


'''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 			UpperCAmelCase			(							unittest.TestCase):

 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



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

 @property
 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    1
    UpperCamelCase__			    =    3
    UpperCamelCase__			    =    (32, 32)

    UpperCamelCase__			    =    floats_tensor((batch_size, num_channels) + sizes,       rng=random.Random(0       )       ).to(a_       )
    return image

 @property
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    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,       )
    return model

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    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,       )
    return model

 @property
 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    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(a_       )

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    def extract(*a_			:						List[Any],       **a_			:						str       ):







       class 			UpperCAmelCase			:

        def __init__(			self			:						Dict       ):





           """simple docstring"""



           UpperCamelCase__			    =    torch.ones([0]       )

        def 		lowercase_    (			self			:						Optional[int],       a_			:						str       ):





           """simple docstring"""



           self.pixel_values.to(a_       )
           return self







       return Out()

    return extract

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"  # ensure determinism for the device-dependent torch.Generator
    UpperCamelCase__			    =    self.dummy_cond_unet
    UpperCamelCase__			    =    DDIMScheduler(
        beta_start=0.00_085,       beta_end=0.012,       beta_schedule="scaled_linear",       clip_sample=a_,       set_alpha_to_one=a_,       )

    UpperCamelCase__			    =    self.dummy_vae
    UpperCamelCase__			    =    self.dummy_text_encoder
    UpperCamelCase__			    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

    # make sure here that pndm scheduler skips prk
    UpperCamelCase__			    =    StableDiffusionPipeline(
        unet=a_,       scheduler=a_,       vae=a_,       text_encoder=a_,       tokenizer=a_,       safety_checker=a_,       feature_extractor=self.dummy_extractor,       )
    UpperCamelCase__			    =    sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "A painting of a squirrel eating a burger"

    UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(0       )
    UpperCamelCase__			    =    sd_pipe([prompt],       generator=a_,       guidance_scale=6.0,       num_inference_steps=2,       output_type="np"       )
    UpperCamelCase__			    =    output.images

    UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(0       )
    UpperCamelCase__			    =    sd_pipe(
        [prompt],       generator=a_,       guidance_scale=6.0,       num_inference_steps=2,       output_type="np",       return_dict=a_,       )[0]

    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    UpperCamelCase__			    =    np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]       )

    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 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"  # ensure determinism for the device-dependent torch.Generator
    UpperCamelCase__			    =    self.dummy_cond_unet
    UpperCamelCase__			    =    PNDMScheduler(skip_prk_steps=a_       )
    UpperCamelCase__			    =    self.dummy_vae
    UpperCamelCase__			    =    self.dummy_text_encoder
    UpperCamelCase__			    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

    # make sure here that pndm scheduler skips prk
    UpperCamelCase__			    =    StableDiffusionPipeline(
        unet=a_,       scheduler=a_,       vae=a_,       text_encoder=a_,       tokenizer=a_,       safety_checker=a_,       feature_extractor=self.dummy_extractor,       )
    UpperCamelCase__			    =    sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "A painting of a squirrel eating a burger"
    UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(0       )
    UpperCamelCase__			    =    sd_pipe([prompt],       generator=a_,       guidance_scale=6.0,       num_inference_steps=2,       output_type="np"       )

    UpperCamelCase__			    =    output.images

    UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(0       )
    UpperCamelCase__			    =    sd_pipe(
        [prompt],       generator=a_,       guidance_scale=6.0,       num_inference_steps=2,       output_type="np",       return_dict=a_,       )[0]

    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    UpperCamelCase__			    =    np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]       )

    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 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained(
        "hf-internal-testing/tiny-stable-diffusion-lms-pipe",       safety_checker=a_       )
    assert isinstance(a_,       a_       )
    assert isinstance(pipe.scheduler,       a_       )
    assert pipe.safety_checker is None

    UpperCamelCase__			    =    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(a_       )
       UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained(a_       )

