

"""simple docstring"""






# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()






class     _UpperCAmelCase			(		__a    ,     __a    ,     __a    ,     unittest.TestCase):
			__a :       Union[str, Any]									=							StableDiffusionControlNetInpaintPipeline
			__a :       Union[str, Any]									=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__a :       Any									=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__a :       Optional[int]									=							frozenset({"""control_image"""})  # skip `image` and `mask` for now, only test for control_image
			__a :       Optional[Any]									=							TEXT_TO_IMAGE_IMAGE_PARAMS
			def 							__snake_case (		self     )    ->    Optional[int]:






				'''simple docstring'''







				torch.manual_seed(0     )
				_UpperCAmelCase    :							Optional[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      ,		)
				torch.manual_seed(0     )
				_UpperCAmelCase    :							Union[str, Any]									=					ControlNetModel(
				    block_out_channels=(32, 64)      ,		layers_per_block=2      ,		in_channels=4      ,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,		cross_attention_dim=32      ,		conditioning_embedding_out_channels=(16, 32)      ,		)
				torch.manual_seed(0     )
				_UpperCAmelCase    :							int									=					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    :							str									=					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      ,		)
				torch.manual_seed(0     )
				_UpperCAmelCase    :							int									=					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=10_00      ,		)
				_UpperCAmelCase    :							str									=					CLIPTextModel(_A     )
				_UpperCAmelCase    :							int									=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

				_UpperCAmelCase    :							Optional[int]									=					{
				    """unet""": unet,
				    """controlnet""": controlnet,
				    """scheduler""": scheduler,
				    """vae""": vae,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """safety_checker""": None,
				    """feature_extractor""": None,
				}
				return components
			def 							__snake_case (		self      ,		_A      ,		_A=0     )    ->    Optional[int]:






				'''simple docstring'''







				if str(_A     ).startswith("""mps"""     ):
					_UpperCAmelCase    :							str									=					torch.manual_seed(_A     )
				else:
					_UpperCAmelCase    :							Optional[Any]									=					torch.Generator(device=_A     ).manual_seed(_A     )

				_UpperCAmelCase    :							List[str]									=					2
				_UpperCAmelCase    :							Tuple									=					randn_tensor(
				    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,		generator=_A      ,		device=torch.device(_A     )      ,		)
				_UpperCAmelCase    :							Any									=					floats_tensor((1, 3, 32, 32)      ,		rng=random.Random(_A     )     ).to(_A     )
				_UpperCAmelCase    :							Dict									=					init_image.cpu().permute(0      ,		2      ,		3      ,		1     )[0]

				_UpperCAmelCase    :							Any									=					Image.fromarray(np.uinta(_A     )     ).convert("""RGB"""     ).resize((64, 64)     )
				_UpperCAmelCase    :							str									=					Image.fromarray(np.uinta(init_image + 4     )     ).convert("""RGB"""     ).resize((64, 64)     )

				_UpperCAmelCase    :							Dict									=					{
				    """prompt""": """A painting of a squirrel eating a burger""",
				    """generator""": generator,
				    """num_inference_steps""": 2,
				    """guidance_scale""": 6.0,
				    """output_type""": """numpy""",
				    """image""": image,
				    """mask_image""": mask_image,
				    """control_image""": control_image,
				}

				return inputs
			def 							__snake_case (		self     )    ->    str:






				'''simple docstring'''







				return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3     )
			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()      ,		reason="""XFormers attention is only available with CUDA and `xformers` installed"""      ,		)
			def 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3     )
			def 							__snake_case (		self     )    ->    Any:






				'''simple docstring'''







				self._test_inference_batch_single_identical(expected_max_diff=2e-3     )






class     _UpperCAmelCase			(		__a):
			__a :       int									=							StableDiffusionControlNetInpaintPipeline
			__a :       Optional[Any]									=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__a :       Tuple									=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__a :       Optional[int]									=							frozenset([])
			def 							__snake_case (		self     )    ->    Dict:






				'''simple docstring'''







				torch.manual_seed(0     )
				_UpperCAmelCase    :							List[str]									=					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      ,		)
				torch.manual_seed(0     )
				_UpperCAmelCase    :							int									=					ControlNetModel(
				    block_out_channels=(32, 64)      ,		layers_per_block=2      ,		in_channels=4      ,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,		cross_attention_dim=32      ,		conditioning_embedding_out_channels=(16, 32)      ,		)
				torch.manual_seed(0     )
				_UpperCAmelCase    :							Any									=					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    :							List[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      ,		)
				torch.manual_seed(0     )
				_UpperCAmelCase    :							Any									=					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=10_00      ,		)
				_UpperCAmelCase    :							Any									=					CLIPTextModel(_A     )
				_UpperCAmelCase    :							Optional[Any]									=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

