






"""simple docstring"""


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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__SCREAMING_SNAKE_CASE      =      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      =      {
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}




class 	__UpperCamelCase    (    UpperCamelCase	):
					lowercase_						:							int			=     """m2m_100"""
					lowercase_						:							Dict			=     ["""past_key_values"""]
					lowercase_						:							Optional[int]			=     {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
					def __init__(       self       :  Dict    ,   UpperCAmelCase       :  List[str]=12_8112    ,   UpperCAmelCase       :  Dict=1024    ,   UpperCAmelCase       :  str=12    ,   UpperCAmelCase       :  Tuple=4096    ,   UpperCAmelCase       :  Union[str, Any]=16    ,   UpperCAmelCase       :  int=12    ,   UpperCAmelCase       :  Union[str, Any]=4096    ,   UpperCAmelCase       :  Optional[int]=16    ,   UpperCAmelCase       :  int=0.0_5    ,   UpperCAmelCase       :  Optional[int]=0.0_5    ,   UpperCAmelCase       :  Any=True    ,   UpperCAmelCase       :  str=True    ,   UpperCAmelCase       :  Tuple="relu"    ,   UpperCAmelCase       :  Dict=1024    ,   UpperCAmelCase       :  Union[str, Any]=0.1    ,   UpperCAmelCase       :  List[str]=0.1    ,   UpperCAmelCase       :  Optional[int]=0.0    ,   UpperCAmelCase       :  Optional[Any]=0.0_2    ,   UpperCAmelCase       :  str=2    ,   UpperCAmelCase       :  Optional[Any]=True    ,   UpperCAmelCase       :  Tuple=1    ,   UpperCAmelCase       :  Optional[int]=0    ,   UpperCAmelCase       :  List[str]=2    ,   **UpperCAmelCase       :  Dict    ,   )  ->					Union[str, Any]:
											lowerCAmelCase      :List[str]				  = vocab_size
											lowerCAmelCase      :str				  = max_position_embeddings
											lowerCAmelCase      :Dict				  = d_model
											lowerCAmelCase      :Dict				  = encoder_ffn_dim
											lowerCAmelCase      :Any				  = encoder_layers
											lowerCAmelCase      :Any				  = encoder_attention_heads
											lowerCAmelCase      :Optional[Any]				  = decoder_ffn_dim
											lowerCAmelCase      :Union[str, Any]				  = decoder_layers
											lowerCAmelCase      :Optional[Any]				  = decoder_attention_heads
											lowerCAmelCase      :Dict				  = dropout
											lowerCAmelCase      :List[Any]				  = attention_dropout
											lowerCAmelCase      :Optional[int]				  = activation_dropout
											lowerCAmelCase      :str				  = activation_function
											lowerCAmelCase      :Tuple				  = init_std
											lowerCAmelCase      :List[str]				  = encoder_layerdrop
											lowerCAmelCase      :int				  = decoder_layerdrop
											lowerCAmelCase      :Dict				  = use_cache
											lowerCAmelCase      :Tuple				  = encoder_layers
											lowerCAmelCase      :Dict				  = scale_embedding  # scale factor will be sqrt(d_model) if True

											super().__init__(
											    pad_token_id=UpperCAmelCase    ,   bos_token_id=UpperCAmelCase    ,   eos_token_id=UpperCAmelCase    ,   is_encoder_decoder=UpperCAmelCase    ,   decoder_start_token_id=UpperCAmelCase    ,   **UpperCAmelCase    ,   )




class 	__UpperCamelCase    (    UpperCamelCase	):
					@property
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					Mapping[str, Mapping[int, str]]:
											lowerCAmelCase      :Optional[Any]				  = OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
											    ]      )

											if self.use_past:
																	lowerCAmelCase      :str				  = {0: 'batch'}
																	lowerCAmelCase      :Optional[Any]				  = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
											else:
																	lowerCAmelCase      :List[Any]				  = {0: 'batch', 1: 'decoder_sequence'}
																	lowerCAmelCase      :str				  = {0: 'batch', 1: 'decoder_sequence'}

											if self.use_past:
																	self.fill_with_past_key_values_(UpperCAmelCase    ,   direction='inputs'      )
											return common_inputs
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  PreTrainedTokenizer    ,   UpperCAmelCase       :  int = -1    ,   UpperCAmelCase       :  int = -1    ,   UpperCAmelCase       :  bool = False    ,   UpperCAmelCase       :  Optional[TensorType] = None    ,   )  ->					Mapping[str, Any]:
											# Copied from OnnxConfig.generate_dummy_inputs
											# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											lowerCAmelCase      :Optional[Any]				  = compute_effective_axis_dimension(
											    UpperCAmelCase    ,   fixed_dimension=OnnxConfig.default_fixed_batch    ,   num_token_to_add=0      )

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											lowerCAmelCase      :List[Any]				  = tokenizer.num_special_tokens_to_add(UpperCAmelCase      )
											lowerCAmelCase      :Optional[int]				  = compute_effective_axis_dimension(
											    UpperCAmelCase    ,   fixed_dimension=OnnxConfig.default_fixed_sequence    ,   num_token_to_add=UpperCAmelCase      )

