




import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation






def  snake_case (							A						,       A						,       A = False						,       A = 1						,       A = 1						,       A = 10000						,       )							->							Dict:




	'''simple docstring'''


	assert len(timesteps.shape      ) == 1, "Timesteps should be a 1d-array"

	lowerCamelCase_							:		Union[str, Any]							       =							embedding_dim // 2
	lowerCamelCase_							:		List[Any]							       =							-math.log(A      ) * torch.arange(
	    start=0						,       end=A						,       dtype=torch.floataa						,       device=timesteps.device      )
	lowerCamelCase_							:		Any							       =							exponent / (half_dim - downscale_freq_shift)

	lowerCamelCase_							:		Optional[int]							       =							torch.exp(A      )
	lowerCamelCase_							:		Dict							       =							timesteps[:, None].float() * emb[None, :]

	# scale embeddings
	lowerCamelCase_							:		Optional[Any]							       =							scale * emb

	# concat sine and cosine embeddings
	lowerCamelCase_							:		Dict							       =							torch.cat([torch.sin(A      ), torch.cos(A      )]						,       dim=-1      )

	# flip sine and cosine embeddings
	if flip_sin_to_cos:
		lowerCamelCase_							:		Tuple							       =							torch.cat([emb[:, half_dim:], emb[:, :half_dim]]						,       dim=-1      )

	# zero pad
	if embedding_dim % 2 == 1:
		lowerCamelCase_							:		Optional[Any]							       =							torch.nn.functional.pad(A						,       (0, 1, 0, 0)      )
	return emb






def  snake_case (							A						,       A						,       A=False						,       A=0      )							->							Tuple:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[int]							       =							np.arange(A						,       dtype=np.floataa      )
	lowerCamelCase_							:		List[str]							       =							np.arange(A						,       dtype=np.floataa      )
	lowerCamelCase_							:		Optional[Any]							       =							np.meshgrid(A						,       A      )  # here w goes first
	lowerCamelCase_							:		Optional[Any]							       =							np.stack(A						,       axis=0      )

	lowerCamelCase_							:		Dict							       =							grid.reshape([2, 1, grid_size, grid_size]      )
	lowerCamelCase_							:		str							       =							get_ad_sincos_pos_embed_from_grid(A						,       A      )
	if cls_token and extra_tokens > 0:
		lowerCamelCase_							:		Dict							       =							np.concatenate([np.zeros([extra_tokens, embed_dim]      ), pos_embed]						,       axis=0      )
	return pos_embed






def  snake_case (							A						,       A      )							->							Tuple:




	'''simple docstring'''


	if embed_dim % 2 != 0:
		raise ValueError('embed_dim must be divisible by 2'      )

	# use half of dimensions to encode grid_h
	lowerCamelCase_							:		int							       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2						,       grid[0]      )  # (H*W, D/2)
	lowerCamelCase_							:		List[Any]							       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2						,       grid[1]      )  # (H*W, D/2)

	lowerCamelCase_							:		Any							       =							np.concatenate([emb_h, emb_w]						,       axis=1      )  # (H*W, D)
	return emb






def  snake_case (							A						,       A      )							->							Any:




	'''simple docstring'''


	if embed_dim % 2 != 0:
		raise ValueError('embed_dim must be divisible by 2'      )

	lowerCamelCase_							:		Optional[Any]							       =							np.arange(embed_dim // 2						,       dtype=np.floataa      )
	omega /= embed_dim / 2.0
	lowerCamelCase_							:		int							       =							1.0 / 10000**omega  # (D/2,)

	lowerCamelCase_							:		Dict							       =							pos.reshape(-1      )  # (M,)
	lowerCamelCase_							:		Union[str, Any]							       =							np.einsum('m,d->md'						,       A						,       A      )  # (M, D/2), outer product

	lowerCamelCase_							:		Any							       =							np.sin(A      )  # (M, D/2)
	lowerCamelCase_							:		str							       =							np.cos(A      )  # (M, D/2)

	lowerCamelCase_							:		Optional[Any]							       =							np.concatenate([emb_sin, emb_cos]						,       axis=1      )  # (M, D)
	return emb


