



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     a_      :      List[str]    =     'platform'

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase=None							,  __UpperCAmelCase=None							,  __UpperCAmelCase=None							,  __UpperCAmelCase=None							,  __UpperCAmelCase=None							,  __UpperCAmelCase=None							,  ):
 if attention_mask is None:
  a__												= np.where(input_ids != config.pad_token_id							,  1							,  0       )
 if decoder_attention_mask is None:
  a__												= np.where(decoder_input_ids != config.pad_token_id							,  1							,  0       )
 if head_mask is None:
  a__												= np.ones((config.encoder_layers, config.encoder_attention_heads)       )
 if decoder_head_mask is None:
  a__												= np.ones((config.decoder_layers, config.decoder_attention_heads)       )
 if cross_attn_head_mask is None:
  a__												= np.ones((config.decoder_layers, config.decoder_attention_heads)       )
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
 }
class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=1_3		,				SCREAMING_SNAKE_CASE=7		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=9_9		,				SCREAMING_SNAKE_CASE=1_6		,				SCREAMING_SNAKE_CASE=2		,				SCREAMING_SNAKE_CASE=4		,				SCREAMING_SNAKE_CASE=4		,				SCREAMING_SNAKE_CASE="gelu"		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=3_2		,				SCREAMING_SNAKE_CASE=2		,				SCREAMING_SNAKE_CASE=1		,				SCREAMING_SNAKE_CASE=0		,				SCREAMING_SNAKE_CASE=0.02		,				) ->			Any:
  a__												= parent
  a__												= batch_size
  a__												= seq_length
  a__												= is_training
  a__												= use_labels
  a__												= vocab_size
  a__												= hidden_size
  a__												= num_hidden_layers
  a__												= num_attention_heads
  a__												= intermediate_size
  a__												= hidden_act
  a__												= hidden_dropout_prob
  a__												= attention_probs_dropout_prob
  a__												= max_position_embeddings
  a__												= eos_token_id
  a__												= pad_token_id
  a__												= bos_token_id
  a__												= initializer_range

 def 						_UpperCAmelCase       (	self      ) ->			str:
  a__												= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,				self.vocab_size      )		,				3		,				self.vocab_size      )
  a__												= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,				dtype=np.intaa      ))		,				-1      )

  a__												= shift_tokens_right(SCREAMING_SNAKE_CASE		,				1		,				2      )

  a__												= BlenderbotConfig(
      vocab_size=self.vocab_size		,				d_model=self.hidden_size		,				encoder_layers=self.num_hidden_layers		,				decoder_layers=self.num_hidden_layers		,				encoder_attention_heads=self.num_attention_heads		,				decoder_attention_heads=self.num_attention_heads		,				encoder_ffn_dim=self.intermediate_size		,				decoder_ffn_dim=self.intermediate_size		,				dropout=self.hidden_dropout_prob		,				attention_dropout=self.attention_probs_dropout_prob		,				max_position_embeddings=self.max_position_embeddings		,				eos_token_id=self.eos_token_id		,				bos_token_id=self.bos_token_id		,				pad_token_id=self.pad_token_id		,				initializer_range=self.initializer_range		,				use_cache=SCREAMING_SNAKE_CASE		,				)
  a__												= prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  return config, inputs_dict

 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__							,			a__												= self.prepare_config_and_inputs()
  return config, inputs_dict

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			List[Any]:
  a__												= 2_0
  a__												= model_class_name(SCREAMING_SNAKE_CASE      )

  a__												= model.encode(inputs_dict['''input_ids''']      )

  a__							,			a__												= (
      inputs_dict['''decoder_input_ids'''],
      inputs_dict['''decoder_attention_mask'''],
  )

  a__												= model.init_cache(decoder_input_ids.shape[0]		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  a__												= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,				dtype='''i4'''      )

  a__												= jnp.broadcast_to(
      jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]		,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,				)
  a__												= model.decode(
      decoder_input_ids[:, :-1]		,				SCREAMING_SNAKE_CASE		,				decoder_attention_mask=SCREAMING_SNAKE_CASE		,				past_key_values=SCREAMING_SNAKE_CASE		,				decoder_position_ids=SCREAMING_SNAKE_CASE		,				)

  a__												= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,				dtype='''i4'''      )
  a__												= model.decode(
      decoder_input_ids[:, -1:]		,				SCREAMING_SNAKE_CASE		,				decoder_attention_mask=SCREAMING_SNAKE_CASE		,				past_key_values=outputs_cache.past_key_values		,				decoder_position_ids=SCREAMING_SNAKE_CASE		,				)

  a__												= model.decode(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

  a__												= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
  self.parent.assertTrue(diff < 1e-3		,				msg=f"Max diff is {diff}"      )



