import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
				import torch

				from transformers.generation import DisjunctiveConstraint



@require_torch
class        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													# For consistency across different places the DisjunctiveConstraint is called,
													# dc.token_ids is a list of integers. It is also initialized only by integers.

													_snake_case						:					List[str]    =   [[1, 2, 4], [1, 2, 3, 4]]
													_snake_case						:					List[Any]    =   DisjunctiveConstraint(lowercase_    )
													self.assertTrue(isinstance(dc.token_ids				,    lowercase_    )    )

													with self.assertRaises(lowercase_    ):
																			DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]    )    )

													with self.assertRaises(lowercase_    ):
																			DisjunctiveConstraint([torch.LongTensor([1, 2, 4]    ), torch.LongTensor([1, 2, 3, 4, 5]    )]    )



							def 	UpperCamelCase	(						self    ):
													# We can't have constraints that are complete subsets of another. This leads to a preverse
													# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
													# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
													# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
													# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
													_snake_case						:					Union[str, Any]    =   [[1, 2], [1, 2, 3, 4]]

													with self.assertRaises(lowercase_    ):
																			DisjunctiveConstraint(lowercase_    )  # fails here



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   [[1, 2, 3], [1, 2, 4]]

													_snake_case						:					List[str]    =   DisjunctiveConstraint(lowercase_    )

													_snake_case    ,_snake_case    ,_snake_case						:					int    =   dc.update(1    )
													_snake_case						:					str    =   stepped is True and completed is False and reset is False
													self.assertTrue(lowercase_    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.current_seq == [1]    )

													_snake_case    ,_snake_case    ,_snake_case						:					Tuple    =   dc.update(2    )
													_snake_case						:					Dict    =   stepped is True and completed is False and reset is False
													self.assertTrue(lowercase_    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.current_seq == [1, 2]    )

													_snake_case    ,_snake_case    ,_snake_case						:					Dict    =   dc.update(3    )
													_snake_case						:					List[str]    =   stepped is True and completed is True and reset is False
													self.assertTrue(lowercase_    )
													self.assertTrue(dc.completed    )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 3]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

													_snake_case						:					str    =   DisjunctiveConstraint(lowercase_    )

													_snake_case    ,_snake_case    ,_snake_case						:					Any    =   dc.update(1    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.current_seq == [1]    )

													_snake_case    ,_snake_case    ,_snake_case						:					Dict    =   dc.update(2    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.current_seq == [1, 2]    )

													_snake_case    ,_snake_case    ,_snake_case						:					int    =   dc.update(4    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.current_seq == [1, 2, 4]    )

													_snake_case    ,_snake_case    ,_snake_case						:					int    =   dc.update(5    )
													self.assertTrue(dc.completed    )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 4, 5]    )

													dc.reset()

													_snake_case    ,_snake_case    ,_snake_case						:					str    =   dc.update(1    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.remaining() == 3    )
													self.assertTrue(dc.current_seq == [1]    )

													_snake_case    ,_snake_case    ,_snake_case						:					Dict    =   dc.update(2    )
													self.assertTrue(not dc.completed    )
													self.assertTrue(dc.remaining() == 2    )
													self.assertTrue(dc.current_seq == [1, 2]    )

													_snake_case    ,_snake_case    ,_snake_case						:					int    =   dc.update(5    )
													self.assertTrue(dc.completed    )  # Completed!
													self.assertTrue(dc.remaining() == 0    )
													self.assertTrue(dc.current_seq == [1, 2, 5]    )
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						)