





'''simple docstring'''


from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize



class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     1
		__lowerCAmelCase								=     2
		__lowerCAmelCase								=     3







		def __repr__(self    :   Union[str, Any]   ):
			return F"""{self.__class__.__name__}.{self.name}"""






def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->float:



	"""simple docstring"""







	A	,     A					=   point
	return degrees(atana(y - miny							,      x - minx     )     )




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Direction:



	"""simple docstring"""







	A	,     A					=   starting
	A	,     A					=   via
	A	,     A					=   target
	A					=   degrees(atana(ya - ya							,      xa - xa     )     )
	via_angle %= 360
	A					=   degrees(atana(ya - ya							,      xa - xa     )     )
	target_angle %= 360
	# t-
	#  \ \
	#   \ v
	#    \|
	#     s
	# via_angle is always lower than target_angle, if direction is left.
	# If they are same, it means they are on a same line of convex hull.
	if target_angle > via_angle:
		return Direction.left
	elif target_angle == via_angle:
		return Direction.straight
	else:
		return Direction.right




def  __a			(  UpperCAmelCase     )   ->list[tuple[int, int]]:



	"""simple docstring"""







	if len(UpperCAmelCase     ) <= 2:
		# There is no convex hull
		raise ValueError("""graham_scan: argument must contain more than 3 points."""     )
	if len(UpperCAmelCase     ) == 3:
		return points
	# find the lowest and the most left point
	A					=   0
	A	,     A					=   maxsize, maxsize
	for i, point in enumerate(UpperCAmelCase     ):
		A					=   point[0]
		A					=   point[1]
		if y < miny:
			A					=   y
			A					=   x
			A					=   i
		if y == miny and x < minx:
			A					=   x
			A					=   i

    # remove the lowest and the most left point from points for preparing for sort
	points.pop(UpperCAmelCase     )

	A					=   sorted(UpperCAmelCase							,      key=lambda UpperCAmelCase     : angle_comparer(UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )     )
	# This insert actually costs complexity,
	# and you should instead add (minx, miny) into stack later.
	# I'm using insert just for easy understanding.
	sorted_points.insert(0							,      (minx, miny)     )

	A					=   deque()
	stack.append(sorted_points[0]     )
	stack.append(sorted_points[1]     )
	stack.append(sorted_points[2]     )
	# In any ways, the first 3 points line are towards left.
	# Because we sort them the angle from minx, miny.
	A					=   Direction.left

	for i in range(3							,      len(UpperCAmelCase     )     ):
		while True:
			A					=   stack[-2]
			A					=   stack[-1]
			A					=   sorted_points[i]
			A					=   check_direction(UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )

			if next_direction == Direction.left:
				A					=   Direction.left
				break
			if next_direction == Direction.straight:
				if current_direction == Direction.left:
					# We keep current_direction as left.
					# Because if the straight line keeps as straight,
					# we want to know if this straight line is towards left.
					break
				elif current_direction == Direction.right:
					# If the straight line is towards right,
					# every previous points on those straigh line is not convex hull.
					stack.pop()
			if next_direction == Direction.right:
				stack.pop()
		stack.append(sorted_points[i]     )
	return list(UpperCAmelCase     )








'''simple docstring'''


import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_lowerCamelCase					:						List[str]					     =							logging.getLogger(__name__)



class 				__UpperCAmelCase	:







		'''simple docstring'''







		def __init__(self    :   Any   ):
			A					=   False







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Any   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   Dict   ):
			if not self.initialized:
				A					=   RagRetriever(
				    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   init_retrieval=_lowerCAmelCase   ,   )
				A					=   True







		def 							A  (self    :   List[str]   ):
			self.retriever.index.init_index()







		def 							A  (self    :   List[str]   ):
			# delete the old self.retriever object before assigning the new index
			del self.retriever
			A					=   False







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   Optional[int]   ):
			A	,     A					=   self.retriever._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   self.retriever.index.get_doc_dicts(_lowerCAmelCase   )
			return doc_ids, retrieved_doc_embeds, doc_dicts






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def __init__(self    :   Any   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]=None   ):
			if index is not None and index.is_initialized() and len(_lowerCAmelCase   ) > 0:
				raise ValueError(
				    """When using Ray for distributed fine-tuning, """
				    """you'll need to provide the paths instead, """
				    """as the dataset and the index are loaded """
				    """separately. More info in examples/rag/use_own_knowledge_dataset.py """   )

			super().__init__(
			    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   init_retrieval=_lowerCAmelCase   ,   )

			A					=   retrieval_workers
			A					=   question_encoder_tokenizer
			A					=   generator_tokenizer
			if len(self.retrieval_workers   ) > 0:
				ray.get(
				    [
				        worker.create_rag_retriever.remote(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
				        for worker in self.retrieval_workers
				    ]   )







		def 							A  (self    :   Optional[Any]   ):
			logger.info("""initializing retrieval"""   )

			if len(self.retrieval_workers   ) > 0:
				ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]   )
			else:
				# Non-distributed training. Load index into this same process.
				self.index.init_index()







		def 							A  (self    :   int   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]   ):
			if len(self.retrieval_workers   ) > 0:
				# Select a random retrieval actor.
				A					=   self.retrieval_workers[random.randint(0   ,   len(self.retrieval_workers   ) - 1   )]
				A	,     A	,     A					=   ray.get(
				    random_worker.retrieve.remote(_lowerCAmelCase   ,   _lowerCAmelCase   )   )
			else:
				A	,     A					=   self._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
				A					=   self.index.get_doc_dicts(_lowerCAmelCase   )
			return retrieved_doc_embeds, doc_ids, doc_dicts







		@classmethod
		def 							A  (cls    :   Dict   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   str=None   ,   **_lowerCAmelCase    :   List[Any]   ):
			return super(_lowerCAmelCase   ,   cls   ).get_tokenizers(_lowerCAmelCase   ,   _lowerCAmelCase   ,   **_lowerCAmelCase   )







		@classmethod
		def 							A  (cls    :   List[str]   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   List[Any]   ,   _lowerCAmelCase    :   Optional[int]=None   ,   **_lowerCAmelCase    :   Optional[int]   ):
			A					=   kwargs.pop("""config"""   ,   _lowerCAmelCase   ) or RagConfig.from_pretrained(_lowerCAmelCase   ,   **_lowerCAmelCase   )
			A					=   RagTokenizer.from_pretrained(_lowerCAmelCase   ,   config=_lowerCAmelCase   )
			A					=   rag_tokenizer.question_encoder
			A					=   rag_tokenizer.generator

			if indexed_dataset is not None:
				A					=   """custom"""
				A					=   CustomHFIndex(config.retrieval_vector_size   ,   _lowerCAmelCase   )
			else:
				A					=   cls._build_index(_lowerCAmelCase   )

			return cls(
			    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   retrieval_workers=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   )







		def 							A  (self    :   str   ):
			logger.info("""re-loading the new dataset with embeddings"""   )
			# access from the training loop

			ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]   )

			# build the index object again
			A					=   self._build_index(self.config   )

			ray.get(
			    [
			        worker.create_rag_retriever.remote(
			            self.config   ,   self.question_encoder_tokenizer   ,   self.generator_tokenizer   ,   _lowerCAmelCase   )
			        for worker in self.retrieval_workers
			    ]   )