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Dict:
  a__												= 2_0
  a__												= model_class_name(SCREAMING_SNAKE_CASE      )

  a__												= model.encode(inputs_dict['''input_ids''']      )

  a__							,			a__												= (
      inputs_dict['''decoder_input_ids'''],
      inputs_dict['''decoder_attention_mask'''],
  )

  a__												= jnp.concatenate(
      [
          decoder_attention_mask,
          jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
      ]		,				axis=-1		,				)

  a__												= model.init_cache(decoder_input_ids.shape[0]		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  a__												= jnp.broadcast_to(
      jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]		,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,				)

  a__												= model.decode(
      decoder_input_ids[:, :-1]		,				SCREAMING_SNAKE_CASE		,				decoder_attention_mask=SCREAMING_SNAKE_CASE		,				past_key_values=SCREAMING_SNAKE_CASE		,				decoder_position_ids=SCREAMING_SNAKE_CASE		,				)
  a__												= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,				dtype='''i4'''      )
  a__												= model.decode(
      decoder_input_ids[:, -1:]		,				SCREAMING_SNAKE_CASE		,				past_key_values=outputs_cache.past_key_values		,				decoder_attention_mask=SCREAMING_SNAKE_CASE		,				decoder_position_ids=SCREAMING_SNAKE_CASE		,				)

  a__												= model.decode(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				decoder_attention_mask=SCREAMING_SNAKE_CASE      )

  a__												= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
  self.parent.assertTrue(diff < 1e-3		,				msg=f"Max diff is {diff}"      )





@require_flax
class    __UpperCamelCase			(       unittest.TestCase				):

 """simple docstring"""



 _lowercase   :    str      						=					99

 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= np.array(
      [
          [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
          [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
          [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
          [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
          [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
          [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
          [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
          [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
          [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
          [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
          [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
          [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
          [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
      ]		,				dtype=np.intaa		,				)

  a__												= input_ids.shape[0]
  a__												= BlenderbotConfig(
      vocab_size=self.vocab_size		,				d_model=2_4		,				encoder_layers=2		,				decoder_layers=2		,				encoder_attention_heads=2		,				decoder_attention_heads=2		,				encoder_ffn_dim=3_2		,				decoder_ffn_dim=3_2		,				max_position_embeddings=4_8		,				eos_token_id=2		,				pad_token_id=1		,				bos_token_id=0		,				)
  return config, input_ids, batch_size

 def 						_UpperCAmelCase       (	self      ) ->			Tuple:
  a__							,			a__							,			a__												= self._get_config_and_data()
  a__												= FlaxBlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE      )
  a__												= lm_model(input_ids=SCREAMING_SNAKE_CASE      )
  a__												= (batch_size, input_ids.shape[1], config.vocab_size)
  self.assertEqual(outputs['''logits'''].shape		,				SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= BlenderbotConfig(
      vocab_size=self.vocab_size		,				d_model=1_4		,				encoder_layers=2		,				decoder_layers=2		,				encoder_attention_heads=2		,				decoder_attention_heads=2		,				encoder_ffn_dim=8		,				decoder_ffn_dim=8		,				max_position_embeddings=4_8		,				)
  a__												= FlaxBlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE      )
  a__												= np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]		,				dtype=np.intaa      )
  a__												= np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]		,				dtype=np.intaa      )
  a__												= lm_model(input_ids=SCREAMING_SNAKE_CASE		,				decoder_input_ids=SCREAMING_SNAKE_CASE      )
  a__												= (*summary.shape, config.vocab_size)
  self.assertEqual(outputs['''logits'''].shape		,				SCREAMING_SNAKE_CASE      )



 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  a__												= np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]		,				dtype=np.intaa      )
  a__												= shift_tokens_right(SCREAMING_SNAKE_CASE		,				1		,				2      )
  a__												= np.equal(SCREAMING_SNAKE_CASE		,				1      ).astype(np.floataa      ).sum()
  a__												= np.equal(SCREAMING_SNAKE_CASE		,				1      ).astype(np.floataa      ).sum()
  self.assertEqual(shifted.shape		,				input_ids.shape      )
  self.assertEqual(SCREAMING_SNAKE_CASE		,				n_pad_before - 1      )
  self.assertTrue(np.equal(shifted[:, 0]		,				2      ).all()      )





@require_flax
class    __UpperCamelCase			(       _lowercase	,	unittest.TestCase	,	_lowercase				):

 """simple docstring"""



 _lowercase   :    Optional[int]      						=					True
 _lowercase   :    int      						=					(
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 _lowercase   :    List[Any]      						=					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

 def 						_UpperCAmelCase       (	self      ) ->			Tuple:
  a__												= FlaxBlenderbotModelTester(self      )

 def 						_UpperCAmelCase       (	self      ) ->			Tuple:
  a__							,			a__												= self.model_tester.prepare_config_and_inputs()
  for model_class in self.all_model_classes:
   self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Any:
  a__							,			a__												= self.model_tester.prepare_config_and_inputs()
  for model_class in self.all_model_classes:
   self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  a__							,			a__												= self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__      ):
    a__												= self._prepare_for_class(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
    a__												= model_class(SCREAMING_SNAKE_CASE      )

