





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				snake_case_       ,				snake_case_       ,				unittest.TestCase					):
      __magic_name__:			Dict       =       StableDiffusionInpaintPipeline
      __magic_name__:			str       =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      __magic_name__:			Dict       =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      __magic_name__:			List[Any]       =       frozenset(
          []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      __magic_name__:			Dict       =       frozenset([]					)






      def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    List[Any]:







       """simple docstring"""


       torch.manual_seed(0	)
       snake_case_		:			List[Any]											=	UNetaDConditionModel(
           block_out_channels=(32, 64)		,   layers_per_block=2		,   sample_size=32		,   in_channels=9		,   out_channels=4		,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,   cross_attention_dim=32		,   attention_head_dim=(2, 4)		,   use_linear_projection=_A		,   )
       snake_case_		:			str											=	PNDMScheduler(skip_prk_steps=_A	)
       torch.manual_seed(0	)
       snake_case_		:			Optional[Any]											=	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		,   sample_size=128		,   )
       torch.manual_seed(0	)
       snake_case_		:			str											=	CLIPTextConfig(
           bos_token_id=0		,   eos_token_id=2		,   hidden_size=32		,   intermediate_size=37		,   layer_norm_eps=1E-05		,   num_attention_heads=4		,   num_hidden_layers=5		,   pad_token_id=1		,   vocab_size=1000		,   hidden_act='gelu'		,   projection_dim=512		,   )
       snake_case_		:			Tuple											=	CLIPTextModel(_A	)
       snake_case_		:			Any											=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

       snake_case_		:			int											=	{
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components






      def UpperCAmelCase_				(      self      :      Dict		,   _A      :      List[str]		,   _A      :      str=0	)							->    Any:







       """simple docstring"""


       snake_case_		:			Any											=	floats_tensor((1, 3, 32, 32)		,   rng=random.Random(_A	)	).to(_A	)
       snake_case_		:			List[Any]											=	image.cpu().permute(0		,   2		,   3		,   1	)[0]
       snake_case_		:			Tuple											=	Image.fromarray(np.uinta(_A	)	).convert('RGB'	).resize((64, 64)	)
       snake_case_		:			Union[str, Any]											=	Image.fromarray(np.uinta(image + 4	)	).convert('RGB'	).resize((64, 64)	)
       if str(_A	).startswith('mps'	):
        snake_case_		:			str											=	torch.manual_seed(_A	)
       else:
        snake_case_		:			Any											=	torch.Generator(device=_A	).manual_seed(_A	)
       snake_case_		:			Optional[Any]											=	{
           'prompt': 'A painting of a squirrel eating a burger',
           'image': init_image,
           'mask_image': mask_image,
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
       }
       return inputs






      def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Dict:







       """simple docstring"""


       snake_case_		:			List[str]											=	'cpu'  # ensure determinism for the device-dependent torch.Generator
       snake_case_		:			List[Any]											=	self.get_dummy_components()
       snake_case_		:			Dict											=	StableDiffusionInpaintPipeline(**_A	)
       snake_case_		:			Union[str, Any]											=	sd_pipe.to(_A	)
       sd_pipe.set_progress_bar_config(disable=_A	)

       snake_case_		:			Tuple											=	self.get_dummy_inputs(_A	)
       snake_case_		:			int											=	sd_pipe(**_A	).images
       snake_case_		:			Optional[Any]											=	image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       snake_case_		:			Tuple											=	np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]	)

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






      def UpperCAmelCase_				(      self      :      Tuple	)							->    Optional[Any]:







       """simple docstring"""


       super().test_inference_batch_single_identical(expected_max_diff=3E-3	)
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






      def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Any:







       """simple docstring"""


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






      def UpperCAmelCase_				(      self      :      List[Any]	)							->    Tuple:







