






'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class _lowercase			:
							a          = MBartConfig
							a          = {}
							a          = """gelu"""




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Dict=13    ,							UpperCamelCase__:       Tuple=7    ,							UpperCamelCase__:       str=True    ,							UpperCamelCase__:       Optional[Any]=False    ,							UpperCamelCase__:       Union[str, Any]=99    ,							UpperCamelCase__:       Dict=32    ,							UpperCamelCase__:       Any=2    ,							UpperCamelCase__:       Tuple=4    ,							UpperCamelCase__:       List[Any]=37    ,							UpperCamelCase__:       Union[str, Any]=0.1    ,							UpperCamelCase__:       Any=0.1    ,							UpperCamelCase__:       List[Any]=20    ,							UpperCamelCase__:       Dict=2    ,							UpperCamelCase__:       str=1    ,							UpperCamelCase__:       Dict=0    ,							):
														lowerCamelCase__      :			Optional[int] 						= parent
														lowerCamelCase__      :			Optional[Any] 						= batch_size
														lowerCamelCase__      :			Optional[int] 						= seq_length
														lowerCamelCase__      :			Dict 						= is_training
														lowerCamelCase__      :			Dict 						= use_labels
														lowerCamelCase__      :			List[Any] 						= vocab_size
														lowerCamelCase__      :			List[Any] 						= hidden_size
														lowerCamelCase__      :			List[str] 						= num_hidden_layers
														lowerCamelCase__      :			str 						= num_attention_heads
														lowerCamelCase__      :			int 						= intermediate_size
														lowerCamelCase__      :			List[str] 						= hidden_dropout_prob
														lowerCamelCase__      :			str 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Optional[int] 						= max_position_embeddings
														lowerCamelCase__      :			str 						= eos_token_id
														lowerCamelCase__      :			Any 						= pad_token_id
														lowerCamelCase__      :			List[str] 						= bos_token_id




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			str 						= ids_tensor([self.batch_size, self.seq_length - 1]    ,							self.vocab_size  )
														lowerCamelCase__      :			str 						= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )    ,							1  )
														lowerCamelCase__      :			Any 						= tf.concat([input_ids, eos_tensor]    ,							axis=1  )

														lowerCamelCase__      :			Optional[Any] 						= ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size  )

														lowerCamelCase__      :			Dict 						= self.config_cls(
														    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_ids=[2]    ,							bos_token_id=self.bos_token_id    ,							pad_token_id=self.pad_token_id    ,							decoder_start_token_id=self.pad_token_id    ,							**self.config_updates    ,							)
														lowerCamelCase__      :			Any 						= prepare_mbart_inputs_dict(__lowerCamelCase    ,							__lowerCamelCase    ,							__lowerCamelCase  )
														return config, inputs_dict




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Dict  ):
														lowerCamelCase__      :			Dict 						= TFMBartModel(config=__lowerCamelCase  ).get_decoder()
														lowerCamelCase__      :			Any 						= inputs_dict["input_ids"]

														lowerCamelCase__      :			Optional[int] 						= input_ids[:1, :]
														lowerCamelCase__      :			Any 						= inputs_dict["attention_mask"][:1, :]
														lowerCamelCase__      :			List[str] 						= inputs_dict["head_mask"]
														lowerCamelCase__      :			Dict 						= 1

														# first forward pass
														lowerCamelCase__      :			str 						= model(__lowerCamelCase    ,							attention_mask=__lowerCamelCase    ,							head_mask=__lowerCamelCase    ,							use_cache=__lowerCamelCase  )

														lowerCamelCase__      :			Any 						= outputs.to_tuple()
														lowerCamelCase__      :			Optional[int] 						= past_key_values[1]





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    )   -> int:




							if attention_mask is None:
														lowerCamelCase__      :			List[str] 						= tf.cast(tf.math.not_equal(_A       ,    config.pad_token_id   )       ,    tf.inta   )
							if decoder_attention_mask is None:
														lowerCamelCase__      :			Optional[int] 						= tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape       ,    dtype=tf.inta   ),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]       ,    config.pad_token_id   )       ,    tf.inta   ),
														    ]       ,    axis=-1       ,    )
							if head_mask is None:
														lowerCamelCase__      :			int 						= tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
							if decoder_head_mask is None:
														lowerCamelCase__      :			Any 						= tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
							if cross_attn_head_mask is None:
														lowerCamelCase__      :			Dict 						= tf.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": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}

