


from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class 	UpperCAmelCase_					(					_lowerCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		42



class 	UpperCAmelCase_					(					nn.Module    ):

   '''simple docstring'''




   def __init__(		self						,				__A=3						,				__A=3						,				__A=("DownEncoderBlock2D",)						,				__A=(64,)						,				__A=2						,				__A=32						,				__A="silu"						,				__A=True						,				):






     """simple docstring"""





     super().__init__()
     lowerCamelCase :					str    =				layers_per_block

     lowerCamelCase :					List[Any]    =				torch.nn.Convad(
         lowercase_						,				block_out_channels[0]						,				kernel_size=3						,				stride=1						,				padding=1						,				)

     lowerCamelCase :					int    =				None
     lowerCamelCase :					List[str]    =				nn.ModuleList([] )

     # down
     lowerCamelCase :					str    =				block_out_channels[0]
     for i, down_block_type in enumerate(lowercase_ ):
       lowerCamelCase :					Tuple    =				output_channel
       lowerCamelCase :					Union[str, Any]    =				block_out_channels[i]
       lowerCamelCase :					Optional[Any]    =				i == len(lowercase_ ) - 1

       lowerCamelCase :					Dict    =				get_down_block(
           lowercase_						,				num_layers=self.layers_per_block						,				in_channels=lowercase_						,				out_channels=lowercase_						,				add_downsample=not is_final_block						,				resnet_eps=1e-6						,				downsample_padding=0						,				resnet_act_fn=lowercase_						,				resnet_groups=lowercase_						,				attention_head_dim=lowercase_						,				temb_channels=lowercase_						,				)
       self.down_blocks.append(lowercase_ )

     # mid
     lowerCamelCase :					Optional[int]    =				UNetMidBlockaD(
         in_channels=block_out_channels[-1]						,				resnet_eps=1e-6						,				resnet_act_fn=lowercase_						,				output_scale_factor=1						,				resnet_time_scale_shift="default"						,				attention_head_dim=block_out_channels[-1]						,				resnet_groups=lowercase_						,				temb_channels=lowercase_						,				)

     # out
     lowerCamelCase :					Dict    =				nn.GroupNorm(num_channels=block_out_channels[-1]						,				num_groups=lowercase_						,				eps=1e-6 )
     lowerCamelCase :					List[Any]    =				nn.SiLU()

     lowerCamelCase :					List[Any]    =				2 * out_channels if double_z else out_channels
     lowerCamelCase :					Optional[Any]    =				nn.Convad(block_out_channels[-1]						,				lowercase_						,				3						,				padding=1 )

     lowerCamelCase :					List[str]    =				False






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				x
     lowerCamelCase :					Union[str, Any]    =				self.conv_in(lowercase_ )

     if self.training and self.gradient_checkpointing:

       def create_custom_forward(__A ):
         def custom_forward(*__A ):
           return module(*lowercase_ )

         return custom_forward

       # down
       if is_torch_version(">="						,				"1.11.0" ):
         for down_block in self.down_blocks:
           lowerCamelCase :					int    =				torch.utils.checkpoint.checkpoint(
               create_custom_forward(lowercase_ )						,				lowercase_						,				use_reentrant=lowercase_ )
         # middle
         lowerCamelCase :					str    =				torch.utils.checkpoint.checkpoint(
             create_custom_forward(self.mid_block )						,				lowercase_						,				use_reentrant=lowercase_ )
       else:
         for down_block in self.down_blocks:
           lowerCamelCase :					Dict    =				torch.utils.checkpoint.checkpoint(create_custom_forward(lowercase_ )						,				lowercase_ )
         # middle
         lowerCamelCase :					Optional[int]    =				torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block )						,				lowercase_ )

     else:
       # down
       for down_block in self.down_blocks:
         lowerCamelCase :					Optional[int]    =				down_block(lowercase_ )

       # middle
       lowerCamelCase :					Any    =				self.mid_block(lowercase_ )

     # post-process
     lowerCamelCase :					str    =				self.conv_norm_out(lowercase_ )
     lowerCamelCase :					Optional[Any]    =				self.conv_act(lowercase_ )
     lowerCamelCase :					List[Any]    =				self.conv_out(lowercase_ )

     return sample



class 	UpperCAmelCase_					(					nn.Module    ):

   '''simple docstring'''




   def __init__(		self						,				__A=3						,				__A=3						,				__A=("UpDecoderBlock2D",)						,				__A=(64,)						,				__A=2						,				__A=32						,				__A="silu"						,				__A="group"						,				):






