



# 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 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _UpperCAmelCase  						=      frozenset({'''control_image'''}						)  # skip `image` and `mask` for now, only test for control_image
   _UpperCAmelCase  						=      TEXT_TO_IMAGE_IMAGE_PARAMS

   def 		A				(   self      :							Dict				)							->					int:


    """simple docstring"""






    torch.manual_seed(0				)
    a							=		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				)
    a							=		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				)
    a							=		DDIMScheduler(
        beta_start=0.0_0_0_8_5				, beta_end=0.0_1_2				, beta_schedule="scaled_linear"				, clip_sample=__lowerCAmelCase				, set_alpha_to_one=__lowerCAmelCase				, )
    torch.manual_seed(0				)
    a							=		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				)
    a							=		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				, )
    a							=		CLIPTextModel(__lowerCAmelCase				)
    a							=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

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

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Tuple=0				)							->					str:


    """simple docstring"""






    if str(__lowerCAmelCase				).startswith("mps"				):
     a							=		torch.manual_seed(__lowerCAmelCase				)
    else:
     a							=		torch.Generator(device=__lowerCAmelCase				).manual_seed(__lowerCAmelCase				)

    a							=		2
    a							=		randn_tensor(
        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowerCAmelCase				, device=torch.device(__lowerCAmelCase				)				, )
    a							=		floats_tensor((1, 3, 32, 32)				, rng=random.Random(__lowerCAmelCase				)				).to(__lowerCAmelCase				)
    a							=		init_image.cpu().permute(0				, 2				, 3				, 1				)[0]

    a							=		Image.fromarray(np.uinta(__lowerCAmelCase				)				).convert("RGB"				).resize((64, 64)				)
    a							=		Image.fromarray(np.uinta(init_image + 4				)				).convert("RGB"				).resize((64, 64)				)

    a							=		{
        "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 		A				(   self      :							Optional[Any]				)							->					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 		A				(   self      :							List[Any]				)							->					Tuple:


    """simple docstring"""






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)




   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _UpperCAmelCase  						=      frozenset([]						)

   def 		A				(   self      :							str				)							->					Optional[int]:


    """simple docstring"""






    torch.manual_seed(0				)
    a							=		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				)
    a							=		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				)
    a							=		DDIMScheduler(
        beta_start=0.0_0_0_8_5				, beta_end=0.0_1_2				, beta_schedule="scaled_linear"				, clip_sample=__lowerCAmelCase				, set_alpha_to_one=__lowerCAmelCase				, )
    torch.manual_seed(0				)
    a							=		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				)
    a							=		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				, )
    a							=		CLIPTextModel(__lowerCAmelCase				)
    a							=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

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



class 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      StableDiffusionControlNetInpaintPipeline
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _UpperCAmelCase  						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

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


    """simple docstring"""






    torch.manual_seed(0				)
    a							=		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(__lowerCAmelCase      :							Tuple				):
     if isinstance(__lowerCAmelCase				, torch.nn.Convad				):
      torch.nn.init.normal(m.weight				)
      m.bias.data.fill_(1.0				)

    a							=		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(__lowerCAmelCase				)

    torch.manual_seed(0				)
    a							=		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(__lowerCAmelCase				)

    torch.manual_seed(0				)
    a							=		DDIMScheduler(
        beta_start=0.0_0_0_8_5				, beta_end=0.0_1_2				, beta_schedule="scaled_linear"				, clip_sample=__lowerCAmelCase				, set_alpha_to_one=__lowerCAmelCase				, )
    torch.manual_seed(0				)
    a							=		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				)
    a							=		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				, )
    a							=		CLIPTextModel(__lowerCAmelCase				)
    a							=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

    a							=		MultiControlNetModel([controlneta, controlneta]				)

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

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							str=0				)							->					str:


    """simple docstring"""






    if str(__lowerCAmelCase				).startswith("mps"				):
     a							=		torch.manual_seed(__lowerCAmelCase				)
    else:
     a							=		torch.Generator(device=__lowerCAmelCase				).manual_seed(__lowerCAmelCase				)

    a							=		2

    a							=		[
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowerCAmelCase				, device=torch.device(__lowerCAmelCase				)				, ),
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				, generator=__lowerCAmelCase				, device=torch.device(__lowerCAmelCase				)				, ),
    ]
    a							=		floats_tensor((1, 3, 32, 32)				, rng=random.Random(__lowerCAmelCase				)				).to(__lowerCAmelCase				)
    a							=		init_image.cpu().permute(0				, 2				, 3				, 1				)[0]

    a							=		Image.fromarray(np.uinta(__lowerCAmelCase				)				).convert("RGB"				).resize((64, 64)				)
    a							=		Image.fromarray(np.uinta(init_image + 4				)				).convert("RGB"				).resize((64, 64)				)

    a							=		{
        "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 		A				(   self      :							Optional[Any]				)							->					List[str]:


