import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


_snake_case			=						get_logger(__name__)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__ = None,    )						->   List[str]:
							# Model states
							for i, state in enumerate(snake_case__							):
														__UpperCAmelCase						:			Optional[int]         =   f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
														__UpperCAmelCase						:			Dict         =   os.path.join(snake_case__,    snake_case__							)
														save(snake_case__,    snake_case__							)
														logger.info(f'''Model weights saved in {output_model_file}'''							)
							# Optimizer states
							for i, opt in enumerate(snake_case__							):
														__UpperCAmelCase						:			Any         =   opt.state_dict()
														__UpperCAmelCase						:			Union[str, Any]         =   f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
														__UpperCAmelCase						:			Optional[int]         =   os.path.join(snake_case__,    snake_case__							)
														save(snake_case__,    snake_case__							)
														logger.info(f'''Optimizer state saved in {output_optimizer_file}'''							)
							# Scheduler states
							for i, scheduler in enumerate(snake_case__							):
														__UpperCAmelCase						:			List[Any]         =   scheduler.state_dict()
														__UpperCAmelCase						:			Any         =   f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
														__UpperCAmelCase						:			Optional[Any]         =   os.path.join(snake_case__,    snake_case__							)
														save(snake_case__,    snake_case__							)
														logger.info(f'''Scheduler state saved in {output_scheduler_file}'''							)
							# GradScaler state
							if scaler is not None:
														__UpperCAmelCase						:			List[Any]         =   scaler.state_dict()
														__UpperCAmelCase						:			int         =   os.path.join(snake_case__,    snake_case__							)
														torch.save(snake_case__,    snake_case__							)
														logger.info(f'''Gradient scaler state saved in {output_scaler_file}'''							)
							# Random number generator states
							__UpperCAmelCase						:			Optional[int]         =   {}
							__UpperCAmelCase						:			int         =   f'''{RNG_STATE_NAME}_{process_index}.pkl'''
							__UpperCAmelCase						:			int         =   random.getstate()
							__UpperCAmelCase						:			Tuple         =   np.random.get_state()
							__UpperCAmelCase						:			Any         =   torch.get_rng_state()
							if is_xpu_available():
														__UpperCAmelCase						:			Union[str, Any]         =   torch.xpu.get_rng_state_all()
							else:
														__UpperCAmelCase						:			List[str]         =   torch.cuda.get_rng_state_all()
							if is_tpu_available():
														__UpperCAmelCase						:			Tuple         =   xm.get_rng_state()
							__UpperCAmelCase						:			List[Any]         =   os.path.join(snake_case__,    snake_case__							)
							torch.save(snake_case__,    snake_case__							)
							logger.info(f'''Random states saved in {output_states_file}'''							)
							return output_dir




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__=None,    snake_case__=None,    **snake_case__,    )						->   int:
							if map_location not in [None, "cpu", "on_device"]:
														raise TypeError(
														    "Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"							)
							if map_location is None:
														__UpperCAmelCase						:			Dict         =   "cpu"
							elif map_location == "on_device":
														__UpperCAmelCase						:			Optional[int]         =   PartialState().device
							# Model states
							for i, model in enumerate(snake_case__							):
														__UpperCAmelCase						:			int         =   f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
														__UpperCAmelCase						:			Optional[int]         =   os.path.join(snake_case__,    snake_case__							)
														models[i].load_state_dict(torch.load(snake_case__,    map_location=snake_case__							),    **snake_case__							)
							logger.info("All model weights loaded successfully"							)

							# Optimizer states
							for i, opt in enumerate(snake_case__							):
														__UpperCAmelCase						:			Union[str, Any]         =   f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
														__UpperCAmelCase						:			Dict         =   os.path.join(snake_case__,    snake_case__							)
														__UpperCAmelCase						:			str         =   torch.load(snake_case__,    map_location=snake_case__							)
														optimizers[i].load_state_dict(snake_case__							)
							logger.info("All optimizer states loaded successfully"							)

							# Scheduler states
							for i, scheduler in enumerate(snake_case__							):
														__UpperCAmelCase						:			str         =   f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
														__UpperCAmelCase						:			int         =   os.path.join(snake_case__,    snake_case__							)
														scheduler.load_state_dict(torch.load(snake_case__							)							)
							logger.info("All scheduler states loaded successfully"							)

							# GradScaler state
							if scaler is not None:
														__UpperCAmelCase						:			Any         =   os.path.join(snake_case__,    snake_case__							)
														scaler.load_state_dict(torch.load(snake_case__							)							)
														logger.info("GradScaler state loaded successfully"							)

