



"""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      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        for model_result in results.values():
          for batch_size, sequence_length in zip(model_result['bs']    ,			model_result['ss']       ):
            lowerCAmelCase_   :				Dict								= model_result['result'][batch_size][sequence_length]
            self.assertIsNotNone(UpperCAmelCase__       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				Any								= 'sshleifer/tiny-gpt2'
        lowerCAmelCase_   :				Dict								= TensorFlowBenchmarkArguments(
            models=[MODEL_ID]    ,			training=UpperCAmelCase__    ,			inference=UpperCAmelCase__    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			eager_mode=UpperCAmelCase__    ,			multi_process=UpperCAmelCase__    ,			)
        lowerCAmelCase_   :				Dict								= TensorFlowBenchmark(UpperCAmelCase__       )
        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      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				Tuple								= 'sgugger/tiny-distilbert-classification'
        lowerCAmelCase_   :				Any								= TensorFlowBenchmarkArguments(
            models=[MODEL_ID]    ,			training=UpperCAmelCase__    ,			inference=UpperCAmelCase__    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=UpperCAmelCase__    ,			only_pretrain_model=UpperCAmelCase__    ,			)
        lowerCAmelCase_   :				Union[str, Any]								= TensorFlowBenchmark(UpperCAmelCase__       )
        lowerCAmelCase_   :				Dict								= benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result       )
        self.check_results_dict_not_empty(results.memory_inference_result       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Tuple								= 'sshleifer/tiny-gpt2'
        lowerCAmelCase_   :				List[Any]								= TensorFlowBenchmarkArguments(
            models=[MODEL_ID]    ,			training=UpperCAmelCase__    ,			inference=UpperCAmelCase__    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=UpperCAmelCase__    ,			)
        lowerCAmelCase_   :				Dict								= TensorFlowBenchmark(UpperCAmelCase__       )
        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      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				Tuple								= 'sshleifer/tiny-gpt2'
        lowerCAmelCase_   :				List[str]								= AutoConfig.from_pretrained(UpperCAmelCase__       )
        lowerCAmelCase_   :				Optional[Any]								= TensorFlowBenchmarkArguments(
            models=[MODEL_ID]    ,			training=UpperCAmelCase__    ,			inference=UpperCAmelCase__    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			eager_mode=UpperCAmelCase__    ,			multi_process=UpperCAmelCase__    ,			)
        lowerCAmelCase_   :				Optional[int]								= TensorFlowBenchmark(UpperCAmelCase__    ,			[config]       )
        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      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				Dict								= 'sshleifer/tiny-gpt2'
        lowerCAmelCase_   :				Union[str, Any]								= AutoConfig.from_pretrained(UpperCAmelCase__       )
        lowerCAmelCase_   :				Dict								= TensorFlowBenchmarkArguments(
            models=[MODEL_ID]    ,			training=UpperCAmelCase__    ,			inference=UpperCAmelCase__    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=UpperCAmelCase__    ,			)
        lowerCAmelCase_   :				Optional[Any]								= TensorFlowBenchmark(UpperCAmelCase__    ,			[config]       )
        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      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				Any								= 'sshleifer/tiny-gpt2'
        lowerCAmelCase_   :				List[Any]								= TensorFlowBenchmarkArguments(
            models=[MODEL_ID]    ,			training=UpperCAmelCase__    ,			inference=UpperCAmelCase__    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=UpperCAmelCase__    ,			)
        lowerCAmelCase_   :				Optional[Any]								= TensorFlowBenchmark(UpperCAmelCase__       )
        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      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				str								= 'sshleifer/tiny-gpt2'
        lowerCAmelCase_   :				Any								= AutoConfig.from_pretrained(UpperCAmelCase__       )
        lowerCAmelCase_   :				int								= TensorFlowBenchmarkArguments(
            models=[MODEL_ID]    ,			training=UpperCAmelCase__    ,			inference=UpperCAmelCase__    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			multi_process=UpperCAmelCase__    ,			)
        lowerCAmelCase_   :				List[Any]								= TensorFlowBenchmark(UpperCAmelCase__    ,			[config]       )
        lowerCAmelCase_   :				str								= benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result       )
        self.check_results_dict_not_empty(results.memory_train_result       )



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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				Optional[Any]								= 'sshleifer/tiny-gpt2'
        with tempfile.TemporaryDirectory() as tmp_dir:
          lowerCAmelCase_   :				str								= TensorFlowBenchmarkArguments(
              models=[MODEL_ID]    ,			inference=UpperCAmelCase__    ,			save_to_csv=UpperCAmelCase__    ,			sequence_lengths=[8]    ,			batch_sizes=[1]    ,			inference_time_csv_file=os.path.join(UpperCAmelCase__    ,			'inf_time.csv'       )    ,			inference_memory_csv_file=os.path.join(UpperCAmelCase__    ,			'inf_mem.csv'       )    ,			env_info_csv_file=os.path.join(UpperCAmelCase__    ,			'env.csv'       )    ,			multi_process=UpperCAmelCase__    ,			)
          lowerCAmelCase_   :				Optional[int]								= TensorFlowBenchmark(UpperCAmelCase__       )
          benchmark.run()
          self.assertTrue(Path(os.path.join(UpperCAmelCase__    ,			'inf_time.csv'       )       ).exists()       )
          self.assertTrue(Path(os.path.join(UpperCAmelCase__    ,			'inf_mem.csv'       )       ).exists()       )
          self.assertTrue(Path(os.path.join(UpperCAmelCase__    ,			'env.csv'       )       ).exists()       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				List[str]								= 'sshleifer/tiny-gpt2'

        def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE_				:      List[str]       ):
          self.assertTrue(hasattr(UpperCAmelCase__    ,			'sequential'       )       )
          self.assertTrue(hasattr(UpperCAmelCase__    ,			'cumulative'       )       )
          self.assertTrue(hasattr(UpperCAmelCase__    ,			'current'       )       )
          self.assertTrue(hasattr(UpperCAmelCase__    ,			'total'       )       )

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









"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if number > 0:
										raise ValueError("input must be a negative integer"   )
					__SCREAMING_SNAKE_CASE						   =				len(bin(lowerCAmelCase_   )[3:]   )
					__SCREAMING_SNAKE_CASE						   =				bin(abs(lowerCAmelCase_   ) - (1 << binary_number_length)   )[3:]
					__SCREAMING_SNAKE_CASE						   =				(
					    (
					        "1"
					        + "0" * (binary_number_length - len(lowerCAmelCase_   ))
					        + twos_complement_number
					    )
					    if number < 0
					    else "0"
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
		import doctest

		doctest.testmod()