    """simple docstring"""






    a							=		self.get_dummy_components()
    a							=		self.pipeline_class(**__lowerCAmelCase				)
    pipe.to(__lowerCAmelCase				)

    a							=		1_0.0
    a							=		4

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				)[0]

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				, control_guidance_start=0.1				, control_guidance_end=0.2				)[0]

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				, control_guidance_start=[0.1, 0.3]				, control_guidance_end=[0.2, 0.7]				)[0]

    a							=		self.get_dummy_inputs(__lowerCAmelCase				)
    a							=		steps
    a							=		scale
    a							=		pipe(**__lowerCAmelCase				, 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 		A				(   self      :							Dict				)							->					List[Any]:


    """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 		A				(   self      :							Optional[int]				)							->					str:


    """simple docstring"""






    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)

   def 		A				(   self      :							Dict				)							->					List[str]:


    """simple docstring"""






    self._test_inference_batch_single_identical(expected_max_diff=2E-3				)




   def 		A				(   self      :							Dict				)							->					List[Any]:


    """simple docstring"""






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



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

   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






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

   def 		A				(   self      :							Optional[int]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"				)

    a							=		StableDiffusionControlNetInpaintPipeline.from_pretrained(
        "runwayml/stable-diffusion-inpainting"				, safety_checker=__lowerCAmelCase				, controlnet=__lowerCAmelCase				)
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=__lowerCAmelCase				)

    a							=		torch.Generator(device="cpu"				).manual_seed(0				)
    a							=		load_image(
        "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"				).resize((512, 512)				)

    a							=		load_image(
        "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
        "/stable_diffusion_inpaint/input_bench_mask.png"				).resize((512, 512)				)

    a							=		"pitch black hole"

    a							=		load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"				).resize((512, 512)				)

    a							=		pipe(
        __lowerCAmelCase				, image=__lowerCAmelCase				, mask_image=__lowerCAmelCase				, control_image=__lowerCAmelCase				, generator=__lowerCAmelCase				, output_type="np"				, num_inference_steps=3				, )

    a							=		output.images[0]

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

    a							=		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 		A				(   self      :							Any				)							->					Dict:


    """simple docstring"""






    a							=		ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"				)

    a							=		StableDiffusionControlNetInpaintPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5"				, safety_checker=__lowerCAmelCase				, controlnet=__lowerCAmelCase				)
    a							=		DDIMScheduler.from_config(pipe.scheduler.config				)
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=__lowerCAmelCase				)

    a							=		torch.Generator(device="cpu"				).manual_seed(33				)

    a							=		load_image(
        "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"				)
    a							=		init_image.resize((512, 512)				)

    a							=		load_image(
        "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"				)
    a							=		mask_image.resize((512, 512)				)

    a							=		"a handsome man with ray-ban sunglasses"

    def make_inpaint_condition(__lowerCAmelCase      :							Any				, __lowerCAmelCase      :							int				):
     a							=		np.array(image.convert("RGB"				)				).astype(np.floataa				) / 2_5_5.0
     a							=		np.array(image_mask.convert("L"				)				).astype(np.floataa				) / 2_5_5.0

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

    a							=		make_inpaint_condition(__lowerCAmelCase				, __lowerCAmelCase				)

    a							=		pipe(
        __lowerCAmelCase				, image=__lowerCAmelCase				, mask_image=__lowerCAmelCase				, control_image=__lowerCAmelCase				, guidance_scale=9.0				, eta=1.0				, generator=__lowerCAmelCase				, num_inference_steps=20				, output_type="np"				, )
    a							=		output.images[0]

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

    a							=		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 itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
    from tests.trainer.test_trainer import (  # noqa
        RegressionModelConfig,
        RegressionPreTrainedModel,
        get_regression_trainer,
    )


set_seed(4_2)

lowerCamelCase				:     Union[str, Any]								=  get_tests_dir("""fixtures""")
lowerCamelCase				:     str								=  os.path.join(dirname(get_tests_dir()))
lowerCamelCase				:     Union[str, Any]								=  dirname(os.path.abspath(__file__))

