import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers.generation import (
				    MaxLengthCriteria,
				    MaxNewTokensCriteria,
				    MaxTimeCriteria,
				    StoppingCriteriaList,
				    validate_stopping_criteria,
				)



@require_torch
class        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					Tuple    =   3
													_snake_case						:					str    =   250

													_snake_case						:					Dict    =   ids_tensor((batch_size, length)				,    lowercase_    )
													_snake_case						:					List[str]    =   torch.ones((batch_size, length)				,    device=lowercase_				,    dtype=torch.float    ) / length
													return input_ids, scores



							def 	UpperCamelCase	(						self    ):
													_snake_case    ,_snake_case						:					Tuple    =   self._get_tensors(5    )

													_snake_case						:					int    =   StoppingCriteriaList(
													    [
													        MaxLengthCriteria(max_length=10    ),
													        MaxTimeCriteria(max_time=0.1    ),
													    ]    )

													self.assertFalse(criteria(lowercase_				,    lowercase_    )    )

													_snake_case    ,_snake_case						:					Any    =   self._get_tensors(9    )
													self.assertFalse(criteria(lowercase_				,    lowercase_    )    )

													_snake_case    ,_snake_case						:					Optional[Any]    =   self._get_tensors(10    )
													self.assertTrue(criteria(lowercase_				,    lowercase_    )    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   MaxLengthCriteria(max_length=10    )

													_snake_case    ,_snake_case						:					List[str]    =   self._get_tensors(5    )
													self.assertFalse(criteria(lowercase_				,    lowercase_    )    )

													_snake_case    ,_snake_case						:					str    =   self._get_tensors(9    )
													self.assertFalse(criteria(lowercase_				,    lowercase_    )    )

													_snake_case    ,_snake_case						:					Optional[Any]    =   self._get_tensors(10    )
													self.assertTrue(criteria(lowercase_				,    lowercase_    )    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   MaxNewTokensCriteria(start_length=5				,    max_new_tokens=5    )

													_snake_case    ,_snake_case						:					int    =   self._get_tensors(5    )
													self.assertFalse(criteria(lowercase_				,    lowercase_    )    )

													_snake_case    ,_snake_case						:					Union[str, Any]    =   self._get_tensors(9    )
													self.assertFalse(criteria(lowercase_				,    lowercase_    )    )

													_snake_case    ,_snake_case						:					Any    =   self._get_tensors(10    )
													self.assertTrue(criteria(lowercase_				,    lowercase_    )    )

													_snake_case						:					Any    =   StoppingCriteriaList([criteria]    )
													self.assertEqual(criteria_list.max_length				,    10    )



							def 	UpperCamelCase	(						self    ):
													_snake_case    ,_snake_case						:					Tuple    =   self._get_tensors(5    )

													_snake_case						:					int    =   MaxTimeCriteria(max_time=0.1    )
													self.assertFalse(criteria(lowercase_				,    lowercase_    )    )

													_snake_case						:					Optional[Any]    =   MaxTimeCriteria(max_time=0.1				,    initial_timestamp=time.time() - 0.2    )
													self.assertTrue(criteria(lowercase_				,    lowercase_    )    )



							def 	UpperCamelCase	(						self    ):
													validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10    )]    )				,    10    )

													with self.assertWarns(lowercase_    ):
																			validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10    )]    )				,    11    )

													_snake_case						:					List[Any]    =   validate_stopping_criteria(StoppingCriteriaList()				,    11    )

													self.assertEqual(len(lowercase_    )				,    1    )
import os

import pytest
from attr import dataclass


__SCREAMING_SNAKE_CASE						:   int         =			'us-east-1'  # defaults region



@dataclass
class        lowercase_   :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		'arn:aws:iam::558105141721:role/sagemaker_execution_role'
							_lowerCamelCase						=		{
							    'task_name': 'mnli',
							    'per_device_train_batch_size': 16,
							    'per_device_eval_batch_size': 16,
							    'do_train': True,
							    'do_eval': True,
							    'do_predict': True,
							    'output_dir': '/opt/ml/model',
							    'overwrite_output_dir': True,
							    'max_steps': 500,
							    'save_steps': 5_500,
							}
							_lowerCamelCase						=		{**hyperparameters, 'max_steps': 1_000}



							@property
							def 	UpperCamelCase	(						self    ):
													if self.framework == "pytorch":
																			return [
																			    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
																			    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
																			    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
																			]
													else:
																			return [
																			    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
																			    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
																			    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
																			]



							@property
							def 	UpperCamelCase	(						self    ):
													return f"""{self.framework}-transfromers-test"""



							@property
							def 	UpperCamelCase	(						self    ):
													return f"""./tests/sagemaker/scripts/{self.framework}"""



							@property
							def 	UpperCamelCase	(						self    ):
													if self.framework == "pytorch":
																			return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
													else:
																			return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"




@pytest.fixture(scope="class"						)
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					List[str]    =   SageMakerTestEnvironment(framework=request.cls.framework						)