






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment


def __A   (				*__lowerCAmelCase     )->  Any:





						"""simple docstring"""



						_UpperCAmelCase     =      torch.cuda.device_count()
						print(F"""Device count: {num_processes}"""     )
						with patch_environment(
						    world_size=__lowerCAmelCase			,		master_addr='127.0.01'			,		master_port='29500'			,		mixed_precision=args[1].mixed_precision     ):
												_UpperCAmelCase     =      PrepareForLaunch(__lowerCAmelCase			,		distributed_type='MULTI_GPU'     )
												torch.multiprocessing.start_processes(__lowerCAmelCase			,		args=__lowerCAmelCase			,		nprocs=__lowerCAmelCase			,		start_method='spawn'     )


if __name__ == "__main__":
		# Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
		# for cloud access setup instructions (if using on-demand hardware), and for API specifications.

		# on-demand GPU
		# gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
		_a				     =  rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
		gpu.up_if_not()

		# on-prem GPU
		# gpu = rh.cluster(
		#           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
		#       )

		# Set up remote function
		_a				     =  [
		    '''pip:./''',
		    '''transformers''',
		    '''datasets''',
		    '''evaluate''',
		    '''tqdm''',
		    '''scipy''',
		    '''scikit-learn''',
		    '''tensorboard''',
		    '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
		]
		_a				     =  rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

		# Define train args/config, run train function
		_a				     =  argparse.Namespace(cpu=False, mixed_precision='''fp16''')
		_a				     =  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		launch_train_gpu(config, train_args, stream_logs=True)

		# Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
		# gpu.install_packages(reqs)
		# gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])








import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=				MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	UpperCamelCase__								=				TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							if isinstance(model.config	,   UpperCAmelCase   ):
													_UpperCAmelCase ,			_UpperCAmelCase     =      list(tokenizer.lang_code_to_id.keys()   )[:2]
													_UpperCAmelCase     =      TranslationPipeline(model=UpperCAmelCase	,   tokenizer=UpperCAmelCase	,   src_lang=UpperCAmelCase	,   tgt_lang=UpperCAmelCase   )
							else:
													_UpperCAmelCase     =      TranslationPipeline(model=UpperCAmelCase	,   tokenizer=UpperCAmelCase   )
							return translator, ["Some string", "Some other text"]

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      translator('Some string'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}]   )

							_UpperCAmelCase     =      translator(['Some string']   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}]   )

							_UpperCAmelCase     =      translator(['Some string', 'other string']   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}, {'translation_text': ANY(UpperCAmelCase   )}]   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_ro'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='pt'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
							                ' Beide Beide'
							            )
							        }
							    ]	,   )

	@require_tf
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_ro'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='tf'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
							                ' Beide Beide'
							            )
							        }
							    ]	,   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_de'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='pt'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
							                ' urine urine urine urine urine urine urine'
							            )
							        }
							    ]	,   )

	@require_tf
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_de'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='tf'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
							                ' urine urine urine urine urine urine urine'
							            )
							        }
							    ]	,   )





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	@require_torch
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							with self.assertRaises(UpperCAmelCase   ):
													pipeline(task='translation_cn_to_ar'   )

							# but we do for this one
							_UpperCAmelCase     =      pipeline(task='translation_en_to_de'   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'en'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'de'   )

	@require_torch
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )
							_UpperCAmelCase     =      MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )

							_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase	,   tokenizer=UpperCAmelCase   )
							# Missing src_lang, tgt_lang
							with self.assertRaises(UpperCAmelCase   ):
													translator('This is a test'   )

							_UpperCAmelCase     =      translator('This is a test'	,   src_lang='en_XX'	,   tgt_lang='ar_AR'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'هذا إختبار'}]   )

							_UpperCAmelCase     =      translator('This is a test'	,   src_lang='en_XX'	,   tgt_lang='hi_IN'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'यह एक परीक्षण है'}]   )

							# src_lang, tgt_lang can be defined at pipeline call time
							_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase	,   tokenizer=UpperCAmelCase	,   src_lang='en_XX'	,   tgt_lang='ar_AR'   )
							_UpperCAmelCase     =      translator('This is a test'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'هذا إختبار'}]   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'patrickvonplaten/t5-tiny-random'
							_UpperCAmelCase     =      pipeline(task='translation_cn_to_ar'	,   model=UpperCAmelCase   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'cn'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'ar'   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'patrickvonplaten/t5-tiny-random'
							with pytest.warns(UpperCAmelCase	,   match=R'.*translation_en_to_de.*'   ):
													_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase   )
							self.assertEqual(translator.task	,   'translation_en_to_de'   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'en'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'de'   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							with self.assertRaises(UpperCAmelCase   ):
													pipeline(task='translation'   )
