
"""simple docstring"""

import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class        _lowercase   (				unittest.TestCase		):




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




	@property
	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Optional[int]		=    1
			lowerCamelCase     :			List[Any]		=    3
			lowerCamelCase     :			Optional[Any]		=    (32, 32)

			lowerCamelCase     :			Optional[int]		=    floats_tensor((batch_size, num_channels) + sizes		, rng=random.Random(0					)					).to(__lowerCamelCase					)
			return image




	@property
	def 			_UpperCamelCase    ( self					)		->							List[str]:
			torch.manual_seed(0					)
			lowerCamelCase     :			Optional[Any]		=    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		, )
			return model




	@property
	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			torch.manual_seed(0					)
			lowerCamelCase     :			Union[str, 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		, )
			return model




	@property
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			torch.manual_seed(0					)
			lowerCamelCase     :			List[Any]		=    CLIPTextConfig(
			    bos_token_id=0		, eos_token_id=2		, hidden_size=32		, intermediate_size=37		, layer_norm_eps=1E-0_5		, num_attention_heads=4		, num_hidden_layers=5		, pad_token_id=1		, vocab_size=1000		, )
			return CLIPTextModel(__lowerCamelCase					)




	@property
	def 			_UpperCamelCase    ( self					)		->							List[str]:
			def extract(*UpperCAmelCase_		, **UpperCAmelCase_					):






					class        _lowercase   :




						def __init__( self					)		->							Optional[int]:
								lowerCamelCase     :			int		=    torch.ones([0]					)




						def 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							List[Any]:
								self.pixel_values.to(__lowerCamelCase					)
								return self


					return Out()

			return extract




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			Dict		=    "cpu"  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase     :			Optional[int]		=    self.dummy_cond_unet
			lowerCamelCase     :			Dict		=    DDIMScheduler(
			    beta_start=0.00085		, beta_end=0.012		, beta_schedule='scaled_linear'		, clip_sample=__lowerCamelCase		, set_alpha_to_one=__lowerCamelCase		, )

			lowerCamelCase     :			Optional[Any]		=    self.dummy_vae
			lowerCamelCase     :			Any		=    self.dummy_text_encoder
			lowerCamelCase     :			Dict		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

			# make sure here that pndm scheduler skips prk
			lowerCamelCase     :			Optional[Any]		=    StableDiffusionPipeline(
			    unet=__lowerCamelCase		, scheduler=__lowerCamelCase		, vae=__lowerCamelCase		, text_encoder=__lowerCamelCase		, tokenizer=__lowerCamelCase		, safety_checker=__lowerCamelCase		, feature_extractor=self.dummy_extractor		, )
			lowerCamelCase     :			str		=    sd_pipe.to(__lowerCamelCase					)
			sd_pipe.set_progress_bar_config(disable=__lowerCamelCase					)

			lowerCamelCase     :			List[Any]		=    "A painting of a squirrel eating a burger"

			lowerCamelCase     :			Tuple		=    torch.Generator(device=__lowerCamelCase					).manual_seed(0					)
			lowerCamelCase     :			int		=    sd_pipe([prompt]		, generator=__lowerCamelCase		, guidance_scale=6.0		, num_inference_steps=2		, output_type='np'					)
			lowerCamelCase     :			Union[str, Any]		=    output.images

			lowerCamelCase     :			List[str]		=    torch.Generator(device=__lowerCamelCase					).manual_seed(0					)
			lowerCamelCase     :			Any		=    sd_pipe(
			    [prompt]		, generator=__lowerCamelCase		, guidance_scale=6.0		, num_inference_steps=2		, output_type='np'		, return_dict=__lowerCamelCase		, )[0]

			lowerCamelCase     :			List[str]		=    image[0, -3:, -3:, -1]
			lowerCamelCase     :			Optional[Any]		=    image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCamelCase     :			str		=    np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]					)

