"""simple docstring"""
from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)



@flax.struct.dataclass
class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      jnp.ndarray





@flax_register_to_config
class 					lowerCAmelCase_      (     nn.Module       ,      lowerCAmelCase       ,      lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      int     					=  32
				_lowerCAmelCase       :      int     					=  4
				_lowerCAmelCase       :      int     					=  4
				_lowerCAmelCase       :      Tuple[str]     					=  (
				    "CrossAttnDownBlock2D",
				    "CrossAttnDownBlock2D",
				    "CrossAttnDownBlock2D",
				    "DownBlock2D",
				)
				_lowerCAmelCase       :      Tuple[str]     					=  ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
				_lowerCAmelCase       :      Union[bool, Tuple[bool]]     					=  False
				_lowerCAmelCase       :      Tuple[int]     					=  (320, 640, 1280, 1280)
				_lowerCAmelCase       :      int     					=  2
				_lowerCAmelCase       :      Union[int, Tuple[int]]     					=  8
				_lowerCAmelCase       :      Optional[Union[int, Tuple[int]]]     					=  None
				_lowerCAmelCase       :      int     					=  1280
				_lowerCAmelCase       :      float     					=  0.0
				_lowerCAmelCase       :      bool     					=  False
				_lowerCAmelCase       :      jnp.dtype     					=  jnp.floataa
				_lowerCAmelCase       :      bool     					=  True
				_lowerCAmelCase       :      int     					=  0
				_lowerCAmelCase       :      bool     					=  False

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     (1, self.in_channels, self.sample_size, self.sample_size)
										snake_case        =     jnp.zeros(lowerCAmelCase						,      dtype=jnp.floataa      )
										snake_case        =     jnp.ones((1,)						,      dtype=jnp.intaa      )
										snake_case        =     jnp.zeros((1, 1, self.cross_attention_dim)						,      dtype=jnp.floataa      )

										snake_case   ,snake_case        =     jax.random.split(lowerCAmelCase      )
										snake_case        =     {'params': params_rng, 'dropout': dropout_rng}

										return self.init(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )["params"]

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.block_out_channels
										snake_case        =     block_out_channels[0] * 4

										if self.num_attention_heads is not None:
																raise ValueError(
																    'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.'      )

										# If `num_attention_heads` is not defined (which is the case for most models)
										# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
										# The reason for this behavior is to correct for incorrectly named variables that were introduced
										# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
										# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
										# which is why we correct for the naming here.
										snake_case        =     self.num_attention_heads or self.attention_head_dim

										# input
										snake_case        =     nn.Conv(
										    block_out_channels[0]						,      kernel_size=(3, 3)						,      strides=(1, 1)						,      padding=((1, 1), (1, 1))						,      dtype=self.dtype						,      )

										# time
										snake_case        =     FlaxTimesteps(
										    block_out_channels[0]						,      flip_sin_to_cos=self.flip_sin_to_cos						,      freq_shift=self.config.freq_shift      )
										snake_case        =     FlaxTimestepEmbedding(lowerCAmelCase						,      dtype=self.dtype      )

										snake_case        =     self.only_cross_attention
										if isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
																snake_case        =     (only_cross_attention,) * len(self.down_block_types      )

										if isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
																snake_case        =     (num_attention_heads,) * len(self.down_block_types      )

										# down
										snake_case        =     []
										snake_case        =     block_out_channels[0]
										for i, down_block_type in enumerate(self.down_block_types      ):
																snake_case        =     output_channel
																snake_case        =     block_out_channels[i]
																snake_case        =     i == len(lowerCAmelCase      ) - 1

																if down_block_type == "CrossAttnDownBlock2D":
																						snake_case        =     FlaxCrossAttnDownBlockaD(
																						    in_channels=lowerCAmelCase						,      out_channels=lowerCAmelCase						,      dropout=self.dropout						,      num_layers=self.layers_per_block						,      num_attention_heads=num_attention_heads[i]						,      add_downsample=not is_final_block						,      use_linear_projection=self.use_linear_projection						,      only_cross_attention=only_cross_attention[i]						,      use_memory_efficient_attention=self.use_memory_efficient_attention						,      dtype=self.dtype						,      )
																else:
																						snake_case        =     FlaxDownBlockaD(
																						    in_channels=lowerCAmelCase						,      out_channels=lowerCAmelCase						,      dropout=self.dropout						,      num_layers=self.layers_per_block						,      add_downsample=not is_final_block						,      dtype=self.dtype						,      )

																down_blocks.append(lowerCAmelCase      )
										snake_case        =     down_blocks

										# mid
										snake_case        =     FlaxUNetMidBlockaDCrossAttn(
										    in_channels=block_out_channels[-1]						,      dropout=self.dropout						,      num_attention_heads=num_attention_heads[-1]						,      use_linear_projection=self.use_linear_projection						,      use_memory_efficient_attention=self.use_memory_efficient_attention						,      dtype=self.dtype						,      )

