
'''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			):



    def 	lowerCamelCase_	(						self   :							Optional[int],__A   :							Any  ):
          for model_result in results.values():
                for batch_size, sequence_length in zip(model_result["bs"],model_result["ss"]  ):
                      _lowerCamelCase					: Optional[Any]		    = model_result["result"][batch_size][sequence_length]
                      self.assertIsNotNone(__A  )



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: List[Any]		    = "sshleifer/tiny-gpt2"
          _lowerCamelCase					: str		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],eager_mode=__A,multi_process=__A,)
          _lowerCamelCase					: Optional[Any]		    = TensorFlowBenchmark(__A  )
          _lowerCamelCase					: Union[str, Any]		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: Optional[int]		    = "sgugger/tiny-distilbert-classification"
          _lowerCamelCase					: List[str]		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],multi_process=__A,only_pretrain_model=__A,)
          _lowerCamelCase					: Dict		    = TensorFlowBenchmark(__A  )
          _lowerCamelCase					: Tuple		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



    def 	lowerCamelCase_	(						self   :							str  ):
          _lowerCamelCase					: List[str]		    = "sshleifer/tiny-gpt2"
          _lowerCamelCase					: Dict		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],multi_process=__A,)
          _lowerCamelCase					: str		    = TensorFlowBenchmark(__A  )
          _lowerCamelCase					: int		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: Any		    = "sshleifer/tiny-gpt2"
          _lowerCamelCase					: List[Any]		    = AutoConfig.from_pretrained(__A  )
          _lowerCamelCase					: Union[str, Any]		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],eager_mode=__A,multi_process=__A,)
          _lowerCamelCase					: Any		    = TensorFlowBenchmark(__A,[config]  )
          _lowerCamelCase					: Optional[Any]		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: Any		    = "sshleifer/tiny-gpt2"
          _lowerCamelCase					: Tuple		    = AutoConfig.from_pretrained(__A  )
          _lowerCamelCase					: Tuple		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],multi_process=__A,)
          _lowerCamelCase					: Optional[Any]		    = TensorFlowBenchmark(__A,[config]  )
          _lowerCamelCase					: List[str]		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          _lowerCamelCase					: int		    = "sshleifer/tiny-gpt2"
          _lowerCamelCase					: List[str]		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],multi_process=__A,)
          _lowerCamelCase					: int		    = TensorFlowBenchmark(__A  )
          _lowerCamelCase					: Any		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result  )
          self.check_results_dict_not_empty(results.memory_train_result  )



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: Dict		    = "sshleifer/tiny-gpt2"
          _lowerCamelCase					: Dict		    = AutoConfig.from_pretrained(__A  )
          _lowerCamelCase					: Any		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],multi_process=__A,)
          _lowerCamelCase					: Union[str, Any]		    = TensorFlowBenchmark(__A,[config]  )
          _lowerCamelCase					: Any		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result  )
          self.check_results_dict_not_empty(results.memory_train_result  )



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: Optional[Any]		    = "patrickvonplaten/t5-tiny-random"
          _lowerCamelCase					: List[str]		    = AutoConfig.from_pretrained(__A  )
          _lowerCamelCase					: Tuple		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],multi_process=__A,)
          _lowerCamelCase					: Dict		    = TensorFlowBenchmark(__A,configs=[config]  )
          _lowerCamelCase					: List[str]		    = 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 	lowerCamelCase_	(						self   :							Optional[int]  ):
          _lowerCamelCase					: Tuple		    = "sshleifer/tiny-gpt2"
          _lowerCamelCase					: List[str]		    = TensorFlowBenchmarkArguments(
              models=[MODEL_ID],training=__A,inference=__A,sequence_lengths=[8],batch_sizes=[1],use_xla=__A,multi_process=__A,)
          _lowerCamelCase					: str		    = TensorFlowBenchmark(__A  )
          _lowerCamelCase					: List[str]		    = benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



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



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: Optional[int]		    = "sshleifer/tiny-gpt2"

          def _check_summary_is_not_empty(__A   :							List[Any]  ):
                self.assertTrue(hasattr(__A,"sequential"  )  )
                self.assertTrue(hasattr(__A,"cumulative"  )  )
                self.assertTrue(hasattr(__A,"current"  )  )
                self.assertTrue(hasattr(__A,"total"  )  )

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







'''simple docstring'''







from __future__ import annotations

from typing import TypedDict





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					42
			snake_case__	=					42





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )

				return [s[i:] + s[:i] for i in range(len(__A   )   )]





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )
				if not s:
								raise ValueError('''The parameter s must not be empty.'''   )

				a_		:	str									=   all_rotations(__A   )
				rotations.sort()  # sort the list of rotations in alphabetically order
				# make a string composed of the last char of each rotation
				a_		:	BWTTransformDict									=   {
				    "bwt_string": "".join([word[-1] for word in rotations]   ),
				    "idx_original_string": rotations.index(__A   ),
				}
				return response





def 		_UpperCAmelCase (				__A				: str    ,		__A				: int   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter bwt_string type must be str.'''   )
				if not bwt_string:
								raise ValueError('''The parameter bwt_string must not be empty.'''   )
				try:
								a_		:	Union[str, Any]									=   int(__A   )
				except ValueError:
								raise TypeError(
								    '''The parameter idx_original_string type must be int or passive'''
								    ''' of cast to int.'''   )
				if idx_original_string < 0:
								raise ValueError('''The parameter idx_original_string must not be lower than 0.'''   )
				if idx_original_string >= len(__A   ):
								raise ValueError(
								    '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''   )

				a_		:	Union[str, Any]									=   [''''''] * len(__A   )
				for _ in range(len(__A   )   ):
								for i in range(len(__A   )   ):
												a_		:	int									=   bwt_string[i] + ordered_rotations[i]
								ordered_rotations.sort()
				return ordered_rotations[idx_original_string]


if __name__ == "__main__":
		__lowerCAmelCase									=   'Provide a string that I will generate its BWT transform: '
		__lowerCAmelCase									=   input(entry_msg).strip()
		__lowerCAmelCase									=   bwt_transform(s)
		print(
		    F"""Burrows Wheeler transform for string '{s}' results """
		    F"""in '{result['bwt_string']}'"""
		)
		__lowerCAmelCase									=   reverse_bwt(result['bwt_string'], result['idx_original_string'])
		print(
		    F"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
		    F"""we get original string '{original_string}'"""
		)