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




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase     :			Optional[int]		=    "cpu"  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase     :			str		=    self.dummy_cond_unet
			lowerCamelCase     :			Tuple		=    PNDMScheduler(skip_prk_steps=__lowerCamelCase					)
			lowerCamelCase     :			Dict		=    self.dummy_vae
			lowerCamelCase     :			List[str]		=    self.dummy_text_encoder
			lowerCamelCase     :			List[Any]		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

			# make sure here that pndm scheduler skips prk
			lowerCamelCase     :			int		=    StableDiffusionPipeline(
			    unet=__lowerCamelCase		, scheduler=__lowerCamelCase		, vae=__lowerCamelCase		, text_encoder=__lowerCamelCase		, tokenizer=__lowerCamelCase		, safety_checker=__lowerCamelCase		, feature_extractor=self.dummy_extractor		, )
			lowerCamelCase     :			Optional[Any]		=    sd_pipe.to(__lowerCamelCase					)
			sd_pipe.set_progress_bar_config(disable=__lowerCamelCase					)

			lowerCamelCase     :			List[Any]		=    "A painting of a squirrel eating a burger"
			lowerCamelCase     :			Optional[int]		=    torch.Generator(device=__lowerCamelCase					).manual_seed(0					)
			lowerCamelCase     :			Any		=    sd_pipe([prompt]		, generator=__lowerCamelCase		, guidance_scale=6.0		, num_inference_steps=2		, output_type='np'					)

			lowerCamelCase     :			Union[str, Any]		=    output.images

			lowerCamelCase     :			Optional[int]		=    torch.Generator(device=__lowerCamelCase					).manual_seed(0					)
			lowerCamelCase     :			Tuple		=    sd_pipe(
			    [prompt]		, generator=__lowerCamelCase		, guidance_scale=6.0		, num_inference_steps=2		, output_type='np'		, return_dict=__lowerCamelCase		, )[0]

			lowerCamelCase     :			Any		=    image[0, -3:, -3:, -1]
			lowerCamelCase     :			Dict		=    image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCamelCase     :			Optional[Any]		=    np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]					)

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




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			List[Any]		=    StableDiffusionPipeline.from_pretrained(
			    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'		, safety_checker=__lowerCamelCase					)
			assert isinstance(__lowerCamelCase		, __lowerCamelCase					)
			assert isinstance(pipe.scheduler		, __lowerCamelCase					)
			assert pipe.safety_checker is None

			lowerCamelCase     :			int		=    pipe('example prompt'		, num_inference_steps=2					).images[0]
			assert image is not None

			# check that there's no error when saving a pipeline with one of the models being None
			with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(__lowerCamelCase					)
					lowerCamelCase     :			Union[str, Any]		=    StableDiffusionPipeline.from_pretrained(__lowerCamelCase					)

			# sanity check that the pipeline still works
			assert pipe.safety_checker is None
			lowerCamelCase     :			Union[str, Any]		=    pipe('example prompt'		, num_inference_steps=2					).images[0]
			assert image is not None




	@unittest.skipIf(torch_device != 'cuda'		, 'This test requires a GPU'					)
	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase     :			List[Any]		=    self.dummy_cond_unet
			lowerCamelCase     :			Union[str, Any]		=    PNDMScheduler(skip_prk_steps=__lowerCamelCase					)
			lowerCamelCase     :			Optional[int]		=    self.dummy_vae
			lowerCamelCase     :			List[str]		=    self.dummy_text_encoder
			lowerCamelCase     :			str		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

			# put models in fp16
			lowerCamelCase     :			List[Any]		=    unet.half()
			lowerCamelCase     :			List[str]		=    vae.half()
			lowerCamelCase     :			int		=    bert.half()

