import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
					import tensorflow as tf



class     _A   (    enum.Enum							):
		SCREAMING_SNAKE_CASE_	:	List[Any]						=0
		SCREAMING_SNAKE_CASE_	:	List[str]						=1
		SCREAMING_SNAKE_CASE_	:	Any						=2



@add_end_docstrings(__UpperCAmelCase							)
class     _A   (    __UpperCAmelCase							):
		SCREAMING_SNAKE_CASE_	:	List[Any]						="""
    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
    voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
    begging for his blessing. <eod> </s> <eos>
    """






		def __init__(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      Dict:





							'''simple docstring'''





							super().__init__(*UpperCAmelCase_   ,				**UpperCAmelCase_	)
							self.check_model_type(
							    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING	)
							if "prefix" not in self._preprocess_params:
												# This is very specific. The logic is quite complex and needs to be done
												# as a "default".
												# It also defines both some preprocess_kwargs and generate_kwargs
												# which is why we cannot put them in their respective methods.
												UpperCamelCase__										=      None
												if self.model.config.prefix is not None:
																	UpperCamelCase__										=      self.model.config.prefix
												if prefix is None and self.model.__class__.__name__ in [
												    "XLNetLMHeadModel",
												    "TransfoXLLMHeadModel",
												    "TFXLNetLMHeadModel",
												    "TFTransfoXLLMHeadModel",
												]:
																	# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
																	UpperCamelCase__										=      self.XL_PREFIX
												if prefix is not None:
																	# Recalculate some generate_kwargs linked to prefix.
																	UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      self._sanitize_parameters(prefix=UpperCAmelCase_   ,				**self._forward_params	)
																	UpperCamelCase__										=      {**self._preprocess_params, **preprocess_params}
																	UpperCamelCase__										=      {**self._forward_params, **forward_params}






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





							'''simple docstring'''





							UpperCamelCase__										=      {}
							if prefix is not None:
												UpperCamelCase__										=      prefix
							if prefix:
												UpperCamelCase__										=      self.tokenizer(
												    UpperCAmelCase_   ,				padding=UpperCAmelCase_   ,				add_special_tokens=UpperCAmelCase_   ,				return_tensors=self.framework	)
												UpperCamelCase__										=      prefix_inputs['''input_ids'''].shape[-1]

							if handle_long_generation is not None:
												if handle_long_generation not in {"hole"}:
																	raise ValueError(
																	    F"{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"
																	    ''' [None, \'hole\']'''	)
												UpperCamelCase__										=      handle_long_generation

							preprocess_params.update(UpperCAmelCase_	)
							UpperCamelCase__										=      generate_kwargs

							UpperCamelCase__										=      {}
							if return_full_text is not None and return_type is None:
												if return_text is not None:
																	raise ValueError('''`return_text` is mutually exclusive with `return_full_text`'''	)
												if return_tensors is not None:
																	raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`'''	)
												UpperCamelCase__										=      ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
							if return_tensors is not None and return_type is None:
												if return_text is not None:
																	raise ValueError('''`return_text` is mutually exclusive with `return_tensors`'''	)
												UpperCamelCase__										=      ReturnType.TENSORS
							if return_type is not None:
												UpperCamelCase__										=      return_type
							if clean_up_tokenization_spaces is not None:
												UpperCamelCase__										=      clean_up_tokenization_spaces

							if stop_sequence is not None:
												UpperCamelCase__										=      self.tokenizer.encode(UpperCAmelCase_   ,				add_special_tokens=UpperCAmelCase_	)
												if len(UpperCAmelCase_	) > 1:
																	warnings.warn(
																	    '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
																	    ''' the stop sequence will be used as the stop sequence string in the interim.'''	)
												UpperCamelCase__										=      stop_sequence_ids[0]

							return preprocess_params, forward_params, postprocess_params






		def 		_a					(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      Any:





							'''simple docstring'''





							if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
												kwargs.update({'''add_space_before_punct_symbol''': True}	)

							return super()._parse_and_tokenize(*UpperCAmelCase_   ,				**UpperCAmelCase_	)






		def __call__(self   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      Tuple:





							'''simple docstring'''





							return super().__call__(UpperCAmelCase_   ,				**UpperCAmelCase_	)






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=""   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      Any:





							'''simple docstring'''





							UpperCamelCase__										=      self.tokenizer(
							    prefix + prompt_text   ,				padding=UpperCAmelCase_   ,				add_special_tokens=UpperCAmelCase_   ,				return_tensors=self.framework	)
							UpperCamelCase__										=      prompt_text

							if handle_long_generation == "hole":
												UpperCamelCase__										=      inputs['''input_ids'''].shape[-1]
												if "max_new_tokens" in generate_kwargs:
																	UpperCamelCase__										=      generate_kwargs['''max_new_tokens''']
												else:
																	UpperCamelCase__										=      generate_kwargs.get('''max_length'''   ,				self.model.config.max_length	) - cur_len
																	if new_tokens < 0:
																						raise ValueError('''We cannot infer how many new tokens are expected'''	)
												if cur_len + new_tokens > self.tokenizer.model_max_length:
																	UpperCamelCase__										=      self.tokenizer.model_max_length - new_tokens
																	if keep_length <= 0:
																						raise ValueError(
																						    '''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
																						    ''' models max length'''	)