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase=224			,	__lowercase=224			,	__lowercase=16			,	__lowercase=3			,	__lowercase=768			,	__lowercase=False			,	__lowercase=True			,	__lowercase=True			,	):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		Any							       =							(height // patch_size) * (width // patch_size)
						lowerCamelCase_							:		List[str]							       =							flatten
						lowerCamelCase_							:		Any							       =							layer_norm

						lowerCamelCase_							:		Optional[Any]							       =							nn.Convad(
						    __lowercase			,	__lowercase			,	kernel_size=(patch_size, patch_size)			,	stride=__lowercase			,	bias=__lowercase )
						if layer_norm:
							lowerCamelCase_							:		Tuple							       =							nn.LayerNorm(__lowercase			,	elementwise_affine=__lowercase			,	eps=1E-6 )
						else:
							lowerCamelCase_							:		Optional[int]							       =							None

						lowerCamelCase_							:		Optional[int]							       =							get_ad_sincos_pos_embed(__lowercase			,	int(num_patches**0.5 ) )
						self.register_buffer('pos_embed'			,	torch.from_numpy(__lowercase ).float().unsqueeze(0 )			,	persistent=__lowercase )





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							self.proj(__lowercase )
						if self.flatten:
							lowerCamelCase_							:		Optional[int]							       =							latent.flatten(2 ).transpose(1			,	2 )  # BCHW -> BNC
						if self.layer_norm:
							lowerCamelCase_							:		Optional[Any]							       =							self.norm(__lowercase )
						return latent + self.pos_embed


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase = "silu"			,	__lowercase = None			,	__lowercase = None			,	__lowercase=None			,	):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		Dict							       =							nn.Linear(__lowercase			,	__lowercase )

						if cond_proj_dim is not None:
							lowerCamelCase_							:		Any							       =							nn.Linear(__lowercase			,	__lowercase			,	bias=__lowercase )
						else:
							lowerCamelCase_							:		List[str]							       =							None

						lowerCamelCase_							:		str							       =							get_activation(__lowercase )

						if out_dim is not None:
							lowerCamelCase_							:		Optional[int]							       =							out_dim
						else:
							lowerCamelCase_							:		str							       =							time_embed_dim
						lowerCamelCase_							:		List[str]							       =							nn.Linear(__lowercase			,	__lowercase )

						if post_act_fn is None:
							lowerCamelCase_							:		str							       =							None
						else:
							lowerCamelCase_							:		List[str]							       =							get_activation(__lowercase )





					def      lowercase_							(    self			,	__lowercase			,	__lowercase=None ):







						'''simple docstring'''




						if condition is not None:
							lowerCamelCase_							:		Dict							       =							sample + self.cond_proj(__lowercase )
						lowerCamelCase_							:		List[Any]							       =							self.linear_a(__lowercase )

						if self.act is not None:
							lowerCamelCase_							:		Optional[int]							       =							self.act(__lowercase )

						lowerCamelCase_							:		Optional[Any]							       =							self.linear_a(__lowercase )

						if self.post_act is not None:
							lowerCamelCase_							:		Dict							       =							self.post_act(__lowercase )
						return sample


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		str							       =							num_channels
						lowerCamelCase_							:		Tuple							       =							flip_sin_to_cos
						lowerCamelCase_							:		List[str]							       =							downscale_freq_shift





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							get_timestep_embedding(
						    __lowercase			,	self.num_channels			,	flip_sin_to_cos=self.flip_sin_to_cos			,	downscale_freq_shift=self.downscale_freq_shift			,	)
						return t_emb


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 256			,	__lowercase = 1.0			,	__lowercase=True			,	__lowercase=True			,	__lowercase=False ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		Tuple							       =							nn.Parameter(torch.randn(__lowercase ) * scale			,	requires_grad=__lowercase )
						lowerCamelCase_							:		Any							       =							log
						lowerCamelCase_							:		List[str]							       =							flip_sin_to_cos

						if set_W_to_weight:
							# to delete later
							lowerCamelCase_							:		Optional[Any]							       =							nn.Parameter(torch.randn(__lowercase ) * scale			,	requires_grad=__lowercase )

							lowerCamelCase_							:		Union[str, Any]							       =							self.W





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						if self.log:
							lowerCamelCase_							:		List[Any]							       =							torch.log(__lowercase )

						lowerCamelCase_							:		Optional[Any]							       =							x[:, None] * self.weight[None, :] * 2 * np.pi

						if self.flip_sin_to_cos:
							lowerCamelCase_							:		int							       =							torch.cat([torch.cos(__lowercase ), torch.sin(__lowercase )]			,	dim=-1 )
						else:
							lowerCamelCase_							:		Optional[int]							       =							torch.cat([torch.sin(__lowercase ), torch.cos(__lowercase )]			,	dim=-1 )
						return out


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		str							       =							height
						lowerCamelCase_							:		str							       =							width
						lowerCamelCase_							:		List[Any]							       =							num_embed
						lowerCamelCase_							:		List[Any]							       =							embed_dim

						lowerCamelCase_							:		List[Any]							       =							nn.Embedding(self.num_embed			,	__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							nn.Embedding(self.height			,	__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							nn.Embedding(self.width			,	__lowercase )