							# Random states
							try:
														__UpperCAmelCase						:			List[Any]         =   torch.load(os.path.join(snake_case__,    f'''{RNG_STATE_NAME}_{process_index}.pkl'''							)							)
														random.setstate(states["random_state"]							)
														np.random.set_state(states["numpy_random_seed"]							)
														torch.set_rng_state(states["torch_manual_seed"]							)
														if is_xpu_available():
																					torch.xpu.set_rng_state_all(states["torch_xpu_manual_seed"]							)
														else:
																					torch.cuda.set_rng_state_all(states["torch_cuda_manual_seed"]							)
														if is_tpu_available():
																					xm.set_rng_state(states["xm_seed"]							)
														logger.info("All random states loaded successfully"							)
							except Exception:
														logger.info("Could not load random states"							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__ = 0							)						->   Any:
							__UpperCAmelCase						:			Any         =   Path(snake_case__							) / f'''custom_checkpoint_{index}.pkl'''
							logger.info(f'''Saving the state of {get_pretty_name(snake_case__							)} to {save_location}'''							)
							torch.save(obj.state_dict(),    snake_case__							)







def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__ = 0							)						->   Union[str, Any]:
							__UpperCAmelCase						:			Union[str, Any]         =   f'''{path}/custom_checkpoint_{index}.pkl'''
							logger.info(f'''Loading the state of {get_pretty_name(snake_case__							)} from {load_location}'''							)
							obj.load_state_dict(torch.load(snake_case__,    map_location="cpu"							)							)





from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize




class 					_snake_case						(						_lowercase				):
					lowerCamelCase__:		Tuple  =     1
					lowerCamelCase__:		Dict  =     2
					lowerCamelCase__:		List[str]  =     3
					def __repr__(  self:				List[Any]     )    ->				int:
												return f'''{self.__class__.__name__}.{self.name}'''




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   float:
							__UpperCAmelCase    ,	__UpperCAmelCase						:			Optional[Any]         =   point
							return degrees(atana(y - miny,    x - minx							)							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   Direction:
							__UpperCAmelCase    ,	__UpperCAmelCase						:			List[Any]         =   starting
							__UpperCAmelCase    ,	__UpperCAmelCase						:			Optional[int]         =   via
							__UpperCAmelCase    ,	__UpperCAmelCase						:			List[str]         =   target
							__UpperCAmelCase						:			Tuple         =   degrees(atana(ya - ya,    xa - xa							)							)
							via_angle %= 360
							__UpperCAmelCase						:			str         =   degrees(atana(ya - ya,    xa - xa							)							)
							target_angle %= 360
							# t-
							#  \ \
							#   \ v
							#    \|
							#     s
							# via_angle is always lower than target_angle, if direction is left.
							# If they are same, it means they are on a same line of convex hull.
							if target_angle > via_angle:
														return Direction.left
							elif target_angle == via_angle:
														return Direction.straight
							else:
														return Direction.right







def   _UpperCamelCase   (			snake_case__							)						->   list[tuple[int, int]]:

							if len(snake_case__							) <= 2:
														# There is no convex hull
														raise ValueError("graham_scan: argument must contain more than 3 points."							)
							if len(snake_case__							) == 3:
														return points
							# find the lowest and the most left point
							__UpperCAmelCase						:			str         =   0
							__UpperCAmelCase    ,	__UpperCAmelCase						:			Any         =   maxsize, maxsize
							for i, point in enumerate(snake_case__							):
														__UpperCAmelCase						:			str         =   point[0]
														__UpperCAmelCase						:			Any         =   point[1]
														if y < miny:
																					__UpperCAmelCase						:			Tuple         =   y
																					__UpperCAmelCase						:			int         =   x
																					__UpperCAmelCase						:			str         =   i
														if y == miny and x < minx:
																					__UpperCAmelCase						:			Any         =   x
																					__UpperCAmelCase						:			Dict         =   i

    # remove the lowest and the most left point from points for preparing for sort
							points.pop(snake_case__							)

							__UpperCAmelCase						:			Any         =   sorted(snake_case__,    key=lambda snake_case__							: angle_comparer(snake_case__,    snake_case__,    snake_case__							)							)
							# This insert actually costs complexity,
							# and you should instead add (minx, miny) into stack later.
							# I'm using insert just for easy understanding.
							sorted_points.insert(0,    (minx, miny)							)

							__UpperCAmelCase						:			deque[tuple[int, int]]         =   deque()
							stack.append(sorted_points[0]							)
							stack.append(sorted_points[1]							)
							stack.append(sorted_points[2]							)
							# In any ways, the first 3 points line are towards left.
							# Because we sort them the angle from minx, miny.
							__UpperCAmelCase						:			Any         =   Direction.left

							for i in range(3,    len(snake_case__							)							):
														while True:
																					__UpperCAmelCase						:			List[Any]         =   stack[-2]
																					__UpperCAmelCase						:			Tuple         =   stack[-1]
																					__UpperCAmelCase						:			Any         =   sorted_points[i]
																					__UpperCAmelCase						:			Optional[Any]         =   check_direction(snake_case__,    snake_case__,    snake_case__							)

																					if next_direction == Direction.left:
																												__UpperCAmelCase						:			Optional[int]         =   Direction.left
																												break
																					if next_direction == Direction.straight:
																												if current_direction == Direction.left:
																																			# We keep current_direction as left.
																																			# Because if the straight line keeps as straight,
																																			# we want to know if this straight line is towards left.
																																			break
																												elif current_direction == Direction.right:
																																			# If the straight line is towards right,
																																			# every previous points on those straigh line is not convex hull.
																																			stack.pop()
																					if next_direction == Direction.right:
																												stack.pop()
														stack.append(sorted_points[i]							)
							return list(snake_case__							)




