



import logging
import random

import ray

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


SCREAMING_SNAKE_CASE__   			=   logging.getLogger(__name__)


class   __lowerCAmelCase  :
					"""simple docstring"""






					def __init__(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     False



					def      _a				(  self       :							List[Any]					,       _snake_case       :							Tuple					,       _snake_case       :							List[str]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								if not self.initialized:
											A__	  =     RagRetriever(
											    _snake_case					,       question_encoder_tokenizer=_snake_case					,       generator_tokenizer=_snake_case					,       index=_snake_case					,       init_retrieval=_snake_case					,       )
											A__	  =     True



					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								self.retriever.index.init_index()



					def      _a				(  self       :							List[str]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								A__  ,			A__	  =     self.retriever._main_retrieve(_snake_case					,       _snake_case       )
								return doc_ids, retrieved_doc_embeds







class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							int					,       _snake_case       :							int					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							Any					,       _snake_case       :							Tuple					,       _snake_case       :							str=None       ):






								"""simple docstring"""


								if index is not None and index.is_initialized() and len(_snake_case       ) > 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__(
								    _snake_case					,       question_encoder_tokenizer=_snake_case					,       generator_tokenizer=_snake_case					,       index=_snake_case					,       init_retrieval=_snake_case					,       )
								A__	  =     retrieval_workers
								if len(self.retrieval_workers       ) > 0:
											ray.get(
											    [
											        worker.create_rag_retriever.remote(_snake_case					,       _snake_case					,       _snake_case					,       _snake_case       )
											        for worker in self.retrieval_workers
											    ]       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								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       :							str					,       _snake_case       :							Optional[Any]					,       _snake_case       :							Optional[int]       ):






								"""simple docstring"""


								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__	  =     ray.get(random_worker.retrieve.remote(_snake_case					,       _snake_case       )       )
								else:
											A__  ,			A__	  =     self._main_retrieve(_snake_case					,       _snake_case       )
								return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_snake_case       )



					@classmethod
					def      _a				(  cls       :							List[Any]					,       _snake_case       :							int					,       _snake_case       :							Union[str, Any]=None					,       **_snake_case       :							int       ):






								"""simple docstring"""


								return super(_snake_case					,       cls       ).get_tokenizers(_snake_case					,       _snake_case					,       **_snake_case       )



					@classmethod
					def      _a				(  cls       :							int					,       _snake_case       :							List[str]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							List[str]=None					,       **_snake_case       :							Any       ):






								"""simple docstring"""


								A__	  =     kwargs.pop('config'					,       _snake_case       ) or RagConfig.from_pretrained(_snake_case					,       **_snake_case       )
								A__	  =     RagTokenizer.from_pretrained(_snake_case					,       config=_snake_case       )
								A__	  =     rag_tokenizer.question_encoder
								A__	  =     rag_tokenizer.generator
								if indexed_dataset is not None:
											A__	  =     'custom'
											A__	  =     CustomHFIndex(config.retrieval_vector_size					,       _snake_case       )
								else:
											A__	  =     cls._build_index(_snake_case       )
								return cls(
								    _snake_case					,       question_encoder_tokenizer=_snake_case					,       generator_tokenizer=_snake_case					,       retrieval_workers=_snake_case					,       index=_snake_case					,       )









import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
							# Create universe of discourse in Python using linspace ()
							SCREAMING_SNAKE_CASE__   			=   np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

							# Create two fuzzy sets by defining any membership function
							# (trapmf(), gbellmf(), gaussmf(), etc).
							SCREAMING_SNAKE_CASE__   			=   [0, 2_5, 5_0]
							SCREAMING_SNAKE_CASE__   			=   [2_5, 5_0, 7_5]
							SCREAMING_SNAKE_CASE__   			=   fuzz.membership.trimf(X, abca)
							SCREAMING_SNAKE_CASE__   			=   fuzz.membership.trimf(X, abca)

							# Compute the different operations using inbuilt functions.
							SCREAMING_SNAKE_CASE__   			=   np.ones(7_5)
							SCREAMING_SNAKE_CASE__   			=   np.zeros((7_5,))
							# 1. Union = max(µA(x), µB(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_or(X, young, X, middle_aged)[1]
							# 2. Intersection = min(µA(x), µB(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, young, X, middle_aged)[1]
							# 3. Complement (A) = (1- min(µA(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_not(young)
							# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
							# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
							SCREAMING_SNAKE_CASE__   			=   young + middle_aged - (young * middle_aged)
							# 6. Algebraic Product = (µA(x) * µB(x))
							SCREAMING_SNAKE_CASE__   			=   young * middle_aged
							# 7. Bounded Sum = min[1,(µA(x), µB(x))]
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
							# 8. Bounded difference = min[0,(µA(x), µB(x))]
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

							# max-min composition
							# max-product composition

							# Plot each set A, set B and each operation result using plot() and subplot().
							from matplotlib import pyplot as plt

							plt.figure()

							plt.subplot(4, 3, 1)
							plt.plot(X, young)
							plt.title('''Young''')
							plt.grid(True)

							plt.subplot(4, 3, 2)
							plt.plot(X, middle_aged)
							plt.title('''Middle aged''')
							plt.grid(True)

							plt.subplot(4, 3, 3)
							plt.plot(X, union)
							plt.title('''union''')
							plt.grid(True)

							plt.subplot(4, 3, 4)
							plt.plot(X, intersection)
							plt.title('''intersection''')
							plt.grid(True)

							plt.subplot(4, 3, 5)
							plt.plot(X, complement_a)
							plt.title('''complement_a''')
							plt.grid(True)

							plt.subplot(4, 3, 6)
							plt.plot(X, difference)
							plt.title('''difference a/b''')
							plt.grid(True)

							plt.subplot(4, 3, 7)
							plt.plot(X, alg_sum)
							plt.title('''alg_sum''')
							plt.grid(True)

							plt.subplot(4, 3, 8)
							plt.plot(X, alg_product)
							plt.title('''alg_product''')
							plt.grid(True)

							plt.subplot(4, 3, 9)
							plt.plot(X, bdd_sum)
							plt.title('''bdd_sum''')
							plt.grid(True)

							plt.subplot(4, 3, 1_0)
							plt.plot(X, bdd_difference)
							plt.title('''bdd_difference''')
							plt.grid(True)

							plt.subplots_adjust(hspace=0.5)
							plt.show()




