


'''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


lowerCamelCase__     						=  '\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'

lowerCamelCase__     						=  logging.getLogger(__name__)






@dataclass
class 						lowerCAmelCase__      :
			lowerCAmelCase			:						Optional[str]							 =  field(
			    default="roberta-large-mnli"     ,							metadata={"help": "The NLI/zero-shot teacher model to be distilled."}						)
			lowerCAmelCase			:						Optional[str]							 =  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			:						Optional[int]							 =  field(
			    default=32     ,							metadata={"help": "Batch size for generating teacher predictions."}						)
			lowerCAmelCase			:						Optional[bool]							 =  field(
			    default=UpperCAmelCase__     ,							metadata={
			        "help": (
			            "Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"
			            "multi-class multi-label classification)."
			        )
			    }     ,							)
			lowerCAmelCase			:						Optional[float]							 =  field(
			    default=1.0     ,							metadata={"help": "Temperature applied to teacher softmax for distillation."}						)






@dataclass
class 						lowerCAmelCase__      :
			lowerCAmelCase			:						Optional[str]							 =  field(
			    default="distilbert-base-uncased"     ,							metadata={"help": "The NLI/zero-shot teacher model to be distilled."}						)






@dataclass
class 						lowerCAmelCase__      :
			lowerCAmelCase			:						str							 =  field(metadata={"help": "Text file with one unlabeled instance per line."}						)
			lowerCAmelCase			:						str							 =  field(metadata={"help": "Text file with one class name per line."}						)
			lowerCAmelCase			:						bool							 =  field(
			    default=UpperCAmelCase__     ,							metadata={"help": "Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."}     ,							)






@dataclass
class 						lowerCAmelCase__      (      UpperCAmelCase__						):
			lowerCAmelCase			:						Optional[str]							 =  field(
			    default=UpperCAmelCase__     ,							metadata={"help": "The output directory where the model predictions and checkpoints will be written."}     ,							)
			lowerCAmelCase			:						int							 =  field(
			    default=32     ,							metadata={"help": "Batch size per GPU/TPU core/CPU for training."}						)
			lowerCAmelCase			:						int							 =  field(
			    default=128     ,							metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}						)
			lowerCAmelCase			:						float							 =  field(default=1.0     ,							metadata={"help": "Total number of training epochs to perform."}						)
			lowerCAmelCase			:						bool							 =  field(default=UpperCAmelCase__     ,							metadata={"help": "Whether to run training of student model."}						)
			lowerCAmelCase			:						bool							 =  field(
			    default=UpperCAmelCase__     ,							metadata={
			        "help": (
			            "Whether to evaluate the agreement of the final student predictions and the teacher predictions"
			            "after training."
			        )
			    }     ,							)
			lowerCAmelCase			:						Optional[int]							 =  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__      (      UpperCAmelCase__						):




			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Union[str, Any]=False			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   inputs['labels']
					_UpperCAmelCase		:							Dict             =   model(inputs["input_ids"]						,	attention_mask=inputs["attention_mask"]			)
					_UpperCAmelCase		:							List[str]             =   outputs[0]

					_UpperCAmelCase		:							Optional[int]             =   -torch.sum(target_p * logits.log_softmax(dim=-1			)						,	axis=-1			).mean()

					if return_outputs:
							return loss, outputs

					return loss






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		_UpperCAmelCase		:							Tuple             =   []
		with open(__lowerCAmelCase   ,			"r" ) as f:
				for line in f:
						_UpperCAmelCase		:							Tuple             =   line.strip()
						if len(__lowerCAmelCase ) > 0:
								lines.append(__lowerCAmelCase )
		return lines






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							int             =   []
		_UpperCAmelCase		:							Union[str, Any]             =   []
		for example in examples:
				for name in class_names:
						premises.append(__lowerCAmelCase )
						hypotheses.append(hypothesis_template.format(__lowerCAmelCase ) )
		return premises, hypotheses






def 			__lowerCAmelCase			(__lowerCAmelCase ):