										# up
										snake_case        =     []
										snake_case        =     list(reversed(lowerCAmelCase      )      )
										snake_case        =     list(reversed(lowerCAmelCase      )      )
										snake_case        =     list(reversed(lowerCAmelCase      )      )
										snake_case        =     reversed_block_out_channels[0]
										for i, up_block_type in enumerate(self.up_block_types      ):
																snake_case        =     output_channel
																snake_case        =     reversed_block_out_channels[i]
																snake_case        =     reversed_block_out_channels[min(i + 1						,      len(lowerCAmelCase      ) - 1      )]

																snake_case        =     i == len(lowerCAmelCase      ) - 1

																if up_block_type == "CrossAttnUpBlock2D":
																						snake_case        =     FlaxCrossAttnUpBlockaD(
																						    in_channels=lowerCAmelCase						,      out_channels=lowerCAmelCase						,      prev_output_channel=lowerCAmelCase						,      num_layers=self.layers_per_block + 1						,      num_attention_heads=reversed_num_attention_heads[i]						,      add_upsample=not is_final_block						,      dropout=self.dropout						,      use_linear_projection=self.use_linear_projection						,      only_cross_attention=only_cross_attention[i]						,      use_memory_efficient_attention=self.use_memory_efficient_attention						,      dtype=self.dtype						,      )
																else:
																						snake_case        =     FlaxUpBlockaD(
																						    in_channels=lowerCAmelCase						,      out_channels=lowerCAmelCase						,      prev_output_channel=lowerCAmelCase						,      num_layers=self.layers_per_block + 1						,      add_upsample=not is_final_block						,      dropout=self.dropout						,      dtype=self.dtype						,      )

																up_blocks.append(lowerCAmelCase      )
																snake_case        =     output_channel
										snake_case        =     up_blocks

										# out
										snake_case        =     nn.GroupNorm(num_groups=32						,      epsilon=1E-5      )
										snake_case        =     nn.Conv(
										    self.out_channels						,      kernel_size=(3, 3)						,      strides=(1, 1)						,      padding=((1, 1), (1, 1))						,      dtype=self.dtype						,      )

				def __call__(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase = True						,      lowerCAmelCase = False						,      ):






										"""simple docstring"""

										if not isinstance(lowerCAmelCase						,      jnp.ndarray      ):
																snake_case        =     jnp.array([timesteps]						,      dtype=jnp.intaa      )
										elif isinstance(lowerCAmelCase						,      jnp.ndarray      ) and len(timesteps.shape      ) == 0:
																snake_case        =     timesteps.astype(dtype=jnp.floataa      )
																snake_case        =     jnp.expand_dims(lowerCAmelCase						,      0      )

										snake_case        =     self.time_proj(lowerCAmelCase      )
										snake_case        =     self.time_embedding(lowerCAmelCase      )

										# 2. pre-process
										snake_case        =     jnp.transpose(lowerCAmelCase						,      (0, 2, 3, 1)      )
										snake_case        =     self.conv_in(lowerCAmelCase      )

										# 3. down
										snake_case        =     (sample,)
										for down_block in self.down_blocks:
																if isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
																						snake_case   ,snake_case        =     down_block(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      deterministic=not train      )
																else:
																						snake_case   ,snake_case        =     down_block(lowerCAmelCase						,      lowerCAmelCase						,      deterministic=not train      )
																down_block_res_samples += res_samples

										if down_block_additional_residuals is not None:
																snake_case        =     ()

																for down_block_res_sample, down_block_additional_residual in zip(
																    lowerCAmelCase						,      lowerCAmelCase      ):
																						down_block_res_sample += down_block_additional_residual
																						new_down_block_res_samples += (down_block_res_sample,)

																snake_case        =     new_down_block_res_samples

										# 4. mid
										snake_case        =     self.mid_block(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      deterministic=not train      )

										if mid_block_additional_residual is not None:
																sample += mid_block_additional_residual

										# 5. up
										for up_block in self.up_blocks:
																snake_case        =     down_block_res_samples[-(self.layers_per_block + 1) :]
																snake_case        =     down_block_res_samples[: -(self.layers_per_block + 1)]
																if isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
																						snake_case        =     up_block(
																						    lowerCAmelCase						,      temb=lowerCAmelCase						,      encoder_hidden_states=lowerCAmelCase						,      res_hidden_states_tuple=lowerCAmelCase						,      deterministic=not train						,      )
																else:
																						snake_case        =     up_block(lowerCAmelCase						,      temb=lowerCAmelCase						,      res_hidden_states_tuple=lowerCAmelCase						,      deterministic=not train      )

        # 6. post-process
										snake_case        =     self.conv_norm_out(lowerCAmelCase      )
										snake_case        =     nn.silu(lowerCAmelCase      )
										snake_case        =     self.conv_out(lowerCAmelCase      )
										snake_case        =     jnp.transpose(lowerCAmelCase						,      (0, 3, 1, 2)      )

										if not return_dict:
																return (sample,)

										return FlaxUNetaDConditionOutput(sample=lowerCAmelCase      )



"""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()