       """simple docstring"""


       snake_case_		:			Tuple											=	load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/sd2-inpaint/init_image.png'	)
       snake_case_		:			str											=	load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'	)
       snake_case_		:			int											=	load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
           '/yellow_cat_sitting_on_a_park_bench.npy'	)

       snake_case_		:			Optional[int]											=	'stabilityai/stable-diffusion-2-inpainting'
       snake_case_		:			Any											=	StableDiffusionInpaintPipeline.from_pretrained(_A		,   safety_checker=_A	)
       pipe.to(_A	)
       pipe.set_progress_bar_config(disable=_A	)
       pipe.enable_attention_slicing()

       snake_case_		:			int											=	'Face of a yellow cat, high resolution, sitting on a park bench'

       snake_case_		:			List[str]											=	torch.manual_seed(0	)
       snake_case_		:			List[Any]											=	pipe(
           prompt=_A		,   image=_A		,   mask_image=_A		,   generator=_A		,   output_type='np'		,   )
       snake_case_		:			Dict											=	output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image	).max() < 9E-3






      def UpperCAmelCase_				(      self      :      List[str]	)							->    List[str]:







       """simple docstring"""


       snake_case_		:			List[Any]											=	load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/sd2-inpaint/init_image.png'	)
       snake_case_		:			Union[str, Any]											=	load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'	)
       snake_case_		:			List[str]											=	load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
           '/yellow_cat_sitting_on_a_park_bench_fp16.npy'	)

       snake_case_		:			Any											=	'stabilityai/stable-diffusion-2-inpainting'
       snake_case_		:			Dict											=	StableDiffusionInpaintPipeline.from_pretrained(
           _A		,   torch_dtype=torch.floataa		,   safety_checker=_A		,   )
       pipe.to(_A	)
       pipe.set_progress_bar_config(disable=_A	)
       pipe.enable_attention_slicing()

       snake_case_		:			int											=	'Face of a yellow cat, high resolution, sitting on a park bench'

       snake_case_		:			Optional[Any]											=	torch.manual_seed(0	)
       snake_case_		:			Tuple											=	pipe(
           prompt=_A		,   image=_A		,   mask_image=_A		,   generator=_A		,   output_type='np'		,   )
       snake_case_		:			List[str]											=	output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image	).max() < 5E-1






      def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    str:







       """simple docstring"""


       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       snake_case_		:			List[Any]											=	load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/sd2-inpaint/init_image.png'	)
       snake_case_		:			List[str]											=	load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'	)

       snake_case_		:			Any											=	'stabilityai/stable-diffusion-2-inpainting'
       snake_case_		:			Union[str, Any]											=	PNDMScheduler.from_pretrained(_A		,   subfolder='scheduler'	)
       snake_case_		:			str											=	StableDiffusionInpaintPipeline.from_pretrained(
           _A		,   safety_checker=_A		,   scheduler=_A		,   torch_dtype=torch.floataa		,   )
       pipe.to(_A	)
       pipe.set_progress_bar_config(disable=_A	)
       pipe.enable_attention_slicing(1	)
       pipe.enable_sequential_cpu_offload()

       snake_case_		:			Any											=	'Face of a yellow cat, high resolution, sitting on a park bench'

       snake_case_		:			Union[str, Any]											=	torch.manual_seed(0	)
       snake_case_		:			str											=	pipe(
           prompt=_A		,   image=_A		,   mask_image=_A		,   generator=_A		,   num_inference_steps=2		,   output_type='np'		,   )

       snake_case_		:			Tuple											=	torch.cuda.max_memory_allocated()
       # make sure that less than 2.65 GB is allocated
       assert mem_bytes < 2.6_5 * 10**9








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 SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							return FSMTTokenizer.from_pretrained(_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	FSMTForConditionalGeneration.from_pretrained(_A	).to(_A	)
							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 UpperCAmelCase_				(      self      :      int		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	F"""facebook/wmt19-{pair}"""
							snake_case_		:			List[Any]											=	self.get_tokenizer(_A	)
							snake_case_		:			List[str]											=	self.get_model(_A	)

							snake_case_		:			Union[str, Any]											=	bleu_data[pair]['src']
							snake_case_		:			List[str]											=	bleu_data[pair]['tgt']

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

