





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowerCamelCase							=     logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase							=     '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Union[str, Any]   ,				UpperCamelCase__:							Dict   ,				UpperCamelCase__:							Optional[Any]=8      ):
		SCREAMING_SNAKE_CASE__								= height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		SCREAMING_SNAKE_CASE__								= width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Tuple=512   ,				UpperCamelCase__:							Optional[Any]=512      ):
		SCREAMING_SNAKE_CASE__								= pil_image.resize((w, h)   ,				resample=Image.BICUBIC   ,				reducing_gap=1      )
		SCREAMING_SNAKE_CASE__								= np.array(pil_image.convert("""RGB"""      )      )
		SCREAMING_SNAKE_CASE__								= arr.astype(np.floataa      ) / 127.5 - 1
		SCREAMING_SNAKE_CASE__								= np.transpose(UpperCamelCase__   ,				[2, 0, 1]      )
		SCREAMING_SNAKE_CASE__								= torch.from_numpy(UpperCamelCase__      ).unsqueeze(0      )
		return image




class 					UpperCamelCase_    (       UpperCamelCase__      ):


							def __init__(							self      :Optional[int]							,			__A      :UNetaDConditionModel							,			__A      :DDPMScheduler							,			__A      :VQModel							,			)				->				List[Any]:



									"""simple docstring"""



									super().__init__()

									self.register_modules(
									    unet=__A							,			scheduler=__A							,			movq=__A							,			)
									SCREAMING_SNAKE_CASE__								= 2 ** (len(self.movq.config.block_out_channels						) - 1)


							def        _snake_case					(							self      :Optional[int]							,			__A      :Optional[Any]							,			__A      :List[Any]							,			__A      :List[str]						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= min(int(num_inference_steps * strength						)							,			__A						)

									SCREAMING_SNAKE_CASE__								= max(num_inference_steps - init_timestep							,			0						)
									SCREAMING_SNAKE_CASE__								= self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start


							def        _snake_case					(							self      :Dict							,			__A      :str							,			__A      :str							,			__A      :Tuple							,			__A      :int							,			__A      :str							,			__A      :List[Any]							,			__A      :Dict=None						)				->				Union[str, Any]:



									"""simple docstring"""



									if not isinstance(__A							,			(torch.Tensor, PIL.Image.Image, list)						):
											raise ValueError(
											    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__A						)}'''						)

									SCREAMING_SNAKE_CASE__								= image.to(device=__A							,			dtype=__A						)

									SCREAMING_SNAKE_CASE__								= batch_size * num_images_per_prompt

									if image.shape[1] == 4:
											SCREAMING_SNAKE_CASE__								= image

									else:
											if isinstance(__A							,			__A						) and len(__A						) != batch_size:
													raise ValueError(
													    f'''You have passed a list of generators of length {len(__A						)}, but requested an effective batch'''
													    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

											elif isinstance(__A							,			__A						):
													SCREAMING_SNAKE_CASE__								= [
													    self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(__A						)
													]
													SCREAMING_SNAKE_CASE__								= torch.cat(__A							,			dim=0						)
											else:
													SCREAMING_SNAKE_CASE__								= self.movq.encode(__A						).latent_dist.sample(__A						)

											SCREAMING_SNAKE_CASE__								= self.movq.config.scaling_factor * init_latents

									SCREAMING_SNAKE_CASE__								= torch.cat([init_latents]							,			dim=0						)

									SCREAMING_SNAKE_CASE__								= init_latents.shape
									SCREAMING_SNAKE_CASE__								= randn_tensor(__A							,			generator=__A							,			device=__A							,			dtype=__A						)

									# get latents
									SCREAMING_SNAKE_CASE__								= self.scheduler.add_noise(__A							,			__A							,			__A						)

									SCREAMING_SNAKE_CASE__								= init_latents

									return latents


							def        _snake_case					(							self      :Tuple							,			__A      :List[Any]=0						)				->				int:



									"""simple docstring"""



									if is_accelerate_available():
											from accelerate import cpu_offload
									else:
											raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

									SCREAMING_SNAKE_CASE__								= torch.device(f'''cuda:{gpu_id}'''						)

									SCREAMING_SNAKE_CASE__								= [
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
													cpu_offload(__A							,			__A						)


							def        _snake_case					(							self      :Tuple							,			__A      :Optional[Any]=0						)				->				Tuple:



									"""simple docstring"""



									if is_accelerate_available() and is_accelerate_version(""">="""							,			"""0.17.0.dev0"""						):
											from accelerate import cpu_offload_with_hook
									else:
											raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""						)

									SCREAMING_SNAKE_CASE__								= torch.device(f'''cuda:{gpu_id}'''						)

									if self.device.type != "cpu":
											self.to("""cpu"""							,			silence_dtype_warnings=__A						)
											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									SCREAMING_SNAKE_CASE__								= None
									for cpu_offloaded_model in [self.unet, self.movq]:
											SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= cpu_offload_with_hook(__A							,			__A							,			prev_module_hook=__A						)