# default torch.distributed port
lowerCamelCase				:     Optional[Any]								=  """10999"""

lowerCamelCase				:     Optional[Any]								=  """t5-small"""

# *** Working Models ***
lowerCamelCase				:     Union[str, Any]								=  """hf-internal-testing/tiny-albert"""
lowerCamelCase				:     int								=  """sshleifer/bart-tiny-random"""
lowerCamelCase				:     List[Any]								=  """hf-internal-testing/tiny-bert"""
lowerCamelCase				:     int								=  """hf-internal-testing/tiny-random-bigbird_pegasus"""
lowerCamelCase				:     List[Any]								=  """hf-internal-testing/tiny-random-big_bird"""
lowerCamelCase				:     Tuple								=  """hf-internal-testing/tiny-random-blenderbot"""
lowerCamelCase				:     str								=  """bigscience/bigscience-small-testing"""
lowerCamelCase				:     int								=  """hf-internal-testing/tiny-random-deberta"""
lowerCamelCase				:     Tuple								=  """hf-internal-testing/tiny-random-deberta-v2"""
lowerCamelCase				:     List[str]								=  """sshleifer/tiny-distilbert-base-cased"""
lowerCamelCase				:     int								=  """hf-internal-testing/tiny-electra"""
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-random-flaubert"""
lowerCamelCase				:     Union[str, Any]								=  """stas/tiny-wmt19-en-de"""
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-random-funnel"""
lowerCamelCase				:     List[str]								=  """sshleifer/tiny-gpt2"""
lowerCamelCase				:     int								=  """hf-internal-testing/tiny-random-gptj"""
lowerCamelCase				:     List[Any]								=  """hf-internal-testing/tiny-random-gpt_neo"""
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-layoutlm"""
lowerCamelCase				:     int								=  """hf-internal-testing/tiny-random-led"""
lowerCamelCase				:     List[str]								=  """hf-internal-testing/tiny-random-longformer"""
lowerCamelCase				:     Tuple								=  """stas/tiny-m2m_100"""  # hf tiny model is unsuitable
lowerCamelCase				:     Optional[int]								=  """sshleifer/tiny-marian-en-de"""
lowerCamelCase				:     Optional[int]								=  """sshleifer/tiny-mbart"""
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-random-mobilebert"""
lowerCamelCase				:     Tuple								=  """hf-internal-testing/tiny-random-mpnet"""
lowerCamelCase				:     Dict								=  """stas/pegasus-cnn_dailymail-tiny-random"""
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-random-prophetnet"""
lowerCamelCase				:     Any								=  """sshleifer/tiny-distilroberta-base"""
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-random-squeezebert"""
lowerCamelCase				:     Tuple								=  """patrickvonplaten/t5-tiny-random"""
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-random-t5-v1.1"""
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-vit"""
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-xlm-roberta"""
lowerCamelCase				:     Dict								=  """sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-mt5"""
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-camembert"""
lowerCamelCase				:     Any								=  """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-convbert"""
lowerCamelCase				:     str								=  """hf-internal-testing/tiny-random-layoutlmv2"""
lowerCamelCase				:     List[Any]								=  """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
lowerCamelCase				:     List[str]								=  """hf-internal-testing/tiny-random-ctrl"""
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
lowerCamelCase				:     Tuple								=  """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
lowerCamelCase				:     List[str]								=  """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
lowerCamelCase				:     str								=  """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
lowerCamelCase				:     List[Any]								=  """"""
# - "luke" Entities classes - no plan to make such example
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
lowerCamelCase				:     Optional[Any]								=  """hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
lowerCamelCase				:     Union[str, Any]								=  """hf-internal-testing/tiny-random-tapas"""


def 							A__ (     UpperCamelCase__=False    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			min(2       ,							get_gpu_count()    ) if distributed else 1
  _SCREAMING_SNAKE_CASE        =			os.environ.get('''DS_TEST_PORT'''       ,							UpperCamelCase__    )
  return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()


def 							A__ (     ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			F'''{FIXTURE_DIRECTORY}/tests_samples'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/wmt_en_ro'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/xsum'''
  _SCREAMING_SNAKE_CASE        =			'''
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        '''.split()

