
"""simple docstring"""


import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 	lowerCamelCase__      (      __magic_name__       ):





   '''simple docstring'''
   lowerCamelCase						      =	42
   lowerCamelCase						      =	None







def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase=0.9_99					,				__UpperCamelCase="cosine"					,				)       ->				Union[str, Any]:
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(__UpperCamelCase ):
   return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(__UpperCamelCase ):
   return math.exp(t * -12.0 )

 else:
  raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

 _lowerCAmelCase			=[]
 for i in range(__UpperCamelCase ):
  _lowerCAmelCase			=i / num_diffusion_timesteps
  _lowerCAmelCase			=(i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase )					,				__UpperCamelCase ) )
 return torch.tensor(__UpperCamelCase					,				dtype=torch.floataa )







class 	lowerCamelCase__      (      __magic_name__				,			__magic_name__       ):





   '''simple docstring'''
   lowerCamelCase						      =	[e.name for e in KarrasDiffusionSchedulers]
   lowerCamelCase						      =	1



   @register_to_config
   def __init__(							self  ,  __UpperCAmelCase = 10_00  ,  __UpperCAmelCase = 0.0_0_0_1  ,  __UpperCAmelCase = 0.0_2  ,  __UpperCAmelCase = "linear"  ,  __UpperCAmelCase = None  ,  __UpperCAmelCase = False  ,  __UpperCAmelCase = "epsilon"  ,  __UpperCAmelCase = "linspace"  ,  __UpperCAmelCase = 0  ,  )				->  Optional[int]:
    if trained_betas is not None:
     _lowerCAmelCase			=torch.tensor(__UpperCAmelCase  ,  dtype=torch.floataa						)
    elif beta_schedule == "linear":
     _lowerCAmelCase			=torch.linspace(__UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  dtype=torch.floataa						)
    elif beta_schedule == "scaled_linear":
     # this schedule is very specific to the latent diffusion model.
     _lowerCAmelCase			=(
         torch.linspace(beta_start**0.5  ,  beta_end**0.5  ,  __UpperCAmelCase  ,  dtype=torch.floataa						) ** 2
     )
    elif beta_schedule == "squaredcos_cap_v2":
     # Glide cosine schedule
     _lowerCAmelCase			=betas_for_alpha_bar(__UpperCAmelCase						)
    else:
     raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''						)

    _lowerCAmelCase			=1.0 - self.betas
    _lowerCAmelCase			=torch.cumprod(self.alphas  ,  dim=0						)

    _lowerCAmelCase			=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
    _lowerCAmelCase			=np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
    _lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						)

    # setable values
    _lowerCAmelCase			=None
    _lowerCAmelCase			=use_karras_sigmas
    self.set_timesteps(__UpperCAmelCase  ,  __UpperCAmelCase						)
    _lowerCAmelCase			=[]
    _lowerCAmelCase			=False



   @property
   def       _lowerCAmelCase	(							self						)				->  List[str]:
    # standard deviation of the initial noise distribution
    if self.config.timestep_spacing in ["linspace", "trailing"]:
     return self.sigmas.max()

    return (self.sigmas.max() ** 2 + 1) ** 0.5



   def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  torch.FloatTensor:
    if isinstance(__UpperCAmelCase  ,  torch.Tensor						):
     _lowerCAmelCase			=timestep.to(self.timesteps.device						)
    _lowerCAmelCase			=(self.timesteps == timestep).nonzero().item()
    _lowerCAmelCase			=self.sigmas[step_index]
    _lowerCAmelCase			=sample / ((sigma**2 + 1) ** 0.5)
    _lowerCAmelCase			=True
    return sample



   def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  Optional[Any]:

    def lms_derivative(__UpperCAmelCase						):
     _lowerCAmelCase			=1.0
     for k in range(__UpperCAmelCase						):
      if current_order == k:
       continue
      prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
     return prod

    _lowerCAmelCase			=integrate.quad(__UpperCAmelCase  ,  self.sigmas[t]  ,  self.sigmas[t + 1]  ,  epsrel=1e-4						)[0]

    return integrated_coeff



   def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase = None						)				->  Tuple:
    _lowerCAmelCase			=num_inference_steps

    # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
    if self.config.timestep_spacing == "linspace":
     _lowerCAmelCase			=np.linspace(0  ,  self.config.num_train_timesteps - 1  ,  __UpperCAmelCase  ,  dtype=__UpperCAmelCase						)[
         ::-1
     ].copy()
    elif self.config.timestep_spacing == "leading":
     _lowerCAmelCase			=self.config.num_train_timesteps // self.num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # casting to int to avoid issues when num_inference_step is power of 3
     _lowerCAmelCase			=(np.arange(0  ,  __UpperCAmelCase						) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase						)
     timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
     _lowerCAmelCase			=self.config.num_train_timesteps / self.num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # casting to int to avoid issues when num_inference_step is power of 3
     _lowerCAmelCase			=(np.arange(self.config.num_train_timesteps  ,  0  ,  -step_ratio						)).round().copy().astype(__UpperCAmelCase						)
     timesteps -= 1
    else:
     raise ValueError(
         f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''						)

    _lowerCAmelCase			=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
    _lowerCAmelCase			=np.log(__UpperCAmelCase						)
    _lowerCAmelCase			=np.interp(__UpperCAmelCase  ,  np.arange(0  ,  len(__UpperCAmelCase						)						)  ,  __UpperCAmelCase						)

    if self.use_karras_sigmas:
     _lowerCAmelCase			=self._convert_to_karras(in_sigmas=__UpperCAmelCase						)
     _lowerCAmelCase			=np.array([self._sigma_to_t(__UpperCAmelCase  ,  __UpperCAmelCase						) for sigma in sigmas]						)

    _lowerCAmelCase			=np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

    _lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						).to(device=__UpperCAmelCase						)
    if str(__UpperCAmelCase						).startswith("""mps"""						):
     # mps does not support float64
     _lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						).to(__UpperCAmelCase  ,  dtype=torch.floataa						)
    else:
     _lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						).to(device=__UpperCAmelCase						)

    _lowerCAmelCase			=[]



   def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  str:
    # get log sigma
    _lowerCAmelCase			=np.log(__UpperCAmelCase						)

    # get distribution
    _lowerCAmelCase			=log_sigma - log_sigmas[:, np.newaxis]

    # get sigmas range
    _lowerCAmelCase			=np.cumsum((dists >= 0)  ,  axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
    _lowerCAmelCase			=low_idx + 1

    _lowerCAmelCase			=log_sigmas[low_idx]
    _lowerCAmelCase			=log_sigmas[high_idx]

    # interpolate sigmas
    _lowerCAmelCase			=(low - log_sigma) / (low - high)
    _lowerCAmelCase			=np.clip(__UpperCAmelCase  ,  0  ,  1						)

    # transform interpolation to time range
    _lowerCAmelCase			=(1 - w) * low_idx + w * high_idx
    _lowerCAmelCase			=t.reshape(sigma.shape						)
    return t



   def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase						)				->  torch.FloatTensor:
    _lowerCAmelCase			=in_sigmas[-1].item()
    _lowerCAmelCase			=in_sigmas[0].item()

    _lowerCAmelCase			=7.0  # 7.0 is the value used in the paper
    _lowerCAmelCase			=np.linspace(0  ,  1  ,  self.num_inference_steps						)
    _lowerCAmelCase			=sigma_min ** (1 / rho)
    _lowerCAmelCase			=sigma_max ** (1 / rho)
    _lowerCAmelCase			=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas



   def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase = 4  ,  __UpperCAmelCase = True  ,  )				->  Union[LMSDiscreteSchedulerOutput, Tuple]:
    if not self.is_scale_input_called:
     warnings.warn(
         """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
         """See `StableDiffusionPipeline` for a usage example."""						)

    if isinstance(__UpperCAmelCase  ,  torch.Tensor						):
     _lowerCAmelCase			=timestep.to(self.timesteps.device						)
    _lowerCAmelCase			=(self.timesteps == timestep).nonzero().item()
    _lowerCAmelCase			=self.sigmas[step_index]

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
     _lowerCAmelCase			=sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
     # * c_out + input * c_skip
     _lowerCAmelCase			=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
    elif self.config.prediction_type == "sample":
     _lowerCAmelCase			=model_output
    else:
     raise ValueError(
         f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

    # 2. Convert to an ODE derivative
    _lowerCAmelCase			=(sample - pred_original_sample) / sigma
    self.derivatives.append(__UpperCAmelCase						)
    if len(self.derivatives						) > order:
     self.derivatives.pop(0						)

    # 3. Compute linear multistep coefficients
    _lowerCAmelCase			=min(step_index + 1  ,  __UpperCAmelCase						)
    _lowerCAmelCase			=[self.get_lms_coefficient(__UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						) for curr_order in range(__UpperCAmelCase						)]

