
from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel
@require_tf
class 			lowercase__:



	"""simple docstring"""




	a      :Optional[Any]   					=     MarianConfig
	a      :Tuple   					=     {}
	a      :Any   					=     'gelu'
	def __init__(	self  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      Dict=1_3	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=7	,							SCREAMING_SNAKE_CASE_  :      Dict=True	,							SCREAMING_SNAKE_CASE_  :      Dict=False	,							SCREAMING_SNAKE_CASE_  :      Dict=9_9	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=3_2	,							SCREAMING_SNAKE_CASE_  :      Tuple=2	,							SCREAMING_SNAKE_CASE_  :      int=4	,							SCREAMING_SNAKE_CASE_  :      Any=3_7	,							SCREAMING_SNAKE_CASE_  :      List[str]=0.1	,							SCREAMING_SNAKE_CASE_  :      List[str]=0.1	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=2_0	,							SCREAMING_SNAKE_CASE_  :      Tuple=2	,							SCREAMING_SNAKE_CASE_  :      str=1	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=0	,							)	->					Any:
					lowercase_					  = parent
					lowercase_					  = batch_size
					lowercase_					  = seq_length
					lowercase_					  = is_training
					lowercase_					  = use_labels
					lowercase_					  = vocab_size
					lowercase_					  = hidden_size
					lowercase_					  = num_hidden_layers
					lowercase_					  = num_attention_heads
					lowercase_					  = intermediate_size

					lowercase_					  = hidden_dropout_prob
					lowercase_					  = attention_probs_dropout_prob
					lowercase_					  = max_position_embeddings
					lowercase_					  = eos_token_id
					lowercase_					  = pad_token_id
					lowercase_					  = bos_token_id
	def 						_lowercase  (	self  :      Optional[int]	)	->					List[Any]:
					lowercase_					  = ids_tensor([self.batch_size, self.seq_length - 1]	,							self.vocab_size	)
					lowercase_					  = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)	,							1	)
					lowercase_					  = tf.concat([input_ids, eos_tensor]	,							axis=1	)

					lowercase_					  = ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size	)

					lowercase_					  = self.config_cls(
					    vocab_size=self.vocab_size	,							d_model=self.hidden_size	,							encoder_layers=self.num_hidden_layers	,							decoder_layers=self.num_hidden_layers	,							encoder_attention_heads=self.num_attention_heads	,							decoder_attention_heads=self.num_attention_heads	,							encoder_ffn_dim=self.intermediate_size	,							decoder_ffn_dim=self.intermediate_size	,							dropout=self.hidden_dropout_prob	,							attention_dropout=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							eos_token_ids=[2]	,							bos_token_id=self.bos_token_id	,							pad_token_id=self.pad_token_id	,							decoder_start_token_id=self.pad_token_id	,							**self.config_updates	,							)
					lowercase_					  = prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
					return config, inputs_dict




	def 						_lowercase  (	self  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]	)	->					Dict:
					lowercase_					  = TFMarianModel(config=SCREAMING_SNAKE_CASE_	).get_decoder()
					lowercase_					  = inputs_dict['''input_ids''']

					lowercase_					  = input_ids[:1, :]
					lowercase_					  = inputs_dict['''attention_mask'''][:1, :]
					lowercase_					  = inputs_dict['''head_mask''']
					lowercase_					  = 1

					# first forward pass
					lowercase_					  = model(SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							head_mask=SCREAMING_SNAKE_CASE_	,							use_cache=SCREAMING_SNAKE_CASE_	)

					lowercase_  ,     lowercase_					  = outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					lowercase_					  = ids_tensor((self.batch_size, 3)	,							config.vocab_size	)
					lowercase_					  = tf.cast(ids_tensor((self.batch_size, 3)	,							2	)	,							tf.inta	)

					# append to next input_ids and
					lowercase_					  = tf.concat([input_ids, next_tokens]	,							axis=-1	)
					lowercase_					  = tf.concat([attention_mask, next_attn_mask]	,							axis=-1	)

					lowercase_					  = model(SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	)[0]
					lowercase_					  = model(SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	,							past_key_values=SCREAMING_SNAKE_CASE_	)[0]

					self.parent.assertEqual(next_tokens.shape[1]	,							output_from_past.shape[1]	)

