





import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

__lowercase	: Any       =(
    """4S 3H 2C 7S 5H""",
    """9D 8H 2C 6S 7H""",
    """2D 6D 9D TH 7D""",
    """TC 8C 2S JH 6C""",
    """JH 8S TH AH QH""",
    """TS KS 5S 9S AC""",
    """KD 6S 9D TH AD""",
    """KS 8D 4D 9S 4S""",  # pair
    """8C 4S KH JS 4D""",  # pair
    """QH 8H KD JH 8S""",  # pair
    """KC 4H KS 2H 8D""",  # pair
    """KD 4S KC 3H 8S""",  # pair
    """AH 8S AS KC JH""",  # pair
    """3H 4C 4H 3S 2H""",  # 2 pairs
    """5S 5D 2C KH KH""",  # 2 pairs
    """3C KH 5D 5S KH""",  # 2 pairs
    """AS 3C KH AD KH""",  # 2 pairs
    """7C 7S 3S 7H 5S""",  # 3 of a kind
    """7C 7S KH 2H 7H""",  # 3 of a kind
    """AC KH QH AH AS""",  # 3 of a kind
    """2H 4D 3C AS 5S""",  # straight (low ace)
    """3C 5C 4C 2C 6H""",  # straight
    """6S 8S 7S 5H 9H""",  # straight
    """JS QS 9H TS KH""",  # straight
    """QC KH TS JS AH""",  # straight (high ace)
    """8C 9C 5C 3C TC""",  # flush
    """3S 8S 9S 5S KS""",  # flush
    """4C 5C 9C 8C KC""",  # flush
    """JH 8H AH KH QH""",  # flush
    """3D 2H 3H 2C 2D""",  # full house
    """2H 2C 3S 3H 3D""",  # full house
    """KH KC 3S 3H 3D""",  # full house
    """JC 6H JS JD JH""",  # 4 of a kind
    """JC 7H JS JD JH""",  # 4 of a kind
    """JC KH JS JD JH""",  # 4 of a kind
    """2S AS 4S 5S 3S""",  # straight flush (low ace)
    """2D 6D 3D 4D 5D""",  # straight flush
    """5C 6C 3C 7C 4C""",  # straight flush
    """JH 9H TH KH QH""",  # straight flush
    """JH AH TH KH QH""",  # royal flush (high ace straight flush)
)

__lowercase	: Union[str, Any]       =(
    ("""2H 3H 4H 5H 6H""", """KS AS TS QS JS""", """Loss"""),
    ("""2H 3H 4H 5H 6H""", """AS AD AC AH JD""", """Win"""),
    ("""AS AH 2H AD AC""", """JS JD JC JH 3D""", """Win"""),
    ("""2S AH 2H AS AC""", """JS JD JC JH AD""", """Loss"""),
    ("""2S AH 2H AS AC""", """2H 3H 5H 6H 7H""", """Win"""),
    ("""AS 3S 4S 8S 2S""", """2H 3H 5H 6H 7H""", """Win"""),
    ("""2H 3H 5H 6H 7H""", """2S 3H 4H 5S 6C""", """Win"""),
    ("""2S 3H 4H 5S 6C""", """3D 4C 5H 6H 2S""", """Tie"""),
    ("""2S 3H 4H 5S 6C""", """AH AC 5H 6H AS""", """Win"""),
    ("""2S 2H 4H 5S 4C""", """AH AC 5H 6H AS""", """Loss"""),
    ("""2S 2H 4H 5S 4C""", """AH AC 5H 6H 7S""", """Win"""),
    ("""6S AD 7H 4S AS""", """AH AC 5H 6H 7S""", """Loss"""),
    ("""2S AH 4H 5S KC""", """AH AC 5H 6H 7S""", """Loss"""),
    ("""2S 3H 6H 7S 9C""", """7H 3C TH 6H 9S""", """Loss"""),
    ("""4S 5H 6H TS AC""", """3S 5H 6H TS AC""", """Win"""),
    ("""2S AH 4H 5S 6C""", """AD 4C 5H 6H 2C""", """Tie"""),
    ("""AS AH 3H AD AC""", """AS AH 2H AD AC""", """Win"""),
    ("""AH AC 5H 5C QS""", """AH AC 5H 5C KS""", """Loss"""),
    ("""AH AC 5H 5C QS""", """KH KC 5H 5C QS""", """Win"""),
    ("""7C 7S KH 2H 7H""", """3C 3S AH 2H 3H""", """Win"""),
    ("""3C 3S AH 2H 3H""", """7C 7S KH 2H 7H""", """Loss"""),
    ("""6H 5H 4H 3H 2H""", """5H 4H 3H 2H AH""", """Win"""),
    ("""5H 4H 3H 2H AH""", """5H 4H 3H 2H AH""", """Tie"""),
    ("""5H 4H 3H 2H AH""", """6H 5H 4H 3H 2H""", """Loss"""),
    ("""AH AD KS KC AC""", """AH KD KH AC KC""", """Win"""),
    ("""2H 4D 3C AS 5S""", """2H 4D 3C 6S 5S""", """Loss"""),
    ("""2H 3S 3C 3H 2S""", """3S 3C 2S 2H 2D""", """Win"""),
    ("""4D 6D 5D 2D JH""", """3S 8S 3H TC KH""", """Loss"""),
    ("""4S 6C 8S 3S 7S""", """AD KS 2D 7D 7C""", """Loss"""),
    ("""6S 4C 7H 8C 3H""", """5H JC AH 9D 9C""", """Loss"""),
    ("""9D 9H JH TC QH""", """3C 2S JS 5C 7H""", """Win"""),
    ("""2H TC 8S AD 9S""", """4H TS 7H 2C 5C""", """Win"""),
    ("""9D 3S 2C 7S 7C""", """JC TD 3C TC 9H""", """Loss"""),
)

