



from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types


def       UpperCamelCase(  __UpperCamelCase							:		Any					,__UpperCamelCase							:		List[Any]					,__UpperCamelCase							:		str			):







		if not inplace:
				lowerCAmelCase_      :					Dict     =			ma + ma
		else:
				ma += ma

		return ma


def       UpperCamelCase(  __UpperCamelCase							:		List[str]					,__UpperCamelCase							:		List[str]			):
		lowerCAmelCase_      :					Union[str, Any]     =			-1 * len(__UpperCamelCase			)
		lowerCAmelCase_      :					List[Any]     =			list(range(len(tensor.shape[:zero_index]			)			)			)
		return tensor.permute(first_inds + [zero_index + i for i in inds]			)


def       UpperCamelCase(  __UpperCamelCase							:		List[Any]					,__UpperCamelCase							:		str			):
		return t.reshape(t.shape[:-no_dims] + (-1,)			)


def       UpperCamelCase(  __UpperCamelCase							:		Any					,__UpperCamelCase							:		Optional[int]					,__UpperCamelCase							:		Tuple					,__UpperCamelCase							:		List[Any] = 1e-4			):
		lowerCAmelCase_      :					Optional[Any]     =			mask.expand(*value.shape			)
		return torch.sum(mask * value					,dim=__UpperCamelCase			) / (eps + torch.sum(__UpperCamelCase					,dim=__UpperCamelCase			))


def       UpperCamelCase(  __UpperCamelCase							:		Optional[Any]					,__UpperCamelCase							:		Union[str, Any] = 2.3_1_2_5					,__UpperCamelCase							:		str = 2_1.6_8_7_5					,__UpperCamelCase							:		List[str] = 64			):
		lowerCAmelCase_      :					Union[str, Any]     =			torch.linspace(__UpperCamelCase					,__UpperCamelCase					,no_bins - 1					,device=pts.device			)
		lowerCAmelCase_      :					Optional[Any]     =			torch.sqrt(torch.sum((pts.unsqueeze(-2			) - pts.unsqueeze(-3			)) ** 2					,dim=-1			)			)
		return torch.bucketize(__UpperCamelCase					,__UpperCamelCase			)


def       UpperCamelCase(  __UpperCamelCase							:		int					,__UpperCamelCase							:		Dict			):
		lowerCAmelCase_      :					Optional[int]     =			dicts[0]
		lowerCAmelCase_      :					str     =			{}
		for k, v in first.items():
				lowerCAmelCase_      :					Any     =			[d[k] for d in dicts]
				if isinstance(__UpperCamelCase					,__UpperCamelCase			):
						lowerCAmelCase_      :					List[str]     =			dict_multimap(__UpperCamelCase					,__UpperCamelCase			)
				else:
						lowerCAmelCase_      :					int     =			fn(__UpperCamelCase			)

		return new_dict


def       UpperCamelCase(  __UpperCamelCase							:		List[str]					,__UpperCamelCase							:		Union[str, Any]			):
		lowerCAmelCase_      :					Union[str, Any]     =			v_bins.view(((1,) * len(x.shape			)) + (len(__UpperCamelCase			),)			)
		lowerCAmelCase_      :					Tuple     =			x[..., None] - reshaped_bins
		lowerCAmelCase_      :					Union[str, Any]     =			torch.argmin(torch.abs(__UpperCamelCase			)					,dim=-1			)
		return nn.functional.one_hot(__UpperCamelCase					,num_classes=len(__UpperCamelCase			)			).float()


def       UpperCamelCase(  __UpperCamelCase							:		int					,__UpperCamelCase							:		str					,__UpperCamelCase							:		Optional[int] = 0					,__UpperCamelCase							:		Dict = 0			):
		lowerCAmelCase_      :					Optional[int]     =			[]
		for i, s in enumerate(data.shape[:no_batch_dims]			):
				lowerCAmelCase_      :					Union[str, Any]     =			torch.arange(__UpperCamelCase			)
				lowerCAmelCase_      :					Optional[Any]     =			r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape			) - i - 1)))			)
				ranges.append(__UpperCamelCase			)

		lowerCAmelCase_      :					List[Any]     =			[slice(__UpperCamelCase			) for _ in range(len(data.shape			) - no_batch_dims			)]
		lowerCAmelCase_      :					List[str]     =			inds
		ranges.extend(__UpperCamelCase			)
		# Matt note: Editing this to get around the behaviour of using a list as an array index changing
		# in recent Numpy versions
		return data[tuple(__UpperCamelCase			)]