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							self.emb(__lowercase )

						lowerCamelCase_							:		str							       =							self.height_emb(torch.arange(self.height			,	device=index.device ).view(1			,	self.height ) )

						# 1 x H x D -> 1 x H x 1 x D
						lowerCamelCase_							:		Dict							       =							height_emb.unsqueeze(2 )

						lowerCamelCase_							:		List[str]							       =							self.width_emb(torch.arange(self.width			,	device=index.device ).view(1			,	self.width ) )

						# 1 x W x D -> 1 x 1 x W x D
						lowerCamelCase_							:		Optional[Any]							       =							width_emb.unsqueeze(1 )

						lowerCamelCase_							:		Union[str, Any]							       =							height_emb + width_emb

						# 1 x H x W x D -> 1 x L xD
						lowerCamelCase_							:		Union[str, Any]							       =							pos_emb.view(1			,	self.height * self.width			,	-1 )

						lowerCamelCase_							:		Any							       =							emb + pos_emb[:, : emb.shape[1], :]

						return emb


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		List[Any]							       =							dropout_prob > 0
						lowerCamelCase_							:		Any							       =							nn.Embedding(num_classes + use_cfg_embedding			,	__lowercase )
						lowerCamelCase_							:		str							       =							num_classes
						lowerCamelCase_							:		Optional[int]							       =							dropout_prob

					def      lowercase_							(    self			,	__lowercase			,	__lowercase=None ):







						'''simple docstring'''




						if force_drop_ids is None:
							lowerCamelCase_							:		Optional[int]							       =							torch.rand(labels.shape[0]			,	device=labels.device ) < self.dropout_prob
						else:
							lowerCamelCase_							:		Optional[int]							       =							torch.tensor(force_drop_ids == 1 )
						lowerCamelCase_							:		int							       =							torch.where(__lowercase			,	self.num_classes			,	__lowercase )
						return labels





					def      lowercase_							(    self			,	__lowercase			,	__lowercase=None ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.dropout_prob > 0
						if (self.training and use_dropout) or (force_drop_ids is not None):
							lowerCamelCase_							:		Union[str, Any]							       =							self.token_drop(__lowercase			,	__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							self.embedding_table(__lowercase )
						return embeddings


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 1_024			,	__lowercase = 768			,	__lowercase = 768			,	__lowercase = 10			,	):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		Union[str, Any]							       =							num_image_text_embeds
						lowerCamelCase_							:		Tuple							       =							nn.Linear(__lowercase			,	self.num_image_text_embeds * cross_attention_dim )
						lowerCamelCase_							:		Optional[int]							       =							nn.Linear(__lowercase			,	__lowercase )





					def      lowercase_							(    self			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							text_embeds.shape[0]

						# image
						lowerCamelCase_							:		Optional[Any]							       =							self.image_embeds(__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							image_text_embeds.reshape(__lowercase			,	self.num_image_text_embeds			,	-1 )

						# text
						lowerCamelCase_							:		str							       =							self.text_proj(__lowercase )

						return torch.cat([image_text_embeds, text_embeds]			,	dim=1 )


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 768			,	__lowercase = 768			,	__lowercase = 32			,	):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		int							       =							num_image_text_embeds
						lowerCamelCase_							:		Optional[int]							       =							nn.Linear(__lowercase			,	self.num_image_text_embeds * cross_attention_dim )
						lowerCamelCase_							:		Optional[int]							       =							nn.LayerNorm(__lowercase )





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							image_embeds.shape[0]

						# image
						lowerCamelCase_							:		str							       =							self.image_embeds(__lowercase )
						lowerCamelCase_							:		Dict							       =							image_embeds.reshape(__lowercase			,	self.num_image_text_embeds			,	-1 )
						lowerCamelCase_							:		Optional[Any]							       =							self.norm(__lowercase )
						return image_embeds


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase=0.1 ):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		Any							       =							Timesteps(num_channels=256			,	flip_sin_to_cos=__lowercase			,	downscale_freq_shift=1 )
						lowerCamelCase_							:		Tuple							       =							TimestepEmbedding(in_channels=256			,	time_embed_dim=__lowercase )
						lowerCamelCase_							:		Dict							       =							LabelEmbedding(__lowercase			,	__lowercase			,	__lowercase )