		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			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			):
		_UpperCAmelCase		:							Union[str, Any]             =   AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase )
		_UpperCAmelCase		:							Optional[Any]             =   model.config
		if not no_cuda and torch.cuda.is_available():
				_UpperCAmelCase		:							List[Any]             =   nn.DataParallel(model.cuda() )
				batch_size *= len(model.device_ids )
		_UpperCAmelCase		:							Any             =   AutoTokenizer.from_pretrained(__lowerCAmelCase   ,			use_fast=__lowerCAmelCase )

		_UpperCAmelCase		:							Optional[int]             =   get_premise_hypothesis_pairs(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase )
		_UpperCAmelCase		:							List[str]             =   []

		for i in tqdm(range(0   ,			len(__lowerCAmelCase )   ,			__lowerCAmelCase ) ):
				_UpperCAmelCase		:							Optional[Any]             =   premises[i : i + batch_size]
				_UpperCAmelCase		:							Optional[int]             =   hypotheses[i : i + batch_size]

				_UpperCAmelCase		:							Any             =   tokenizer(
				    __lowerCAmelCase   ,			__lowerCAmelCase   ,			padding=__lowerCAmelCase   ,			truncation="only_first"   ,			return_tensors="pt"   ,			)

				with torch.cuda.amp.autocast(enabled=__lowerCAmelCase ):
						with torch.no_grad():
								_UpperCAmelCase		:							Optional[Any]             =   model(**__lowerCAmelCase )
				logits.append(outputs.logits.detach().cpu().float() )

		_UpperCAmelCase		:							int             =   get_entailment_id(__lowerCAmelCase )
		_UpperCAmelCase		:							Union[str, Any]             =   -1 if entail_id == 0 else 0
		_UpperCAmelCase		:							List[str]             =   torch.cat(__lowerCAmelCase   ,			dim=0 )  # N*K x 3
		_UpperCAmelCase		:							Any             =   logits.reshape(len(__lowerCAmelCase )   ,			len(__lowerCAmelCase )   ,			-1 )[..., [contr_id, entail_id]]  # N x K x 2

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

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






def 			__lowerCAmelCase			():
		_UpperCAmelCase		:							Dict             =   HfArgumentParser(
		    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)   ,			description=__lowerCAmelCase   ,			)

		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.
				_UpperCAmelCase		:							List[Any]             =   parser.parse_json_file(
				    json_file=os.path.abspath(sys.argv[1] ) )
		else:
				_UpperCAmelCase		:							Dict             =   parser.parse_args_into_dataclasses()

		# Detecting last checkpoint.
		_UpperCAmelCase		:							Optional[int]             =   None
		if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
				_UpperCAmelCase		:							Dict             =   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
		_UpperCAmelCase		:							List[Any]             =   read_lines(data_args.data_file )
		_UpperCAmelCase		:							Any             =   read_lines(data_args.class_names_file )

		# 2. get teacher predictions and load into dataset
		logger.info("Generating predictions from zero-shot teacher model" )
		_UpperCAmelCase		:							Optional[int]             =   get_teacher_predictions(
		    teacher_args.teacher_name_or_path   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			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   ,			)
		_UpperCAmelCase		:							Any             =   Dataset.from_dict(
		    {
		        "text": examples,
		        "labels": teacher_soft_preds,
		    } )

		# 3. create student
		logger.info("Initializing student model" )
		_UpperCAmelCase		:							Union[str, Any]             =   AutoModelForSequenceClassification.from_pretrained(
		    student_args.student_name_or_path   ,			num_labels=len(__lowerCAmelCase ) )
		_UpperCAmelCase		:							int             =   AutoTokenizer.from_pretrained(student_args.student_name_or_path   ,			use_fast=data_args.use_fast_tokenizer )
		_UpperCAmelCase		:							Any             =   dict(enumerate(__lowerCAmelCase ) )
		_UpperCAmelCase		:							Dict             =   {label: i for i, label in enumerate(__lowerCAmelCase )}

