




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


SCREAMING_SNAKE_CASE__       :      Any  =   logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   {
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Tuple	     =      """codegen"""
    lowerCamelCase_      :	Optional[int]	     =      {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(    self     ,  UpperCamelCase__=5_0400     ,  UpperCamelCase__=2048     ,  UpperCamelCase__=2048     ,  UpperCamelCase__=4096     ,  UpperCamelCase__=28     ,  UpperCamelCase__=16     ,  UpperCamelCase__=64     ,  UpperCamelCase__=None     ,  UpperCamelCase__="gelu_new"     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=1e-5     ,  UpperCamelCase__=0.02     ,  UpperCamelCase__=True     ,  UpperCamelCase__=5_0256     ,  UpperCamelCase__=5_0256     ,  UpperCamelCase__=False     ,  **UpperCamelCase__     ,  )     ->      List[str]:
           lowerCamelCase   :      Union[str, Any]			 =					vocab_size
           lowerCamelCase   :      Optional[Any]			 =					n_ctx
           lowerCamelCase   :      Optional[int]			 =					n_positions
           lowerCamelCase   :      int			 =					n_embd
           lowerCamelCase   :      Optional[Any]			 =					n_layer
           lowerCamelCase   :      List[Any]			 =					n_head
           lowerCamelCase   :      Optional[int]			 =					n_inner
           lowerCamelCase   :      Optional[int]			 =					rotary_dim
           lowerCamelCase   :      int			 =					activation_function
           lowerCamelCase   :      List[str]			 =					resid_pdrop
           lowerCamelCase   :      Optional[int]			 =					embd_pdrop
           lowerCamelCase   :      Tuple			 =					attn_pdrop
           lowerCamelCase   :      int			 =					layer_norm_epsilon
           lowerCamelCase   :      Optional[Any]			 =					initializer_range
           lowerCamelCase   :      str			 =					use_cache

           lowerCamelCase   :      List[Any]			 =					bos_token_id
           lowerCamelCase   :      Tuple			 =					eos_token_id

           super().__init__(
               bos_token_id=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     ,  tie_word_embeddings=UpperCamelCase__     ,  **UpperCamelCase__     )




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = "default"     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = False     ,  )     ->      List[Any]:
           super().__init__(UpperCamelCase__     ,  task=UpperCamelCase__     ,  patching_specs=UpperCamelCase__     ,  use_past=UpperCamelCase__     )
           if not getattr(self._config     ,  "pad_token_id"     ,  UpperCamelCase__     ):
                  # TODO: how to do that better?
                  lowerCamelCase   :      Union[str, Any]			 =					0







    @property
    def     _lowercase     (    self     )     ->      Mapping[str, Mapping[int, str]]:
           lowerCamelCase   :      List[Any]			 =					OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
           if self.use_past:
                  self.fill_with_past_key_values_(UpperCamelCase__     ,  direction="inputs"     )
                  lowerCamelCase   :      Optional[Any]			 =					{0: "batch", 1: "past_sequence + sequence"}
           else:
                  lowerCamelCase   :      Dict			 =					{0: "batch", 1: "sequence"}

           return common_inputs







    @property
    def     _lowercase     (    self     )     ->      int:
           return self._config.n_layer







    @property
    def     _lowercase     (    self     )     ->      int:
           return self._config.n_head







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = -1     ,  UpperCamelCase__ = -1     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  )     ->      Mapping[str, Any]:
           lowerCamelCase   :      int			 =					super(UpperCamelCase__     ,  self     ).generate_dummy_inputs(
               UpperCamelCase__     ,  batch_size=UpperCamelCase__     ,  seq_length=UpperCamelCase__     ,  is_pair=UpperCamelCase__     ,  framework=UpperCamelCase__     )

           # We need to order the input in the way they appears in the forward()
           lowerCamelCase   :      int			 =					OrderedDict({"input_ids": common_inputs["input_ids"]}     )

           # Need to add the past_keys
           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."     )
                  else:
                         import torch

