"""simple docstring"""
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 					lowerCAmelCase_      (     nn.Module   ):



				"""simple docstring"""

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






										"""simple docstring"""

										super().__init__()
										snake_case        =     only_cross_attention

										snake_case        =     (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
										snake_case        =     (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:
																snake_case        =     AdaLayerNorm(lowerCAmelCase						,      lowerCAmelCase      )
										elif self.use_ada_layer_norm_zero:
																snake_case        =     AdaLayerNormZero(lowerCAmelCase						,      lowerCAmelCase      )
										else:
																snake_case        =     nn.LayerNorm(lowerCAmelCase						,      elementwise_affine=lowerCAmelCase      )
										snake_case        =     Attention(
										    query_dim=lowerCAmelCase						,      heads=lowerCAmelCase						,      dim_head=lowerCAmelCase						,      dropout=lowerCAmelCase						,      bias=lowerCAmelCase						,      cross_attention_dim=cross_attention_dim if only_cross_attention else None						,      upcast_attention=lowerCAmelCase						,      )

										# 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.
																snake_case        =     (
																    AdaLayerNorm(lowerCAmelCase						,      lowerCAmelCase      )
																    if self.use_ada_layer_norm
																    else nn.LayerNorm(lowerCAmelCase						,      elementwise_affine=lowerCAmelCase      )
																)
																snake_case        =     Attention(
																    query_dim=lowerCAmelCase						,      cross_attention_dim=cross_attention_dim if not double_self_attention else None						,      heads=lowerCAmelCase						,      dim_head=lowerCAmelCase						,      dropout=lowerCAmelCase						,      bias=lowerCAmelCase						,      upcast_attention=lowerCAmelCase						,      )  # is self-attn if encoder_hidden_states is none
										else:
																snake_case        =     None
																snake_case        =     None

										# 3. Feed-forward
										snake_case        =     nn.LayerNorm(lowerCAmelCase						,      elementwise_affine=lowerCAmelCase      )
										snake_case        =     FeedForward(lowerCAmelCase						,      dropout=lowerCAmelCase						,      activation_fn=lowerCAmelCase						,      final_dropout=lowerCAmelCase      )

										# let chunk size default to None
										snake_case        =     None
										snake_case        =     0

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     chunk_size
										snake_case        =     dim

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										if self.use_ada_layer_norm:
																snake_case        =     self.norma(lowerCAmelCase						,      lowerCAmelCase      )
										elif self.use_ada_layer_norm_zero:
																snake_case   ,snake_case   ,snake_case   ,snake_case   ,snake_case        =     self.norma(
																    lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      hidden_dtype=hidden_states.dtype      )
										else:
																snake_case        =     self.norma(lowerCAmelCase      )

										snake_case        =     cross_attention_kwargs if cross_attention_kwargs is not None else {}

										snake_case        =     self.attna(
										    lowerCAmelCase						,      encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None						,      attention_mask=lowerCAmelCase						,      **lowerCAmelCase						,      )
										if self.use_ada_layer_norm_zero:
																snake_case        =     gate_msa.unsqueeze(1      ) * attn_output
										snake_case        =     attn_output + hidden_states

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

																snake_case        =     self.attna(
																    lowerCAmelCase						,      encoder_hidden_states=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      **lowerCAmelCase						,      )
																snake_case        =     attn_output + hidden_states

										# 3. Feed-forward
										snake_case        =     self.norma(lowerCAmelCase      )

										if self.use_ada_layer_norm_zero:
																snake_case        =     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`."""      )

																snake_case        =     norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
																snake_case        =     torch.cat(
																    [self.ff(lowerCAmelCase      ) for hid_slice in norm_hidden_states.chunk(lowerCAmelCase						,      dim=self._chunk_dim      )]						,      dim=self._chunk_dim						,      )
										else:
																snake_case        =     self.ff(lowerCAmelCase      )

										if self.use_ada_layer_norm_zero:
																snake_case        =     gate_mlp.unsqueeze(1      ) * ff_output

										snake_case        =     ff_output + hidden_states

										return hidden_states





class 					lowerCAmelCase_      (     nn.Module   ):



				"""simple docstring"""

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






										"""simple docstring"""

										super().__init__()
										snake_case        =     int(dim * mult      )
										snake_case        =     dim_out if dim_out is not None else dim

										if activation_fn == "gelu":
																snake_case        =     GELU(lowerCAmelCase						,      lowerCAmelCase      )
										if activation_fn == "gelu-approximate":
																snake_case        =     GELU(lowerCAmelCase						,      lowerCAmelCase						,      approximate='tanh'      )
										elif activation_fn == "geglu":
																snake_case        =     GEGLU(lowerCAmelCase						,      lowerCAmelCase      )
										elif activation_fn == "geglu-approximate":
																snake_case        =     ApproximateGELU(lowerCAmelCase						,      lowerCAmelCase      )

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

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										for module in self.net:
																snake_case        =     module(lowerCAmelCase      )
										return hidden_states





class 					lowerCAmelCase_      (     nn.Module   ):



				"""simple docstring"""

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






										"""simple docstring"""

										super().__init__()
										snake_case        =     nn.Linear(lowerCAmelCase						,      lowerCAmelCase      )
										snake_case        =     approximate

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										if gate.device.type != "mps":
																return F.gelu(lowerCAmelCase						,      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 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.proj(lowerCAmelCase      )
										snake_case        =     self.gelu(lowerCAmelCase      )
										return hidden_states





class 					lowerCAmelCase_      (     nn.Module   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										super().__init__()
										snake_case        =     nn.Linear(lowerCAmelCase						,      dim_out * 2      )

