

from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings

@maybe_allow_in_graph
class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_						,					A_						,					A_=0.0						,					A_ = None						,					A_ = "geglu"						,					A_ = None						,					A_ = False						,					A_ = False						,					A_ = False						,					A_ = False						,					A_ = True						,					A_ = "layer_norm"						,					A_ = False						,					):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		int 		=    only_cross_attention

										_UpperCAmelCase       :		str 		=    (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
										_UpperCAmelCase       :		List[Any] 		=    (num_embeds_ada_norm is not None) and norm_type == "ada_norm"

										if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
														raise ValueError(
														    f'`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'
														    f' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'	)

										# Define 3 blocks. Each block has its own normalization layer.
										# 1. Self-Attn
										if self.use_ada_layer_norm:
														_UpperCAmelCase       :		Tuple 		=    AdaLayerNorm(A_						,					A_	)
										elif self.use_ada_layer_norm_zero:
														_UpperCAmelCase       :		List[str] 		=    AdaLayerNormZero(A_						,					A_	)
										else:
														_UpperCAmelCase       :		int 		=    nn.LayerNorm(A_						,					elementwise_affine=A_	)
										_UpperCAmelCase       :		Tuple 		=    Attention(
										    query_dim=A_						,					heads=A_						,					dim_head=A_						,					dropout=A_						,					bias=A_						,					cross_attention_dim=cross_attention_dim if only_cross_attention else None						,					upcast_attention=A_						,					)

										# 2. Cross-Attn
										if cross_attention_dim is not None or double_self_attention:
														# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
														# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
														# the second cross attention block.
														_UpperCAmelCase       :		Optional[Any] 		=    (
														    AdaLayerNorm(A_						,					A_	)
														    if self.use_ada_layer_norm
														    else nn.LayerNorm(A_						,					elementwise_affine=A_	)
														)
														_UpperCAmelCase       :		Tuple 		=    Attention(
														    query_dim=A_						,					cross_attention_dim=cross_attention_dim if not double_self_attention else None						,					heads=A_						,					dim_head=A_						,					dropout=A_						,					bias=A_						,					upcast_attention=A_						,					)  # is self-attn if encoder_hidden_states is none
										else:
														_UpperCAmelCase       :		Dict 		=    None
														_UpperCAmelCase       :		Tuple 		=    None

										# 3. Feed-forward
										_UpperCAmelCase       :		Tuple 		=    nn.LayerNorm(A_						,					elementwise_affine=A_	)
										_UpperCAmelCase       :		List[Any] 		=    FeedForward(A_						,					dropout=A_						,					activation_fn=A_						,					final_dropout=A_	)

										# let chunk size default to None
										_UpperCAmelCase       :		str 		=    None
										_UpperCAmelCase       :		Union[str, Any] 		=    0


						def        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    chunk_size
										_UpperCAmelCase       :		int 		=    dim





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					):







										'''simple docstring'''


										if self.use_ada_layer_norm:
														_UpperCAmelCase       :		Optional[Any] 		=    self.norma(A_						,					A_	)
										elif self.use_ada_layer_norm_zero:
														_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    self.norma(
														    A_						,					A_						,					A_						,					hidden_dtype=hidden_states.dtype	)
										else:
														_UpperCAmelCase       :		str 		=    self.norma(A_	)

										_UpperCAmelCase       :		Any 		=    cross_attention_kwargs if cross_attention_kwargs is not None else {}

										_UpperCAmelCase       :		Union[str, Any] 		=    self.attna(
										    A_						,					encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None						,					attention_mask=A_						,					**A_						,					)
										if self.use_ada_layer_norm_zero:
														_UpperCAmelCase       :		Optional[int] 		=    gate_msa.unsqueeze(1	) * attn_output
										_UpperCAmelCase       :		Tuple 		=    attn_output + hidden_states

										# 2. Cross-Attention
										if self.attna is not None:
														_UpperCAmelCase       :		int 		=    (
														    self.norma(A_						,					A_	) if self.use_ada_layer_norm else self.norma(A_	)
														)

														_UpperCAmelCase       :		List[str] 		=    self.attna(
														    A_						,					encoder_hidden_states=A_						,					attention_mask=A_						,					**A_						,					)
														_UpperCAmelCase       :		List[Any] 		=    attn_output + hidden_states