     """simple docstring"""





     super().__init__()
     lowerCamelCase :					Union[str, Any]    =				layers_per_block

     lowerCamelCase :					str    =				nn.Convad(
         lowercase_						,				block_out_channels[-1]						,				kernel_size=3						,				stride=1						,				padding=1						,				)

     lowerCamelCase :					Union[str, Any]    =				None
     lowerCamelCase :					Any    =				nn.ModuleList([] )

     lowerCamelCase :					Optional[Any]    =				in_channels if norm_type == "spatial" else None

     # mid
     lowerCamelCase :					List[Any]    =				UNetMidBlockaD(
         in_channels=block_out_channels[-1]						,				resnet_eps=1e-6						,				resnet_act_fn=lowercase_						,				output_scale_factor=1						,				resnet_time_scale_shift="default" if norm_type == "group" else norm_type						,				attention_head_dim=block_out_channels[-1]						,				resnet_groups=lowercase_						,				temb_channels=lowercase_						,				)

     # up
     lowerCamelCase :					Union[str, Any]    =				list(reversed(lowercase_ ) )
     lowerCamelCase :					Optional[Any]    =				reversed_block_out_channels[0]
     for i, up_block_type in enumerate(lowercase_ ):
       lowerCamelCase :					Optional[Any]    =				output_channel
       lowerCamelCase :					List[str]    =				reversed_block_out_channels[i]

       lowerCamelCase :					Optional[int]    =				i == len(lowercase_ ) - 1

       lowerCamelCase :					Dict    =				get_up_block(
           lowercase_						,				num_layers=self.layers_per_block + 1						,				in_channels=lowercase_						,				out_channels=lowercase_						,				prev_output_channel=lowercase_						,				add_upsample=not is_final_block						,				resnet_eps=1e-6						,				resnet_act_fn=lowercase_						,				resnet_groups=lowercase_						,				attention_head_dim=lowercase_						,				temb_channels=lowercase_						,				resnet_time_scale_shift=lowercase_						,				)
       self.up_blocks.append(lowercase_ )
       lowerCamelCase :					List[Any]    =				output_channel

     # out
     if norm_type == "spatial":
       lowerCamelCase :					Optional[int]    =				SpatialNorm(block_out_channels[0]						,				lowercase_ )
     else:
       lowerCamelCase :					Dict    =				nn.GroupNorm(num_channels=block_out_channels[0]						,				num_groups=lowercase_						,				eps=1e-6 )
     lowerCamelCase :					Any    =				nn.SiLU()
     lowerCamelCase :					Union[str, Any]    =				nn.Convad(block_out_channels[0]						,				lowercase_						,				3						,				padding=1 )

     lowerCamelCase :					Optional[Any]    =				False






   def _snake_case    (		self						,				__A						,				__A=None ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				z
     lowerCamelCase :					Optional[Any]    =				self.conv_in(lowercase_ )

     lowerCamelCase :					Optional[Any]    =				next(iter(self.up_blocks.parameters() ) ).dtype
     if self.training and self.gradient_checkpointing:

       def create_custom_forward(__A ):
         def custom_forward(*__A ):
           return module(*lowercase_ )

         return custom_forward

       if is_torch_version(">="						,				"1.11.0" ):
         # middle
         lowerCamelCase :					int    =				torch.utils.checkpoint.checkpoint(
             create_custom_forward(self.mid_block )						,				lowercase_						,				lowercase_						,				use_reentrant=lowercase_ )
         lowerCamelCase :					Optional[Any]    =				sample.to(lowercase_ )

         # up
         for up_block in self.up_blocks:
           lowerCamelCase :					str    =				torch.utils.checkpoint.checkpoint(
               create_custom_forward(lowercase_ )						,				lowercase_						,				lowercase_						,				use_reentrant=lowercase_ )
       else:
         # middle
         lowerCamelCase :					Optional[Any]    =				torch.utils.checkpoint.checkpoint(
             create_custom_forward(self.mid_block )						,				lowercase_						,				lowercase_ )
         lowerCamelCase :					Optional[int]    =				sample.to(lowercase_ )

         # up
         for up_block in self.up_blocks:
           lowerCamelCase :					int    =				torch.utils.checkpoint.checkpoint(create_custom_forward(lowercase_ )						,				lowercase_						,				lowercase_ )
     else:
       # middle
       lowerCamelCase :					str    =				self.mid_block(lowercase_						,				lowercase_ )
       lowerCamelCase :					str    =				sample.to(lowercase_ )