					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase=None ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							self.time_proj(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							self.timestep_embedder(timesteps_proj.to(dtype=__lowercase ) )  # (N, D)

						lowerCamelCase_							:		int							       =							self.class_embedder(__lowercase )  # (N, D)

						lowerCamelCase_							:		int							       =							timesteps_emb + class_labels  # (N, D)

						return conditioning


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase = 64 ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		Optional[Any]							       =							nn.LayerNorm(__lowercase )
						lowerCamelCase_							:		str							       =							AttentionPooling(__lowercase			,	__lowercase )
						lowerCamelCase_							:		List[Any]							       =							nn.Linear(__lowercase			,	__lowercase )
						lowerCamelCase_							:		Dict							       =							nn.LayerNorm(__lowercase )





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							self.norma(__lowercase )
						lowerCamelCase_							:		str							       =							self.pool(__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							self.proj(__lowercase )
						lowerCamelCase_							:		Dict							       =							self.norma(__lowercase )
						return hidden_states


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 768			,	__lowercase = 768			,	__lowercase = 1_536 ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		List[Any]							       =							nn.Linear(__lowercase			,	__lowercase )
						lowerCamelCase_							:		Dict							       =							nn.LayerNorm(__lowercase )
						lowerCamelCase_							:		Any							       =							nn.Linear(__lowercase			,	__lowercase )





					def      lowercase_							(    self			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.text_proj(__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							self.text_norm(__lowercase )

						# image
						lowerCamelCase_							:		Optional[int]							       =							self.image_proj(__lowercase )

						return time_image_embeds + time_text_embeds


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 768			,	__lowercase = 1_536 ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		List[Any]							       =							nn.Linear(__lowercase			,	__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							nn.LayerNorm(__lowercase )





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.image_proj(__lowercase )
						lowerCamelCase_							:		List[Any]							       =							self.image_norm(__lowercase )
						return time_image_embeds


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase = 768			,	__lowercase = 1_536 ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		Any							       =							nn.Linear(__lowercase			,	__lowercase )
						lowerCamelCase_							:		str							       =							nn.LayerNorm(__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							nn.Sequential(
						    nn.Convad(3			,	16			,	3			,	padding=1 )			,	nn.SiLU()			,	nn.Convad(16			,	16			,	3			,	padding=1 )			,	nn.SiLU()			,	nn.Convad(16			,	32			,	3			,	padding=1			,	stride=2 )			,	nn.SiLU()			,	nn.Convad(32			,	32			,	3			,	padding=1 )			,	nn.SiLU()			,	nn.Convad(32			,	96			,	3			,	padding=1			,	stride=2 )			,	nn.SiLU()			,	nn.Convad(96			,	96			,	3			,	padding=1 )			,	nn.SiLU()			,	nn.Convad(96			,	256			,	3			,	padding=1			,	stride=2 )			,	nn.SiLU()			,	nn.Convad(256			,	4			,	3			,	padding=1 )			,	)





					def      lowercase_							(    self			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.image_proj(__lowercase )
						lowerCamelCase_							:		int							       =							self.image_norm(__lowercase )
						lowerCamelCase_							:		List[Any]							       =							self.input_hint_block(__lowercase )
						return time_image_embeds, hint


class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase=None ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_							:		Tuple							       =							dtype
						lowerCamelCase_							:		Optional[Any]							       =							nn.Parameter(torch.randn(1			,	__lowercase ) / embed_dim**0.5 )
						lowerCamelCase_							:		Tuple							       =							nn.Linear(__lowercase			,	__lowercase			,	dtype=self.dtype )
						lowerCamelCase_							:		List[str]							       =							nn.Linear(__lowercase			,	__lowercase			,	dtype=self.dtype )
						lowerCamelCase_							:		Union[str, Any]							       =							nn.Linear(__lowercase			,	__lowercase			,	dtype=self.dtype )
						lowerCamelCase_							:		str							       =							num_heads
						lowerCamelCase_							:		Dict							       =							embed_dim // self.num_heads





					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							x.size()

						def shape(__lowercase ):
							# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
							lowerCamelCase_							:		Union[str, Any]							       =							x.view(__lowercase			,	-1			,	self.num_heads			,	self.dim_per_head )
							# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
							lowerCamelCase_							:		Any							       =							x.transpose(1			,	2 )
							# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
							lowerCamelCase_							:		str							       =							x.reshape(bs * self.num_heads			,	-1			,	self.dim_per_head )
							# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
							lowerCamelCase_							:		Tuple							       =							x.transpose(1			,	2 )
							return x

						lowerCamelCase_							:		Optional[int]							       =							x.mean(dim=1			,	keepdim=__lowercase ) + self.positional_embedding.to(x.dtype )
						lowerCamelCase_							:		Tuple							       =							torch.cat([class_token, x]			,	dim=1 )  # (bs, length+1, width)