										# 3. Feed-forward
										_UpperCAmelCase       :		List[Any] 		=    self.norma(A_	)

										if self.use_ada_layer_norm_zero:
														_UpperCAmelCase       :		List[Any] 		=    norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

										if self._chunk_size is not None:
														# "feed_forward_chunk_size" can be used to save memory
														if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																		raise ValueError(
																		    f'`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'	)

														_UpperCAmelCase       :		Optional[Any] 		=    norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
														_UpperCAmelCase       :		Optional[Any] 		=    torch.cat(
														    [self.ff(A_	) for hid_slice in norm_hidden_states.chunk(A_						,					dim=self._chunk_dim	)]						,					dim=self._chunk_dim						,					)
										else:
														_UpperCAmelCase       :		Dict 		=    self.ff(A_	)

										if self.use_ada_layer_norm_zero:
														_UpperCAmelCase       :		List[str] 		=    gate_mlp.unsqueeze(1	) * ff_output

										_UpperCAmelCase       :		Optional[int] 		=    ff_output + hidden_states

										return hidden_states







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_ = None						,					A_ = 4						,					A_ = 0.0						,					A_ = "geglu"						,					A_ = False						,					):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		str 		=    int(dim * mult	)
										_UpperCAmelCase       :		Union[str, Any] 		=    dim_out if dim_out is not None else dim

										if activation_fn == "gelu":
														_UpperCAmelCase       :		List[Any] 		=    GELU(A_						,					A_	)
										if activation_fn == "gelu-approximate":
														_UpperCAmelCase       :		Tuple 		=    GELU(A_						,					A_						,					approximate="tanh"	)
										elif activation_fn == "geglu":
														_UpperCAmelCase       :		List[str] 		=    GEGLU(A_						,					A_	)
										elif activation_fn == "geglu-approximate":
														_UpperCAmelCase       :		Tuple 		=    ApproximateGELU(A_						,					A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    nn.ModuleList([]	)
										# project in
										self.net.append(A_	)
										# project dropout
										self.net.append(nn.Dropout(A_	)	)
										# project out
										self.net.append(nn.Linear(A_						,					A_	)	)
										# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
										if final_dropout:
														self.net.append(nn.Dropout(A_	)	)





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										for module in self.net:
														_UpperCAmelCase       :		Dict 		=    module(A_	)
										return hidden_states







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_						,					A_ = "none"	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		Optional[int] 		=    nn.Linear(A_						,					A_	)
										_UpperCAmelCase       :		List[str] 		=    approximate


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										if gate.device.type != "mps":
														return F.gelu(A_						,					approximate=self.approximate	)
										# mps: gelu is not implemented for float16
										return F.gelu(gate.to(dtype=torch.floataa	)						,					approximate=self.approximate	).to(dtype=gate.dtype	)





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.proj(A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    self.gelu(A_	)
										return hidden_states







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		Dict 		=    nn.Linear(A_						,					dim_out * 2	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										if gate.device.type != "mps":
														return F.gelu(A_	)
										# mps: gelu is not implemented for float16
										return F.gelu(gate.to(dtype=torch.floataa	)	).to(dtype=gate.dtype	)





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    self.proj(A_	).chunk(2						,					dim=-1	)
										return hidden_states * self.gelu(A_	)







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		str 		=    nn.Linear(A_						,					A_	)





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.proj(A_	)
										return x * torch.sigmoid(1.7_02 * x	)







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		List[Any] 		=    nn.Embedding(A_						,					A_	)
										_UpperCAmelCase       :		List[str] 		=    nn.SiLU()
										_UpperCAmelCase       :		List[Any] 		=    nn.Linear(A_						,					embedding_dim * 2	)
										_UpperCAmelCase       :		Optional[int] 		=    nn.LayerNorm(A_						,					elementwise_affine=A_	)





						def        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.linear(self.silu(self.emb(A_	)	)	)
										_UpperCAmelCase		,							_UpperCAmelCase       :		List[str] 		=    torch.chunk(A_						,					2	)
										_UpperCAmelCase       :		Dict 		=    self.norm(A_	) * (1 + scale) + shift
										return x