			# make sure here that pndm scheduler skips prk
			lowerCamelCase     :			Any		=    StableDiffusionPipeline(
			    unet=__lowerCamelCase		, scheduler=__lowerCamelCase		, vae=__lowerCamelCase		, text_encoder=__lowerCamelCase		, tokenizer=__lowerCamelCase		, safety_checker=__lowerCamelCase		, feature_extractor=self.dummy_extractor		, )
			lowerCamelCase     :			str		=    sd_pipe.to(__lowerCamelCase					)
			sd_pipe.set_progress_bar_config(disable=__lowerCamelCase					)

			lowerCamelCase     :			Any		=    "A painting of a squirrel eating a burger"
			lowerCamelCase     :			int		=    sd_pipe([prompt]		, num_inference_steps=2		, output_type='np'					).images

			assert image.shape == (1, 64, 64, 3)






@nightly
@require_torch_gpu
class        _lowercase   (				unittest.TestCase		):




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase     :			List[Any]		=    "cuda"
			lowerCamelCase     :			Optional[int]		=    StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'					)
			lowerCamelCase     :			Any		=    pipe.to(__lowerCamelCase					)
			pipe.set_progress_bar_config(disable=__lowerCamelCase					)

			lowerCamelCase     :			str		=    "a photo of a cat"
			lowerCamelCase     :			Any		=    {
			    "editing_prompt": ["sunglasses"],
			    "reverse_editing_direction": [False],
			    "edit_warmup_steps": 10,
			    "edit_guidance_scale": 6,
			    "edit_threshold": 0.95,
			    "edit_momentum_scale": 0.5,
			    "edit_mom_beta": 0.6,
			}

			lowerCamelCase     :			Union[str, Any]		=    3
			lowerCamelCase     :			Union[str, Any]		=    7

			# no sega enabled
			lowerCamelCase     :			Tuple		=    torch.Generator(__lowerCamelCase					)
			generator.manual_seed(__lowerCamelCase					)
			lowerCamelCase     :			Optional[Any]		=    pipe(
			    [prompt]		, generator=__lowerCamelCase		, guidance_scale=__lowerCamelCase		, num_inference_steps=50		, output_type='np'		, width=512		, height=512		, )

			lowerCamelCase     :			List[str]		=    output.images
			lowerCamelCase     :			List[Any]		=    image[0, -3:, -3:, -1]
			lowerCamelCase     :			str		=    [
			    0.34673113,
			    0.38492733,
			    0.37597352,
			    0.34086335,
			    0.35650748,
			    0.35579205,
			    0.3384763,
			    0.34340236,
			    0.3573271,
			]

			assert image.shape == (1, 512, 512, 3)

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

			# with sega enabled
			# generator = torch.manual_seed(seed)
			generator.manual_seed(__lowerCamelCase					)
			lowerCamelCase     :			Any		=    pipe(
			    [prompt]		, generator=__lowerCamelCase		, guidance_scale=__lowerCamelCase		, num_inference_steps=50		, output_type='np'		, width=512		, height=512		, **__lowerCamelCase		, )

			lowerCamelCase     :			int		=    output.images
			lowerCamelCase     :			Union[str, Any]		=    image[0, -3:, -3:, -1]
			lowerCamelCase     :			List[str]		=    [
			    0.41887826,
			    0.37728766,
			    0.30138272,
			    0.41416335,
			    0.41664985,
			    0.36283392,
			    0.36191246,
			    0.43364465,
			    0.43001732,
			]

			assert image.shape == (1, 512, 512, 3)

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




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Optional[Any]		=    "cuda"
			lowerCamelCase     :			Dict		=    StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'					)
			lowerCamelCase     :			Tuple		=    pipe.to(__lowerCamelCase					)
			pipe.set_progress_bar_config(disable=__lowerCamelCase					)

			lowerCamelCase     :			Optional[Any]		=    "an image of a crowded boulevard, realistic, 4k"
			lowerCamelCase     :			Optional[Any]		=    {
			    "editing_prompt": "crowd, crowded, people",
			    "reverse_editing_direction": True,
			    "edit_warmup_steps": 10,
			    "edit_guidance_scale": 8.3,
			    "edit_threshold": 0.9,
			    "edit_momentum_scale": 0.5,
			    "edit_mom_beta": 0.6,
			}

			lowerCamelCase     :			List[str]		=    9
			lowerCamelCase     :			Optional[int]		=    7

