



"""simple docstring"""
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


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




@require_torch
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self			,     a_      ):
									return FSMTTokenizer.from_pretrained(a_      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Union[str, Any] 						=     FSMTForConditionalGeneration.from_pretrained(a_      ).to(a_      )
									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 snake_case_  (   self			,     a_			,     a_      ):
									# 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
									a_     :      Union[str, Any] 						=     F"""facebook/wmt19-{pair}"""
									a_     :      List[str] 						=     self.get_tokenizer(a_      )
									a_     :      int 						=     self.get_model(a_      )

									a_     :      List[Any] 						=     bleu_data[pair]["src"]
									a_     :      List[Any] 						=     bleu_data[pair]["tgt"]

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




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			snake_case_						(						unittest.TestCase				):






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      Any 						=     UNetaDModel(
									    block_out_channels=(3_2, 6_4)			,     layers_per_block=2			,     sample_size=3_2			,     in_channels=3			,     out_channels=3			,     down_block_types=("DownBlock2D", "AttnDownBlock2D")			,     up_block_types=("AttnUpBlock2D", "UpBlock2D")			,     )
									return model






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.dummy_uncond_unet
									a_     :      Any 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )

									a_     :      Tuple 						=     torch.manual_seed(0      )
									a_     :      Union[str, Any] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"      ).images

									a_     :      int 						=     torch.manual_seed(0      )
									a_     :      Optional[int] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"			,     return_dict=a_      )[0]

									a_     :      List[Any] 						=     image[0, -3:, -3:, -1]
									a_     :      Union[str, Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      List[Any] 						=     np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "google/ddpm-cifar10-32"

									a_     :      Optional[int] 						=     UNetaDModel.from_pretrained(a_      )
									a_     :      Union[str, Any] 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )
									a_     :      Optional[int] 						=     torch.manual_seed(0      )
									a_     :      List[Any] 						=     pndm(generator=a_			,     output_type="numpy"      ).images

									a_     :      str 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      Optional[Any] 						=     np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2