					# select random slice
					lowercase_					  = int(ids_tensor((1,)	,							output_from_past.shape[-1]	)	)
					lowercase_					  = output_from_no_past[:, -3:, random_slice_idx]
					lowercase_					  = output_from_past[:, :, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							rtol=1e-3	)




def     a						(							snake_case__: int						,       snake_case__: Optional[Any]						,       snake_case__: Optional[int]						,       snake_case__: List[str]=None						,       snake_case__: List[str]=None						,       snake_case__: Union[str, Any]=None						,       snake_case__: str=None						,       snake_case__: Optional[Any]=None						,       ):


				'''simple docstring'''
				if attention_mask is None:
								lowercase_					  = tf.cast(tf.math.not_equal(snake_case__						,       config.pad_token_id )						,       tf.inta )
				if decoder_attention_mask is None:
								lowercase_					  = tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape						,       dtype=tf.inta ),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,       config.pad_token_id )						,       tf.inta ),
								    ]						,       axis=-1						,       )
				if head_mask is None:
								lowercase_					  = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
				if decoder_head_mask is None:
								lowercase_					  = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
				if cross_attn_head_mask is None:
								lowercase_					  = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}
@require_tf
class 			lowercase__(    UpperCAmelCase			,						UpperCAmelCase			,						unittest.TestCase							):



	"""simple docstring"""




	a      :Dict   					=     (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
	a      :List[str]   					=     (TFMarianMTModel,) if is_tf_available() else ()
	a      :List[str]   					=     (
	    {
	        'conversational': TFMarianMTModel,
	        'feature-extraction': TFMarianModel,
	        'summarization': TFMarianMTModel,
	        'text2text-generation': TFMarianMTModel,
	        'translation': TFMarianMTModel,
	    }
	    if is_tf_available()
	    else {}
	)
	a      :Any   					=     True
	a      :Tuple   					=     False
	a      :Any   					=     False
	def 						_lowercase  (	self  :      Tuple	)	->					str:
					lowercase_					  = TFMarianModelTester(self	)
					lowercase_					  = ConfigTester(self	,							config_class=SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Optional[Any]	)	->					Dict:
					self.config_tester.run_common_tests()




	def 						_lowercase  (	self  :      List[str]	)	->					Tuple:
					lowercase_					  = self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_	)

@require_tf
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	a      :List[Any]   					=     1_000  # show more chars for failing integration tests
	@classmethod
	def 						_lowercase  (	cls  :      List[str]	)	->					None:
					lowercase_					  = f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
					return cls
	@cached_property
	def 						_lowercase  (	self  :      int	)	->					MarianTokenizer:
					return AutoTokenizer.from_pretrained(self.model_name	)
	@property
	def 						_lowercase  (	self  :      str	)	->					int:
					return self.tokenizer.eos_token_id
	@cached_property
	def 						_lowercase  (	self  :      str	)	->					Tuple:
					warnings.simplefilter('''error'''	)
					lowercase_					  = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
					assert isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
					lowercase_					  = model.config
					self.assertListEqual(c.bad_words_ids	,							[[c.pad_token_id]]	)
					self.assertEqual(c.max_length	,							5_1_2	)
					self.assertEqual(c.decoder_start_token_id	,							c.pad_token_id	)
					return model
	def 						_lowercase  (	self  :      int	,							**SCREAMING_SNAKE_CASE_  :      Any	)	->					Any:
					lowercase_					  = self.translate_src_text(**SCREAMING_SNAKE_CASE_	)
					self.assertListEqual(self.expected_text	,							SCREAMING_SNAKE_CASE_	)




	def 						_lowercase  (	self  :      Union[str, Any]	,							**SCREAMING_SNAKE_CASE_  :      Union[str, Any]	)	->					Optional[int]:
					lowercase_					  = self.tokenizer(self.src_text	,							**SCREAMING_SNAKE_CASE_	,							padding=SCREAMING_SNAKE_CASE_	,							return_tensors='''tf'''	)
					lowercase_					  = self.model.generate(
					    model_inputs.input_ids	,							attention_mask=model_inputs.attention_mask	,							num_beams=2	,							max_length=1_2_8	)
					lowercase_					  = self.tokenizer.batch_decode(generated_ids.numpy()	,							skip_special_tokens=SCREAMING_SNAKE_CASE_	)
					return generated_words