    @jax.jit
    def encode_jitted(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ):
     return model.encode(input_ids=SCREAMING_SNAKE_CASE		,				attention_mask=SCREAMING_SNAKE_CASE      )

    with self.subTest('''JIT Enabled'''      ):
     a__												= encode_jitted(**SCREAMING_SNAKE_CASE      ).to_tuple()

    with self.subTest('''JIT Disabled'''      ):
     with jax.disable_jit():
      a__												= encode_jitted(**SCREAMING_SNAKE_CASE      ).to_tuple()

    self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,				len(SCREAMING_SNAKE_CASE      )      )
    for jitted_output, output in zip(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
     self.assertEqual(jitted_output.shape		,				output.shape      )

 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__							,			a__												= self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__      ):
    a__												= model_class(SCREAMING_SNAKE_CASE      )
    a__												= model.encode(inputs_dict['''input_ids''']		,				inputs_dict['''attention_mask''']      )

    a__												= {
        '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
        '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
        '''encoder_outputs''': encoder_outputs,
    }

    @jax.jit
    def decode_jitted(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
     return model.decode(
         decoder_input_ids=SCREAMING_SNAKE_CASE		,				decoder_attention_mask=SCREAMING_SNAKE_CASE		,				encoder_outputs=SCREAMING_SNAKE_CASE		,				)

    with self.subTest('''JIT Enabled'''      ):
     a__												= decode_jitted(**SCREAMING_SNAKE_CASE      ).to_tuple()

    with self.subTest('''JIT Disabled'''      ):
     with jax.disable_jit():
      a__												= decode_jitted(**SCREAMING_SNAKE_CASE      ).to_tuple()

    self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,				len(SCREAMING_SNAKE_CASE      )      )
    for jitted_output, output in zip(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
     self.assertEqual(jitted_output.shape		,				output.shape      )

 @slow
 def 						_UpperCAmelCase       (	self      ) ->			int:
  for model_class_name in self.all_model_classes:
   a__												= model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''      )
   # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
   a__												= np.ones((1, 1)      ) * model.config.eos_token_id
   a__												= model(SCREAMING_SNAKE_CASE      )
   self.assertIsNotNone(SCREAMING_SNAKE_CASE      )



 @unittest.skipUnless(jax_device != '''cpu'''		,				'''3B test too slow on CPU.'''      )
 @slow
 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  a__												= {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
  a__												= {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

  a__												= FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''		,				from_pt=SCREAMING_SNAKE_CASE      )
  a__												= BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''      )

  a__												= ['''Sam''']
  a__												= tokenizer(SCREAMING_SNAKE_CASE		,				return_tensors='''jax'''      )

  a__												= model.generate(**SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  a__												= '''Sam is a great name. It means "sun" in Gaelic.'''

  a__												= tokenizer.batch_decode(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  assert generated_txt[0].strip() == tgt_text










from statistics import mean

import numpy as np






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= 0
 # Number of processes finished
 a__												= 0
 # Displays the finished process.
 # If it is 0, the performance is completed if it is 1, before the performance.
 a__												= [0] * no_of_process
 # List to include calculation results
 a__												= [0] * no_of_process

 # Sort by arrival time.
 a__												= [burst_time[i] for i in np.argsort(__UpperCAmelCase       )]
 a__												= [process_name[i] for i in np.argsort(__UpperCAmelCase       )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  a__												= 0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   a__												= arrival_time[i]

  a__												= 0
  # Index showing the location of the process being performed
  a__												= 0
  # Saves the current response ratio.
  a__												= 0
  for i in range(0							,  __UpperCAmelCase       ):
   if finished_process[i] == 0 and arrival_time[i] <= current_time:
    a__												= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
        i
    ]
   if response_ratio < temp:
    a__												= temp
    a__												= i

        # Calculate the turn around time
  a__												= current_time + burst_time[loc] - arrival_time[loc]
  current_time += burst_time[loc]
  # Indicates that the process has been performed.
  a__												= 1
  # Increase finished_process_count by 1
  finished_process_count += 1

 return turn_around_time






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= [0] * no_of_process
 for i in range(0							,  __UpperCAmelCase       ):
  a__												= turn_around_time[i] - burst_time[i]
 return waiting_time


if __name__ == "__main__":
     a_      :      List[Any]    =     5
     a_      :      Optional[int]    =     ['A', 'B', 'C', 'D', 'E']
     a_      :      Union[str, Any]    =     [1, 2, 3, 4, 5]
     a_      :      Dict    =     [1, 2, 3, 4, 5]

     a_      :      List[Any]    =     calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     a_      :      int    =     calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
     for i in range(0, no_of_process):
          print(
              f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
              f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
          )

     print(f'average waiting time : {mean(waiting_time):.5f}')
     print(f'average turn around time : {mean(turn_around_time):.5f}')