						# (bs*n_heads, class_token_length, dim_per_head)
						lowerCamelCase_							:		str							       =							shape(self.q_proj(__lowercase ) )
						# (bs*n_heads, length+class_token_length, dim_per_head)
						lowerCamelCase_							:		str							       =							shape(self.k_proj(__lowercase ) )
						lowerCamelCase_							:		Optional[Any]							       =							shape(self.v_proj(__lowercase ) )

						# (bs*n_heads, class_token_length, length+class_token_length):
						lowerCamelCase_							:		Dict							       =							1 / math.sqrt(math.sqrt(self.dim_per_head ) )
						lowerCamelCase_							:		Dict							       =							torch.einsum('bct,bcs->bts'			,	q * scale			,	k * scale )  # More stable with f16 than dividing afterwards
						lowerCamelCase_							:		int							       =							torch.softmax(weight.float()			,	dim=-1 ).type(weight.dtype )

						# (bs*n_heads, dim_per_head, class_token_length)
						lowerCamelCase_							:		Optional[int]							       =							torch.einsum('bts,bcs->bct'			,	__lowercase			,	__lowercase )

						# (bs, length+1, width)
						lowerCamelCase_							:		Dict							       =							a.reshape(__lowercase			,	-1			,	1 ).transpose(1			,	2 )

						return a[:, 0, :]  # cls_token






from __future__ import annotations






def  snake_case (							A						,       A      )							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							       =							0
	lowerCamelCase_							:		str							       =							len(A      ) - 1
	while left <= right:
		lowerCamelCase_							:		str							       =							(left + right) // 2
		if left == right:
			if sorted_list[middle] < item:
				lowerCamelCase_							:		List[Any]							       =							middle + 1
			break
		elif sorted_list[middle] < item:
			lowerCamelCase_							:		Any							       =							middle + 1
		else:
			lowerCamelCase_							:		Optional[int]							       =							middle - 1
	sorted_list.insert(A						,       A      )
	return sorted_list






def  snake_case (							A						,       A      )							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							       =							[]
	while left and right:
		if left[0][0] < right[0][0]:
			result.append(left.pop(0      )      )
		else:
			result.append(right.pop(0      )      )
	return result + left + right






def  snake_case (							A      )							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							       =							len(A      )
	if length <= 1:
		return list_ad
	lowerCamelCase_							:		Any							       =							length // 2
	return merge(sortlist_ad(list_ad[:middle]      )						,       sortlist_ad(list_ad[middle:]      )      )






def  snake_case (							A      )							->							list[int]:




	'''simple docstring'''


	if len(A      ) <= 1:
		return collection
	lowerCamelCase_							:		Tuple							       =							[]
	lowerCamelCase_							:		int							       =							False
	for i in range(0						,       len(A      )						,       2      ):
		if i == len(A      ) - 1:
			lowerCamelCase_							:		Any							       =							True
		else:
			if collection[i] < collection[i + 1]:
				two_paired_list.append([collection[i], collection[i + 1]]      )
			else:
				two_paired_list.append([collection[i + 1], collection[i]]      )
	lowerCamelCase_							:		Union[str, Any]							       =							sortlist_ad(A      )
	lowerCamelCase_							:		int							       =							[i[0] for i in sorted_list_ad]
	result.append(sorted_list_ad[-1][1]      )
	if has_last_odd_item:
		lowerCamelCase_							:		List[str]							       =							collection[-1]
		lowerCamelCase_							:		Optional[Any]							       =							binary_search_insertion(A						,       A      )
	lowerCamelCase_							:		Optional[Any]							       =							False
	for i in range(len(A      ) - 1      ):
		if result[i] == collection[-1] and has_last_odd_item:
			lowerCamelCase_							:		Optional[Any]							       =							True
		lowerCamelCase_							:		Dict							       =							sorted_list_ad[i][1]
		# If last_odd_item is inserted before the item's index,
		# you should forward index one more.
		if is_last_odd_item_inserted_before_this_index:
			lowerCamelCase_							:		Dict							       =							result[: i + 2] + binary_search_insertion(result[i + 2 :]						,       A      )
		else:
			lowerCamelCase_							:		str							       =							result[: i + 1] + binary_search_insertion(result[i + 1 :]						,       A      )

	return result


if __name__ == "__main__":
							UpperCamelCase__		=						input('Enter numbers separated by a comma:\n').strip()
							UpperCamelCase__		=						[int(item) for item in user_input.split(',')]
							print(merge_insertion_sort(unsorted))