       # up
       for up_block in self.up_blocks:
         lowerCamelCase :					Any    =				up_block(lowercase_						,				lowercase_ )

        # post-process
     if latent_embeds is None:
       lowerCamelCase :					Optional[Any]    =				self.conv_norm_out(lowercase_ )
     else:
       lowerCamelCase :					Optional[Any]    =				self.conv_norm_out(lowercase_						,				lowercase_ )
     lowerCamelCase :					str    =				self.conv_act(lowercase_ )
     lowerCamelCase :					List[Any]    =				self.conv_out(lowercase_ )

     return sample



class 	UpperCAmelCase_					(					nn.Module    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A						,				__A=None						,				__A="random"						,				__A=False						,				__A=True ):






     """simple docstring"""





     super().__init__()
     lowerCamelCase :					Any    =				n_e
     lowerCamelCase :					int    =				vq_embed_dim
     lowerCamelCase :					Tuple    =				beta
     lowerCamelCase :					List[str]    =				legacy

     lowerCamelCase :					List[Any]    =				nn.Embedding(self.n_e						,				self.vq_embed_dim )
     self.embedding.weight.data.uniform_(-1.0 / self.n_e						,				1.0 / self.n_e )

     lowerCamelCase :					Optional[Any]    =				remap
     if self.remap is not None:
       self.register_buffer("used"						,				torch.tensor(np.load(self.remap ) ) )
       lowerCamelCase :					Dict    =				self.used.shape[0]
       lowerCamelCase :					List[str]    =				unknown_index  # "random" or "extra" or integer
       if self.unknown_index == "extra":
         lowerCamelCase :					List[str]    =				self.re_embed
         lowerCamelCase :					Tuple    =				self.re_embed + 1
       print(
           F"""Remapping {self.n_e} indices to {self.re_embed} indices. """
           F"""Using {self.unknown_index} for unknown indices.""" )
     else:
       lowerCamelCase :					Any    =				n_e

     lowerCamelCase :					Optional[int]    =				sane_index_shape






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				inds.shape
     assert len(lowercase_ ) > 1
     lowerCamelCase :					Dict    =				inds.reshape(ishape[0]						,				-1 )
     lowerCamelCase :					Tuple    =				self.used.to(lowercase_ )
     lowerCamelCase :					Union[str, Any]    =				(inds[:, :, None] == used[None, None, ...]).long()
     lowerCamelCase :					Tuple    =				match.argmax(-1 )
     lowerCamelCase :					Any    =				match.sum(2 ) < 1
     if self.unknown_index == "random":
       lowerCamelCase :					Dict    =				torch.randint(0						,				self.re_embed						,				size=new[unknown].shape ).to(device=new.device )
     else:
       lowerCamelCase :					Tuple    =				self.unknown_index
     return new.reshape(lowercase_ )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					int    =				inds.shape
     assert len(lowercase_ ) > 1
     lowerCamelCase :					int    =				inds.reshape(ishape[0]						,				-1 )
     lowerCamelCase :					int    =				self.used.to(lowercase_ )
     if self.re_embed > self.used.shape[0]:  # extra token
       lowerCamelCase :					Any    =				0  # simply set to zero
     lowerCamelCase :					Dict    =				torch.gather(used[None, :][inds.shape[0] * [0], :]						,				1						,				lowercase_ )
     return back.reshape(lowercase_ )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				z.permute(0						,				2						,				3						,				1 ).contiguous()
     lowerCamelCase :					Tuple    =				z.view(-1						,				self.vq_embed_dim )

     # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
     lowerCamelCase :					int    =				torch.argmin(torch.cdist(lowercase_						,				self.embedding.weight )						,				dim=1 )

     lowerCamelCase :					Any    =				self.embedding(lowercase_ ).view(z.shape )
     lowerCamelCase :					List[Any]    =				None
     lowerCamelCase :					Any    =				None

     # compute loss for embedding
     if not self.legacy:
       lowerCamelCase :					Tuple    =				self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
     else:
       lowerCamelCase :					Tuple    =				torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )

     # preserve gradients
     lowerCamelCase :					List[str]    =				z + (z_q - z).detach()

     # reshape back to match original input shape
     lowerCamelCase :					Optional[int]    =				z_q.permute(0						,				3						,				1						,				2 ).contiguous()

     if self.remap is not None:
       lowerCamelCase :					Any    =				min_encoding_indices.reshape(z.shape[0]						,				-1 )  # add batch axis
       lowerCamelCase :					Union[str, Any]    =				self.remap_to_used(lowercase_ )
       lowerCamelCase :					Any    =				min_encoding_indices.reshape(-1						,				1 )  # flatten

     if self.sane_index_shape:
       lowerCamelCase :					List[Any]    =				min_encoding_indices.reshape(z_q.shape[0]						,				z_q.shape[2]						,				z_q.shape[3] )

     return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     if self.remap is not None:
       lowerCamelCase :					List[Any]    =				indices.reshape(shape[0]						,				-1 )  # add batch axis
       lowerCamelCase :					Any    =				self.unmap_to_all(lowercase_ )
       lowerCamelCase :					Union[str, Any]    =				indices.reshape(-1 )  # flatten again

     # get quantized latent vectors
     lowerCamelCase :					Optional[Any]    =				self.embedding(lowercase_ )

     if shape is not None:
       lowerCamelCase :					Optional[int]    =				z_q.view(lowercase_ )
       # reshape back to match original input shape
       lowerCamelCase :					str    =				z_q.permute(0						,				3						,				1						,				2 ).contiguous()

     return z_q



class 	UpperCAmelCase_					(					_lowerCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A=False ):






     """simple docstring"""





     lowerCamelCase :					str    =				parameters
     lowerCamelCase    ,	lowerCamelCase :					int    =				torch.chunk(lowercase_						,				2						,				dim=1 )
     lowerCamelCase :					str    =				torch.clamp(self.logvar						,				-30.0						,				20.0 )
     lowerCamelCase :					Optional[int]    =				deterministic
     lowerCamelCase :					Optional[Any]    =				torch.exp(0.5 * self.logvar )
     lowerCamelCase :					Optional[Any]    =				torch.exp(self.logvar )
     if self.deterministic:
       lowerCamelCase :					int    =				torch.zeros_like(
           self.mean						,				device=self.parameters.device						,				dtype=self.parameters.dtype )






   def _snake_case    (		self						,				__A = None ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				randn_tensor(
         self.mean.shape						,				generator=lowercase_						,				device=self.parameters.device						,				dtype=self.parameters.dtype )
     lowerCamelCase :					Optional[Any]    =				self.mean + self.std * sample
     return x






   def _snake_case    (		self						,				__A=None ):






     """simple docstring"""





     if self.deterministic:
       return torch.Tensor([0.0] )
     else:
       if other is None:
         return 0.5 * torch.sum(torch.pow(self.mean						,				2 ) + self.var - 1.0 - self.logvar						,				dim=[1, 2, 3] )
       else:
         return 0.5 * torch.sum(
             torch.pow(self.mean - other.mean						,				2 ) / other.var
             + self.var / other.var
             - 1.0
             - self.logvar
             + other.logvar						,				dim=[1, 2, 3]						,				)






   def _snake_case    (		self						,				__A						,				__A=[1, 2, 3] ):






     """simple docstring"""





     if self.deterministic:
       return torch.Tensor([0.0] )
     lowerCamelCase :					List[Any]    =				np.log(2.0 * np.pi )
     return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean						,				2 ) / self.var						,				dim=lowercase_ )






   def _snake_case    (		self ):






     """simple docstring"""





     return self.mean









'''simple docstring'''

import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
a  :				Dict									=  (720, 1280)  # Height, Width
a  :				Tuple									=  (0.4, 0.6)  # if height or width lower than this scale, drop it.
a  :				Dict									=  1 / 100
a  :				str									=  ''
a  :				Any									=  ''
a  :				Optional[int]									=  ''
a  :				List[str]									=  250


def 		__magic_name__    (	)		->					None:

					'''simple docstring'''

					snake_case_			,snake_case_									=				get_dataset(__UpperCAmelCase,      __UpperCAmelCase	)
					for index in range(__UpperCAmelCase	):
										snake_case_									=				random.sample(range(len(__UpperCAmelCase	)	),      4	)
										snake_case_			,snake_case_			,snake_case_									=				update_image_and_anno(
										    __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      filter_scale=__UpperCAmelCase,      )

										# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
										snake_case_									=				random_chars(32	)
										snake_case_									=				path.split(os.sep	)[-1].rsplit('''.''',      1	)[0]
										snake_case_									=				F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
										cva.imwrite(F"{file_root}.jpg",      __UpperCAmelCase,      [cva.IMWRITE_JPEG_QUALITY, 85]	)
										print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"	)
										snake_case_									=				[]
										for anno in new_annos:
															snake_case_									=				anno[3] - anno[1]
															snake_case_									=				anno[4] - anno[2]
															snake_case_									=				anno[1] + width / 2
															snake_case_									=				anno[2] + height / 2
															snake_case_									=				F"{anno[0]} {x_center} {y_center} {width} {height}"
															annos_list.append(__UpperCAmelCase	)
										with open(F"{file_root}.txt",      '''w'''	) as outfile:
															outfile.write('''\n'''.join(line for line in annos_list	)	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					tuple[list, list]:

					'''simple docstring'''

					snake_case_									=				[]
					snake_case_									=				[]
					for label_file in glob.glob(os.path.join(__UpperCAmelCase,      '''*.txt'''	)	):
										snake_case_									=				label_file.split(os.sep	)[-1].rsplit('''.''',      1	)[0]
										with open(__UpperCAmelCase	) as in_file:
															snake_case_									=				in_file.readlines()
										snake_case_									=				os.path.join(__UpperCAmelCase,      F"{label_name}.jpg"	)

										snake_case_									=				[]
										for obj_list in obj_lists:
															snake_case_									=				obj_list.rstrip('''\n'''	).split(''' '''	)
															snake_case_									=				float(obj[1]	) - float(obj[3]	) / 2
															snake_case_									=				float(obj[2]	) - float(obj[4]	) / 2
															snake_case_									=				float(obj[1]	) + float(obj[3]	) / 2
															snake_case_									=				float(obj[2]	) + float(obj[4]	) / 2

															boxes.append([int(obj[0]	), xmin, ymin, xmax, ymax]	)
										if not boxes:
															continue
										img_paths.append(__UpperCAmelCase	)
										labels.append(__UpperCAmelCase	)
					return img_paths, labels


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase = 0.0,      )		->					tuple[list, list, str]:

					'''simple docstring'''

					snake_case_									=				np.zeros([output_size[0], output_size[1], 3],      dtype=np.uinta	)
					snake_case_									=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					snake_case_									=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					snake_case_									=				int(scale_x * output_size[1]	)
					snake_case_									=				int(scale_y * output_size[0]	)

					snake_case_									=				[]
					snake_case_									=				[]
					for i, index in enumerate(__UpperCAmelCase	):
										snake_case_									=				all_img_list[index]
										path_list.append(__UpperCAmelCase	)
										snake_case_									=				all_annos[index]
										snake_case_									=				cva.imread(__UpperCAmelCase	)
										if i == 0:  # top-left
															snake_case_									=				cva.resize(__UpperCAmelCase,      (divid_point_x, divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				bbox[1] * scale_x
																				snake_case_									=				bbox[2] * scale_y
																				snake_case_									=				bbox[3] * scale_x
																				snake_case_									=				bbox[4] * scale_y
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
										elif i == 1:  # top-right
															snake_case_									=				cva.resize(__UpperCAmelCase,      (output_size[1] - divid_point_x, divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				scale_x + bbox[1] * (1 - scale_x)
																				snake_case_									=				bbox[2] * scale_y
																				snake_case_									=				scale_x + bbox[3] * (1 - scale_x)
																				snake_case_									=				bbox[4] * scale_y
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
										elif i == 2:  # bottom-left
															snake_case_									=				cva.resize(__UpperCAmelCase,      (divid_point_x, output_size[0] - divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				bbox[1] * scale_x
																				snake_case_									=				scale_y + bbox[2] * (1 - scale_y)
																				snake_case_									=				bbox[3] * scale_x
																				snake_case_									=				scale_y + bbox[4] * (1 - scale_y)
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
										else:  # bottom-right
															snake_case_									=				cva.resize(
															    __UpperCAmelCase,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				scale_x + bbox[1] * (1 - scale_x)
																				snake_case_									=				scale_y + bbox[2] * (1 - scale_y)
																				snake_case_									=				scale_x + bbox[3] * (1 - scale_x)
																				snake_case_									=				scale_y + bbox[4] * (1 - scale_y)
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)

    # Remove bounding box small than scale of filter
					if filter_scale > 0:
										snake_case_									=				[
										    anno
										    for anno in new_anno
										    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
										]

					return output_img, new_anno, path_list[0]


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					assert number_char > 1, "The number of character should greater than 1"
					snake_case_									=				ascii_lowercase + digits
					return "".join(random.choice(__UpperCAmelCase	) for _ in range(__UpperCAmelCase	)	)


if __name__ == "__main__":
							main()
							print('DONE ✅')