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






class     _UpperCAmelCase			(		__a    ,     __a    ,     unittest.TestCase):
			__a :       Optional[Any]									=							StableDiffusionControlNetInpaintPipeline
			__a :       Union[str, Any]									=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__a :       Any									=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			def 							__snake_case (		self     )    ->    Optional[int]:






				'''simple docstring'''







				torch.manual_seed(0     )
				_UpperCAmelCase    :							Optional[int]									=					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      ,		)
				torch.manual_seed(0     )

				def init_weights(_A     ):
					if isinstance(_A      ,		torch.nn.Convad     ):
						torch.nn.init.normal(m.weight     )
						m.bias.data.fill_(1.0     )

				_UpperCAmelCase    :							Tuple									=					ControlNetModel(
				    block_out_channels=(32, 64)      ,		layers_per_block=2      ,		in_channels=4      ,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,		cross_attention_dim=32      ,		conditioning_embedding_out_channels=(16, 32)      ,		)
				controlneta.controlnet_down_blocks.apply(_A     )

				torch.manual_seed(0     )
				_UpperCAmelCase    :							List[str]									=					ControlNetModel(
				    block_out_channels=(32, 64)      ,		layers_per_block=2      ,		in_channels=4      ,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,		cross_attention_dim=32      ,		conditioning_embedding_out_channels=(16, 32)      ,		)
				controlneta.controlnet_down_blocks.apply(_A     )

				torch.manual_seed(0     )
				_UpperCAmelCase    :							List[str]									=					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    :							Dict									=					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      ,		)
				torch.manual_seed(0     )
				_UpperCAmelCase    :							Tuple									=					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=10_00      ,		)
				_UpperCAmelCase    :							int									=					CLIPTextModel(_A     )
				_UpperCAmelCase    :							str									=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

				_UpperCAmelCase    :							Optional[int]									=					MultiControlNetModel([controlneta, controlneta]     )

				_UpperCAmelCase    :							Optional[int]									=					{
				    """unet""": unet,
				    """controlnet""": controlnet,
				    """scheduler""": scheduler,
				    """vae""": vae,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """safety_checker""": None,
				    """feature_extractor""": None,
				}
				return components
			def 							__snake_case (		self      ,		_A      ,		_A=0     )    ->    Optional[int]:






				'''simple docstring'''







				if str(_A     ).startswith("""mps"""     ):
					_UpperCAmelCase    :							int									=					torch.manual_seed(_A     )
				else:
					_UpperCAmelCase    :							int									=					torch.Generator(device=_A     ).manual_seed(_A     )

				_UpperCAmelCase    :							str									=					2

				_UpperCAmelCase    :							List[str]									=					[
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,		generator=_A      ,		device=torch.device(_A     )      ,		),
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,		generator=_A      ,		device=torch.device(_A     )      ,		),
				]
				_UpperCAmelCase    :							Optional[Any]									=					floats_tensor((1, 3, 32, 32)      ,		rng=random.Random(_A     )     ).to(_A     )
				_UpperCAmelCase    :							Tuple									=					init_image.cpu().permute(0      ,		2      ,		3      ,		1     )[0]

				_UpperCAmelCase    :							Union[str, Any]									=					Image.fromarray(np.uinta(_A     )     ).convert("""RGB"""     ).resize((64, 64)     )
				_UpperCAmelCase    :							Optional[Any]									=					Image.fromarray(np.uinta(init_image + 4     )     ).convert("""RGB"""     ).resize((64, 64)     )

				_UpperCAmelCase    :							List[str]									=					{
				    """prompt""": """A painting of a squirrel eating a burger""",
				    """generator""": generator,
				    """num_inference_steps""": 2,
				    """guidance_scale""": 6.0,
				    """output_type""": """numpy""",
				    """image""": image,
				    """mask_image""": mask_image,
				    """control_image""": control_image,
				}

				return inputs
			def 							__snake_case (		self     )    ->    int:






				'''simple docstring'''







				_UpperCAmelCase    :							List[Any]									=					self.get_dummy_components()
				_UpperCAmelCase    :							Dict									=					self.pipeline_class(**_A     )
				pipe.to(_A     )

				_UpperCAmelCase    :							Optional[Any]									=					10.0
				_UpperCAmelCase    :							str									=					4