__lowercase	: List[str]       =(
    ("""2H 3H 4H 5H 6H""", True),
    ("""AS AH 2H AD AC""", False),
    ("""2H 3H 5H 6H 7H""", True),
    ("""KS AS TS QS JS""", True),
    ("""8H 9H QS JS TH""", False),
    ("""AS 3S 4S 8S 2S""", True),
)

__lowercase	: str       =(
    ("""2H 3H 4H 5H 6H""", True),
    ("""AS AH 2H AD AC""", False),
    ("""2H 3H 5H 6H 7H""", False),
    ("""KS AS TS QS JS""", True),
    ("""8H 9H QS JS TH""", True),
)

__lowercase	: Union[str, Any]       =(
    ("""2H 4D 3C AS 5S""", True, [5, 4, 3, 2, 14]),
    ("""2H 5D 3C AS 5S""", False, [14, 5, 5, 3, 2]),
    ("""JH QD KC AS TS""", False, [14, 13, 12, 11, 10]),
    ("""9D 3S 2C 7S 7C""", False, [9, 7, 7, 3, 2]),
)

__lowercase	: str       =(
    ("""JH AH TH KH QH""", 0),
    ("""JH 9H TH KH QH""", 0),
    ("""JC KH JS JD JH""", 7),
    ("""KH KC 3S 3H 3D""", 6),
    ("""8C 9C 5C 3C TC""", 0),
    ("""JS QS 9H TS KH""", 0),
    ("""7C 7S KH 2H 7H""", 3),
    ("""3C KH 5D 5S KH""", 2),
    ("""QH 8H KD JH 8S""", 1),
    ("""2D 6D 9D TH 7D""", 0),
)

__lowercase	: int       =(
    ("""JH AH TH KH QH""", 23),
    ("""JH 9H TH KH QH""", 22),
    ("""JC KH JS JD JH""", 21),
    ("""KH KC 3S 3H 3D""", 20),
    ("""8C 9C 5C 3C TC""", 19),
    ("""JS QS 9H TS KH""", 18),
    ("""7C 7S KH 2H 7H""", 17),
    ("""3C KH 5D 5S KH""", 16),
    ("""QH 8H KD JH 8S""", 15),
    ("""2D 6D 9D TH 7D""", 14),
)




def      a__			(       ):






   '''simple docstring'''






   UpperCAmelCase_    ,							UpperCAmelCase_           =randrange(len(lowercase__	)	), randrange(len(lowercase__	)	)
   UpperCAmelCase_           =["Loss", "Tie", "Win"][(play >= oppo) + (play > oppo)]
   UpperCAmelCase_    ,							UpperCAmelCase_           =SORTED_HANDS[play], SORTED_HANDS[oppo]
   return hand, other, expected




def      a__			(       lowercase__ = 1_0_0	):






   '''simple docstring'''






   return (generate_random_hand() for _ in range(lowercase__	))




@pytest.mark.parametrize("hand, expected"	,     lowercase__	)
def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   assert PokerHand(lowercase__	)._is_flush() == expected




@pytest.mark.parametrize("hand, expected"	,     lowercase__	)
def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   assert PokerHand(lowercase__	)._is_straight() == expected




@pytest.mark.parametrize("hand, expected, card_values"	,     lowercase__	)
def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =PokerHand(lowercase__	)
   assert player._is_five_high_straight() == expected
   assert player._card_values == card_values




@pytest.mark.parametrize("hand, expected"	,     lowercase__	)
def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   assert PokerHand(lowercase__	)._is_same_kind() == expected




@pytest.mark.parametrize("hand, expected"	,     lowercase__	)
def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   assert PokerHand(lowercase__	)._hand_type == expected




@pytest.mark.parametrize("hand, other, expected"	,     lowercase__	)
def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   assert PokerHand(lowercase__	).compare_with(PokerHand(lowercase__	)	) == expected




