




'''simple docstring'''



import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers.models.esm.modeling_esmfold import EsmForProteinFolding





class      lowerCAmelCase :


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=13     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=19     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=5     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=37     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=512     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=None     ,	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	seq_length
            __snake_case            =	is_training
            __snake_case            =	use_input_mask
            __snake_case            =	use_token_type_ids
            __snake_case            =	use_labels
            __snake_case            =	vocab_size
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	max_position_embeddings
            __snake_case            =	type_vocab_size
            __snake_case            =	type_sequence_label_size
            __snake_case            =	initializer_range
            __snake_case            =	num_labels
            __snake_case            =	num_choices
            __snake_case            =	scope


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size	)

            __snake_case            =	None
            if self.use_input_mask:
                 __snake_case            =	random_attention_mask([self.batch_size, self.seq_length]	)

            __snake_case            =	None
            __snake_case            =	None
            __snake_case            =	None
            if self.use_labels:
                 __snake_case            =	ids_tensor([self.batch_size]     ,	self.type_sequence_label_size	)
                 __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.num_labels	)
                 __snake_case            =	ids_tensor([self.batch_size]     ,	self.num_choices	)

            __snake_case            =	self.get_config()

            return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	EsmConfig(
                vocab_size=33     ,	hidden_size=self.hidden_size     ,	pad_token_id=1     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	initializer_range=self.initializer_range     ,	is_folding_model=__SCREAMING_SNAKE_CASE     ,	esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False}     ,	)
            return config


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	EsmForProteinFolding(config=__SCREAMING_SNAKE_CASE	).float()
            model.to(__SCREAMING_SNAKE_CASE	)
            model.eval()
            __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)

            self.parent.assertEqual(result.positions.shape     ,	(8, self.batch_size, self.seq_length, 14, 3)	)
            self.parent.assertEqual(result.angles.shape     ,	(8, self.batch_size, self.seq_length, 7, 2)	)




       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	self.prepare_config_and_inputs()
            (
                (
                __snake_case
            )       ,     (
                __snake_case
            )       ,     (
                __snake_case
            )       ,     (
                __snake_case
            )       ,     (
                __snake_case
            )       ,     (
                __snake_case
            )       ,     
            )            =	config_and_inputs
            __snake_case            =	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
            return config, inputs_dict





@require_torch
class      lowerCAmelCase (			__lowerCAmelCase					, __lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   List[str]										=      False

       __lowercase	:   Dict										=      (EsmForProteinFolding,) if is_torch_available() else ()
       __lowercase	:   Union[str, Any]										=      ()
       __lowercase	:   List[str]										=      {} if is_torch_available() else {}
       __lowercase	:   List[Any]										=      False


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	EsmFoldModelTester(self	)
            __snake_case            =	ConfigTester(self     ,	config_class=__SCREAMING_SNAKE_CASE     ,	hidden_size=37	)


       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            self.config_tester.run_common_tests()


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)


       @unittest.skip('''Does not support attention outputs'''	)
       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            pass


       @unittest.skip
       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            pass


       @unittest.skip('''Esm does not support embedding resizing'''	)
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            pass


       @unittest.skip('''Esm does not support embedding resizing'''	)
       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold does not support passing input embeds!'''	)
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold does not support head pruning.'''	)
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold does not support head pruning.'''	)
       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold does not support head pruning.'''	)
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold does not support head pruning.'''	)
       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold does not support head pruning.'''	)
       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold does not output hidden states in the normal way.'''	)
       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMfold does not output hidden states in the normal way.'''	)
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold only has one output format.'''	)
       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            pass


       @unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality'''	)
       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold does not support input chunking.'''	)
       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'''	)
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''	)
       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''	)
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''	)
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            pass


       @unittest.skip('''ESMFold doesn\'t support data parallel.'''	)
       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            pass




       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            pass





@require_torch
class      lowerCAmelCase (			__lowerCAmelCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1'''	).float()
            model.eval()
            __snake_case            =	torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)['''positions''']
            __snake_case            =	torch.tensor([2.5_828, 0.7_993, -10.9_334]     ,	dtype=torch.floataa	)
            self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)










from __future__ import annotations

from statistics import mean







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				list[int]:
  snake_case__						= [0] * no_of_processes
  snake_case__						= [0] * no_of_processes

  # Initialize remaining_time to waiting_time.

  for i in range(__lowerCAmelCase				):
    snake_case__						= burst_time[i]
  snake_case__						= []

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

  return waiting_time







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				list[int]:
  snake_case__						= [0] * no_of_processes
  for i in range(__lowerCAmelCase				):
    snake_case__						= burst_time[i] + waiting_time[i]
  return turn_around_time


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

   lowerCamelCase__							:						Tuple							     =   4
   lowerCamelCase__							:						Union[str, Any]							     =   [2, 5, 3, 7]
   lowerCamelCase__							:						Optional[Any]							     =   [0, 0, 0, 0]
   lowerCamelCase__							:						Dict							     =   calculate_waitingtime(arrival_time, burst_time, no_of_processes)
   lowerCamelCase__							:						Union[str, Any]							     =   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}""")

