

import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD

class     __snake_case    (				nn.Module  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							42
       __SCREAMING_SNAKE_CASE				    =							42
       __SCREAMING_SNAKE_CASE				    =							0.0
       __SCREAMING_SNAKE_CASE				    =							1
       __SCREAMING_SNAKE_CASE				    =							1
       __SCREAMING_SNAKE_CASE				    =							True
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							jnp.floataa


       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							[]
              __snake_case       	=							[]

              for i in range(self.num_layers   ):
                     __snake_case       	=							self.in_channels if i == 0 else self.out_channels

                     __snake_case       	=							FlaxResnetBlockaD(
                         in_channels=_UpperCamelCase      , out_channels=self.out_channels      , dropout_prob=self.dropout      , dtype=self.dtype      , )
                     resnets.append(_UpperCamelCase   )

                     __snake_case       	=							FlaxTransformeraDModel(
                         in_channels=self.out_channels      , n_heads=self.num_attention_heads      , d_head=self.out_channels // self.num_attention_heads      , depth=1      , use_linear_projection=self.use_linear_projection      , only_cross_attention=self.only_cross_attention      , use_memory_efficient_attention=self.use_memory_efficient_attention      , dtype=self.dtype      , )
                     attentions.append(_UpperCamelCase   )

              __snake_case       	=							resnets
              __snake_case       	=							attentions

              if self.add_downsample:
                     __snake_case       	=							FlaxDownsampleaD(self.out_channels      , dtype=self.dtype   )






       def __call__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase=True   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							()

              for resnet, attn in zip(self.resnets      , self.attentions   ):
                     __snake_case       	=							resnet(_UpperCamelCase      , _UpperCamelCase      , deterministic=_UpperCamelCase   )
                     __snake_case       	=							attn(_UpperCamelCase      , _UpperCamelCase      , deterministic=_UpperCamelCase   )
                     output_states += (hidden_states,)

              if self.add_downsample:
                     __snake_case       	=							self.downsamplers_a(_UpperCamelCase   )
                     output_states += (hidden_states,)

              return hidden_states, output_states






class     __snake_case    (				nn.Module  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							42
       __SCREAMING_SNAKE_CASE				    =							42
       __SCREAMING_SNAKE_CASE				    =							0.0
       __SCREAMING_SNAKE_CASE				    =							1
       __SCREAMING_SNAKE_CASE				    =							True
       __SCREAMING_SNAKE_CASE				    =							jnp.floataa


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							[]

              for i in range(self.num_layers   ):
                     __snake_case       	=							self.in_channels if i == 0 else self.out_channels

                     __snake_case       	=							FlaxResnetBlockaD(
                         in_channels=_UpperCamelCase      , out_channels=self.out_channels      , dropout_prob=self.dropout      , dtype=self.dtype      , )
                     resnets.append(_UpperCamelCase   )
              __snake_case       	=							resnets

              if self.add_downsample:
                     __snake_case       	=							FlaxDownsampleaD(self.out_channels      , dtype=self.dtype   )






       def __call__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase=True   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							()

              for resnet in self.resnets:
                     __snake_case       	=							resnet(_UpperCamelCase      , _UpperCamelCase      , deterministic=_UpperCamelCase   )
                     output_states += (hidden_states,)

              if self.add_downsample:
                     __snake_case       	=							self.downsamplers_a(_UpperCamelCase   )
                     output_states += (hidden_states,)

              return hidden_states, output_states






class     __snake_case    (				nn.Module  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							42
       __SCREAMING_SNAKE_CASE				    =							42
       __SCREAMING_SNAKE_CASE				    =							42
       __SCREAMING_SNAKE_CASE				    =							0.0
       __SCREAMING_SNAKE_CASE				    =							1
       __SCREAMING_SNAKE_CASE				    =							1
       __SCREAMING_SNAKE_CASE				    =							True
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							jnp.floataa


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							[]
              __snake_case       	=							[]

              for i in range(self.num_layers   ):
                     __snake_case       	=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
                     __snake_case       	=							self.prev_output_channel if i == 0 else self.out_channels

                     __snake_case       	=							FlaxResnetBlockaD(
                         in_channels=resnet_in_channels + res_skip_channels      , out_channels=self.out_channels      , dropout_prob=self.dropout      , dtype=self.dtype      , )
                     resnets.append(_UpperCamelCase   )

                     __snake_case       	=							FlaxTransformeraDModel(
                         in_channels=self.out_channels      , n_heads=self.num_attention_heads      , d_head=self.out_channels // self.num_attention_heads      , depth=1      , use_linear_projection=self.use_linear_projection      , only_cross_attention=self.only_cross_attention      , use_memory_efficient_attention=self.use_memory_efficient_attention      , dtype=self.dtype      , )
                     attentions.append(_UpperCamelCase   )

              __snake_case       	=							resnets
              __snake_case       	=							attentions

              if self.add_upsample:
                     __snake_case       	=							FlaxUpsampleaD(self.out_channels      , dtype=self.dtype   )






       def __call__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase=True   )      ->							List[str]:






              """simple docstring"""


              for resnet, attn in zip(self.resnets      , self.attentions   ):
                     # pop res hidden states
                     __snake_case       	=							res_hidden_states_tuple[-1]
                     __snake_case       	=							res_hidden_states_tuple[:-1]
                     __snake_case       	=							jnp.concatenate((hidden_states, res_hidden_states)      , axis=-1   )

                     __snake_case       	=							resnet(_UpperCamelCase      , _UpperCamelCase      , deterministic=_UpperCamelCase   )
                     __snake_case       	=							attn(_UpperCamelCase      , _UpperCamelCase      , deterministic=_UpperCamelCase   )

              if self.add_upsample:
                     __snake_case       	=							self.upsamplers_a(_UpperCamelCase   )

              return hidden_states






class     __snake_case    (				nn.Module  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							42
       __SCREAMING_SNAKE_CASE				    =							42
       __SCREAMING_SNAKE_CASE				    =							42
       __SCREAMING_SNAKE_CASE				    =							0.0
       __SCREAMING_SNAKE_CASE				    =							1
       __SCREAMING_SNAKE_CASE				    =							True
       __SCREAMING_SNAKE_CASE				    =							jnp.floataa


       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							[]

              for i in range(self.num_layers   ):
                     __snake_case       	=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
                     __snake_case       	=							self.prev_output_channel if i == 0 else self.out_channels

                     __snake_case       	=							FlaxResnetBlockaD(
                         in_channels=resnet_in_channels + res_skip_channels      , out_channels=self.out_channels      , dropout_prob=self.dropout      , dtype=self.dtype      , )
                     resnets.append(_UpperCamelCase   )

              __snake_case       	=							resnets

              if self.add_upsample:
                     __snake_case       	=							FlaxUpsampleaD(self.out_channels      , dtype=self.dtype   )






       def __call__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase=True   )      ->							int:






              """simple docstring"""


              for resnet in self.resnets:
                     # pop res hidden states
                     __snake_case       	=							res_hidden_states_tuple[-1]
                     __snake_case       	=							res_hidden_states_tuple[:-1]
                     __snake_case       	=							jnp.concatenate((hidden_states, res_hidden_states)      , axis=-1   )

                     __snake_case       	=							resnet(_UpperCamelCase      , _UpperCamelCase      , deterministic=_UpperCamelCase   )

              if self.add_upsample:
                     __snake_case       	=							self.upsamplers_a(_UpperCamelCase   )

              return hidden_states






class     __snake_case    (				nn.Module  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							42
       __SCREAMING_SNAKE_CASE				    =							0.0
       __SCREAMING_SNAKE_CASE				    =							1
       __SCREAMING_SNAKE_CASE				    =							1
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							False
       __SCREAMING_SNAKE_CASE				    =							jnp.floataa


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							[
                  FlaxResnetBlockaD(
                      in_channels=self.in_channels      , out_channels=self.in_channels      , dropout_prob=self.dropout      , dtype=self.dtype      , )
              ]

              __snake_case       	=							[]

              for _ in range(self.num_layers   ):
                     __snake_case       	=							FlaxTransformeraDModel(
                         in_channels=self.in_channels      , n_heads=self.num_attention_heads      , d_head=self.in_channels // self.num_attention_heads      , depth=1      , use_linear_projection=self.use_linear_projection      , use_memory_efficient_attention=self.use_memory_efficient_attention      , dtype=self.dtype      , )
                     attentions.append(_UpperCamelCase   )

                     __snake_case       	=							FlaxResnetBlockaD(
                         in_channels=self.in_channels      , out_channels=self.in_channels      , dropout_prob=self.dropout      , dtype=self.dtype      , )
                     resnets.append(_UpperCamelCase   )

              __snake_case       	=							resnets
              __snake_case       	=							attentions






       def __call__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase=True   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.resnets[0](_UpperCamelCase      , _UpperCamelCase   )
              for attn, resnet in zip(self.attentions      , self.resnets[1:]   ):
                     __snake_case       	=							attn(_UpperCamelCase      , _UpperCamelCase      , deterministic=_UpperCamelCase   )
                     __snake_case       	=							resnet(_UpperCamelCase      , _UpperCamelCase      , deterministic=_UpperCamelCase   )

              return hidden_states




from PIL import Image







def 						lowerCamelCase__      ( __A    :Image		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							image.size
       __snake_case       	=							0
       __snake_case       	=							image.load()
       for i in range(__A		):
              for j in range(__A		):
                     __snake_case       	=							pixels[j, i]
                     mean += pixel
       mean //= width * height

       for j in range(__A		):
              for i in range(__A		):
                     __snake_case       	=							2_5_5 if pixels[i, j] > mean else 0
       return image


if __name__ == "__main__":
    UpperCamelCase__		 =     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
    image.save('''output_image_path''')