A__  :  Optional[Any]       = TypeVar('''T''')


def       UpperCamelCase(  __UpperCamelCase							:		List[str]					,__UpperCamelCase							:		Dict					,__UpperCamelCase							:		Dict			):
		lowerCAmelCase_      :					Union[str, Any]     =			{}
		for k, v in dic.items():
				if isinstance(__UpperCamelCase					,__UpperCamelCase			):
						lowerCAmelCase_      :					Optional[Any]     =			dict_map(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase			)
				else:
						lowerCAmelCase_      :					List[str]     =			tree_map(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase			)

		return new_dict


@overload
def       UpperCamelCase(  __UpperCamelCase							:		Optional[Any]					,__UpperCamelCase							:		Dict					,__UpperCamelCase							:		Any			):
		...


@overload
def       UpperCamelCase(  __UpperCamelCase							:		List[Any]					,__UpperCamelCase							:		Union[str, Any]					,__UpperCamelCase							:		List[Any]			):
		...


@overload
def       UpperCamelCase(  __UpperCamelCase							:		str					,__UpperCamelCase							:		Any					,__UpperCamelCase							:		str			):
		...


@overload
def       UpperCamelCase(  __UpperCamelCase							:		Optional[Any]					,__UpperCamelCase							:		Optional[int]					,__UpperCamelCase							:		Dict			):
		...


def       UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]					,__UpperCamelCase							:		Optional[int]					,__UpperCamelCase							:		str			):







		if isinstance(__UpperCamelCase					,__UpperCamelCase			):
				return dict_map(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase			)
		elif isinstance(__UpperCamelCase					,__UpperCamelCase			):
				return [tree_map(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase			) for x in tree]
		elif isinstance(__UpperCamelCase					,__UpperCamelCase			):
				return tuple(tree_map(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase			) for x in tree			)
		elif isinstance(__UpperCamelCase					,__UpperCamelCase			):
				return fn(__UpperCamelCase			)
		else:
				print(type(__UpperCamelCase			)			)
				raise ValueError('''Not supported'''			)





A__  :  List[Any]       = partial(tree_map, leaf_type=torch.Tensor)










A__  :  str       = 1000


def       UpperCamelCase(  __UpperCamelCase							:		float			):
		return x * x


def       UpperCamelCase(  __UpperCamelCase							:		Optional[int]					,__UpperCamelCase							:		float					,__UpperCamelCase							:		float					,__UpperCamelCase							:		int = 4			):
		assert callable(
		    __UpperCamelCase			), f"""the function(object) passed should be callable your input : {function}"""
		assert isinstance(__UpperCamelCase					,(float, int)			), f"""a should be float or integer your input : {a}"""
		assert isinstance(function(__UpperCamelCase			)					,(float, int)			), (
		    "the function should return integer or float return type of your function, "
		    f"""{type(__UpperCamelCase			)}"""
		)
		assert isinstance(__UpperCamelCase					,(float, int)			), f"""b should be float or integer your input : {b}"""
		assert (
		    isinstance(__UpperCamelCase					,__UpperCamelCase			) and precision > 0
		), f"""precision should be positive integer your input : {precision}"""

		# just applying the formula of simpson for approximate integration written in
		# mentioned article in first comment of this file and above this function

		lowerCAmelCase_      :					List[str]     =			(b - a) / N_STEPS
		lowerCAmelCase_      :					str     =			function(__UpperCamelCase			) + function(__UpperCamelCase			)

		for i in range(1					,__UpperCamelCase			):
				lowerCAmelCase_      :					Optional[Any]     =			a + h * i
				result += function(__UpperCamelCase			) * (4 if i % 2 else 2)

		result *= h / 3
		return round(__UpperCamelCase					,__UpperCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





