


'''simple docstring'''




import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
       import os

       import jax.numpy as jnp
       from jax import jit

       from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
       from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

       __snake_case    						="""0.12"""  # assumed parallelism: 8


if is_torch_available():
       import torch





def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     Dict	, lowerCamelCase	:     Optional[Any]=None				):
   if rng is None:
      lowerCAmelCase     =			random.Random()

   lowerCAmelCase     =			1
   for dim in shape:
      total_dims *= dim

   lowerCAmelCase     =			[]
   for _ in range(lowerCamelCase				):
      values.append(rng.randint(0	, vocab_size - 1				)				)

   lowerCAmelCase     =			np.array(lowerCamelCase	, dtype=jnp.intaa				).reshape(lowerCamelCase				)

   return output





def 	a_					( lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Tuple=None				):
   lowerCAmelCase     =			ids_tensor(lowerCamelCase	, vocab_size=2	, rng=lowerCamelCase				)
   # make sure that at least one token is attended to for each batch
   lowerCAmelCase     =			1
   return attn_mask

@require_flax
class 		UpperCAmelCase_      :
     lowerCamelCase				:	Dict		=     None
     lowerCamelCase				:	Dict		=     ()





     def 							__UpperCAmelCase      ( self       :      str    )				->  Optional[int]:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        # cut to half length & take max batch_size 3
        lowerCAmelCase     =			2
        lowerCAmelCase     =			inputs['input_ids'].shape[-1] // 2
        lowerCAmelCase     =			inputs['input_ids'][:max_batch_size, :sequence_length]

        lowerCAmelCase     =			jnp.ones_like(UpperCAmelCase__    )
        lowerCAmelCase     =			attention_mask[:max_batch_size, :sequence_length]

        # generate max 5 tokens
        lowerCAmelCase     =			input_ids.shape[-1] + 5
        if config.eos_token_id is not None and config.pad_token_id is None:
           # hack to allow generate for models such as GPT2 as is done in `generate()`
           lowerCAmelCase     =			config.eos_token_id
        return config, input_ids, attention_mask, max_length





     @is_pt_flax_cross_test
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[str]:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			False
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			0

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
           lowerCAmelCase     =			getattr(UpperCAmelCase__    ,      UpperCAmelCase__    )
           lowerCAmelCase     =			pt_model_class(UpperCAmelCase__    ).eval()
           lowerCAmelCase     =			load_flax_weights_in_pytorch_model(UpperCAmelCase__    ,      flax_model.params    )

           lowerCAmelCase     =			flax_model.generate(UpperCAmelCase__    ).sequences
           lowerCAmelCase     =			pt_model.generate(torch.tensor(UpperCAmelCase__    ,      dtype=torch.long    )    )

           if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
              lowerCAmelCase     =			flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

           self.assertListEqual(pt_generation_outputs.numpy().tolist()    ,      flax_generation_outputs.tolist()    )





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Optional[Any]:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			False
        lowerCAmelCase     =			max_length

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			model.generate(UpperCAmelCase__    ).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

           self.assertListEqual(generation_outputs.tolist()    ,      jit_generation_outputs.tolist()    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			True
        lowerCAmelCase     =			max_length

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			model.generate(UpperCAmelCase__    ).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

           self.assertListEqual(generation_outputs.tolist()    ,      jit_generation_outputs.tolist()    )





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Dict:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			False
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			2

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			model.generate(UpperCAmelCase__    ).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

           self.assertListEqual(generation_outputs.tolist()    ,      jit_generation_outputs.tolist()    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Any:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			False
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			2
        lowerCAmelCase     =			2

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			model.generate(UpperCAmelCase__    ).sequences
           self.assertEqual(generation_outputs.shape[0]    ,      input_ids.shape[0] * config.num_return_sequences    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			True
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			0.8
        lowerCAmelCase     =			1_0
        lowerCAmelCase     =			0.3
        lowerCAmelCase     =			1
        lowerCAmelCase     =			8
        lowerCAmelCase     =			9

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			model.generate(UpperCAmelCase__    ).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

           self.assertListEqual(generation_outputs.tolist()    ,      jit_generation_outputs.tolist()    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Dict:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			1
        lowerCAmelCase     =			8
        lowerCAmelCase     =			9

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			model.generate(UpperCAmelCase__    ).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

           self.assertListEqual(generation_outputs.tolist()    ,      jit_generation_outputs.tolist()    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  int:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			2
        lowerCAmelCase     =			1
        lowerCAmelCase     =			8
        lowerCAmelCase     =			9

