
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 __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           for model_result in results.values():
                  for batch_size, sequence_length in zip(model_result["""bs"""]		,model_result["""ss"""]				):
                         lowercase							:					Optional[Any]	     =				model_result["""result"""][batch_size][sequence_length]
                         self.assertIsNotNone(_snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				"""sshleifer/tiny-gpt2"""
           lowercase							:					Union[str, Any]	     =				TensorFlowBenchmarkArguments(
               models=[MODEL_ID]		,training=_snake_case		,inference=_snake_case		,sequence_lengths=[8]		,batch_sizes=[1]		,eager_mode=_snake_case		,multi_process=_snake_case		,)
           lowercase							:					Dict	     =				TensorFlowBenchmark(_snake_case				)
           lowercase							:					int	     =				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				):



           '''simple docstring'''







           lowercase							:					str	     =				"""sgugger/tiny-distilbert-classification"""
           lowercase							:					int	     =				TensorFlowBenchmarkArguments(
               models=[MODEL_ID]		,training=_snake_case		,inference=_snake_case		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=_snake_case		,only_pretrain_model=_snake_case		,)
           lowercase							:					str	     =				TensorFlowBenchmark(_snake_case				)
           lowercase							:					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				):



           '''simple docstring'''







           lowercase							:					Any	     =				"""sshleifer/tiny-gpt2"""
           lowercase							:					int	     =				TensorFlowBenchmarkArguments(
               models=[MODEL_ID]		,training=_snake_case		,inference=_snake_case		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=_snake_case		,)
           lowercase							:					Tuple	     =				TensorFlowBenchmark(_snake_case				)
           lowercase							:					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				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				"""sshleifer/tiny-gpt2"""
           lowercase							:					Union[str, Any]	     =				AutoConfig.from_pretrained(_snake_case				)
           lowercase							:					Tuple	     =				TensorFlowBenchmarkArguments(
               models=[MODEL_ID]		,training=_snake_case		,inference=_snake_case		,sequence_lengths=[8]		,batch_sizes=[1]		,eager_mode=_snake_case		,multi_process=_snake_case		,)
           lowercase							:					List[str]	     =				TensorFlowBenchmark(_snake_case		,[config]				)
           lowercase							:					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				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				"""sshleifer/tiny-gpt2"""
           lowercase							:					Dict	     =				AutoConfig.from_pretrained(_snake_case				)
           lowercase							:					List[Any]	     =				TensorFlowBenchmarkArguments(
               models=[MODEL_ID]		,training=_snake_case		,inference=_snake_case		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=_snake_case		,)
           lowercase							:					Tuple	     =				TensorFlowBenchmark(_snake_case		,[config]				)
           lowercase							:					List[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				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				"""sshleifer/tiny-gpt2"""
           lowercase							:					Optional[Any]	     =				TensorFlowBenchmarkArguments(
               models=[MODEL_ID]		,training=_snake_case		,inference=_snake_case		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=_snake_case		,)
           lowercase							:					Optional[Any]	     =				TensorFlowBenchmark(_snake_case				)
           lowercase							:					Tuple	     =				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				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				"""sshleifer/tiny-gpt2"""
           lowercase							:					Optional[Any]	     =				AutoConfig.from_pretrained(_snake_case				)
           lowercase							:					List[str]	     =				TensorFlowBenchmarkArguments(
               models=[MODEL_ID]		,training=_snake_case		,inference=_snake_case		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=_snake_case		,)
           lowercase							:					Optional[Any]	     =				TensorFlowBenchmark(_snake_case		,[config]				)
           lowercase							:					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				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				"""patrickvonplaten/t5-tiny-random"""
           lowercase							:					Optional[Any]	     =				AutoConfig.from_pretrained(_snake_case				)
           lowercase							:					Optional[Any]	     =				TensorFlowBenchmarkArguments(
               models=[MODEL_ID]		,training=_snake_case		,inference=_snake_case		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=_snake_case		,)
           lowercase							:					Any	     =				TensorFlowBenchmark(_snake_case		,configs=[config]				)
           lowercase							:					List[Any]	     =				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				):



