




import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)




def 			__magic_name__				( A		:					List[Any],  A		:					List[str],  A		:					Optional[Any]   ):






			'''simple docstring'''







			if isinstance(A,  torch.Tensor   ):
						return image
			elif isinstance(A,  PIL.Image.Image   ):
						a    =			[image]

			if isinstance(image[0],  PIL.Image.Image   ):
						a    =			[np.array(i.resize((w, h),  resample=PIL_INTERPOLATION["lanczos"]   )   )[None, :] for i in image]
						a    =			np.concatenate(A,  axis=0   )
						a    =			np.array(A   ).astype(np.floataa   ) / 2_55.0
						a    =			image.transpose(0,  3,  1,  2   )
						a    =			2.0 * image - 1.0
						a    =			torch.from_numpy(A   )
			elif isinstance(image[0],  torch.Tensor   ):
						a    =			torch.cat(A,  dim=0   )
			return image




def 			__magic_name__				( A		:					str,  A		:					Optional[int],  A		:					Tuple,  A		:					Tuple=0.99_95   ):






			'''simple docstring'''







			if not isinstance(A,  np.ndarray   ):
						a    =			True
						a    =			va.device
						a    =			va.cpu().numpy()
						a    =			va.cpu().numpy()

			a    =			np.sum(va * va / (np.linalg.norm(A   ) * np.linalg.norm(A   ))   )
			if np.abs(A   ) > DOT_THRESHOLD:
						a    =			(1 - t) * va + t * va
			else:
						a    =			np.arccos(A   )
						a    =			np.sin(A   )
						a    =			theta_a * t
						a    =			np.sin(A   )
						a    =			np.sin(theta_a - theta_t   ) / sin_theta_a
						a    =			sin_theta_t / sin_theta_a
						a    =			sa * va + sa * va

			if inputs_are_torch:
						a    =			torch.from_numpy(A   ).to(A   )

			return va




def 			__magic_name__				( A		:					int,  A		:					Optional[int]   ):






			'''simple docstring'''







			a    =			F.normalize(A,  dim=-1   )
			a    =			F.normalize(A,  dim=-1   )
			return (x - y).norm(dim=-1   ).div(2   ).arcsin().pow(2   ).mul(2   )




def 			__magic_name__				( A		:					List[str],  A		:					str   ):






			'''simple docstring'''







			for param in model.parameters():
						a    =			value






class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     Optional[Any]					,      __lowerCamelCase				:     AutoencoderKL					,      __lowerCamelCase				:     CLIPTextModel					,      __lowerCamelCase				:     CLIPModel					,      __lowerCamelCase				:     CLIPTokenizer					,      __lowerCamelCase				:     UNetaDConditionModel					,      __lowerCamelCase				:     Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]					,      __lowerCamelCase				:     CLIPFeatureExtractor					,      __lowerCamelCase				:     List[Any]=None					,      __lowerCamelCase				:     str=None					,      __lowerCamelCase				:     Tuple=None					,      )					->     Optional[int]:
							super().__init__()
							self.register_modules(
							    vae=__lowerCamelCase					,      text_encoder=__lowerCamelCase					,      clip_model=__lowerCamelCase					,      tokenizer=__lowerCamelCase					,      unet=__lowerCamelCase					,      scheduler=__lowerCamelCase					,      feature_extractor=__lowerCamelCase					,      coca_model=__lowerCamelCase					,      coca_tokenizer=__lowerCamelCase					,      coca_transform=__lowerCamelCase					,      )
							a    =			(
							    feature_extractor.size
							    if isinstance(feature_extractor.size					,      __lowerCamelCase							)
							    else feature_extractor.size["shortest_edge"]
							)
							a    =			transforms.Normalize(mean=feature_extractor.image_mean					,      std=feature_extractor.image_std							)
							set_requires_grad(self.text_encoder					,      __lowerCamelCase							)
							set_requires_grad(self.clip_model					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     Optional[Union[str, int]] = "auto"							)					->     List[Any]:
							if slice_size == "auto":
										# half the attention head size is usually a good trade-off between
										# speed and memory
										a    =			self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     List[Any]:
							self.enable_attention_slicing(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Union[str, Any]:
							set_requires_grad(self.vae					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[Any]:
							set_requires_grad(self.vae					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[Any]:
							set_requires_grad(self.unet					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     str:
							set_requires_grad(self.unet					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     List[str]							)					->     Optional[int]:
							# get the original timestep using init_timestep
							a    =			min(int(num_inference_steps * strength							)					,      __lowerCamelCase							)