			# no sega enabled
			lowerCamelCase     :			Optional[Any]		=    torch.Generator(__lowerCamelCase					)
			generator.manual_seed(__lowerCamelCase					)
			lowerCamelCase     :			int		=    pipe(
			    [prompt]		, generator=__lowerCamelCase		, guidance_scale=__lowerCamelCase		, num_inference_steps=50		, output_type='np'		, width=512		, height=512		, )

			lowerCamelCase     :			int		=    output.images
			lowerCamelCase     :			Optional[int]		=    image[0, -3:, -3:, -1]
			lowerCamelCase     :			List[Any]		=    [
			    0.43497998,
			    0.91814065,
			    0.7540739,
			    0.55580205,
			    0.8467265,
			    0.5389691,
			    0.62574506,
			    0.58897763,
			    0.50926757,
			]

			assert image.shape == (1, 512, 512, 3)

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

			# with sega enabled
			# generator = torch.manual_seed(seed)
			generator.manual_seed(__lowerCamelCase					)
			lowerCamelCase     :			List[Any]		=    pipe(
			    [prompt]		, generator=__lowerCamelCase		, guidance_scale=__lowerCamelCase		, num_inference_steps=50		, output_type='np'		, width=512		, height=512		, **__lowerCamelCase		, )

			lowerCamelCase     :			Union[str, Any]		=    output.images
			lowerCamelCase     :			str		=    image[0, -3:, -3:, -1]
			lowerCamelCase     :			str		=    [
			    0.3089719,
			    0.30500144,
			    0.29016042,
			    0.30630964,
			    0.325687,
			    0.29419225,
			    0.2908091,
			    0.28723598,
			    0.27696294,
			]

			assert image.shape == (1, 512, 512, 3)

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




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			str		=    "cuda"
			lowerCamelCase     :			Optional[Any]		=    StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'					)
			lowerCamelCase     :			Optional[Any]		=    pipe.to(__lowerCamelCase					)
			pipe.set_progress_bar_config(disable=__lowerCamelCase					)

			lowerCamelCase     :			Optional[int]		=    "a castle next to a river"
			lowerCamelCase     :			List[Any]		=    {
			    "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
			    "reverse_editing_direction": False,
			    "edit_warmup_steps": [15, 18],
			    "edit_guidance_scale": 6,
			    "edit_threshold": [0.9, 0.8],
			    "edit_momentum_scale": 0.5,
			    "edit_mom_beta": 0.6,
			}

			lowerCamelCase     :			Union[str, Any]		=    48
			lowerCamelCase     :			Optional[int]		=    7

			# no sega enabled
			lowerCamelCase     :			int		=    torch.Generator(__lowerCamelCase					)
			generator.manual_seed(__lowerCamelCase					)
			lowerCamelCase     :			Tuple		=    pipe(
			    [prompt]		, generator=__lowerCamelCase		, guidance_scale=__lowerCamelCase		, num_inference_steps=50		, output_type='np'		, width=512		, height=512		, )

			lowerCamelCase     :			List[Any]		=    output.images
			lowerCamelCase     :			Any		=    image[0, -3:, -3:, -1]
			lowerCamelCase     :			Dict		=    [
			    0.75163555,
			    0.76037145,
			    0.61785,
			    0.9189673,
			    0.8627701,
			    0.85189694,
			    0.8512813,
			    0.87012076,
			    0.8312857,
			]

			assert image.shape == (1, 512, 512, 3)

