






import os

import pytest
from attr import dataclass


UpperCamelCase								=		"us-east-1"  # defaults region

@dataclass
class 				lowerCAmelCase_	:
				_UpperCamelCase					:       str
				_UpperCamelCase					:       int							       = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
				_UpperCamelCase					:       Dict							       = {
				    "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": 5500,
				}
				_UpperCamelCase					:       int							       = {**hyperparameters, "max_steps": 1000}


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


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


				@property
				def 						__a					(					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    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Any:
							_lowercase							:   List[Any] 							=   SageMakerTestEnvironment(framework=request.cls.framework				)









'''simple docstring'''





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Union[str, Any]		=			CTRLTokenizer
 __lowerCamelCase       :			Union[str, Any]		=			False
 __lowerCamelCase       :			Any		=			False



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _a           =    ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
      _a           =    dict(zip(A					,  range(len(A			)			)			)			)
      _a           =    ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
      _a           =    {'''unk_token''': '''<unk>'''}

      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''merges_file''']			)
      with open(self.vocab_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write(json.dumps(A			) + '''\n'''			)
      with open(self.merges_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write('''\n'''.join(A			)			)



 def    a__    (self					,  **A			)       ->	int:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return CTRLTokenizer.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    '''adapt react readapt apt'''
      _a           =    '''adapt react readapt apt'''
      return input_text, output_text



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    CTRLTokenizer(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map			)
      _a           =    '''adapt react readapt apt'''
      _a           =    '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
      _a           =    tokenizer.tokenize(A			)
      self.assertListEqual(A					,  A			)

      _a           =    tokens + [tokenizer.unk_token]

      _a           =    [0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  A			)