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

							return timesteps, num_inference_steps - t_start





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     str=None							)					->     Dict:
							if not isinstance(__lowerCamelCase					,      torch.Tensor							):
										raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(__lowerCamelCase							)}"""							)

							a    =			image.to(device=__lowerCamelCase					,      dtype=__lowerCamelCase							)

							if isinstance(__lowerCamelCase					,      __lowerCamelCase							):
										a    =			[
										    self.vae.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(__lowerCamelCase							)
										]
										a    =			torch.cat(__lowerCamelCase					,      dim=0							)
							else:
										a    =			self.vae.encode(__lowerCamelCase							).latent_dist.sample(__lowerCamelCase							)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							a    =			0.18_215 * init_latents
							a    =			init_latents.repeat_interleave(__lowerCamelCase					,      dim=0							)

							a    =			randn_tensor(init_latents.shape					,      generator=__lowerCamelCase					,      device=__lowerCamelCase					,      dtype=__lowerCamelCase							)

							# get latents
							a    =			self.scheduler.add_noise(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)
							a    =			init_latents

							return latents





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Optional[int]							)					->     int:
							a    =			self.coca_transform(__lowerCamelCase							).unsqueeze(0							)
							with torch.no_grad(), torch.cuda.amp.autocast():
										a    =			self.coca_model.generate(transformed_image.to(device=self.device					,      dtype=self.coca_model.dtype							)							)
							a    =			self.coca_tokenizer.decode(generated[0].cpu().numpy()							)
							return generated.split("<end_of_text>"							)[0].replace("<start_of_text>"					,      ""							).rstrip(" .,"							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Optional[Any]							)					->     Any:
							a    =			self.feature_extractor.preprocess(__lowerCamelCase							)
							a    =			torch.from_numpy(clip_image_input["pixel_values"][0]							).unsqueeze(0							).to(self.device							).half()
							a    =			self.clip_model.get_image_features(__lowerCamelCase							)
							a    =			image_embeddings_clip / image_embeddings_clip.norm(p=2					,      dim=-1					,      keepdim=__lowerCamelCase							)
							a    =			image_embeddings_clip.repeat_interleave(__lowerCamelCase					,      dim=0							)
							return image_embeddings_clip





				@torch.enable_grad()
				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Union[str, Any]					,      )					->     List[str]:
							a    =			latents.detach().requires_grad_()

							a    =			self.scheduler.scale_model_input(__lowerCamelCase					,      __lowerCamelCase							)

							# predict the noise residual
							a    =			self.unet(__lowerCamelCase					,      __lowerCamelCase					,      encoder_hidden_states=__lowerCamelCase							).sample

							if isinstance(self.scheduler					,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)							):
										a    =			self.scheduler.alphas_cumprod[timestep]
										a    =			1 - alpha_prod_t
										# compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										a    =			(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

										a    =			torch.sqrt(__lowerCamelCase							)
										a    =			pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler					,      __lowerCamelCase							):
										a    =			self.scheduler.sigmas[index]
										a    =			latents - sigma * noise_pred
							else:
										raise ValueError(f"""scheduler type {type(self.scheduler							)} not supported"""							)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							a    =			1 / 0.18_215 * sample
							a    =			self.vae.decode(__lowerCamelCase							).sample
							a    =			(image / 2 + 0.5).clamp(0					,      1							)

							a    =			transforms.Resize(self.feature_extractor_size							)(__lowerCamelCase							)
							a    =			self.normalize(__lowerCamelCase							).to(latents.dtype							)

							a    =			self.clip_model.get_image_features(__lowerCamelCase							)
							a    =			image_embeddings_clip / image_embeddings_clip.norm(p=2					,      dim=-1					,      keepdim=__lowerCamelCase							)

							a    =			spherical_dist_loss(__lowerCamelCase					,      __lowerCamelCase							).mean() * clip_guidance_scale

							a    =			-torch.autograd.grad(__lowerCamelCase					,      __lowerCamelCase							)[0]

							if isinstance(self.scheduler					,      __lowerCamelCase							):
										a    =			latents.detach() + grads * (sigma**2)
										a    =			noise_pred_original
							else:
										a    =			noise_pred_original - torch.sqrt(__lowerCamelCase							) * grads
							return noise_pred, latents





