




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(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = ComputeEnvironment.AMAZON_SAGEMAKER
    lowerCAmelCase         = True
    lowerCAmelCase         = '''ml.p3.2xlarge'''
    lowerCAmelCase         = '''accelerate_sagemaker_execution_role'''
    lowerCAmelCase         = '''hf-sm'''
    lowerCAmelCase         = '''us-east-1'''
    lowerCAmelCase         = 1
    lowerCAmelCase         = '''accelerate-sagemaker-1'''
    lowerCAmelCase         = '''1.6'''
    lowerCAmelCase         = '''4.4'''
    lowerCAmelCase         = '''train.py'''
    lowerCAmelCase         = [
        '''--model_name_or_path''',
        '''bert''',
        '''--do_train''',
        '''False''',
        '''--epochs''',
        '''3''',
        '''--learning_rate''',
        '''5e-5''',
        '''--max_steps''',
        '''50.5''',
    ]
    lowerCAmelCase         = [
        '''--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  ):






    """simple docstring"""





    def 				a__ ( self    )					->						List[Any]:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         UpperCAmelCase_     :				Union[str, Any]  =     _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    )

"""simple docstring"""


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
						import optax
						from flax.training.common_utils import onehot

						from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
						from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class  __lowerCamelCase  ( unittest.TestCase			):




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''google/mt5-small'''   )

							lowerCamelCase_					=  tokenizer('''Hello there'''							,						return_tensors='''np'''   ).input_ids
							lowerCamelCase_					=  tokenizer('''Hi I am'''							,						return_tensors='''np'''   ).input_ids

							lowerCamelCase_					=  shift_tokens_right(UpperCAmelCase							,						model.config.pad_token_id							,						model.config.decoder_start_token_id   )

							lowerCamelCase_					=  model(UpperCAmelCase							,						decoder_input_ids=UpperCAmelCase   ).logits
							lowerCamelCase_					=  optax.softmax_cross_entropy(UpperCAmelCase							,						onehot(UpperCAmelCase							,						logits.shape[-1]   )   ).mean()

							lowerCamelCase_					=  -(labels.shape[-1] * loss.item())

							lowerCamelCase_					=  -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )