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			model.generate(UpperCAmelCase__    ).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ).sequences

           self.assertListEqual(generation_outputs.tolist()    ,      jit_generation_outputs.tolist()    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Dict:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()

        # pad attention mask on the left
        lowerCAmelCase     =			attention_mask.at[(0, 0)].set(0    )

        lowerCAmelCase     =			False
        lowerCAmelCase     =			max_length

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			model.generate(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ).sequences

           self.assertListEqual(generation_outputs.tolist()    ,      jit_generation_outputs.tolist()    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  int:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()

        # pad attention mask on the left
        lowerCAmelCase     =			attention_mask.at[(0, 0)].set(0    )

        lowerCAmelCase     =			True
        lowerCAmelCase     =			max_length

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			model.generate(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ).sequences

           self.assertListEqual(generation_outputs.tolist()    ,      jit_generation_outputs.tolist()    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self._get_input_ids_and_config()

        # pad attention mask on the left
        lowerCAmelCase     =			attention_mask.at[(0, 0)].set(0    )

        lowerCAmelCase     =			2
        lowerCAmelCase     =			max_length

        for model_class in self.all_generative_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			model.generate(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			jit(model.generate    )
           lowerCAmelCase     =			jit_generate(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ).sequences

           self.assertListEqual(generation_outputs.tolist()    ,      jit_generation_outputs.tolist()    )




@require_flax
class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Any:
        lowerCAmelCase     =			AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'    )
        lowerCAmelCase     =			FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'    )

        lowerCAmelCase     =			'Hello world'
        lowerCAmelCase     =			tokenizer(UpperCAmelCase__    ,      return_tensors='np'    ).input_ids

        # typos are quickly detected (the correct argument is `do_sample`)
        with self.assertRaisesRegex(UpperCAmelCase__    ,      'do_samples'    ):
           model.generate(UpperCAmelCase__    ,      do_samples=UpperCAmelCase__    )

        # arbitrary arguments that will not be used anywhere are also not accepted
        with self.assertRaisesRegex(UpperCAmelCase__    ,      'foo'    ):
           lowerCAmelCase     =			{'foo': 'bar'}
           model.generate(UpperCAmelCase__    ,      **UpperCAmelCase__    )









'''simple docstring'''




from __future__ import annotations

from statistics import mean





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     list[int]	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			[0] * no_of_processes
   lowerCAmelCase     =			[0] * no_of_processes

   # Initialize remaining_time to waiting_time.

   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i]
   lowerCAmelCase     =			[]

   lowerCAmelCase     =			0
   lowerCAmelCase     =			0

   # When processes are not completed,
   # A process whose arrival time has passed \
   # and has remaining execution time is put into the ready_process.
   # The shortest process in the ready_process, target_process is executed.

   while completed != no_of_processes:
      lowerCAmelCase     =			[]
      lowerCAmelCase     =			-1

      for i in range(lowerCamelCase				):
         if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
            ready_process.append(lowerCamelCase				)

      if len(lowerCamelCase				) > 0:
         lowerCAmelCase     =			ready_process[0]
         for i in ready_process:
            if remaining_time[i] < remaining_time[target_process]:
               lowerCAmelCase     =			i
         total_time += burst_time[target_process]
         completed += 1
         lowerCAmelCase     =			0
         lowerCAmelCase     =			(
             total_time - arrival_time[target_process] - burst_time[target_process]
         )
      else:
         total_time += 1

   return waiting_time





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     int	, lowerCamelCase	:     list[int]				):
   lowerCAmelCase     =			[0] * no_of_processes
   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i] + waiting_time[i]
   return turn_around_time


if __name__ == "__main__":
       print("""[TEST CASE 01]""")

       __snake_case    						=4
       __snake_case    						=[2, 5, 3, 7]
       __snake_case    						=[0, 0, 0, 0]
       __snake_case    						=calculate_waitingtime(arrival_time, burst_time, no_of_processes)
       __snake_case    						=calculate_turnaroundtime(
           burst_time, no_of_processes, waiting_time
       )

       # Printing the Result
       print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
       for i, process_id in enumerate(list(range(1, 5))):
              print(
                  F'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
                  F'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
              )
       print(F'''\nAverage waiting time = {mean(waiting_time):.5f}''')
       print(F'''Average turnaround time = {mean(turn_around_time):.5f}''')