				@torch.no_grad()
				def __call__(      self				:     Optional[int]					,      __lowerCamelCase				:     Union[torch.FloatTensor, PIL.Image.Image]					,      __lowerCamelCase				:     Union[torch.FloatTensor, PIL.Image.Image]					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[int] = 5_12					,      __lowerCamelCase				:     Optional[int] = 5_12					,      __lowerCamelCase				:     float = 0.6					,      __lowerCamelCase				:     Optional[int] = 50					,      __lowerCamelCase				:     Optional[float] = 7.5					,      __lowerCamelCase				:     Optional[int] = 1					,      __lowerCamelCase				:     float = 0.0					,      __lowerCamelCase				:     Optional[float] = 1_00					,      __lowerCamelCase				:     Optional[torch.Generator] = None					,      __lowerCamelCase				:     Optional[str] = "pil"					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     float = 0.8					,      __lowerCamelCase				:     float = 0.1					,      __lowerCamelCase				:     float = 0.1					,      )					->     Dict:
							if isinstance(__lowerCamelCase					,      __lowerCamelCase							) and len(__lowerCamelCase							) != batch_size:
										raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(__lowerCamelCase							)} generators."""							)

							if height % 8 != 0 or width % 8 != 0:
										raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""							)

							if isinstance(__lowerCamelCase					,      torch.Generator							) and batch_size > 1:
										a    =			[generator] + [None] * (batch_size - 1)

							a    =			[
							    ("model", self.coca_model is None),
							    ("tokenizer", self.coca_tokenizer is None),
							    ("transform", self.coca_transform is None),
							]
							a    =			[x[0] for x in coca_is_none if x[1]]
							a    =			", ".join(__lowerCamelCase							)
							# generate prompts with coca model if prompt is None
							if content_prompt is None:
										if len(__lowerCamelCase							):
													raise ValueError(
													    f"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
													    f"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""							)
										a    =			self.get_image_description(__lowerCamelCase							)
							if style_prompt is None:
										if len(__lowerCamelCase							):
													raise ValueError(
													    f"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
													    f""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""							)
										a    =			self.get_image_description(__lowerCamelCase							)

							# get prompt text embeddings for content and style
							a    =			self.tokenizer(
							    __lowerCamelCase					,      padding="max_length"					,      max_length=self.tokenizer.model_max_length					,      truncation=__lowerCamelCase					,      return_tensors="pt"					,      )
							a    =			self.text_encoder(content_text_input.input_ids.to(self.device							)							)[0]

							a    =			self.tokenizer(
							    __lowerCamelCase					,      padding="max_length"					,      max_length=self.tokenizer.model_max_length					,      truncation=__lowerCamelCase					,      return_tensors="pt"					,      )
							a    =			self.text_encoder(style_text_input.input_ids.to(self.device							)							)[0]

							a    =			slerp(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)

							# duplicate text embeddings for each generation per prompt
							a    =			text_embeddings.repeat_interleave(__lowerCamelCase					,      dim=0							)

							# set timesteps
							a    =			"offset" in set(inspect.signature(self.scheduler.set_timesteps							).parameters.keys()							)
							a    =			{}
							if accepts_offset:
										a    =			1

							self.scheduler.set_timesteps(__lowerCamelCase					,      **__lowerCamelCase							)
							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							self.scheduler.timesteps.to(self.device							)

							a    ,		a    =			self.get_timesteps(__lowerCamelCase					,      __lowerCamelCase					,      self.device							)
							a    =			timesteps[:1].repeat(__lowerCamelCase							)

							# Preprocess image
							a    =			preprocess(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)
							a    =			self.prepare_latents(
							    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      text_embeddings.dtype					,      self.device					,      __lowerCamelCase							)

							a    =			preprocess(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)
							a    =			self.prepare_latents(
							    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      text_embeddings.dtype					,      self.device					,      __lowerCamelCase							)

							a    =			slerp(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)

							if clip_guidance_scale > 0:
										a    =			self.get_clip_image_embeddings(__lowerCamelCase					,      __lowerCamelCase							)
										a    =			self.get_clip_image_embeddings(__lowerCamelCase					,      __lowerCamelCase							)
										a    =			slerp(
										    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							a    =			guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
										a    =			content_text_input.input_ids.shape[-1]
										a    =			self.tokenizer([""]					,      padding="max_length"					,      max_length=__lowerCamelCase					,      return_tensors="pt"							)
										a    =			self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
										# duplicate unconditional embeddings for each generation per prompt
										a    =			uncond_embeddings.repeat_interleave(__lowerCamelCase					,      dim=0							)

										# For classifier free guidance, we need to do two forward passes.
										# Here we concatenate the unconditional and text embeddings into a single batch
										# to avoid doing two forward passes
										a    =			torch.cat([uncond_embeddings, text_embeddings]							)