											# Generate dummy inputs according to compute batch and sequence
											lowerCAmelCase      :Union[str, Any]				  = [' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
											lowerCAmelCase      :List[Any]				  = dict(tokenizer(UpperCAmelCase    ,   return_tensors=UpperCAmelCase      )      )
											return common_inputs
					def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  PreTrainedTokenizer    ,   UpperCAmelCase       :  int = -1    ,   UpperCAmelCase       :  int = -1    ,   UpperCAmelCase       :  bool = False    ,   UpperCAmelCase       :  Optional[TensorType] = None    ,   )  ->					Mapping[str, Any]:
											lowerCAmelCase      :Optional[int]				  = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )

											# Generate decoder inputs
											lowerCAmelCase      :int				  = seq_length if not self.use_past else 1
											lowerCAmelCase      :Tuple				  = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
											lowerCAmelCase      :List[Any]				  = {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
											lowerCAmelCase      :str				  = dict(**UpperCAmelCase    ,   **UpperCAmelCase      )

											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      :List[Any]				  = common_inputs['input_ids'].shape
																	lowerCAmelCase      :List[Any]				  = common_inputs['decoder_input_ids'].shape[1]
																	lowerCAmelCase  ,   lowerCAmelCase      :Tuple				  = self.num_attention_heads
																	lowerCAmelCase      :Optional[Any]				  = (
																	    batch,
																	    num_encoder_attention_heads,
																	    encoder_seq_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)
																	lowerCAmelCase      :Optional[Any]				  = decoder_seq_length + 3
																	lowerCAmelCase      :List[str]				  = (
																	    batch,
																	    num_decoder_attention_heads,
																	    decoder_past_length,
																	    self._config.hidden_size // num_decoder_attention_heads,
																	)

																	lowerCAmelCase      :List[str]				  = torch.cat(
																	    [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase    ,   UpperCAmelCase      )]    ,   dim=1      )

																	lowerCAmelCase      :Union[str, Any]				  = []
																	# If the number of encoder and decoder layers are present in the model configuration, both are considered
																	lowerCAmelCase  ,   lowerCAmelCase      :Union[str, Any]				  = self.num_layers
																	lowerCAmelCase      :Union[str, Any]				  = min(UpperCAmelCase    ,   UpperCAmelCase      )
																	lowerCAmelCase      :List[str]				  = max(UpperCAmelCase    ,   UpperCAmelCase      ) - min_num_layers
																	lowerCAmelCase      :Any				  = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

																	for _ in range(UpperCAmelCase      ):
																							common_inputs["past_key_values"].append(
																							    (
																							        torch.zeros(UpperCAmelCase      ),
																							        torch.zeros(UpperCAmelCase      ),
																							        torch.zeros(UpperCAmelCase      ),
																							        torch.zeros(UpperCAmelCase      ),
																							    )      )
																	# TODO: test this.
																	lowerCAmelCase      :List[Any]				  = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
																	for _ in range(UpperCAmelCase    ,   UpperCAmelCase      ):
																							common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase      ), torch.zeros(UpperCAmelCase      ))      )
											return common_inputs
					lowercase_						:							str			=     _generate_dummy_inputs_for_default_and_seqaseq_lm







"""simple docstring"""


from __future__ import annotations


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Union[str, Any]				  = [0] * len(a__   )
						for i in range(1    ,			len(a__   )   ):
												lowerCAmelCase      :Optional[int]				  = duration_times[i - 1] + waiting_times[i - 1]
						return waiting_times


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						return [
						    duration_time + waiting_times[i]
						    for i, duration_time in enumerate(a__   )
						]


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						return sum(a__   ) / len(a__   )


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						return sum(a__   ) / len(a__   )


if __name__ == "__main__":
		# process id's
		__SCREAMING_SNAKE_CASE      =      [1, 2, 3]

		# ensure that we actually have processes
		if len(processes) == 0:
				print('Zero amount of processes')
				raise SystemExit(0)

		# duration time of all processes
		__SCREAMING_SNAKE_CASE      =      [19, 8, 9]

		# ensure we can match each id to a duration time
		if len(duration_times) != len(processes):
				print('Unable to match all id\'s with their duration time')
				raise SystemExit(0)

		# get the waiting times and the turnaround times
		__SCREAMING_SNAKE_CASE      =      calculate_waiting_times(duration_times)
		__SCREAMING_SNAKE_CASE      =      calculate_turnaround_times(duration_times, waiting_times)

		# get the average times
		__SCREAMING_SNAKE_CASE      =      calculate_average_waiting_time(waiting_times)
		__SCREAMING_SNAKE_CASE      =      calculate_average_turnaround_time(turnaround_times)

		# print all the results
		print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
		for i, process in enumerate(processes):
				print(
				    F"""{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"""
				    F"""{turnaround_times[i]}"""
				)
		print(F"""Average waiting time = {average_waiting_time}""")
		print(F"""Average turn around time = {average_turnaround_time}""")