






"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class  lowerCamelCase__ (	snake_case   ,					snake_case   ,					snake_case   ,					unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      AltDiffusionPipeline
    SCREAMING_SNAKE_CASE		=      TEXT_TO_IMAGE_PARAMS
    SCREAMING_SNAKE_CASE		=      TEXT_TO_IMAGE_BATCH_PARAMS
    SCREAMING_SNAKE_CASE		=      TEXT_TO_IMAGE_IMAGE_PARAMS
    SCREAMING_SNAKE_CASE		=      TEXT_TO_IMAGE_IMAGE_PARAMS






    def     _UpperCamelCase							(			self ):
          torch.manual_seed(0 )
          UpperCAmelCase          =	UNetaDConditionModel(
              block_out_channels=(32, 64)     ,layers_per_block=2     ,sample_size=32     ,in_channels=4     ,out_channels=4     ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,cross_attention_dim=32     ,)
          UpperCAmelCase          =	DDIMScheduler(
              beta_start=0.00085     ,beta_end=0.012     ,beta_schedule="""scaled_linear"""     ,clip_sample=A     ,set_alpha_to_one=A     ,)
          torch.manual_seed(0 )
          UpperCAmelCase          =	AutoencoderKL(
              block_out_channels=[32, 64]     ,in_channels=3     ,out_channels=3     ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,latent_channels=4     ,)

          # TODO: address the non-deterministic text encoder (fails for save-load tests)
          # torch.manual_seed(0)
          # text_encoder_config = RobertaSeriesConfig(
          #     hidden_size=32,
          #     project_dim=32,
          #     intermediate_size=37,
          #     layer_norm_eps=1e-05,
          #     num_attention_heads=4,
          #     num_hidden_layers=5,
          #     vocab_size=5002,
          # )
          # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

          torch.manual_seed(0 )
          UpperCAmelCase          =	CLIPTextConfig(
              bos_token_id=0     ,eos_token_id=2     ,hidden_size=32     ,projection_dim=32     ,intermediate_size=37     ,layer_norm_eps=1e-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=5_002     ,)
          UpperCAmelCase          =	CLIPTextModel(A )

          UpperCAmelCase          =	XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
          UpperCAmelCase          =	77

          UpperCAmelCase          =	{
              """unet""": unet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components






    def     _UpperCamelCase							(			self     ,A     ,A=0 ):
          if str(A ).startswith("""mps""" ):
                UpperCAmelCase          =	torch.manual_seed(A )
          else:
                UpperCAmelCase          =	torch.Generator(device=A ).manual_seed(A )
          UpperCAmelCase          =	{
              """prompt""": """A painting of a squirrel eating a burger""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
          }
          return inputs






    def     _UpperCamelCase							(			self ):
          super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )






    def     _UpperCamelCase							(			self ):
          super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

          UpperCAmelCase          =	self.get_dummy_components()
          torch.manual_seed(0 )
          UpperCAmelCase          =	RobertaSeriesConfig(
              hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1e-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5_002     ,)
          # TODO: remove after fixing the non-deterministic text encoder
          UpperCAmelCase          =	RobertaSeriesModelWithTransformation(A )
          UpperCAmelCase          =	text_encoder

          UpperCAmelCase          =	AltDiffusionPipeline(**A )
          UpperCAmelCase          =	alt_pipe.to(A )
          alt_pipe.set_progress_bar_config(disable=A )

          UpperCAmelCase          =	self.get_dummy_inputs(A )
          UpperCAmelCase          =	"""A photo of an astronaut"""
          UpperCAmelCase          =	alt_pipe(**A )
          UpperCAmelCase          =	output.images
          UpperCAmelCase          =	image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCAmelCase          =	np.array(
              [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093] )

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






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

          UpperCAmelCase          =	self.get_dummy_components()
          UpperCAmelCase          =	PNDMScheduler(skip_prk_steps=A )
          torch.manual_seed(0 )
          UpperCAmelCase          =	RobertaSeriesConfig(
              hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1e-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5_002     ,)
          # TODO: remove after fixing the non-deterministic text encoder
          UpperCAmelCase          =	RobertaSeriesModelWithTransformation(A )
          UpperCAmelCase          =	text_encoder
          UpperCAmelCase          =	AltDiffusionPipeline(**A )
          UpperCAmelCase          =	alt_pipe.to(A )
          alt_pipe.set_progress_bar_config(disable=A )

          UpperCAmelCase          =	self.get_dummy_inputs(A )
          UpperCAmelCase          =	alt_pipe(**A )
          UpperCAmelCase          =	output.images
          UpperCAmelCase          =	image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCAmelCase          =	np.array(
              [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237] )

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







@slow
@require_torch_gpu
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






    def     _UpperCamelCase							(			self ):
          # make sure here that pndm scheduler skips prk
          UpperCAmelCase          =	AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""     ,safety_checker=A )
          UpperCAmelCase          =	alt_pipe.to(A )
          alt_pipe.set_progress_bar_config(disable=A )

          UpperCAmelCase          =	"""A painting of a squirrel eating a burger"""
          UpperCAmelCase          =	torch.manual_seed(0 )
          UpperCAmelCase          =	alt_pipe([prompt]     ,generator=A     ,guidance_scale=6.0     ,num_inference_steps=20     ,output_type="""np""" )

          UpperCAmelCase          =	output.images

          UpperCAmelCase          =	image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCAmelCase          =	np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586] )

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






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""     ,subfolder="""scheduler""" )

          UpperCAmelCase          =	AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""     ,scheduler=A     ,safety_checker=A )
          UpperCAmelCase          =	alt_pipe.to(A )
          alt_pipe.set_progress_bar_config(disable=A )

          UpperCAmelCase          =	"""A painting of a squirrel eating a burger"""
          UpperCAmelCase          =	torch.manual_seed(0 )

          UpperCAmelCase          =	alt_pipe([prompt]     ,generator=A     ,num_inference_steps=2     ,output_type="""numpy""" )
          UpperCAmelCase          =	output.images

          UpperCAmelCase          =	image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCAmelCase          =	np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323] )

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











"""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


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







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self     ,A ):
          return FSMTTokenizer.from_pretrained(A )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	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     _UpperCamelCase							(			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
          UpperCAmelCase          =	F'''facebook/wmt19-{pair}'''
          UpperCAmelCase          =	self.get_tokenizer(A )
          UpperCAmelCase          =	self.get_model(A )

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

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