		# 4. train student on teacher predictions
		_UpperCAmelCase		:							Tuple             =   dataset.map(__lowerCAmelCase   ,			input_columns="text" )
		dataset.set_format("torch" )

		def compute_metrics(__lowerCAmelCase   ,			__lowerCAmelCase=False ):
				_UpperCAmelCase		:							Dict             =   p.predictions.argmax(-1 )
				_UpperCAmelCase		:							List[Any]             =   p.label_ids.argmax(-1 )  # "label_ids" are actually distributions
				return {"agreement": (preds == proxy_labels).mean().item()}

		_UpperCAmelCase		:							List[str]             =   DistillationTrainer(
		    model=__lowerCAmelCase   ,			tokenizer=__lowerCAmelCase   ,			args=__lowerCAmelCase   ,			train_dataset=__lowerCAmelCase   ,			compute_metrics=__lowerCAmelCase   ,			)

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

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

		trainer.save_model()


if __name__ == "__main__":
						main()









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


UpperCAmelCase__    :							Any       		=     [
    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)

UpperCAmelCase__    :							Union[str, Any]       		=     logging.getLogger()


def 		_lowerCamelCase					(       )      ->		str:





  """simple docstring"""





  _lowercase:			int      =     argparse.ArgumentParser()
  parser.add_argument('-f'				)
  _lowercase:			Optional[Any]      =     parser.parse_args()
  return args.f


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Dict:





  """simple docstring"""





  _lowercase:			str      =     {}
  _lowercase:			Union[str, Any]      =     os.path.join(UpperCamelCase       ,  'all_results.json'				)
  if os.path.exists(UpperCamelCase				):
    with open(UpperCamelCase       ,  'r'				) as f:
      _lowercase:			Dict      =     json.load(UpperCamelCase				)
  else:
    raise ValueError(F'can\'t find {path}'				)
  return results


def 		_lowerCamelCase					(       )      ->		Any:





  """simple docstring"""





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


UpperCAmelCase__    :							List[Any]       		=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class   UpperCAmelCase    (      A ):






   @skip('Skipping until shape inference for to_tf_dataset PR is merged.'  )
   def 			_a    (						self :							Union[str, Any]  )->      Optional[Any]:
     _lowercase:			List[str]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			List[str]      =     f'\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.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'  )
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['eval_accuracy']    ,       0.75  )






   def 			_a    (						self :							Optional[Any]  )->      int:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			str      =     f'\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.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()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       1_00  )






   def 			_a    (						self :							Union[str, Any]  )->      Optional[int]:
     _lowercase:			int      =     self.get_auto_remove_tmp_dir()
     _lowercase:			int      =     f'\n            run_mlm.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            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_mlm.main()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       42  )






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

     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Optional[int]      =     f'\n            run_ner.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            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\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        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_ner.main()
       _lowercase:			Optional[Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.75  )






   def 			_a    (						self :							Tuple  )->      Optional[int]:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			int      =     f'\n            run_qa.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            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_squad.main()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['f1']    ,       30  )
       self.assertGreaterEqual(result['exact']    ,       30  )






   def 			_a    (						self :							List[str]  )->      Tuple:
     _lowercase:			str      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Any      =     f'\n            run_swag.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            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_swag.main()
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['val_accuracy']    ,       0.8  )






   @slow
   def 			_a    (						self :							Dict  )->      Tuple:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			str      =     f'\n            run_summarization.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            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_summarization.main()
       _lowercase:			List[str]      =     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 			_a    (						self :							Tuple  )->      str:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			List[str]      =     f'\n            run_translation.py\n            --model_name_or_path Rocketknight1/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            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\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        '.split()

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_translation.main()
       _lowercase:			Optional[int]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['bleu']    ,       30  )






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

     with patch.object(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_image_classification.main()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.7  )