    # 4. Compute previous sample based on the derivatives path
    _lowerCAmelCase			=sample + sum(
        coeff * derivative for coeff, derivative in zip(__UpperCAmelCase  ,  reversed(self.derivatives						)						)						)

    if not return_dict:
     return (prev_sample,)

    return LMSDiscreteSchedulerOutput(prev_sample=__UpperCAmelCase  ,  pred_original_sample=__UpperCAmelCase						)



   def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  )				->  torch.FloatTensor:
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    _lowerCAmelCase			=self.sigmas.to(device=original_samples.device  ,  dtype=original_samples.dtype						)
    if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase						):
     # mps does not support float64
     _lowerCAmelCase			=self.timesteps.to(original_samples.device  ,  dtype=torch.floataa						)
     _lowerCAmelCase			=timesteps.to(original_samples.device  ,  dtype=torch.floataa						)
    else:
     _lowerCAmelCase			=self.timesteps.to(original_samples.device						)
     _lowerCAmelCase			=timesteps.to(original_samples.device						)

    _lowerCAmelCase			=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

    _lowerCAmelCase			=sigmas[step_indices].flatten()
    while len(sigma.shape						) < len(original_samples.shape						):
     _lowerCAmelCase			=sigma.unsqueeze(-1						)

    _lowerCAmelCase			=original_samples + noise * sigma
    return noisy_samples



   def __len__(							self						)				->  Any:
    return self.config.num_train_timesteps





"""simple docstring"""


import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__A 		= [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
			import run_clm
			import run_image_classification
			import run_mlm
			import run_ner
			import run_qa as run_squad
			import run_summarization
			import run_swag
			import run_text_classification
			import run_translation


logging.basicConfig(level=logging.DEBUG)

__A 		= logging.getLogger()



def 					_lowerCamelCase()       ->				str:
	_lowerCAmelCase			=argparse.ArgumentParser()
	parser.add_argument("""-f""" )
	_lowerCAmelCase			=parser.parse_args()
	return args.f



def 					_lowerCamelCase(__UpperCamelCase )       ->				List[str]:
	_lowerCAmelCase			={}
	_lowerCAmelCase			=os.path.join(__UpperCamelCase					,				"""all_results.json""" )
	if os.path.exists(__UpperCamelCase ):
		with open(__UpperCamelCase					,				"""r""" ) as f:
			_lowerCAmelCase			=json.load(__UpperCamelCase )
	else:
		raise ValueError(F'''can\'t find {path}''' )
	return results



def 					_lowerCamelCase()       ->				List[Any]:
	return bool(tf.config.list_physical_devices("""GPU""" ) )


__A 		= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""						)
			def       _lowerCAmelCase	(							self						)				->  List[str]:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

				if is_cuda_available():
					testargs.append("""--fp16"""						)

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_text_classification.main()
					# Reset the mixed precision policy so we don't break other tests
					tf.keras.mixed_precision.set_global_policy("""float32"""						)
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""eval_accuracy"""]  ,  0.7_5						)



			def       _lowerCAmelCase	(							self						)				->  int:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

				if len(tf.config.list_physical_devices("""GPU"""						)						) > 1:
					# Skipping because there are not enough batches to train the model + would need a drop_last to work.
					return

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_clm.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertLess(result["""eval_perplexity"""]  ,  1_00						)



			def       _lowerCAmelCase	(							self						)				->  str:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_mlm.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertLess(result["""eval_perplexity"""]  ,  42						)



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
				_lowerCAmelCase			=7 if get_gpu_count() > 1 else 2

				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_ner.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""accuracy"""]  ,  0.7_5						)



			def       _lowerCAmelCase	(							self						)				->  str:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_squad.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""f1"""]  ,  30						)
					self.assertGreaterEqual(result["""exact"""]  ,  30						)



			def       _lowerCAmelCase	(							self						)				->  int:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_swag.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""val_accuracy"""]  ,  0.8						)



			@slow
			def       _lowerCAmelCase	(							self						)				->  Tuple:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_summarization.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""rouge1"""]  ,  10						)
					self.assertGreaterEqual(result["""rouge2"""]  ,  2						)
					self.assertGreaterEqual(result["""rougeL"""]  ,  7						)
					self.assertGreaterEqual(result["""rougeLsum"""]  ,  7						)



			@slow
			def       _lowerCAmelCase	(							self						)				->  Any:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_translation.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""bleu"""]  ,  30						)



			def       _lowerCAmelCase	(							self						)				->  Optional[int]:
				_lowerCAmelCase			=self.get_auto_remove_tmp_dir()
				_lowerCAmelCase			=f'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

				with patch.object(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_image_classification.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""accuracy"""]  ,  0.7						)



