


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

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




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]
             _lowerCamelCase       :		Dict		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		Union[str, Any]		=self.in_channels if i == 0 else self.out_channels

                    _lowerCamelCase       :		Any		=FlaxResnetBlockaD(
                        in_channels=lowercase_			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

                    _lowerCamelCase       :		Dict		=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(lowercase_ )

             _lowerCamelCase       :		Optional[int]		=resnets
             _lowerCamelCase       :		Dict		=attentions

             if self.add_downsample:
                    _lowerCamelCase       :		Union[str, Any]		=FlaxDownsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     Any			,   lowercase_						:     Optional[Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     List[str]=True )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=()

             for resnet, attn in zip(self.resnets			,   self.attentions ):
                    _lowerCamelCase       :		Union[str, Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    output_states += (hidden_states,)

             if self.add_downsample:
                    _lowerCamelCase       :		Optional[int]		=self.downsamplers_a(lowercase_ )
                    output_states += (hidden_states,)

             return hidden_states, output_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     Optional[int] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]

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

                    _lowerCamelCase       :		Any		=FlaxResnetBlockaD(
                        in_channels=lowercase_			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )
             _lowerCamelCase       :		Union[str, Any]		=resnets

             if self.add_downsample:
                    _lowerCamelCase       :		List[str]		=FlaxDownsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     Any			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Optional[Any]=True )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=()

             for resnet in self.resnets:
                    _lowerCamelCase       :		Tuple		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    output_states += (hidden_states,)

             if self.add_downsample:
                    _lowerCamelCase       :		Tuple		=self.downsamplers_a(lowercase_ )
                    output_states += (hidden_states,)

             return hidden_states, output_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     Dict )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		str		=[]
             _lowerCamelCase       :		List[str]		=[]

             for i in range(self.num_layers ):
                    _lowerCamelCase       :		List[str]		=self.in_channels if (i == self.num_layers - 1) else self.out_channels
                    _lowerCamelCase       :		Tuple		=self.prev_output_channel if i == 0 else self.out_channels

                    _lowerCamelCase       :		List[str]		=FlaxResnetBlockaD(
                        in_channels=resnet_in_channels + res_skip_channels			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

                    _lowerCamelCase       :		Tuple		=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(lowercase_ )

             _lowerCamelCase       :		int		=resnets
             _lowerCamelCase       :		Dict		=attentions

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






      def __call__(					self						:     Optional[Any]			,   lowercase_						:     Any			,   lowercase_						:     Any			,   lowercase_						:     List[Any]			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]=True )					->						Optional[int]:
             """simple docstring"""




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

                    _lowerCamelCase       :		Optional[Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		List[Any]		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             if self.add_upsample:
                    _lowerCamelCase       :		Optional[Any]		=self.upsamplers_a(lowercase_ )

             return hidden_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=[]

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

                    _lowerCamelCase       :		str		=FlaxResnetBlockaD(
                        in_channels=resnet_in_channels + res_skip_channels			,   out_channels=self.out_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

             _lowerCamelCase       :		str		=resnets

             if self.add_upsample:
                    _lowerCamelCase       :		List[str]		=FlaxUpsampleaD(self.out_channels			,   dtype=self.dtype )






      def __call__(					self						:     List[str]			,   lowercase_						:     str			,   lowercase_						:     Dict			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Any=True )					->						int:
             """simple docstring"""




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

                    _lowerCamelCase       :		Optional[Any]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             if self.add_upsample:
                    _lowerCamelCase       :		Union[str, Any]		=self.upsamplers_a(lowercase_ )

             return hidden_states




class A  (   nn.Module     ):
      UpperCamelCase__							:  int
      UpperCamelCase__							:  float      =0.0
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  int      =1
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  jnp.dtype      =jnp.floataa






      def        lowerCamelCase       (					self						:     int )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=[
                 FlaxResnetBlockaD(
                     in_channels=self.in_channels			,   out_channels=self.in_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
             ]

             _lowerCamelCase       :		Any		=[]

             for _ in range(self.num_layers ):
                    _lowerCamelCase       :		Optional[int]		=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(lowercase_ )

                    _lowerCamelCase       :		Tuple		=FlaxResnetBlockaD(
                        in_channels=self.in_channels			,   out_channels=self.in_channels			,   dropout_prob=self.dropout			,   dtype=self.dtype			,   )
                    resnets.append(lowercase_ )

