





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

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




class        SCREAMING_SNAKE_CASE__					(		nn.Module ):
 __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 							UpperCamelCase     (				self					):
        A__		   =		[]
        A__		   =		[]

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

               A__		   =		FlaxResnetBlockaD(
                   in_channels=__lowerCamelCase,out_channels=self.out_channels,dropout_prob=self.dropout,dtype=self.dtype,)
               resnets.append(__lowerCamelCase					)

               A__		   =		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(__lowerCamelCase					)

        A__		   =		resnets
        A__		   =		attentions

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




 def __call__(				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=True					):
        A__		   =		()

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

        if self.add_downsample:
               A__		   =		self.downsamplers_a(__lowerCamelCase					)
               output_states += (hidden_states,)

        return hidden_states, output_states




class        SCREAMING_SNAKE_CASE__					(		nn.Module ):
 __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 							UpperCamelCase     (				self					):
        A__		   =		[]

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

               A__		   =		FlaxResnetBlockaD(
                   in_channels=__lowerCamelCase,out_channels=self.out_channels,dropout_prob=self.dropout,dtype=self.dtype,)
               resnets.append(__lowerCamelCase					)
        A__		   =		resnets

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




 def __call__(				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=True					):
        A__		   =		()

        for resnet in self.resnets:
               A__		   =		resnet(__lowerCamelCase,__lowerCamelCase,deterministic=__lowerCamelCase					)
               output_states += (hidden_states,)

        if self.add_downsample:
               A__		   =		self.downsamplers_a(__lowerCamelCase					)
               output_states += (hidden_states,)

        return hidden_states, output_states




class        SCREAMING_SNAKE_CASE__					(		nn.Module ):
 __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 							UpperCamelCase     (				self					):
        A__		   =		[]
        A__		   =		[]

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

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

               A__		   =		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(__lowerCamelCase					)

        A__		   =		resnets
        A__		   =		attentions

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




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

               A__		   =		resnet(__lowerCamelCase,__lowerCamelCase,deterministic=__lowerCamelCase					)
               A__		   =		attn(__lowerCamelCase,__lowerCamelCase,deterministic=__lowerCamelCase					)

        if self.add_upsample:
               A__		   =		self.upsamplers_a(__lowerCamelCase					)

        return hidden_states




class        SCREAMING_SNAKE_CASE__					(		nn.Module ):
 __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 							UpperCamelCase     (				self					):
        A__		   =		[]

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

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

        A__		   =		resnets

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




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

               A__		   =		resnet(__lowerCamelCase,__lowerCamelCase,deterministic=__lowerCamelCase					)

        if self.add_upsample:
               A__		   =		self.upsamplers_a(__lowerCamelCase					)

        return hidden_states




class        SCREAMING_SNAKE_CASE__					(		nn.Module ):
 __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 							UpperCamelCase     (				self					):
        # there is always at least one resnet
        A__		   =		[
            FlaxResnetBlockaD(
                in_channels=self.in_channels,out_channels=self.in_channels,dropout_prob=self.dropout,dtype=self.dtype,)
        ]

        A__		   =		[]

        for _ in range(self.num_layers					):
               A__		   =		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(__lowerCamelCase					)

               A__		   =		FlaxResnetBlockaD(
                   in_channels=self.in_channels,out_channels=self.in_channels,dropout_prob=self.dropout,dtype=self.dtype,)
               resnets.append(__lowerCamelCase					)

        A__		   =		resnets
        A__		   =		attentions




 def __call__(				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=True					):
        A__		   =		self.resnets[0](__lowerCamelCase,__lowerCamelCase					)
        for attn, resnet in zip(self.attentions,self.resnets[1:]					):
               A__		   =		attn(__lowerCamelCase,__lowerCamelCase,deterministic=__lowerCamelCase					)
               A__		   =		resnet(__lowerCamelCase,__lowerCamelCase,deterministic=__lowerCamelCase					)

        return hidden_states












def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       A__		   =		set()

       # Replace all the whitespace in our sentence
       A__		   =		input_str.replace(''' '''					,	''''''    )
       for alpha in input_str:
              if "a" <= alpha.lower() <= "z":
                     frequency.add(alpha.lower()    )
       return len(UpperCamelCase__    ) == 26


def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       A__		   =		[False] * 26
       for char in input_str:
              if char.islower():
                     A__		   =		True
              elif char.isupper():
                     A__		   =		True
       return all(UpperCamelCase__    )


def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       return len({char for char in input_str.lower() if char.isalpha()}    ) == 26


def       UpperCamelCase__(			)->None:
       from timeit import timeit

       A__		   =		'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
       print(timeit('''is_pangram()'''					,	setup=UpperCamelCase__    )    )
       print(timeit('''is_pangram_faster()'''					,	setup=UpperCamelCase__    )    )
       print(timeit('''is_pangram_fastest()'''					,	setup=UpperCamelCase__    )    )
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()