           '''simple docstring'''







           lowercase							:					Dict	     =				"""sshleifer/tiny-gpt2"""
           lowercase							:					int	     =				TensorFlowBenchmarkArguments(
               models=[MODEL_ID]		,training=_snake_case		,inference=_snake_case		,sequence_lengths=[8]		,batch_sizes=[1]		,use_xla=_snake_case		,multi_process=_snake_case		,)
           lowercase							:					Tuple	     =				TensorFlowBenchmark(_snake_case				)
           lowercase							:					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				):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:					Any	     =				"""sshleifer/tiny-gpt2"""

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

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


def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     lowercase							:					str	     =				mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              else:
                     lowercase							:					Optional[int]	     =				max(
                         mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     j - wt[i - 1]    ) + val[i - 1] ,     )
              lowercase							:					List[Any]	     =				val
       return f[i][j]







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Any:
       lowercase							:					Optional[int]	     =				[[0] * (w + 1) for _ in range(n + 1    )]

       for i in range(1 ,     n + 1    ):
              for w_ in range(1 ,     w + 1    ):
                     if wt[i - 1] <= w_:
                            lowercase							:					Union[str, Any]	     =				max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,     dp[i - 1][w_]    )
                     else:
                            lowercase							:					Any	     =				dp[i - 1][w_]

       return dp[n][w_], dp







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       if not (isinstance(SCREAMING_SNAKE_CASE__ ,     (list, tuple)    ) and isinstance(SCREAMING_SNAKE_CASE__ ,     (list, tuple)    )):
              raise ValueError(
                  """Both the weights and values vectors must be either lists or tuples"""    )

       lowercase							:					Optional[Any]	     =				len(SCREAMING_SNAKE_CASE__    )
       if num_items != len(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					Optional[Any]	     =				(
                  """The number of weights must be the same as the number of values.\n"""
                  f"But got {num_items} weights and {len(SCREAMING_SNAKE_CASE__    )} values"
              )
              raise ValueError(SCREAMING_SNAKE_CASE__    )
       for i in range(SCREAMING_SNAKE_CASE__    ):
              if not isinstance(wt[i] ,     SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Tuple	     =				(
                         """All weights must be integers but got weight of """
                         f"type {type(wt[i]    )} at index {i}"
                     )
                     raise TypeError(SCREAMING_SNAKE_CASE__    )

       lowercase     ,      lowercase							:					Any	     =				knapsack(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase							:					set	     =				set()
       _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       return optimal_val, example_optional_set







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       # for the current item i at a maximum weight j to be part of an optimal subset,
       # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
       # where i - 1 means considering only the previous items at the given maximum weight
       if i > 0 and j > 0:
              if dp[i - 1][j] == dp[i][j]:
                     _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              else:
                     optimal_set.add(SCREAMING_SNAKE_CASE__    )
                     _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     i - 1 ,     j - wt[i - 1] ,     SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
  lowercase  : Dict						   =							[3, 2, 4, 4]
  lowercase  : List[Any]						   =							[4, 3, 2, 3]
  lowercase  : Tuple						   =							4
  lowercase  : Tuple						   =							6
  lowercase  : Tuple						   =							[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
  lowercase   ,  lowercase  : List[str]						   =							knapsack(w, wt, val, n)
  print(optimal_solution)
  print(mf_knapsack(n, wt, val, w))  # switched the n and w

  # testing the dynamic programming problem with example
  # the optimal subset for the above example are items 3 and 4
  lowercase   ,  lowercase  : Optional[int]						   =							knapsack_with_example_solution(w, wt, val)
  assert optimal_solution == 8
  assert optimal_subset == {3, 4}
  print("""optimal_value = """, optimal_solution)
  print("""An optimal subset corresponding to the optimal value""", optimal_subset)