@pytest.mark.parametrize("hand, other, expected"	,     generate_random_hands()	)
def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   assert PokerHand(lowercase__	).compare_with(PokerHand(lowercase__	)	) == expected




def      a__			(       ):






   '''simple docstring'''






   UpperCAmelCase_           =[PokerHand(lowercase__	) for hand in SORTED_HANDS]
   UpperCAmelCase_           =poker_hands.copy()
   shuffle(lowercase__	)
   UpperCAmelCase_           =chain(sorted(lowercase__	)	)
   for index, hand in enumerate(lowercase__	):
      assert hand == poker_hands[index]




def      a__			(       ):






   '''simple docstring'''






   UpperCAmelCase_           =[PokerHand("2D AC 3H 4H 5S"	), PokerHand("2S 3H 4H 5S 6C"	)]
   pokerhands.sort(reverse=lowercase__	)
   assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"




def      a__			(       ):






   '''simple docstring'''






   UpperCAmelCase_           =PokerHand("2C 4S AS 3D 5C"	)
   UpperCAmelCase_           =True
   UpperCAmelCase_           =[5, 4, 3, 2, 1_4]
   for _ in range(1_0	):
      assert pokerhand._is_five_high_straight() == expected
      assert pokerhand._card_values == expected_card_values





def      a__			(       ):






   '''simple docstring'''






   UpperCAmelCase_           =0
   UpperCAmelCase_           =os.path.abspath(os.path.dirname(lowercase__	)	)
   UpperCAmelCase_           =os.path.join(lowercase__	,     "poker_hands.txt"	)
   with open(lowercase__	) as file_hand:
      for line in file_hand:
         UpperCAmelCase_           =line[:1_4].strip()
         UpperCAmelCase_           =line[1_5:].strip()
         UpperCAmelCase_    ,							UpperCAmelCase_           =PokerHand(lowercase__	), PokerHand(lowercase__	)
         UpperCAmelCase_           =player.compare_with(lowercase__	)
         if output == "Win":
            answer += 1
   assert answer == 3_7_6











'''simple docstring'''

import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

__lowerCAmelCase				:      Dict						      =logging.getLogger()





def 		UpperCamelCase							(				):
    A__							=     argparse.ArgumentParser()
    parser.add_argument("-f"			)
    A__							=     parser.parse_args()
    return args.f





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





def 		UpperCamelCase							(				):
    A__							=     torch.cuda.is_available() and torch_device == "cuda"
    return is_using_cuda and is_apex_available()


__lowerCAmelCase				:      Union[str, Any]						      =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @classmethod
 def 	UpperCAmelCase_				(							cls :List[Any]				)->				Union[str, Any]:
     # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
     A__							=     tempfile.mkdtemp()
     A__							=     os.path.join(cls.tmpdir		,			"default_config.yml"				)
     write_basic_config(save_location=cls.configPath				)
     A__							=     ["accelerate", "launch", "--config_file", cls.configPath]
 @classmethod
 def 	UpperCAmelCase_				(							cls :Tuple				)->				List[Any]:
     shutil.rmtree(cls.tmpdir				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Any:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

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

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.7_5				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"glue_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Tuple:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     if torch.cuda.device_count() > 1:
         # Skipping because there are not enough batches to train the model + would need a drop_last to work.
         return

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertLess(result["perplexity"]		,			1_00				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"clm_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Any				)->				str:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertLess(result["perplexity"]		,			42				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"mlm_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				List[str]:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     A__							=     7 if get_gpu_count() > 1 else 2

     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.7_5				)
     self.assertLess(result["train_loss"]		,			0.5				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"ner_no_trainer"				)				)				)
 @unittest.skip(reason="Fix me @muellerzr"				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Any				)->				Optional[int]:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
     self.assertGreaterEqual(result["eval_f1"]		,			28				)
     self.assertGreaterEqual(result["eval_exact"]		,			28				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"qa_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[str]:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.8				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"swag_no_trainer"				)				)				)
 @slow
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				Any:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_rouge1"]		,			10				)
     self.assertGreaterEqual(result["eval_rouge2"]		,			2				)
     self.assertGreaterEqual(result["eval_rougeL"]		,			7				)
     self.assertGreaterEqual(result["eval_rougeLsum"]		,			7				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"summarization_no_trainer"				)				)				)
 @slow
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_bleu"]		,			30				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"translation_no_trainer"				)				)				)
 @slow
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[Any]:
     A__							=     logging.StreamHandler(sys.stdout				)
     logger.addHandler(lowercase_				)

     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_overall_accuracy"]		,			0.1_0				)



 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        ".split()

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

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     # The base model scores a 25%
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.6				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"step_1"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"image_classification_no_trainer"				)				)				)