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.proj(lowerCAmelCase      ).chunk(2						,      dim=-1      )
										return hidden_states * self.gelu(lowerCAmelCase      )





class 					lowerCAmelCase_      (     nn.Module   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										super().__init__()
										snake_case        =     nn.Linear(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.proj(lowerCAmelCase      )
										return x * torch.sigmoid(1.7_02 * x      )





class 					lowerCAmelCase_      (     nn.Module   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										super().__init__()
										snake_case        =     nn.Embedding(lowerCAmelCase						,      lowerCAmelCase      )
										snake_case        =     nn.SiLU()
										snake_case        =     nn.Linear(lowerCAmelCase						,      embedding_dim * 2      )
										snake_case        =     nn.LayerNorm(lowerCAmelCase						,      elementwise_affine=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.linear(self.silu(self.emb(lowerCAmelCase      )      )      )
										snake_case   ,snake_case        =     torch.chunk(lowerCAmelCase						,      2      )
										snake_case        =     self.norm(lowerCAmelCase      ) * (1 + scale) + shift
										return x





class 					lowerCAmelCase_      (     nn.Module   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										super().__init__()

										snake_case        =     CombinedTimestepLabelEmbeddings(lowerCAmelCase						,      lowerCAmelCase      )

										snake_case        =     nn.SiLU()
										snake_case        =     nn.Linear(lowerCAmelCase						,      6 * embedding_dim						,      bias=lowerCAmelCase      )
										snake_case        =     nn.LayerNorm(lowerCAmelCase						,      elementwise_affine=lowerCAmelCase						,      eps=1E-6      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase=None      ):






										"""simple docstring"""

										snake_case        =     self.linear(self.silu(self.emb(lowerCAmelCase						,      lowerCAmelCase						,      hidden_dtype=lowerCAmelCase      )      )      )
										snake_case   ,snake_case   ,snake_case   ,snake_case   ,snake_case   ,snake_case        =     emb.chunk(6						,      dim=1      )
										snake_case        =     self.norm(lowerCAmelCase      ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
										return x, gate_msa, shift_mlp, scale_mlp, gate_mlp





class 					lowerCAmelCase_      (     nn.Module   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = 1E-5      ):






										"""simple docstring"""

										super().__init__()
										snake_case        =     num_groups
										snake_case        =     eps

										if act_fn is None:
																snake_case        =     None
										else:
																snake_case        =     get_activation(lowerCAmelCase      )

										snake_case        =     nn.Linear(lowerCAmelCase						,      out_dim * 2      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										if self.act:
																snake_case        =     self.act(lowerCAmelCase      )
										snake_case        =     self.linear(lowerCAmelCase      )
										snake_case        =     emb[:, :, None, None]
										snake_case   ,snake_case        =     emb.chunk(2						,      dim=1      )

										snake_case        =     F.group_norm(lowerCAmelCase						,      self.num_groups						,      eps=self.eps      )
										snake_case        =     x * (1 + scale) + shift
										return x



"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int = 8   )   ->			str:





						"""simple docstring"""






						snake_case        =     ascii_letters + digits + punctuation
						return "".join(secrets.choice(_UpperCamelCase   ) for _ in range(_UpperCamelCase   )   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			int   )   ->			str:





						"""simple docstring"""






						i -= len(_UpperCamelCase   )
						snake_case        =     i // 3
						snake_case        =     i % 3
						# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
						#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
						snake_case        =     (
						    chars_incl
						    + random(_UpperCamelCase	,     quotient + remainder   )
						    + random(_UpperCamelCase	,     _UpperCamelCase   )
						    + random(_UpperCamelCase	,     _UpperCamelCase   )
						)
						snake_case        =     list(_UpperCamelCase   )
						shuffle(_UpperCamelCase   )
						return "".join(_UpperCamelCase   )

						# random is a generalised function for letters, characters and numbers





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			int   )   ->			str:





						"""simple docstring"""






						return "".join(secrets.choice(_UpperCamelCase   ) for _ in range(_UpperCamelCase   )   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Optional[Any]   )   ->			List[Any]:





						"""simple docstring"""






						pass  # Put your code here...





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			Optional[Any]   )   ->			Any:





						"""simple docstring"""






						pass  # Put your code here...





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Optional[Any]   )   ->			Union[str, Any]:





						"""simple docstring"""






						pass  # Put your code here...





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			int = 8   )   ->			bool:





						"""simple docstring"""






						if len(_UpperCamelCase   ) < min_length:
												# Your Password must be at least 8 characters long
												return False

						snake_case        =     any(char in ascii_uppercase for char in password   )
						snake_case        =     any(char in ascii_lowercase for char in password   )
						snake_case        =     any(char in digits for char in password   )
						snake_case        =     any(char in punctuation for char in password   )

						return upper and lower and num and spec_char
						# Passwords should contain UPPERCASE, lowerase
						# numbers, and special characters





def 				lowerCAmelCase__      (     )   ->			Any:





						"""simple docstring"""






						snake_case        =     int(input('Please indicate the max length of your password: '   ).strip()   )
						snake_case        =     input(
						    'Please indicate the characters that must be in your password: '   ).strip()
						print('Password generated:'	,     password_generator(_UpperCamelCase   )   )
						print(
						    'Alternative Password generated:'	,     alternative_password_generator(_UpperCamelCase	,     _UpperCamelCase   )	,     )
						print('[If you are thinking of using this passsword, You better save it.]'   )


if __name__ == "__main__":
					main()