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_	):







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase       :		Tuple 		=    CombinedTimestepLabelEmbeddings(A_						,					A_	)

										_UpperCAmelCase       :		List[Any] 		=    nn.SiLU()
										_UpperCAmelCase       :		Tuple 		=    nn.Linear(A_						,					6 * embedding_dim						,					bias=A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    nn.LayerNorm(A_						,					elementwise_affine=A_						,					eps=1e-6	)





						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_=None	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.linear(self.silu(self.emb(A_						,					A_						,					hidden_dtype=A_	)	)	)
										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    emb.chunk(6						,					dim=1	)
										_UpperCAmelCase       :		Union[str, Any] 		=    self.norm(A_	) * (1 + scale_msa[:, None]) + shift_msa[:, None]
										return x, gate_msa, shift_mlp, scale_mlp, gate_mlp







class 				a  (	nn.Module				):


						def __init__(       self						,					A_						,					A_						,					A_						,					A_ = None						,					A_ = 1e-5	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		List[Any] 		=    num_groups
										_UpperCAmelCase       :		Optional[Any] 		=    eps

										if act_fn is None:
														_UpperCAmelCase       :		Union[str, Any] 		=    None
										else:
														_UpperCAmelCase       :		int 		=    get_activation(A_	)

										_UpperCAmelCase       :		str 		=    nn.Linear(A_						,					out_dim * 2	)





						def        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


										if self.act:
														_UpperCAmelCase       :		Tuple 		=    self.act(A_	)
										_UpperCAmelCase       :		Any 		=    self.linear(A_	)
										_UpperCAmelCase       :		Tuple 		=    emb[:, :, None, None]
										_UpperCAmelCase		,							_UpperCAmelCase       :		Tuple 		=    emb.chunk(2						,					dim=1	)

										_UpperCAmelCase       :		List[str] 		=    F.group_norm(A_						,					self.num_groups						,					eps=self.eps	)
										_UpperCAmelCase       :		Union[str, Any] 		=    x * (1 + scale) + shift
										return x





from __future__ import annotations

import pandas as pd

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       list[int]:
				_UpperCAmelCase       :		List[Any] 		=    [0] * no_of_processes
				_UpperCAmelCase       :		Dict 		=    [0] * no_of_processes
				# Copy the burst time into remaining_time[]
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		List[str] 		=    burst_time[i]

				_UpperCAmelCase       :		Any 		=    0
				_UpperCAmelCase       :		Union[str, Any] 		=    0
				_UpperCAmelCase       :		str 		=    9_9999_9999
				_UpperCAmelCase       :		List[Any] 		=    0
				_UpperCAmelCase       :		Any 		=    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:
																				_UpperCAmelCase       :		Optional[int] 		=    remaining_time[j]
																				_UpperCAmelCase       :		int 		=    j
																				_UpperCAmelCase       :		Tuple 		=    True

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

								_UpperCAmelCase       :		List[Any] 		=    remaining_time[short]
								if minm == 0:
												_UpperCAmelCase       :		List[Any] 		=    9_9999_9999

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

												# Find finish time of current process
												_UpperCAmelCase       :		Optional[int] 		=    increment_time + 1

												# Calculate waiting time
												_UpperCAmelCase       :		str 		=    finish_time - arrival_time[short]
												_UpperCAmelCase       :		Union[str, Any] 		=    finar - burst_time[short]

												if waiting_time[short] < 0:
																_UpperCAmelCase       :		Optional[int] 		=    0

        # Increment time
								increment_time += 1
				return waiting_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  list[int] )   ->       list[int]:
				_UpperCAmelCase       :		str 		=    [0] * no_of_processes
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		str 		=    burst_time[i] + waiting_time[i]
				return turn_around_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       None:
				_UpperCAmelCase       :		Dict 		=    0
				_UpperCAmelCase       :		List[str] 		=    0
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Union[str, Any] 		=    total_waiting_time + waiting_time[i]
								_UpperCAmelCase       :		int 		=    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')
					SCREAMING_SNAKE_CASE_        =      int(input())
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      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))
										SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_        =      map(int, input().split())

					SCREAMING_SNAKE_CASE_        =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      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)


