

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict






@dataclass
class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Tuple										=				ComputeEnvironment.AMAZON_SAGEMAKER
							_UpperCAmelCase   :  Optional[Any]										=				True
							_UpperCAmelCase   :  List[Any]										=				'''ml.p3.2xlarge'''
							_UpperCAmelCase   :  Dict										=				'''accelerate_sagemaker_execution_role'''
							_UpperCAmelCase   :  List[str]										=				'''hf-sm'''
							_UpperCAmelCase   :  Any										=				'''us-east-1'''
							_UpperCAmelCase   :  Dict										=				1
							_UpperCAmelCase   :  List[Any]										=				'''accelerate-sagemaker-1'''
							_UpperCAmelCase   :  Optional[Any]										=				'''1.6'''
							_UpperCAmelCase   :  str										=				'''4.4'''
							_UpperCAmelCase   :  List[str]										=				'''train.py'''
							_UpperCAmelCase   :  Tuple										=				[
							    '''--model_name_or_path''',
							    '''bert''',
							    '''--do_train''',
							    '''False''',
							    '''--epochs''',
							    '''3''',
							    '''--learning_rate''',
							    '''5e-5''',
							    '''--max_steps''',
							    '''50.5''',
							]
							_UpperCAmelCase   :  Optional[int]										=				[
							    '''--model_name_or_path''',
							    '''bert''',
							    '''--do_train''',
							    '''--do_test''',
							    '''False''',
							    '''--do_predict''',
							    '''--epochs''',
							    '''3''',
							    '''--learning_rate''',
							    '''5e-5''',
							    '''--max_steps''',
							    '''50.5''',
							]






class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      List[str]):
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__lowerCamelCase    :    int 	=			_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args)
								assert isinstance(converted_args['model_name_or_path']       ,SCREAMING_SNAKE_CASE__)
								assert isinstance(converted_args['do_train']       ,SCREAMING_SNAKE_CASE__)
								assert isinstance(converted_args['epochs']       ,SCREAMING_SNAKE_CASE__)
								assert isinstance(converted_args['learning_rate']       ,SCREAMING_SNAKE_CASE__)
								assert isinstance(converted_args['max_steps']       ,SCREAMING_SNAKE_CASE__)

								with pytest.raises(SCREAMING_SNAKE_CASE__):
									_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args)







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class 	A_		(	unittest.TestCase    ):
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Tuple 	=			TFCamembertModel.from_pretrained('jplu/tf-camembert-base')

								__lowerCamelCase    :    str 	=			tf.convert_to_tensor(
								    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]       ,dtype=tf.intaa       ,)  # J'aime le camembert !"

								__lowerCamelCase    :    int 	=			model(SCREAMING_SNAKE_CASE__)['last_hidden_state']
								__lowerCamelCase    :    Dict 	=			tf.TensorShape((1, 1_0, 7_6_8))
								self.assertEqual(output.shape       ,SCREAMING_SNAKE_CASE__)
								# compare the actual values for a slice.
								__lowerCamelCase    :    Any 	=			tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]       ,dtype=tf.floataa       ,)
								# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
								# camembert.eval()
								# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,expected_slice.numpy()       ,atol=1E-4))