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							a    =			(batch_size, self.unet.config.in_channels, height // 8, width // 8)
							a    =			text_embeddings.dtype
							if latents is None:
										if self.device.type == "mps":
													# randn does not work reproducibly on mps
													a    =			torch.randn(__lowerCamelCase					,      generator=__lowerCamelCase					,      device="cpu"					,      dtype=__lowerCamelCase							).to(
													    self.device							)
										else:
													a    =			torch.randn(__lowerCamelCase					,      generator=__lowerCamelCase					,      device=self.device					,      dtype=__lowerCamelCase							)
							else:
										if latents.shape != latents_shape:
													raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""							)
										a    =			latents.to(self.device							)

							# scale the initial noise by the standard deviation required by the scheduler
							a    =			latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							a    =			"eta" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
							a    =			{}
							if accepts_eta:
										a    =			eta

							# check if the scheduler accepts generator
							a    =			"generator" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
							if accepts_generator:
										a    =			generator

							with self.progress_bar(total=__lowerCamelCase							):
										for i, t in enumerate(__lowerCamelCase							):
													# expand the latents if we are doing classifier free guidance
													a    =			torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
													a    =			self.scheduler.scale_model_input(__lowerCamelCase					,      __lowerCamelCase							)

													# predict the noise residual
													a    =			self.unet(__lowerCamelCase					,      __lowerCamelCase					,      encoder_hidden_states=__lowerCamelCase							).sample

													# perform classifier free guidance
													if do_classifier_free_guidance:
																a    ,		a    =			noise_pred.chunk(2							)
																a    =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# perform clip guidance
													if clip_guidance_scale > 0:
																a    =			(
																    text_embeddings.chunk(2							)[1] if do_classifier_free_guidance else text_embeddings
																)
																a    ,		a    =			self.cond_fn(
																    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      )

													# compute the previous noisy sample x_t -> x_t-1
													a    =			self.scheduler.step(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      **__lowerCamelCase							).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							a    =			1 / 0.18_215 * latents
							a    =			self.vae.decode(__lowerCamelCase							).sample

							a    =			(image / 2 + 0.5).clamp(0					,      1							)
							a    =			image.cpu().permute(0					,      2					,      3					,      1							).numpy()

							if output_type == "pil":
										a    =			self.numpy_to_pil(__lowerCamelCase							)

							if not return_dict:
										return (image, None)

							return StableDiffusionPipelineOutput(images=__lowerCamelCase					,      nsfw_content_detected=__lowerCamelCase							)








# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


A     :	Optional[Any]    = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(4_2)

A     :	List[str]    = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

A     :	Optional[int]    = 'zero2'
A     :	str    = 'zero3'
A     :	Tuple    = [ZEROa, ZEROa]


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  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
				__a										=    parameterized.to_safe_name('''_'''.join(str(a__  ) for x in param.args  )  )
				return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
A     :	Union[str, Any]    = list(itertools.product(stages, models.keys()))







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




				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								self.run_and_check(
								    stage=_snake_case		,     model=_snake_case		,     distributed=_snake_case		,     fpaa=_snake_case		,     )




				@require_torch_multi_gpu
				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      int:


								'''simple docstring'''




								self.run_and_check(
								    stage=_snake_case		,     model=_snake_case		,     distributed=_snake_case		,     fpaa=_snake_case		,     )




				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      str:


								'''simple docstring'''




								self.run_and_check(
								    stage=_snake_case		,     model=_snake_case		,     distributed=_snake_case		,     fpaa=_snake_case		,     )




				@require_torch_multi_gpu
				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								self.run_and_check(
								    stage=_snake_case		,     model=_snake_case		,     distributed=_snake_case		,     fpaa=_snake_case		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = 10		,     _snake_case = True		,     _snake_case = True		,     _snake_case = True		,     )    ->      Any:


								'''simple docstring'''




								__a										=    models[model]

								__a										=    self.run_trainer(
								    stage=_snake_case		,     model_name=_snake_case		,     eval_steps=_snake_case		,     num_train_epochs=1		,     distributed=_snake_case		,     fpaa=_snake_case		,     )

								self.do_checks(_snake_case				)

								return output_dir




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = 10		,     _snake_case = 1		,     _snake_case = True		,     _snake_case = True		,     )    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.get_auto_remove_tmp_dir('''./xxx'''		,     after=_snake_case				)
								__a										=    F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_snake_case				)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

								if fpaa:
												args.extend(['''--fp16''']				)

								# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
								# hence the separate config files
								__a										=    F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
								__a										=    [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
								__a										=    self.get_launcher(_snake_case				)

								__a										=    launcher + script + args + ds_args
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
								execute_subprocess_async(_snake_case		,     env=self.get_env()				)

								return output_dir




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=False				)    ->      List[str]:


								'''simple docstring'''




								__a										=    min(2		,     get_gpu_count()				) if distributed else 1
								return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()