerCamelCase					)
	return ans





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  np.chararray((n, n)					)
	for i in range(lowerCamelCase					):
		for j in range(lowerCamelCase					):
			__lowercase             =  """*"""

	for i in range(lowerCamelCase					):
		for j in range(lowerCamelCase					):
			if (j, (n - 1) - i) in blocks:
				__lowercase             =  """#"""

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

	for i in range(lowerCamelCase					):
		for j in range(lowerCamelCase					):
			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:-"""					)
	__lowercase             =  back_pointer[goal]
	while x != start:
		print(lowerCamelCase				,  end=""" """					)
		__lowercase             =  back_pointer[x]
	print(lowerCamelCase					)
	sys.exit()





def 				snake_case  (     lowerCamelCase					):




	'''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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  ):




	'''simple docstring'''







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

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

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





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  []
	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


__UpperCamelCase      :      Optional[int]     						=						{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__UpperCamelCase      :      Optional[Any]     						=						[
    (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),
]
__UpperCamelCase      :      Optional[Any]     						=						make_common_ground()


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

# start and end destination
__UpperCamelCase      :      str     						=						(0, 0)
__UpperCamelCase      :      str     						=						(n - 1, n - 1)

__UpperCamelCase      :      Optional[Any]     						=						1





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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

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

	__lowercase             =  []
	__lowercase             =  []
	while open_list[0].minkey() < float("""inf"""					):
		for i in range(1				,  lowerCamelCase					):
			# 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(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
				else:
					__lowercase       ,						__lowercase             =  open_list[i].top_show()
					visited.add(lowerCamelCase					)
					expand_state(
					    lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  )
					close_list_inad.append(lowerCamelCase					)
			else:
				if g_function[goal] <= open_list[0].minkey():
					if g_function[goal] < float("""inf"""					):
						do_something(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
				else:
					__lowercase             =  open_list[0].top_show()
					visited.add(lowerCamelCase					)
					expand_state(
					    lowerCamelCase				,  0				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  )
					close_list_anchor.append(lowerCamelCase					)
	print("""No path found to goal"""					)
	print()
	for i in range(n - 1				,  -1				,  -1					):
		for j in range(lowerCamelCase					):
			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)

