




'''simple docstring'''





import os

import pytest
from attr import dataclass


_SCREAMING_SNAKE_CASE							:   str      		= "us-east-1"  # defaults region




@dataclass
class  _snake_case		:
			lowerCAmelCase_     :      str
			lowerCAmelCase_     :      Optional[Any]						       =    "arn:aws:iam::558105141721:role/sagemaker_execution_role"
			lowerCAmelCase_     :      Optional[Any]						       =    {
			    "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,
			}
			lowerCAmelCase_     :      Optional[Any]						       =    {**hyperparameters, "max_steps": 1000}




			@property
			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										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    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										return F'{self.framework}-transfromers-test'




			@property
			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										return F'./tests/sagemaker/scripts/{self.framework}'




			@property
			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										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      UpperCamelCase_(	snake_case							:       Any     ):

							'''simple docstring'''






							snake_case_						=      SageMakerTestEnvironment(framework=request.cls.framework     )









from collections import defaultdict





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				first_str.lower().strip()
    __a						       =				second_str.lower().strip()

    # Remove whitespace
    __a						       =				first_str.replace(''' '''   , ''''''				)
    __a						       =				second_str.replace(''' '''   , ''''''				)

    # Strings of different lengths are not anagrams
    if len(_UpperCAmelCase				) != len(_UpperCAmelCase				):
        return False

    # Default values for count should be 0
    __a						       =				defaultdict(_UpperCAmelCase				)

    # For each character in input strings,
    # increment count in the corresponding
    for i in range(len(_UpperCAmelCase				)				):
        count[first_str[i]] += 1
        count[second_str[i]] -= 1

    return all(_count == 0 for _count in count.values()				)


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 __snake_case						:Any      		=     input('''Enter the first string ''').strip()
 __snake_case						:int      		=     input('''Enter the second string ''').strip()

 __snake_case						:int      		=     check_anagrams(input_a, input_b)
 print(f'{input_a} and {input_b} are {"" if status else "not "}anagrams.')





