
"""simple docstring"""


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__A 		= '\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n'

__A 		= logging.getLogger(__name__)







@dataclass
class 	lowerCamelCase__      :





			'''simple docstring'''
			lowerCamelCase						      =	field(
			    default='''roberta-large-mnli'''				,			metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}       )
			lowerCamelCase						      =	field(
			    default='''This example is {}.'''				,			metadata={
			        '''help''': (
			            '''Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'''
			            '''where class name is inserted.'''
			        )
			    }				,			)
			lowerCamelCase						      =	field(
			    default=32				,			metadata={'''help''': '''Batch size for generating teacher predictions.'''}       )
			lowerCamelCase						      =	field(
			    default=__magic_name__				,			metadata={
			        '''help''': (
			            '''Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'''
			            '''multi-class multi-label classification).'''
			        )
			    }				,			)
			lowerCamelCase						      =	field(
			    default=1.0				,			metadata={'''help''': '''Temperature applied to teacher softmax for distillation.'''}       )







@dataclass
class 	lowerCamelCase__      :





			'''simple docstring'''
			lowerCamelCase						      =	field(
			    default='''distilbert-base-uncased'''				,			metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}       )







@dataclass
class 	lowerCamelCase__      :





			'''simple docstring'''
			lowerCamelCase						      =	field(metadata={'''help''': '''Text file with one unlabeled instance per line.'''}       )
			lowerCamelCase						      =	field(metadata={'''help''': '''Text file with one class name per line.'''}       )
			lowerCamelCase						      =	field(
			    default=__magic_name__				,			metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'''}				,			)







@dataclass
class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	field(
			    default=__magic_name__				,			metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}				,			)
			lowerCamelCase						      =	field(
			    default=32				,			metadata={'''help''': '''Batch size per GPU/TPU core/CPU for training.'''}       )
			lowerCamelCase						      =	field(
			    default=128				,			metadata={'''help''': '''Batch size per GPU/TPU core/CPU for evaluation.'''}       )
			lowerCamelCase						      =	field(default=1.0				,			metadata={'''help''': '''Total number of training epochs to perform.'''}       )
			lowerCamelCase						      =	field(default=__magic_name__				,			metadata={'''help''': '''Whether to run training of student model.'''}       )
			lowerCamelCase						      =	field(
			    default=__magic_name__				,			metadata={
			        '''help''': (
			            '''Whether to evaluate the agreement of the final student predictions and the teacher predictions'''
			            '''after training.'''
			        )
			    }				,			)
			lowerCamelCase						      =	field(
			    default=0				,			metadata={
			        '''help''': (
			            '''Limit the total amount of checkpoints.'''
			            '''Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'''
			        )
			    }				,			)







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase=False						)				->  Optional[Any]:
				_lowerCAmelCase			=inputs["""labels"""]
				_lowerCAmelCase			=model(inputs["""input_ids"""]  ,  attention_mask=inputs["""attention_mask"""]						)
				_lowerCAmelCase			=outputs[0]

				_lowerCAmelCase			=-torch.sum(target_p * logits.log_softmax(dim=-1						)  ,  axis=-1						).mean()

				if return_outputs:
					return loss, outputs

				return loss







def 					_lowerCamelCase(__UpperCamelCase )       ->				Optional[Any]:
	_lowerCAmelCase			=[]
	with open(__UpperCamelCase					,				"""r""" ) as f:
		for line in f:
			_lowerCAmelCase			=line.strip()
			if len(__UpperCamelCase ) > 0:
				lines.append(__UpperCamelCase )
	return lines



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				Union[str, Any]:
	_lowerCAmelCase			=[]
	_lowerCAmelCase			=[]
	for example in examples:
		for name in class_names:
			premises.append(__UpperCamelCase )
			hypotheses.append(hypothesis_template.format(__UpperCamelCase ) )
	return premises, hypotheses



def 					_lowerCamelCase(__UpperCamelCase )       ->				Optional[Any]:
	for label, ind in config.labelaid.items():
		if label.lower().startswith("""entail""" ):
			return ind
	logger.warning("""Could not identify entailment dimension from teacher config label2id. Setting to -1.""" )
	return -1



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				)       ->				Dict:
	_lowerCAmelCase			=AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase )
	_lowerCAmelCase			=model.config
	if not no_cuda and torch.cuda.is_available():
		_lowerCAmelCase			=nn.DataParallel(model.cuda() )
		batch_size *= len(model.device_ids )
	_lowerCAmelCase			=AutoTokenizer.from_pretrained(__UpperCamelCase					,				use_fast=__UpperCamelCase )

	_lowerCAmelCase		,							_lowerCAmelCase			=get_premise_hypothesis_pairs(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )
	_lowerCAmelCase			=[]

	for i in tqdm(range(0					,				len(__UpperCamelCase )					,				__UpperCamelCase ) ):
		_lowerCAmelCase			=premises[i : i + batch_size]
		_lowerCAmelCase			=hypotheses[i : i + batch_size]