									# We'll offload the last model manually.
									SCREAMING_SNAKE_CASE__								= hook


							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        _snake_case					(							self      :Optional[int]						)				->				Tuple:



									"""simple docstring"""



									if not hasattr(self.unet							,			"""_hf_hook"""						):
											return self.device
									for module in self.unet.modules():
											if (
											    hasattr(__A							,			"""_hf_hook"""						)
											    and hasattr(module._hf_hook							,			"""execution_device"""						)
											    and module._hf_hook.execution_device is not None
											):
													return torch.device(module._hf_hook.execution_device						)
									return self.device


							@torch.no_grad()
							@replace_example_docstring(__A						)
							def __call__(							self      :str							,			__A      :Union[torch.FloatTensor, List[torch.FloatTensor]]							,			__A      :Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]							,			__A      :Union[torch.FloatTensor, List[torch.FloatTensor]]							,			__A      :torch.FloatTensor							,			__A      :int = 512							,			__A      :int = 512							,			__A      :int = 100							,			__A      :float = 4.0							,			__A      :float = 0.3							,			__A      :int = 1							,			__A      :Optional[Union[torch.Generator, List[torch.Generator]]] = None							,			__A      :Optional[str] = "pil"							,			__A      :bool = True							,			)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self._execution_device

									SCREAMING_SNAKE_CASE__								= guidance_scale > 1.0

									if isinstance(__A							,			__A						):
											SCREAMING_SNAKE_CASE__								= torch.cat(__A							,			dim=0						)
									if isinstance(__A							,			__A						):
											SCREAMING_SNAKE_CASE__								= torch.cat(__A							,			dim=0						)
									if isinstance(__A							,			__A						):
											SCREAMING_SNAKE_CASE__								= torch.cat(__A							,			dim=0						)

									SCREAMING_SNAKE_CASE__								= image_embeds.shape[0]

									if do_classifier_free_guidance:
											SCREAMING_SNAKE_CASE__								= image_embeds.repeat_interleave(__A							,			dim=0						)
											SCREAMING_SNAKE_CASE__								= negative_image_embeds.repeat_interleave(__A							,			dim=0						)
											SCREAMING_SNAKE_CASE__								= hint.repeat_interleave(__A							,			dim=0						)

									SCREAMING_SNAKE_CASE__								= torch.cat([negative_image_embeds, image_embeds]							,			dim=0						).to(dtype=self.unet.dtype							,			device=__A						)
									SCREAMING_SNAKE_CASE__								= torch.cat([hint, hint]							,			dim=0						).to(dtype=self.unet.dtype							,			device=__A						)

									if not isinstance(__A							,			__A						):
											SCREAMING_SNAKE_CASE__								= [image]
									if not all(isinstance(__A							,			(PIL.Image.Image, torch.Tensor)						) for i in image						):
											raise ValueError(
											    f'''Input is in incorrect format: {[type(__A						) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''						)

									SCREAMING_SNAKE_CASE__								= torch.cat([prepare_image(__A							,			__A							,			__A						) for i in image]							,			dim=0						)
									SCREAMING_SNAKE_CASE__								= image.to(dtype=image_embeds.dtype							,			device=__A						)

									SCREAMING_SNAKE_CASE__								= self.movq.encode(__A						)["""latents"""]
									SCREAMING_SNAKE_CASE__								= latents.repeat_interleave(__A							,			dim=0						)
									self.scheduler.set_timesteps(__A							,			device=__A						)
									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self.get_timesteps(__A							,			__A							,			__A						)
									SCREAMING_SNAKE_CASE__								= timesteps[:1].repeat(batch_size * num_images_per_prompt						)
									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= downscale_height_and_width(__A							,			__A							,			self.movq_scale_factor						)
									SCREAMING_SNAKE_CASE__								= self.prepare_latents(
									    __A							,			__A							,			__A							,			__A							,			image_embeds.dtype							,			__A							,			__A						)
									for i, t in enumerate(self.progress_bar(__A						)						):
											# expand the latents if we are doing classifier free guidance
											SCREAMING_SNAKE_CASE__								= torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

											SCREAMING_SNAKE_CASE__								= {"""image_embeds""": image_embeds, """hint""": hint}
											SCREAMING_SNAKE_CASE__								= self.unet(
											    sample=__A							,			timestep=__A							,			encoder_hidden_states=__A							,			added_cond_kwargs=__A							,			return_dict=__A							,			)[0]

											if do_classifier_free_guidance:
													SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= noise_pred.split(latents.shape[1]							,			dim=1						)
													SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= noise_pred.chunk(2						)
													SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= variance_pred.chunk(2						)
													SCREAMING_SNAKE_CASE__								= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
													SCREAMING_SNAKE_CASE__								= torch.cat([noise_pred, variance_pred_text]							,			dim=1						)

											if not (
											    hasattr(self.scheduler.config							,			"""variance_type"""						)
											    and self.scheduler.config.variance_type in ["learned", "learned_range"]
											):
													SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= noise_pred.split(latents.shape[1]							,			dim=1						)