				_UpperCAmelCase    :							Any									=					self.get_dummy_inputs(_A     )
				_UpperCAmelCase    :							Dict									=					steps
				_UpperCAmelCase    :							Any									=					scale
				_UpperCAmelCase    :							str									=					pipe(**_A     )[0]

				_UpperCAmelCase    :							Optional[int]									=					self.get_dummy_inputs(_A     )
				_UpperCAmelCase    :							List[Any]									=					steps
				_UpperCAmelCase    :							Union[str, Any]									=					scale
				_UpperCAmelCase    :							int									=					pipe(**_A      ,		control_guidance_start=0.1      ,		control_guidance_end=0.2     )[0]

				_UpperCAmelCase    :							Union[str, Any]									=					self.get_dummy_inputs(_A     )
				_UpperCAmelCase    :							Dict									=					steps
				_UpperCAmelCase    :							List[Any]									=					scale
				_UpperCAmelCase    :							Any									=					pipe(**_A      ,		control_guidance_start=[0.1, 0.3]      ,		control_guidance_end=[0.2, 0.7]     )[0]

				_UpperCAmelCase    :							Optional[int]									=					self.get_dummy_inputs(_A     )
				_UpperCAmelCase    :							Optional[Any]									=					steps
				_UpperCAmelCase    :							Optional[Any]									=					scale
				_UpperCAmelCase    :							Optional[int]									=					pipe(**_A      ,		control_guidance_start=0.4      ,		control_guidance_end=[0.5, 0.8]     )[0]

				# make sure that all outputs are different
				assert np.sum(np.abs(output_a - output_a     )     ) > 1e-3
				assert np.sum(np.abs(output_a - output_a     )     ) > 1e-3
				assert np.sum(np.abs(output_a - output_a     )     ) > 1e-3
			def 							__snake_case (		self     )    ->    int:






				'''simple docstring'''







				return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3     )
			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()      ,		reason="""XFormers attention is only available with CUDA and `xformers` installed"""      ,		)
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3     )
			def 							__snake_case (		self     )    ->    Optional[Any]:






				'''simple docstring'''







				self._test_inference_batch_single_identical(expected_max_diff=2e-3     )
			def 							__snake_case (		self     )    ->    List[str]:






				'''simple docstring'''







				_UpperCAmelCase    :							str									=					self.get_dummy_components()
				_UpperCAmelCase    :							Tuple									=					self.pipeline_class(**_A     )
				pipe.to(_A     )
				pipe.set_progress_bar_config(disable=_A     )
				with tempfile.TemporaryDirectory() as tmpdir:
					try:
						# save_pretrained is not implemented for Multi-ControlNet
						pipe.save_pretrained(_A     )
					except NotImplementedError:
						pass






@slow
@require_torch_gpu
class     _UpperCAmelCase			(		unittest.TestCase):
			def 							__snake_case (		self     )    ->    Dict:






				'''simple docstring'''







				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 							__snake_case (		self     )    ->    Tuple:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""     )

				_UpperCAmelCase    :							int									=					StableDiffusionControlNetInpaintPipeline.from_pretrained(
				    """runwayml/stable-diffusion-inpainting"""      ,		safety_checker=_A      ,		controlnet=_A     )
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=_A     )

				_UpperCAmelCase    :							List[str]									=					torch.Generator(device="""cpu"""     ).manual_seed(0     )
				_UpperCAmelCase    :							int									=					load_image(
				    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""     ).resize((5_12, 5_12)     )

				_UpperCAmelCase    :							Optional[int]									=					load_image(
				    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
				    """/stable_diffusion_inpaint/input_bench_mask.png"""     ).resize((5_12, 5_12)     )

				_UpperCAmelCase    :							Union[str, Any]									=					"""pitch black hole"""

				_UpperCAmelCase    :							Dict									=					load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""     ).resize((5_12, 5_12)     )

				_UpperCAmelCase    :							int									=					pipe(
				    _A      ,		image=_A      ,		mask_image=_A      ,		control_image=_A      ,		generator=_A      ,		output_type="""np"""      ,		num_inference_steps=3      ,		)

				_UpperCAmelCase    :							Tuple									=					output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				_UpperCAmelCase    :							Optional[Any]									=					load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"""     )

				assert np.abs(expected_image - image     ).max() < 9e-2
			def 							__snake_case (		self     )    ->    Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[str]									=					ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""     )

				_UpperCAmelCase    :							str									=					StableDiffusionControlNetInpaintPipeline.from_pretrained(
				    """runwayml/stable-diffusion-v1-5"""      ,		safety_checker=_A      ,		controlnet=_A     )
				_UpperCAmelCase    :							int									=					DDIMScheduler.from_config(pipe.scheduler.config     )
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=_A     )