@require_sentencepiece
@require_tokenizers
@require_tf
class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :Any   					=     'mt'
	a      :List[Any]   					=     'en'
	a      :Any   					=     ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
	a      :int   					=     ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']
	@unittest.skip('''Skipping until #12647 is resolved.'''	)
	@slow
	def 						_lowercase  (	self  :      List[Any]	)	->					Optional[int]:
					self._assert_generated_batch_equal_expected()

@require_sentencepiece
@require_tokenizers
@require_tf
class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :List[str]   					=     'en'
	a      :Tuple   					=     'zh'
	a      :Optional[int]   					=     ['My name is Wolfgang and I live in Berlin']
	a      :int   					=     ['我叫沃尔夫冈 我住在柏林']
	@unittest.skip('''Skipping until #12647 is resolved.'''	)
	@slow
	def 						_lowercase  (	self  :      Dict	)	->					List[str]:
					self._assert_generated_batch_equal_expected()

@require_sentencepiece
@require_tokenizers
@require_tf
class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :List[Any]   					=     'en'
	a      :List[str]   					=     'ROMANCE'
	a      :Optional[int]   					=     [
	    '>>fr<< Don\'t spend so much time watching TV.',
	    '>>pt<< Your message has been sent.',
	    '>>es<< He\'s two years older than me.',
	]
	a      :Optional[int]   					=     [
	    'Ne passez pas autant de temps à regarder la télé.',
	    'A sua mensagem foi enviada.',
	    'Es dos años más viejo que yo.',
	]
	@unittest.skip('''Skipping until #12647 is resolved.'''	)
	@slow
	def 						_lowercase  (	self  :      Union[str, Any]	)	->					Optional[Any]:
					self._assert_generated_batch_equal_expected()




	@unittest.skip('''Skipping until #12647 is resolved.'''	)
	@slow
	def 						_lowercase  (	self  :      List[str]	)	->					Union[str, Any]:
					lowercase_					  = TranslationPipeline(self.model	,							self.tokenizer	,							framework='''tf'''	)
					lowercase_					  = pipeline(self.src_text	)
					self.assertEqual(self.expected_text	,							[x['''translation_text'''] for x in output]	)






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     a						(							):


				'''simple docstring'''
				lowercase_					  = argparse.ArgumentParser()
				parser.add_argument('''-f''' )
				lowercase_					  = parser.parse_args()
				return args.f




def     a						(							snake_case__: List[Any] ):


				'''simple docstring'''
				lowercase_					  = {}
				lowercase_					  = os.path.join(snake_case__						,       '''all_results.json''' )
				if os.path.exists(snake_case__ ):
								with open(snake_case__						,       '''r''' ) as f:
												lowercase_					  = json.load(snake_case__ )
				else:
								raise ValueError(F'''can\'t find {path}''' )
				return results




def     a						(							):


				'''simple docstring'''
				return bool(tf.config.list_physical_devices('''GPU''' ) )


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


class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''	)
	def 						_lowercase  (	self  :      Tuple	)	->					List[Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = 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(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_text_classification.main()
									# Reset the mixed precision policy so we don't break other tests
									tf.keras.mixed_precision.set_global_policy('''float32'''	)
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''eval_accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Optional[Any]	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = 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(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_clm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							1_0_0	)
	def 						_lowercase  (	self  :      Any	)	->					Union[str, Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = 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(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_mlm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							4_2	)
	def 						_lowercase  (	self  :      Any	)	->					str:
					# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
					lowercase_					  = 7 if get_gpu_count() > 1 else 2

					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = 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(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_ner.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Any	)	->					Optional[Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = 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(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_squad.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''f1''']	,							3_0	)
									self.assertGreaterEqual(result['''exact''']	,							3_0	)
	def 						_lowercase  (	self  :      str	)	->					Dict:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = 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(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_swag.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''val_accuracy''']	,							0.8	)
	@slow
	def 						_lowercase  (	self  :      int	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = 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(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_summarization.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''rouge1''']	,							1_0	)
									self.assertGreaterEqual(result['''rouge2''']	,							2	)
									self.assertGreaterEqual(result['''rougeL''']	,							7	)
									self.assertGreaterEqual(result['''rougeLsum''']	,							7	)
	@slow
	def 						_lowercase  (	self  :      Dict	)	->					Any:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = 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(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_translation.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''bleu''']	,							3_0	)




	def 						_lowercase  (	self  :      int	)	->					List[str]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = 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(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_image_classification.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''accuracy''']	,							0.7	)