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

			# with sega enabled
			# generator = torch.manual_seed(seed)
			generator.manual_seed(__lowerCamelCase					)
			lowerCamelCase     :			Optional[int]		=    pipe(
			    [prompt]		, generator=__lowerCamelCase		, guidance_scale=__lowerCamelCase		, num_inference_steps=50		, output_type='np'		, width=512		, height=512		, **__lowerCamelCase		, )

			lowerCamelCase     :			List[str]		=    output.images
			lowerCamelCase     :			Any		=    image[0, -3:, -3:, -1]
			lowerCamelCase     :			Dict		=    [
			    0.73553365,
			    0.7537271,
			    0.74341905,
			    0.66480356,
			    0.6472925,
			    0.63039416,
			    0.64812905,
			    0.6749717,
			    0.6517102,
			]

			assert image.shape == (1, 512, 512, 3)

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




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Optional[Any]		=    "cuda"
			lowerCamelCase     :			List[Any]		=    StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'		, torch_dtype=torch.floataa					)
			lowerCamelCase     :			Any		=    pipe.to(__lowerCamelCase					)
			pipe.set_progress_bar_config(disable=__lowerCamelCase					)

			lowerCamelCase     :			Dict		=    "a photo of a cat"
			lowerCamelCase     :			List[Any]		=    {
			    "editing_prompt": ["sunglasses"],
			    "reverse_editing_direction": [False],
			    "edit_warmup_steps": 10,
			    "edit_guidance_scale": 6,
			    "edit_threshold": 0.95,
			    "edit_momentum_scale": 0.5,
			    "edit_mom_beta": 0.6,
			}

			lowerCamelCase     :			Dict		=    3
			lowerCamelCase     :			List[Any]		=    7

			# no sega enabled
			lowerCamelCase     :			List[Any]		=    torch.Generator(__lowerCamelCase					)
			generator.manual_seed(__lowerCamelCase					)
			lowerCamelCase     :			Union[str, Any]		=    pipe(
			    [prompt]		, generator=__lowerCamelCase		, guidance_scale=__lowerCamelCase		, num_inference_steps=50		, output_type='np'		, width=512		, height=512		, )

			lowerCamelCase     :			Union[str, Any]		=    output.images
			lowerCamelCase     :			List[str]		=    image[0, -3:, -3:, -1]
			lowerCamelCase     :			List[Any]		=    [
			    0.34887695,
			    0.3876953,
			    0.375,
			    0.34423828,
			    0.3581543,
			    0.35717773,
			    0.3383789,
			    0.34570312,
			    0.359375,
			]

			assert image.shape == (1, 512, 512, 3)

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

			# with sega enabled
			# generator = torch.manual_seed(seed)
			generator.manual_seed(__lowerCamelCase					)
			lowerCamelCase     :			Tuple		=    pipe(
			    [prompt]		, generator=__lowerCamelCase		, guidance_scale=__lowerCamelCase		, num_inference_steps=50		, output_type='np'		, width=512		, height=512		, **__lowerCamelCase		, )

			lowerCamelCase     :			List[str]		=    output.images
			lowerCamelCase     :			Any		=    image[0, -3:, -3:, -1]
			lowerCamelCase     :			Union[str, Any]		=    [
			    0.42285156,
			    0.36914062,
			    0.29077148,
			    0.42041016,
			    0.41918945,
			    0.35498047,
			    0.3618164,
			    0.4423828,
			    0.43115234,
			]

			assert image.shape == (1, 512, 512, 3)

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













# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       @classmethod
       def 			UpperCamelCase_	(					cls							)	->   Tuple:
            return cls







       @slow
       @require_torch_gpu
       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							F"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

            # we want this test to run even if there is only one GPU, but if there are more we use them all
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							get_gpu_count()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							F"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
            _SCREAMING_SNAKE_CASE				:		int  			=							[sys.executable] + distributed_args + args
            execute_subprocess_async(__lowerCamelCase			,  env=self.get_env()							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							os.path.join(__lowerCamelCase			,  "test_bleu.json"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							load_json(__lowerCamelCase							)
            # print(metrics)
            self.assertGreaterEqual(metrics["bleu"]			,  2_5							)