






"""simple docstring"""
# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowerCAmelCase       =				TypeVar("T")



class   SCREAMING_SNAKE_CASE	(					Generic[T]    ):





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_ = True				):
		A_		:    dict[T, list[T]]						     = {}  # dictionary of lists
		A_		:    Any						     = directed





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):

		if not self.directed:  # For undirected graphs
			# if both source vertex and destination vertex are both present in the
			# adjacency list, add destination vertex to source vertex list of adjacent
			# vertices and add source vertex to destination vertex list of adjacent
			# vertices.
			if source_vertex in self.adj_list and destination_vertex in self.adj_list:
				self.adj_list[source_vertex].append(lowerCAmelCase_				)
				self.adj_list[destination_vertex].append(lowerCAmelCase_				)
			# if only source vertex is present in adjacency list, add destination vertex
			# to source vertex list of adjacent vertices, then create a new vertex with
			# destination vertex as key and assign a list containing the source vertex
			# as it's first adjacent vertex.
			elif source_vertex in self.adj_list:
				self.adj_list[source_vertex].append(lowerCAmelCase_				)
				A_		:    Tuple						     = [source_vertex]
			# if only destination vertex is present in adjacency list, add source vertex
			# to destination vertex list of adjacent vertices, then create a new vertex
			# with source vertex as key and assign a list containing the source vertex
			# as it's first adjacent vertex.
			elif destination_vertex in self.adj_list:
				self.adj_list[destination_vertex].append(lowerCAmelCase_				)
				A_		:    Any						     = [destination_vertex]
			# if both source vertex and destination vertex are not present in adjacency
			# list, create a new vertex with source vertex as key and assign a list
			# containing the destination vertex as it's first adjacent vertex also
			# create a new vertex with destination vertex as key and assign a list
			# containing the source vertex as it's first adjacent vertex.
			else:
				A_		:    Optional[Any]						     = [destination_vertex]
				A_		:    Optional[Any]						     = [source_vertex]
		else:  # For directed graphs
			# if both source vertex and destination vertex are present in adjacency
			# list, add destination vertex to source vertex list of adjacent vertices.
			if source_vertex in self.adj_list and destination_vertex in self.adj_list:
				self.adj_list[source_vertex].append(lowerCAmelCase_				)
			# if only source vertex is present in adjacency list, add destination
			# vertex to source vertex list of adjacent vertices and create a new vertex
			# with destination vertex as key, which has no adjacent vertex
			elif source_vertex in self.adj_list:
				self.adj_list[source_vertex].append(lowerCAmelCase_				)
				A_		:    int						     = []
			# if only destination vertex is present in adjacency list, create a new
			# vertex with source vertex as key and assign a list containing destination
			# vertex as first adjacent vertex
			elif destination_vertex in self.adj_list:
				A_		:    int						     = [destination_vertex]
			# if both source vertex and destination vertex are not present in adjacency
			# list, create a new vertex with source vertex as key and a list containing
			# destination vertex as it's first adjacent vertex. Then create a new vertex
			# with destination vertex as key, which has no adjacent vertex
			else:
				A_		:    str						     = [destination_vertex]
				A_		:    Optional[Any]						     = []

		return self





	def __repr__(self				):
		return pformat(self.adj_list				)









"""simple docstring"""
from typing import Dict

from .base import GenericTensor, Pipeline



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if tokenize_kwargs is None:
			A_		:    Dict						     = {}

		if truncation is not None:
			if "truncation" in tokenize_kwargs:
				raise ValueError(
				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""				)
			A_		:    Optional[Any]						     = truncation

		A_		:    Optional[int]						     = tokenize_kwargs

		A_		:    Optional[Any]						     = {}
		if return_tensors is not None:
			A_		:    Optional[Any]						     = return_tensors

		return preprocess_params, {}, postprocess_params





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				):
		A_		:    List[Any]						     = self.framework
		A_		:    Optional[int]						     = self.tokenizer(lowerCAmelCase_  ,  return_tensors=lowerCAmelCase_  ,  **lowerCAmelCase_				)
		return model_inputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.model(**lowerCAmelCase_				)
		return model_outputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		# [0] is the first available tensor, logits or last_hidden_state.
		if return_tensors:
			return model_outputs[0]
		if self.framework == "pt":
			return model_outputs[0].tolist()
		elif self.framework == "tf":
			return model_outputs[0].numpy().tolist()





	def __call__(self  ,  *lowerCAmelCase_  ,  **lowerCAmelCase_				):
		return super().__call__(*lowerCAmelCase_  ,  **lowerCAmelCase_				)