    # sanity check that the pipeline still works
    assert pipe.safety_checker is None
    UpperCamelCase__			    =    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 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.dummy_cond_unet
    UpperCamelCase__			    =    PNDMScheduler(skip_prk_steps=a_       )
    UpperCamelCase__			    =    self.dummy_vae
    UpperCamelCase__			    =    self.dummy_text_encoder
    UpperCamelCase__			    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

    # put models in fp16
    UpperCamelCase__			    =    unet.half()
    UpperCamelCase__			    =    vae.half()
    UpperCamelCase__			    =    bert.half()

    # make sure here that pndm scheduler skips prk
    UpperCamelCase__			    =    StableDiffusionPipeline(
        unet=a_,       scheduler=a_,       vae=a_,       text_encoder=a_,       tokenizer=a_,       safety_checker=a_,       feature_extractor=self.dummy_extractor,       )
    UpperCamelCase__			    =    sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "A painting of a squirrel eating a burger"
    UpperCamelCase__			    =    sd_pipe([prompt],       num_inference_steps=2,       output_type="np"       ).images

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







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

 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cuda"
    UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       )
    UpperCamelCase__			    =    pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "a photo of a cat"
    UpperCamelCase__			    =    {
        "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,
    }

    UpperCamelCase__			    =    3
    UpperCamelCase__			    =    7

    # no sega enabled
    UpperCamelCase__			    =    torch.Generator(a_       )
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.34_673_113,
        0.38_492_733,
        0.37_597_352,
        0.34_086_335,
        0.35_650_748,
        0.35_579_205,
        0.3_384_763,
        0.34_340_236,
        0.3_573_271,
    ]

    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(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       **a_,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.41_887_826,
        0.37_728_766,
        0.30_138_272,
        0.41_416_335,
        0.41_664_985,
        0.36_283_392,
        0.36_191_246,
        0.43_364_465,
        0.43_001_732,
    ]

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

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

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cuda"
    UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       )
    UpperCamelCase__			    =    pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "an image of a crowded boulevard, realistic, 4k"
    UpperCamelCase__			    =    {
        "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,
    }

    UpperCamelCase__			    =    9
    UpperCamelCase__			    =    7

    # no sega enabled
    UpperCamelCase__			    =    torch.Generator(a_       )
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.43_497_998,
        0.91_814_065,
        0.7_540_739,
        0.55_580_205,
        0.8_467_265,
        0.5_389_691,
        0.62_574_506,
        0.58_897_763,
        0.50_926_757,
    ]

    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(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       **a_,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.3_089_719,
        0.30_500_144,
        0.29_016_042,
        0.30_630_964,
        0.325_687,
        0.29_419_225,
        0.2_908_091,
        0.28_723_598,
        0.27_696_294,
    ]

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

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

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cuda"
    UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       )
    UpperCamelCase__			    =    pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "a castle next to a river"
    UpperCamelCase__			    =    {
        "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,
    }

    UpperCamelCase__			    =    48
    UpperCamelCase__			    =    7

    # no sega enabled
    UpperCamelCase__			    =    torch.Generator(a_       )
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.75_163_555,
        0.76_037_145,
        0.61_785,
        0.9_189_673,
        0.8_627_701,
        0.85_189_694,
        0.8_512_813,
        0.87_012_076,
        0.8_312_857,
    ]

    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(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       **a_,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.73_553_365,
        0.7_537_271,
        0.74_341_905,
        0.66_480_356,
        0.6_472_925,
        0.63_039_416,
        0.64_812_905,
        0.6_749_717,
        0.6_517_102,
    ]

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

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

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cuda"
    UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",       torch_dtype=torch.floataa       )
    UpperCamelCase__			    =    pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "a photo of a cat"
    UpperCamelCase__			    =    {
        "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,
    }

    UpperCamelCase__			    =    3
    UpperCamelCase__			    =    7

    # no sega enabled
    UpperCamelCase__			    =    torch.Generator(a_       )
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.34_887_695,
        0.3_876_953,
        0.375,
        0.34_423_828,
        0.3_581_543,
        0.35_717_773,
        0.3_383_789,
        0.34_570_312,
        0.359_375,
    ]