  # try to cover as many models as possible once (it's enough to run on one task per model)
  # but need a tiny model for each
  #
  # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
  _SCREAMING_SNAKE_CASE        =			{
      '''trans''': [
          '''bart''',
          '''fsmt''',
          '''m2m_100''',
          '''marian''',
          '''mbart''',
          '''t5''',
          '''t5_v1''',
          # "mt5", missing model files
      ],
      '''sum''': [
          '''pegasus''',
      ],
      '''clm''': [
          '''big_bird''',
          '''bigbird_pegasus''',
          '''blenderbot''',
          '''bloom''',
          '''gpt2''',
          '''gpt_neo''',
          '''gptj''',
          '''xlm-roberta''',
          '''prophetnet''',
          # "camembert", missing model files
      ],
      '''mlm''': [
          '''albert''',
          '''deberta''',
          '''deberta-v2''',
          '''distilbert''',
          '''electra''',
          '''flaubert''',
          '''funnel''',
          '''layoutlm''',
          # "reformer", # multiple issues with either mlm/qa/clas
      ],
      '''qa''': [
          '''led''',
          '''longformer''',
          '''mobilebert''',
          '''mpnet''',
          '''roberta''',
          '''squeezebert''',
          # "convbert", # missing tokenizer files
          # "layoutlmv2", missing model files
      ],
      '''clas''': [
          '''bert''',
          '''xlnet''',
          # "hubert", # missing tokenizer files
          # "ibert", # multiple issues with either mlm/qa/clas
          # "transfo-xl", # tokenizer issues as ctrl
          # "ctrl", # tokenizer issues
          # "openai-gpt", missing model files
          # "tapas", multiple issues
      ],
      '''img_clas''': [
          '''vit''',
      ],
  }

  _SCREAMING_SNAKE_CASE        =			F'''{ROOT_DIRECTORY}/examples/pytorch'''

  _SCREAMING_SNAKE_CASE        =			{
      '''trans''': F'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
      '''sum''': F'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
      '''clm''': F'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
      '''mlm''': F'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
      '''qa''': F'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
      '''clas''': F'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
      '''img_clas''': F'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
  }

  _SCREAMING_SNAKE_CASE        =			get_launcher(distributed=UpperCamelCase__    )

  _SCREAMING_SNAKE_CASE        =			{}
  for task, args in tasks.items():
    _SCREAMING_SNAKE_CASE        =			args.split()
    for model in tasksamodels[task]:
      _SCREAMING_SNAKE_CASE        =			globals()[F'''{model.upper().replace("-"       ,							"_"    )}_TINY''']
      _SCREAMING_SNAKE_CASE        =			F'''--model_name_or_path {model_name}'''.split()
      _SCREAMING_SNAKE_CASE        =			launcher + args + args_model + args_main

      # # generation special case
      # if task == "gen":
      #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
      #     args_model += f"--model_type {model}".split()
      #     cmds[f"{task}_{model}"] = launcher + args + args_model
      # else:

  return cmds


lowerCamelCase				:     List[str]								=  make_task_cmds()

lowerCamelCase				:     Any								=  """zero2"""
lowerCamelCase				:     List[Any]								=  """zero3"""

lowerCamelCase				:     int								=  [ZEROa, ZEROa]


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			parameterized.to_safe_name('''_'''.join(str(UpperCamelCase__    ) for x in param.args    )    )
  return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
lowerCamelCase				:     Optional[Any]								=  list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class    __snake_case(					__A       ):




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      if task not in task_cmds:
        raise ValueError(F'''don\'t know of task {task}, have {task_cmds.keys()}'''			)

      _SCREAMING_SNAKE_CASE        =			task_cmds[task]
      _SCREAMING_SNAKE_CASE        =			F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()
      _SCREAMING_SNAKE_CASE        =			F'''--output_dir {output_dir}'''.split()

      cmd += args_ds + args_out

      return cmd, output_dir




    @parameterized.expand(A_    ,							name_func=A_			)
    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_task_cmd(A_    ,							A_			)

      # 1. generate the checkpoint
      cmd += "--save_steps 1".split()
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
      execute_subprocess_async(A_    ,							env=self.get_env()			)

      # 2. test that the fp32 weights get reconsolidated
      _SCREAMING_SNAKE_CASE        =			F'''{output_dir}/checkpoint-1'''
      _SCREAMING_SNAKE_CASE        =			F'''{chkpt_dir}/out.bin'''
      _SCREAMING_SNAKE_CASE        =			F'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
      subprocess.check_call(A_    ,							shell=A_			)
      assert os.path.exists(A_			), F'''{recovered_model_path} was not found'''

      # possibly could also test that the resulting saved model is usable but given that we use
      # random models we won't know if it's any good
