






'''simple docstring'''







from collections import defaultdict
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  first_str.lower().strip()
						lowerCamelCase__         =  second_str.lower().strip()

						# Remove whitespace
						lowerCamelCase__         =  first_str.replace(""" """   ,    """"""						)
						lowerCamelCase__         =  second_str.replace(""" """   ,    """"""						)

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

						# Default values for count should be 0
						lowerCamelCase__         =  defaultdict(__lowerCAmelCase						)

						# For each character in input strings,
						# increment count in the corresponding
						for i in range(len(__lowerCAmelCase						)						):
												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()
							UpperCamelCase    :      List[Any]  			=		input('Enter the first string ').strip()
							UpperCamelCase    :      List[str]  			=		input('Enter the second string ').strip()

							UpperCamelCase    :      Union[str, Any]  			=		check_anagrams(input_a, input_b)
							print(F'{input_a} and {input_b} are {"" if status else "not "}anagrams.')









'''simple docstring'''







import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf

							from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments




@require_tf
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								for model_result in results.values():
														for batch_size, sequence_length in zip(model_result["""bs"""]   ,model_result["""ss"""]       ):
																				lowerCamelCase__         =  model_result["""result"""][batch_size][sequence_length]
																				self.assertIsNotNone(_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,eager_mode=_lowerCAmelCase   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sgugger/tiny-distilbert-classification"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,only_pretrain_model=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,eager_mode=_lowerCAmelCase   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,[config]       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,[config]       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result       )
								self.check_results_dict_not_empty(results.memory_train_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,[config]       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result       )
								self.check_results_dict_not_empty(results.memory_train_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """patrickvonplaten/t5-tiny-random"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,configs=[config]       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""       )       ) == 0   ,"""Cannot do xla on CPU."""       )
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,use_xla=_lowerCAmelCase   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result       )
								self.check_results_dict_not_empty(results.memory_inference_result       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								with tempfile.TemporaryDirectory() as tmp_dir:
														lowerCamelCase__         =  TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]   ,inference=_lowerCAmelCase   ,save_to_csv=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,inference_time_csv_file=os.path.join(_lowerCAmelCase   ,"""inf_time.csv"""       )   ,inference_memory_csv_file=os.path.join(_lowerCAmelCase   ,"""inf_mem.csv"""       )   ,env_info_csv_file=os.path.join(_lowerCAmelCase   ,"""env.csv"""       )   ,multi_process=_lowerCAmelCase   ,)
														lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
														benchmark.run()
														self.assertTrue(Path(os.path.join(_lowerCAmelCase   ,"""inf_time.csv"""       )       ).exists()       )
														self.assertTrue(Path(os.path.join(_lowerCAmelCase   ,"""inf_mem.csv"""       )       ).exists()       )
														self.assertTrue(Path(os.path.join(_lowerCAmelCase   ,"""env.csv"""       )       ).exists()       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""

								def _check_summary_is_not_empty(_lowerCAmelCase       ):
														self.assertTrue(hasattr(_lowerCAmelCase   ,"""sequential"""       )       )
														self.assertTrue(hasattr(_lowerCAmelCase   ,"""cumulative"""       )       )
														self.assertTrue(hasattr(_lowerCAmelCase   ,"""current"""       )       )
														self.assertTrue(hasattr(_lowerCAmelCase   ,"""total"""       )       )

								with tempfile.TemporaryDirectory() as tmp_dir:
														lowerCamelCase__         =  TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,log_filename=os.path.join(_lowerCAmelCase   ,"""log.txt"""       )   ,log_print=_lowerCAmelCase   ,trace_memory_line_by_line=_lowerCAmelCase   ,eager_mode=_lowerCAmelCase   ,multi_process=_lowerCAmelCase   ,)
														lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
														lowerCamelCase__         =  benchmark.run()
														_check_summary_is_not_empty(result.inference_summary       )
														self.assertTrue(Path(os.path.join(_lowerCAmelCase   ,"""log.txt"""       )       ).exists()       )

