
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


__A				=get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
			__A				=json.load(f)




@require_torch
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       str:
					return FSMTTokenizer.from_pretrained(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			FSMTForConditionalGeneration.from_pretrained(lowercase ).to(lowercase )
					if torch_device == "cuda":
									model.half()
					return model



	@parameterized.expand(
	    [
	        ["en-ru", 2_6.0],
	        ["ru-en", 2_2.0],
	        ["en-de", 2_2.0],
	        ["de-en", 2_9.0],
	    ] )
	@slow
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       str:
					# note: this test is not testing the best performance since it only evals a small batch
					# but it should be enough to detect a regression in the output quality
					lowerCamelCase_						=			f'facebook/wmt19-{pair}'
					lowerCamelCase_						=			self.get_tokenizer(lowercase )
					lowerCamelCase_						=			self.get_model(lowercase )

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

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




from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def 			lowerCamelCase_ (				):
				lowerCamelCase_	,    lowerCamelCase_						=			9, 1_4  # noqa: F841
				lowerCamelCase_						=			[
				    [0, 1, 4],
				    [0, 7, 8],
				    [1, 2, 8],
				    [7, 8, 7],
				    [7, 6, 1],
				    [2, 8, 2],
				    [8, 6, 6],
				    [2, 3, 7],
				    [2, 5, 4],
				    [6, 5, 2],
				    [3, 5, 1_4],
				    [3, 4, 9],
				    [5, 4, 1_0],
				    [1, 7, 1_1],
				]

				lowerCamelCase_						=			defaultdict(lowerCamelCase__    )
				for nodea, nodea, cost in edges:
								adjancency[nodea].append([nodea, cost]    )
								adjancency[nodea].append([nodea, cost]    )

				lowerCamelCase_						=			mst(lowerCamelCase__    )

				lowerCamelCase_						=			[
				    [7, 6, 1],
				    [2, 8, 2],
				    [6, 5, 2],
				    [0, 1, 4],
				    [2, 5, 4],
				    [2, 3, 7],
				    [0, 7, 8],
				    [3, 4, 9],
				]

				for answer in expected:
								lowerCamelCase_						=			tuple(answer[:2]    )
								lowerCamelCase_						=			tuple(edge[::-1]    )
								assert edge in result or reverse in result


