





'''simple docstring'''



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							question_encoder
				__lowercase										=							generator
				__lowercase										=							self.question_encoder


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				if os.path.isfile(_lowerCamelCase ):
							raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file" )
				os.makedirs(_lowerCamelCase     ,exist_ok=_lowerCamelCase )
				__lowercase										=							os.path.join(_lowerCamelCase     ,'''question_encoder_tokenizer''' )
				__lowercase										=							os.path.join(_lowerCamelCase     ,'''generator_tokenizer''' )
				self.question_encoder.save_pretrained(_lowerCamelCase )
				self.generator.save_pretrained(_lowerCamelCase )


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase     ,**_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				from ..auto.tokenization_auto import AutoTokenizer

				__lowercase										=							kwargs.pop('''config'''     ,_lowerCamelCase )

				if config is None:
							__lowercase										=							RagConfig.from_pretrained(_lowerCamelCase )

				__lowercase										=							AutoTokenizer.from_pretrained(
				    _lowerCamelCase     ,config=config.question_encoder     ,subfolder='''question_encoder_tokenizer''' )
				__lowercase										=							AutoTokenizer.from_pretrained(
				    _lowerCamelCase     ,config=config.generator     ,subfolder='''generator_tokenizer''' )
				return cls(question_encoder=_lowerCamelCase     ,generator=_lowerCamelCase )


	def __call__(self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							List[str]:







				'''simple docstring'''





				return self.current_tokenizer(*_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							Dict:







				'''simple docstring'''





				return self.generator.batch_decode(*_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							Union[str, Any]:







				'''simple docstring'''





				return self.generator.decode(*_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							self.question_encoder


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							self.generator


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = "longest"     ,_lowerCamelCase = None     ,_lowerCamelCase = True     ,**_lowerCamelCase     ,)							->							BatchEncoding:







				'''simple docstring'''





				warnings.warn(
				    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
				    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
				    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
				    '''details'''     ,_lowerCamelCase     ,)
				if max_length is None:
							__lowercase										=							self.current_tokenizer.model_max_length
				__lowercase										=							self(
				    _lowerCamelCase     ,add_special_tokens=_lowerCamelCase     ,return_tensors=_lowerCamelCase     ,max_length=_lowerCamelCase     ,padding=_lowerCamelCase     ,truncation=_lowerCamelCase     ,**_lowerCamelCase     ,)
				if tgt_texts is None:
							return model_inputs
				# Process tgt_texts
				if max_target_length is None:
							__lowercase										=							self.current_tokenizer.model_max_length
				__lowercase										=							self(
				    text_target=_lowerCamelCase     ,add_special_tokens=_lowerCamelCase     ,return_tensors=_lowerCamelCase     ,padding=_lowerCamelCase     ,max_length=_lowerCamelCase     ,truncation=_lowerCamelCase     ,**_lowerCamelCase     ,)
				__lowercase										=							labels['''input_ids''']
				return model_inputs












'''simple docstring'''



from __future__ import annotations

from typing import Any






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				__lowercase										=							num_of_nodes
				__lowercase										=							[]
				__lowercase										=							{}


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				self.m_edges.append([u_node, v_node, weight] )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				if self.m_component[u_node] == u_node:
							return u_node
				return self.find_component(self.m_component[u_node] )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				if self.m_component[u_node] != u_node:
							for k in self.m_component:
										__lowercase										=							self.find_component(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				if component_size[u_node] <= component_size[v_node]:
							__lowercase										=							v_node
							component_size[v_node] += component_size[u_node]
							self.set_component(_lowerCamelCase )

				elif component_size[u_node] >= component_size[v_node]:
							__lowercase										=							self.find_component(_lowerCamelCase )
							component_size[u_node] += component_size[v_node]
							self.set_component(_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							None:







				'''simple docstring'''





				__lowercase										=							[]
				__lowercase										=							0

				__lowercase										=							[-1] * self.m_num_of_nodes

				# A list of components (initialized to all of the nodes)
				for node in range(self.m_num_of_nodes ):
							self.m_component.update({node: node} )
							component_size.append(1 )

				__lowercase										=							self.m_num_of_nodes

				while num_of_components > 1:
							for edge in self.m_edges:
										__lowercase						,		__lowercase						,		__lowercase										=							edge

										__lowercase										=							self.m_component[u]
										__lowercase										=							self.m_component[v]

										if u_component != v_component:

													for component in (u_component, v_component):
																if (
																    minimum_weight_edge[component] == -1
																    or minimum_weight_edge[component][2] > w
																):
																			__lowercase										=							[u, v, w]

							for edge in minimum_weight_edge:
										if isinstance(_lowerCamelCase     ,_lowerCamelCase ):
													__lowercase						,		__lowercase						,		__lowercase										=							edge

													__lowercase										=							self.m_component[u]
													__lowercase										=							self.m_component[v]

													if u_component != v_component:
																mst_weight += w
																self.union(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )
																print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n" )
																num_of_components -= 1

							__lowercase										=							[-1] * self.m_num_of_nodes
				print(f"The total weight of the minimal spanning tree is: {mst_weight}" )





def    _lowerCAmelCase						(				):
			pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()