@require_tf
class _lowercase			(       lowercase__       ,   lowercase__       ,   unittest.TestCase	):
							a          = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							a          = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
							a          = (
							    {
							        """conversational""": TFMBartForConditionalGeneration,
							        """feature-extraction""": TFMBartModel,
							        """summarization""": TFMBartForConditionalGeneration,
							        """text2text-generation""": TFMBartForConditionalGeneration,
							        """translation""": TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							a          = True
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Tuple  ):



														if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																					# Exception encountered when calling layer '...'
																					return True

														return False




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			List[str] 						= TFMBartModelTester(self  )
														lowerCamelCase__      :			str 						= ConfigTester(self    ,							config_class=__lowerCamelCase  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														self.config_tester.run_common_tests()




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase  )



@require_sentencepiece
@require_tokenizers
@require_tf
class _lowercase			(       unittest.TestCase	):
							a          = [
							    """ UN Chief Says There Is No Military Solution in Syria""",
							]
							a          = [
							    """Şeful ONU declară că nu există o soluţie militară în Siria""",
							]
							a          = """facebook/mbart-large-en-ro"""




							@cached_property
							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														return AutoTokenizer.from_pretrained(self.model_name  )




							@cached_property
							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			Dict 						= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
														return model




							def 	lowerCamelCase_       (							self:       Any    ,							**UpperCamelCase__:       Tuple  ):
														lowerCamelCase__      :			Tuple 						= self.translate_src_text(**__lowerCamelCase  )
														self.assertListEqual(self.expected_text    ,							__lowerCamelCase  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							**UpperCamelCase__:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= self.tokenizer(self.src_text    ,							**__lowerCamelCase    ,							return_tensors="""tf"""  )
														lowerCamelCase__      :			Tuple 						= self.model.generate(
														    model_inputs.input_ids    ,							attention_mask=model_inputs.attention_mask    ,							num_beams=2  )
														lowerCamelCase__      :			List[Any] 						= self.tokenizer.batch_decode(__lowerCamelCase    ,							skip_special_tokens=__lowerCamelCase  )
														return generated_words




							@slow
							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														self._assert_generated_batch_equal_expected()









'''simple docstring'''







from statistics import mean

import numpy as np





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			Optional[int] 						= 0
							# Number of processes finished
							lowerCamelCase__      :			Union[str, Any] 						= 0
							# Displays the finished process.
							# If it is 0, the performance is completed if it is 1, before the performance.
							lowerCamelCase__      :			Tuple 						= [0] * no_of_process
							# List to include calculation results
							lowerCamelCase__      :			List[str] 						= [0] * no_of_process

							# Sort by arrival time.
							lowerCamelCase__      :			Union[str, Any] 						= [burst_time[i] for i in np.argsort(UpperCamelCase   )]
							lowerCamelCase__      :			List[Any] 						= [process_name[i] for i in np.argsort(UpperCamelCase   )]
							arrival_time.sort()

							while no_of_process > finished_process_count:
														lowerCamelCase__      :			str 						= 0
														while finished_process[i] == 1:
																					i += 1
														if current_time < arrival_time[i]:
																					lowerCamelCase__      :			Union[str, Any] 						= arrival_time[i]

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

        # Calculate the turn around time
														lowerCamelCase__      :			Optional[int] 						= current_time + burst_time[loc] - arrival_time[loc]
														current_time += burst_time[loc]
														# Indicates that the process has been performed.
														lowerCamelCase__      :			List[str] 						= 1
														# Increase finished_process_count by 1
														finished_process_count += 1

							return turn_around_time





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			int 						= [0] * no_of_process
							for i in range(0       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[Any] 						= turn_around_time[i] - burst_time[i]
							return waiting_time


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

			_A			:    Any					  =calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			_A			:    Optional[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}')