																	UpperCamelCase__										=      inputs['''input_ids'''][:, -keep_length:]
																	if "attention_mask" in inputs:
																						UpperCamelCase__										=      inputs['''attention_mask'''][:, -keep_length:]

							return inputs






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





							'''simple docstring'''





							UpperCamelCase__										=      model_inputs['''input_ids''']
							UpperCamelCase__										=      model_inputs.get('''attention_mask'''   ,				UpperCAmelCase_	)
							# Allow empty prompts
							if input_ids.shape[1] == 0:
												UpperCamelCase__										=      None
												UpperCamelCase__										=      None
												UpperCamelCase__										=      1
							else:
												UpperCamelCase__										=      input_ids.shape[0]
							UpperCamelCase__										=      model_inputs.pop('''prompt_text'''	)

							# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
							# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
							UpperCamelCase__										=      generate_kwargs.pop('''prefix_length'''   ,				0	)
							if prefix_length > 0:
												UpperCamelCase__										=      '''max_new_tokens''' in generate_kwargs or (
												    '''generation_config''' in generate_kwargs
												    and generate_kwargs['''generation_config'''].max_new_tokens is not None
												)
												if not has_max_new_tokens:
																	UpperCamelCase__										=      generate_kwargs.get('''max_length'''	) or self.model.config.max_length
																	generate_kwargs["max_length"] += prefix_length
												UpperCamelCase__										=      '''min_new_tokens''' in generate_kwargs or (
												    '''generation_config''' in generate_kwargs
												    and generate_kwargs['''generation_config'''].min_new_tokens is not None
												)
												if not has_min_new_tokens and "min_length" in generate_kwargs:
																	generate_kwargs["min_length"] += prefix_length

        # BS x SL
							UpperCamelCase__										=      self.model.generate(input_ids=UpperCAmelCase_   ,				attention_mask=UpperCAmelCase_   ,				**UpperCAmelCase_	)
							UpperCamelCase__										=      generated_sequence.shape[0]
							if self.framework == "pt":
												UpperCamelCase__										=      generated_sequence.reshape(UpperCAmelCase_   ,				out_b // in_b   ,				*generated_sequence.shape[1:]	)
							elif self.framework == "tf":
												UpperCamelCase__										=      tf.reshape(UpperCAmelCase_   ,				(in_b, out_b // in_b, *generated_sequence.shape[1:])	)
							return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=ReturnType.FULL_TEXT   ,				SCREAMING_SNAKE_CASE_=True	)		->      Any:





							'''simple docstring'''





							UpperCamelCase__										=      model_outputs['''generated_sequence'''][0]
							UpperCamelCase__										=      model_outputs['''input_ids''']
							UpperCamelCase__										=      model_outputs['''prompt_text''']
							UpperCamelCase__										=      generated_sequence.numpy().tolist()
							UpperCamelCase__										=      []
							for sequence in generated_sequence:
												if return_type == ReturnType.TENSORS:
																	UpperCamelCase__										=      {'''generated_token_ids''': sequence}
												elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
																	# Decode text
																	UpperCamelCase__										=      self.tokenizer.decode(
																	    UpperCAmelCase_   ,				skip_special_tokens=UpperCAmelCase_   ,				clean_up_tokenization_spaces=UpperCAmelCase_   ,				)

																	# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
																	if input_ids is None:
																						UpperCamelCase__										=      0
																	else:
																						UpperCamelCase__										=      len(
																						    self.tokenizer.decode(
																						        input_ids[0]   ,				skip_special_tokens=UpperCAmelCase_   ,				clean_up_tokenization_spaces=UpperCAmelCase_   ,				)	)

																	if return_type == ReturnType.FULL_TEXT:
																						UpperCamelCase__										=      prompt_text + text[prompt_length:]
																	else:
																						UpperCamelCase__										=      text[prompt_length:]

																	UpperCamelCase__										=      {'''generated_text''': all_text}
												records.append(UpperCAmelCase_	)

							return records




from __future__ import annotations

import pandas as pd







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     UpperCamelCase__										=      [0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i]

     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      999999999
     UpperCamelCase__										=      0
     UpperCamelCase__										=      False

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

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

          UpperCamelCase__										=      remaining_time[short]
          if minm == 0:
               UpperCamelCase__										=      999999999

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

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

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

               if waiting_time[short] < 0:
                    UpperCamelCase__										=      0

        # Increment time
          increment_time += 1
     return waiting_time







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i] + waiting_time[i]
     return turn_around_time







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     for i in range(A							):
          UpperCamelCase__										=      total_waiting_time + waiting_time[i]
          UpperCamelCase__										=      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''')
     __magic_name__								=int(input())
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=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))
          __magic_name__		,  __magic_name__								=map(int, input().split())

     __magic_name__								=calculate_waitingtime(arrival_time, burst_time, no_of_processes)

     __magic_name__								=burst_time
     __magic_name__								=no_of_processes
     __magic_name__								=waiting_time
     __magic_name__								=calculate_turnaroundtime(bt, n, wt)

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     __magic_name__								=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)