				_UpperCAmelCase    :							List[str]									=					torch.Generator(device="""cpu"""     ).manual_seed(33     )

				_UpperCAmelCase    :							Optional[Any]									=					load_image(
				    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""     )
				_UpperCAmelCase    :							List[Any]									=					init_image.resize((5_12, 5_12)     )

				_UpperCAmelCase    :							List[Any]									=					load_image(
				    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""     )
				_UpperCAmelCase    :							Optional[int]									=					mask_image.resize((5_12, 5_12)     )

				_UpperCAmelCase    :							Tuple									=					"""a handsome man with ray-ban sunglasses"""

				def make_inpaint_condition(_A      ,		_A     ):
					_UpperCAmelCase    :							Union[str, Any]									=					np.array(image.convert("""RGB"""     )     ).astype(np.floataa     ) / 255.0
					_UpperCAmelCase    :							int									=					np.array(image_mask.convert("""L"""     )     ).astype(np.floataa     ) / 255.0

					assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
					_UpperCAmelCase    :							List[str]									=					-1.0  # set as masked pixel
					_UpperCAmelCase    :							Optional[int]									=					np.expand_dims(_A      ,		0     ).transpose(0      ,		3      ,		1      ,		2     )
					_UpperCAmelCase    :							Any									=					torch.from_numpy(_A     )
					return image

				_UpperCAmelCase    :							Optional[Any]									=					make_inpaint_condition(_A      ,		_A     )

				_UpperCAmelCase    :							Union[str, Any]									=					pipe(
				    _A      ,		image=_A      ,		mask_image=_A      ,		control_image=_A      ,		guidance_scale=9.0      ,		eta=1.0      ,		generator=_A      ,		num_inference_steps=20      ,		output_type="""np"""      ,		)
				_UpperCAmelCase    :							Tuple									=					output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				_UpperCAmelCase    :							Union[str, Any]									=					load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"""     )

				assert np.abs(expected_image - image     ).max() < 9e-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


lowerCamelCase__				:  Any													=  torch.cuda.is_available()
lowerCamelCase__				:  Optional[Any]													=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCamelCase__				:  List[str]													=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCamelCase__				:  int													=  {
    '''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       UpperCamelCase					(			_lowerCAmelCase     :						Path,  _lowerCAmelCase     :						list )				->			str:
	_UpperCAmelCase    :							Dict									=					"""\n""".join(_lowerCAmelCase )
	Path(_lowerCAmelCase ).open("""w""" ).writelines(_lowerCAmelCase )
def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[int] )				->			Optional[Any]:
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.source''' ),  _lowerCAmelCase )
		_dump_articles(os.path.join(_lowerCAmelCase,  f'''{split}.target''' ),  _lowerCAmelCase )
	return tmp_dir






class     _UpperCAmelCase			(		__a):
			@classmethod
			def 							__snake_case (		cls     )    ->    Optional[int]:






				'''simple docstring'''







				return cls
			@require_torch_multi_gpu
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[str]									=					{
				    """no_teacher""": True,
				    """freeze_encoder""": True,
				    """gpus""": 2,
				    """overwrite_output_dir""": True,
				    """sortish_sampler""": True,
				}
				self._test_distiller_cli_fork(_A      ,		check_contents=_A     )
			def 							__snake_case (		self      ,		_A      ,		_A=True     )    ->    int:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					{
				    """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(_A     )
				_UpperCAmelCase    :							dict									=					CHEAP_ARGS.copy()
				_UpperCAmelCase    :							List[Any]									=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
				_UpperCAmelCase    :							Optional[Any]									=					self.get_auto_remove_tmp_dir()
				args_d.update(data_dir=_A      ,		output_dir=_A      ,		**_A     )

				def convert(_A      ,		_A     ):
					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}'''

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

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

				self.assertIn("""test_generations.txt"""      ,		_A     )
				self.assertIn("""test_results.txt"""      ,		_A     )

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

				_UpperCAmelCase    :							List[Any]									=					load_json(_A     )
				# {'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(_A     )
				_UpperCAmelCase    :							str									=					metrics["""val"""][-1]
				self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]      ,		0.01     )
				self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']      ,		_A     )
				self.assertEqual(len(metrics["""test"""]     )      ,		1     )
				_UpperCAmelCase    :							Optional[int]									=					int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1     )
				self.assertEqual(len(metrics["""val"""]     )      ,		_A     )





