



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		):
						"""simple docstring"""

						a					     =   {}




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						print(self.vertex		)
						for i in self.vertex:
											print(lowerCamelCase_		,       " -> "		,       " -> ".join([str(lowerCamelCase_		) for j in self.vertex[i]]		)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						if from_vertex in self.vertex:
											self.vertex[from_vertex].append(lowerCamelCase_		)
						else:
											# else make a new vertex
											a					     =   [to_vertex]




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   [False] * len(self.vertex		)

						# call the recursive helper function
						for i in range(len(self.vertex		)		):
											if not visited[i]:
																self.dfs_recursive(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   True

						print(lowerCamelCase_		,       end=" "		)

						# Recur for all the vertices that are adjacent to this node
						for i in self.vertex:
											if not visited[i]:
																self.dfs_recursive(lowerCamelCase_		,       lowerCamelCase_		)


if __name__ == "__main__":
				_lowercase: int      			=		Graph()
				g.add_edge(0, 1)
				g.add_edge(0, 2)
				g.add_edge(1, 2)
				g.add_edge(2, 0)
				g.add_edge(2, 3)
				g.add_edge(3, 3)

				g.print_graph()
				print("DFS:")
				g.dfs()

				# OUTPUT:
				# 0  ->  1 -> 2
				# 1  ->  2
				# 2  ->  0 -> 3
				# 3  ->  3
				# DFS:
				#  0 1 2 3





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowercase: Union[str, Any]      			=		get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
				_lowercase: List[str]      			=		json.load(f)



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
						if torch_device == "cuda":
											model.half()
						return model




	@parameterized.expand(
	    [
	        ["en-ru", 26.0],
	        ["ru-en", 22.0],
	        ["en-de", 22.0],
	        ["de-en", 29.0],
	    ]		)
	@slow
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="pt"		,       truncation=lowerCamelCase_		,       padding="longest"		).to(lowerCamelCase_		)
						a					     =   model.generate(
						    input_ids=batch.input_ids		,       num_beams=8		,       )
						a					     =   tokenizer.batch_decode(
						    lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_		)
						a					     =   calculate_bleu(lowerCamelCase_		,       lowerCamelCase_		)
						print(lowerCamelCase_		)
						self.assertGreaterEqual(scores["bleu"]		,       lowerCamelCase_		)