    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(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       **a_,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.42_285_156,
        0.36_914_062,
        0.29_077_148,
        0.42_041_016,
        0.41_918_945,
        0.35_498_047,
        0.3_618_164,
        0.4_423_828,
        0.43_115_234,
    ]

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

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






'''simple docstring'''






import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


snake_case_     			=						torch.cuda.is_available()
snake_case_     			=						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
snake_case_     			=						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
snake_case_     			=						{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}
def 				_lowerCamelCase(	UpperCamelCase__     :			Path     ,							UpperCamelCase__     :			list		)		->	str:
    A							:							List[Any]				=    '''\n'''.join(UpperCamelCase__		)
    Path(UpperCamelCase__		).open('''w'''		).writelines(UpperCamelCase__		)
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	Dict:
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(UpperCamelCase__     ,							F'''{split}.source'''		)     ,							UpperCamelCase__		)
        _dump_articles(os.path.join(UpperCamelCase__     ,							F'''{split}.target'''		)     ,							UpperCamelCase__		)
    return tmp_dir





class 	_lowercase    (				a					):


   @classmethod
   def        snake_case					(			cls       ):
       return cls


   @require_torch_multi_gpu
   def        snake_case					(			self       ):
       A							:							Optional[int]				=    {
           '''no_teacher''': True,
           '''freeze_encoder''': True,
           '''gpus''': 2,
           '''overwrite_output_dir''': True,
           '''sortish_sampler''': True,
       }
       self._test_distiller_cli_fork(_UpperCAmelCase     , check_contents=_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=True       ):
       A							:							List[Any]				=    {
           '''label_smoothing''': 0.0,
           '''early_stopping_patience''': -1,
           '''train_batch_size''': 1,
           '''eval_batch_size''': 2,
           '''max_epochs''': 2,
           '''alpha_mlm''': 0.2,
           '''alpha_ce''': 0.8,
           '''do_predict''': True,
           '''model_name_or_path''': '''sshleifer/tinier_bart''',
           '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
           '''val_check_interval''': 0.5,
       }
       default_updates.update(_UpperCAmelCase       )
       A							:							dict				=    CHEAP_ARGS.copy()
       A							:							Union[str, Any]				=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
       A							:							Optional[int]				=    self.get_auto_remove_tmp_dir()
       args_d.update(data_dir=_UpperCAmelCase     , output_dir=_UpperCAmelCase     , **_UpperCAmelCase       )

       def convert(_UpperCAmelCase     , _UpperCAmelCase       ):
           if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
               return ""
           if v is False or v is None:
               return ""
           if v is True:  # or len(str(v))==0:
               return f'''--{k}'''
           return f'''--{k}={v}'''

       A							:							int				=    [x for x in (convert(_UpperCAmelCase     , _UpperCAmelCase       ) for k, v in args_d.items()) if len(_UpperCAmelCase       )]
       A							:							Union[str, Any]				=    [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
       execute_subprocess_async(_UpperCAmelCase     , env=self.get_env()       )

       A							:							Dict				=    os.listdir(_UpperCAmelCase       )
       A							:							Optional[int]				=    {os.path.basename(_UpperCAmelCase       ) for p in contents}
       A							:							Union[str, Any]				=    [p for p in contents if p.endswith('''ckpt'''       )]
       assert len(_UpperCAmelCase       ) > 0

       self.assertIn('''test_generations.txt'''     , _UpperCAmelCase       )
       self.assertIn('''test_results.txt'''     , _UpperCAmelCase       )

       # get the following from the module, (we don't have access to `model` here)
       A							:							Optional[Any]				=    os.path.join(_UpperCAmelCase     , '''metrics.json'''       )
       A							:							List[Any]				=    '''rouge2'''

       A							:							Union[str, Any]				=    load_json(_UpperCAmelCase       )
       # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
       print(_UpperCAmelCase       )
       A							:							Optional[Any]				=    metrics['''val'''][-1]
       self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']     , 0.01       )
       self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']     , _UpperCAmelCase       )
       self.assertEqual(len(metrics['''test''']       )     , 1       )
       A							:							Tuple				=    int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1       )
       self.assertEqual(len(metrics['''val''']       )     , _UpperCAmelCase       )