                         lowerCamelCase ,      lowerCamelCase   :      Optional[int]			 =					common_inputs["input_ids"].shape
                         # Not using the same length for past_key_values
                         lowerCamelCase   :      List[Any]			 =					seqlen + 2
                         lowerCamelCase   :      Any			 =					(
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowerCamelCase   :      Union[str, Any]			 =					[
                             (torch.zeros(UpperCamelCase__     ), torch.zeros(UpperCamelCase__     )) for _ in range(self.num_layers     )
                         ]

           lowerCamelCase   :      str			 =					common_inputs["attention_mask"]
           if self.use_past:
                  lowerCamelCase   :      Optional[Any]			 =					ordered_inputs["attention_mask"].dtype
                  lowerCamelCase   :      Union[str, Any]			 =					torch.cat(
                      [ordered_inputs["attention_mask"], torch.ones(UpperCamelCase__     ,  UpperCamelCase__     ,  dtype=UpperCamelCase__     )]     ,  dim=1     )

           return ordered_inputs







    @property
    def     _lowercase     (    self     )     ->      int:
           return 13










"""simple docstring"""







from __future__ import annotations

import pandas as pd




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					# Copy the burst time into remaining_time[]
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				burst_time[i]

					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				9_9999_9999
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				False

					# Process until all processes are completed
					while complete != no_of_processes:
										for j in range(lowerCAmelCase_   ):
															if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																				if remaining_time[j] < minm:
																									__SCREAMING_SNAKE_CASE						   =				remaining_time[j]
																									__SCREAMING_SNAKE_CASE						   =				j
																									__SCREAMING_SNAKE_CASE						   =				True

										if not check:
															increment_time += 1
															continue
										remaining_time[short] -= 1

										__SCREAMING_SNAKE_CASE						   =				remaining_time[short]
										if minm == 0:
															__SCREAMING_SNAKE_CASE						   =				9_9999_9999

										if remaining_time[short] == 0:
															complete += 1
															__SCREAMING_SNAKE_CASE						   =				False

															# Find finish time of current process
															__SCREAMING_SNAKE_CASE						   =				increment_time + 1

															# Calculate waiting time
															__SCREAMING_SNAKE_CASE						   =				finish_time - arrival_time[short]
															__SCREAMING_SNAKE_CASE						   =				finar - burst_time[short]

															if waiting_time[short] < 0:
																				__SCREAMING_SNAKE_CASE						   =				0

        # Increment time
										increment_time += 1
					return waiting_time




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				burst_time[i] + waiting_time[i]
					return turn_around_time




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				0
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				total_waiting_time + waiting_time[i]
										__SCREAMING_SNAKE_CASE						   =				total_turn_around_time + turn_around_time[i]
					print(f"""Average waiting time = {total_waiting_time / no_of_processes:.5f}"""   )
					print("Average turn around time ="  ,						total_turn_around_time / no_of_processes   )


if __name__ == "__main__":
		print('''Enter how many process you want to analyze''')
		a__				:							Optional[Any]						=     int(input())
		a__				:							Optional[int]						=     [0] * no_of_processes
		a__				:							int						=     [0] * no_of_processes
		a__				:							List[Any]						=     list(range(1, no_of_processes + 1))

		for i in range(no_of_processes):
				print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
				a__			,     a__				:							Tuple						=     map(int, input().split())

		a__				:							int						=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

		a__				:							Dict						=     burst_time
		a__				:							Any						=     no_of_processes
		a__				:							Optional[int]						=     waiting_time
		a__				:							Union[str, Any]						=     calculate_turnaroundtime(bt, n, wt)

		calculate_average_times(waiting_time, turn_around_time, no_of_processes)

		a__				:							str						=     pd.DataFrame(
		    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
		    columns=[
		        '''Process''',
		        '''BurstTime''',
		        '''ArrivalTime''',
		        '''WaitingTime''',
		        '''TurnAroundTime''',
		    ],
		)

		# Printing the dataFrame
		pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
		print(fcfs)