											# compute the previous noisy sample x_t -> x_t-1

											SCREAMING_SNAKE_CASE__								= self.scheduler.step(
											    __A							,			__A							,			__A							,			generator=__A							,			)[0]

									# post-processing
									SCREAMING_SNAKE_CASE__								= self.movq.decode(__A							,			force_not_quantize=__A						)["""sample"""]

									if output_type not in ["pt", "np", "pil"]:
											raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''						)

									if output_type in ["np", "pil"]:
											SCREAMING_SNAKE_CASE__								= image * 0.5 + 0.5
											SCREAMING_SNAKE_CASE__								= image.clamp(0							,			1						)
											SCREAMING_SNAKE_CASE__								= image.cpu().permute(0							,			2							,			3							,			1						).float().numpy()

									if output_type == "pil":
											SCREAMING_SNAKE_CASE__								= self.numpy_to_pil(__A						)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=__A						)






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(42)

_lowerCamelCase							=     get_tests_dir('fixtures')
_lowerCamelCase							=     os.path.join(dirname(get_tests_dir()))
_lowerCamelCase							=     dirname(os.path.abspath(__file__))

# default torch.distributed port
_lowerCamelCase							=     '10999'

_lowerCamelCase							=     't5-small'

# *** Working Models ***
_lowerCamelCase							=     'hf-internal-testing/tiny-albert'
_lowerCamelCase							=     'sshleifer/bart-tiny-random'
_lowerCamelCase							=     'hf-internal-testing/tiny-bert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-bigbird_pegasus'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-big_bird'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-blenderbot'
_lowerCamelCase							=     'bigscience/bigscience-small-testing'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-deberta'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-deberta-v2'
_lowerCamelCase							=     'sshleifer/tiny-distilbert-base-cased'
_lowerCamelCase							=     'hf-internal-testing/tiny-electra'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-flaubert'
_lowerCamelCase							=     'stas/tiny-wmt19-en-de'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-funnel'
_lowerCamelCase							=     'sshleifer/tiny-gpt2'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-gptj'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-gpt_neo'
_lowerCamelCase							=     'hf-internal-testing/tiny-layoutlm'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-led'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-longformer'
_lowerCamelCase							=     'stas/tiny-m2m_100'  # hf tiny model is unsuitable
_lowerCamelCase							=     'sshleifer/tiny-marian-en-de'
_lowerCamelCase							=     'sshleifer/tiny-mbart'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-mobilebert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-mpnet'
_lowerCamelCase							=     'stas/pegasus-cnn_dailymail-tiny-random'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-prophetnet'
_lowerCamelCase							=     'sshleifer/tiny-distilroberta-base'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-squeezebert'
_lowerCamelCase							=     'patrickvonplaten/t5-tiny-random'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-t5-v1.1'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-vit'
_lowerCamelCase							=     'hf-internal-testing/tiny-xlm-roberta'
_lowerCamelCase							=     'sshleifer/tiny-xlnet-base-cased'


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_lowerCamelCase							=     'hf-internal-testing/tiny-random-mt5'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-camembert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-openai-gpt'

# missing tokenizer files
_lowerCamelCase							=     'hf-internal-testing/tiny-random-convbert'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-layoutlmv2'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-hubert'

# issues with tokenizer
_lowerCamelCase							=     'hf-internal-testing/tiny-random-ctrl'
_lowerCamelCase							=     'hf-internal-testing/tiny-random-transfo-xl'  # same as ctrl

# other issues with tiny models
_lowerCamelCase							=     'hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
_lowerCamelCase							=     '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							=     'hf-internal-testing/tiny-random-dpr'
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_lowerCamelCase							=     'hf-internal-testing/tiny-random-rag'
# - "rag" research_projects
_lowerCamelCase							=     ''
# - "luke" Entities classes - no plan to make such example
_lowerCamelCase							=     'hf-internal-testing/tiny-random-lxmert'
# - "lxmert" doesn't work with run_qa.py
_lowerCamelCase							=     'hf-internal-testing/tiny-random-clip'
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_lowerCamelCase							=     '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							=     'hf-internal-testing/tiny-random-tapas'






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Optional[Any]=False      ):
		# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
		# - it won't be able to handle that
		# 2. for now testing with just 2 gpus max (since some quality tests may give different
		# results with mode gpus because we use very little data)
		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 			SCREAMING_SNAKE_CASE__							(    ):
		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							=     make_task_cmds()

_lowerCamelCase							=     'zero2'
_lowerCamelCase							=     'zero3'

_lowerCamelCase							=     [ZEROa, ZEROa]






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							str   ,				UpperCamelCase__:							Tuple      ):
		# customize the test name generator function as we want both params to appear in the sub-test
		# name, as by default it shows only the first param
		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							=     list(itertools.product(stages, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class 					UpperCamelCase_    (       UpperCamelCase__      ):


							def        _snake_case					(							self      :Tuple							,			__A      :int							,			__A      :str						)				->				List[Any]:



									"""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        _snake_case					(							self      :List[Any]							,			__A      :Tuple							,			__A      :int						)				->				Tuple:



									"""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