		_lowerCAmelCase			=tokenizer(
		    __UpperCamelCase					,				__UpperCamelCase					,				padding=__UpperCamelCase					,				truncation="""only_first"""					,				return_tensors="""pt"""					,				)

		with torch.cuda.amp.autocast(enabled=__UpperCamelCase ):
			with torch.no_grad():
				_lowerCAmelCase			=model(**__UpperCamelCase )
		logits.append(outputs.logits.detach().cpu().float() )

	_lowerCAmelCase			=get_entailment_id(__UpperCamelCase )
	_lowerCAmelCase			=-1 if entail_id == 0 else 0
	_lowerCAmelCase			=torch.cat(__UpperCamelCase					,				dim=0 )  # N*K x 3
	_lowerCAmelCase			=logits.reshape(len(__UpperCamelCase )					,				len(__UpperCamelCase )					,				-1 )[..., [contr_id, entail_id]]  # N x K x 2

	if multi_label:
		# softmax over (contr, entail) logits for each class independently
		_lowerCAmelCase			=(nli_logits / temperature).softmax(-1 )
	else:
		# softmax over entail logits across classes s.t. class probabilities sum to 1.
		_lowerCAmelCase			=(nli_logits / temperature).softmax(1 )

	return nli_prob[..., 1]  # N x K



def 					_lowerCamelCase()       ->				List[str]:
	_lowerCAmelCase			=HfArgumentParser(
	    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)					,				description=__UpperCamelCase					,				)

	if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=parser.parse_json_file(
		    json_file=os.path.abspath(sys.argv[1] ) )
	else:
		_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	_lowerCAmelCase			=None
	if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
		_lowerCAmelCase			=get_last_checkpoint(training_args.output_dir )
		if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
			raise ValueError(
			    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
			    """Use --overwrite_output_dir to overcome.""" )
		elif last_checkpoint is not None:
			logger.info(
			    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
			    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )

    # Setup logging
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					,				datefmt="""%m/%d/%Y %H:%M:%S"""					,				handlers=[logging.StreamHandler(sys.stdout )]					,				)
	logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )

	# Log on each process the small summary:
	logger.warning(
	    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
	    + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank ):
		utils.logging.set_verbosity_info()
		utils.logging.enable_default_handler()
		utils.logging.enable_explicit_format()

	if training_args.local_rank != -1:
		raise ValueError("""Distributed training is not currently supported.""" )
	if training_args.tpu_num_cores is not None:
		raise ValueError("""TPU acceleration is not currently supported.""" )

	logger.info(F'''Training/evaluation parameters {training_args}''' )

	# Set seed before initializing model.
	set_seed(training_args.seed )

	# 1. read in data
	_lowerCAmelCase			=read_lines(data_args.data_file )
	_lowerCAmelCase			=read_lines(data_args.class_names_file )

	# 2. get teacher predictions and load into dataset
	logger.info("""Generating predictions from zero-shot teacher model""" )
	_lowerCAmelCase			=get_teacher_predictions(
	    teacher_args.teacher_name_or_path					,				__UpperCamelCase					,				__UpperCamelCase					,				teacher_args.hypothesis_template					,				teacher_args.teacher_batch_size					,				teacher_args.temperature					,				teacher_args.multi_label					,				data_args.use_fast_tokenizer					,				training_args.no_cuda					,				training_args.fpaa					,				)
	_lowerCAmelCase			=Dataset.from_dict(
	    {
	        """text""": examples,
	        """labels""": teacher_soft_preds,
	    } )

	# 3. create student
	logger.info("""Initializing student model""" )
	_lowerCAmelCase			=AutoModelForSequenceClassification.from_pretrained(
	    student_args.student_name_or_path					,				num_labels=len(__UpperCamelCase ) )
	_lowerCAmelCase			=AutoTokenizer.from_pretrained(student_args.student_name_or_path					,				use_fast=data_args.use_fast_tokenizer )
	_lowerCAmelCase			=dict(enumerate(__UpperCamelCase ) )
	_lowerCAmelCase			={label: i for i, label in enumerate(__UpperCamelCase )}

	# 4. train student on teacher predictions
	_lowerCAmelCase			=dataset.map(__UpperCamelCase					,				input_columns="""text""" )
	dataset.set_format("""torch""" )

	def compute_metrics(__UpperCamelCase					,				__UpperCamelCase=False ):
		_lowerCAmelCase			=p.predictions.argmax(-1 )
		_lowerCAmelCase			=p.label_ids.argmax(-1 )  # "label_ids" are actually distributions
		return {"agreement": (preds == proxy_labels).mean().item()}

	_lowerCAmelCase			=DistillationTrainer(
	    model=__UpperCamelCase					,				tokenizer=__UpperCamelCase					,				args=__UpperCamelCase					,				train_dataset=__UpperCamelCase					,				compute_metrics=__UpperCamelCase					,				)

	if training_args.do_train:
		logger.info("""Training student model on teacher predictions""" )
		trainer.train()

	if training_args.do_eval:
		_lowerCAmelCase			=trainer.evaluate(eval_dataset=__UpperCamelCase )["""eval_agreement"""]
		logger.info(F'''Agreement of student and teacher predictions: {agreement * 100:0.2f}%''' )

	trainer.save_model()


if __name__ == "__main__":
			main()





"""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						)