             _lowerCamelCase       :		List[Any]		=resnets
             _lowerCamelCase       :		List[str]		=attentions






      def __call__(					self						:     Optional[int]			,   lowercase_						:     List[Any]			,   lowercase_						:     Dict			,   lowercase_						:     Tuple			,   lowercase_						:     List[str]=True )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.resnets[0](lowercase_			,   lowercase_ )
             for attn, resnet in zip(self.attentions			,   self.resnets[1:] ):
                    _lowerCamelCase       :		Tuple		=attn(lowercase_			,   lowercase_			,   deterministic=lowercase_ )
                    _lowerCamelCase       :		List[str]		=resnet(lowercase_			,   lowercase_			,   deterministic=lowercase_ )

             return hidden_states






from collections.abc import Generator
from math import sin




def 	a_      (    SCREAMING_SNAKE_CASE__					:      bytes     ):
       '''simple docstring'''





       if len(SCREAMING_SNAKE_CASE__     ) != 32:
              raise ValueError('Input must be of length 32'     )

       _lowerCamelCase       :		Any		=b''
       for i in [3, 2, 1, 0]:
              little_endian += string_aa[8 * i : 8 * i + 8]
       return little_endian




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





       if i < 0:
              raise ValueError('Input must be non-negative'     )

       _lowerCamelCase       :		Optional[int]		=format(SCREAMING_SNAKE_CASE__		,			'08x'     )[-8:]
       _lowerCamelCase       :		List[Any]		=b''
       for i in [3, 2, 1, 0]:
              little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8'     )
       return little_endian_hex




def 	a_      (    SCREAMING_SNAKE_CASE__					:      bytes     ):
       '''simple docstring'''





       _lowerCamelCase       :		Dict		=b''
       for char in message:
              bit_string += format(SCREAMING_SNAKE_CASE__		,			'08b'     ).encode('utf-8'     )
       _lowerCamelCase       :		Dict		=format(len(SCREAMING_SNAKE_CASE__     )		,			'064b'     ).encode('utf-8'     )

       # Pad bit_string to a multiple of 512 chars
       bit_string += b"1"
       while len(SCREAMING_SNAKE_CASE__     ) % 512 != 448:
              bit_string += b"0"
       bit_string += to_little_endian(start_len[32:]     ) + to_little_endian(start_len[:32]     )

       return bit_string




def 	a_      (    SCREAMING_SNAKE_CASE__					:      bytes     ):
       '''simple docstring'''





       if len(SCREAMING_SNAKE_CASE__     ) % 512 != 0:
              raise ValueError('Input must have length that\'s a multiple of 512'     )

       for pos in range(0		,			len(SCREAMING_SNAKE_CASE__     )		,			512     ):
              _lowerCamelCase       :		str		=bit_string[pos : pos + 512]
              _lowerCamelCase       :		Tuple		=[]
              for i in range(0		,			512		,			32     ):
                     block_words.append(int(to_little_endian(block[i : i + 32]     )		,			2     )     )
              yield block_words




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





       if i < 0:
              raise ValueError('Input must be non-negative'     )

       _lowerCamelCase       :		List[str]		=format(SCREAMING_SNAKE_CASE__		,			'032b'     )
       _lowerCamelCase       :		List[str]		=''
       for c in i_str:
              new_str += "1" if c == "0" else "0"
       return int(SCREAMING_SNAKE_CASE__		,			2     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





       return (a + b) % 2**32




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





       if i < 0:
              raise ValueError('Input must be non-negative'     )
       if shift < 0:
              raise ValueError('Shift must be non-negative'     )
       return ((i << shift) ^ (i >> (32 - shift))) % 2**32




def 	a_      (    SCREAMING_SNAKE_CASE__					:      bytes     ):
       '''simple docstring'''





       _lowerCamelCase       :		int		=preprocess(SCREAMING_SNAKE_CASE__     )

       _lowerCamelCase       :		Dict		=[int(2**32 * abs(sin(i + 1     )     )     ) for i in range(64     )]

       # Starting states
       _lowerCamelCase       :		str		=0X67_45_23_01
       _lowerCamelCase       :		Union[str, Any]		=0Xef_cd_ab_89
       _lowerCamelCase       :		List[str]		=0X98_ba_dc_fe
       _lowerCamelCase       :		int		=0X10_32_54_76

       _lowerCamelCase       :		Optional[int]		=[
           7,
           12,
           17,
           22,
           7,
           12,
           17,
           22,
           7,
           12,
           17,
           22,
           7,
           12,
           17,
           22,
           5,
           9,
           14,
           20,
           5,
           9,
           14,
           20,
           5,
           9,
           14,
           20,
           5,
           9,
           14,
           20,
           4,
           11,
           16,
           23,
           4,
           11,
           16,
           23,
           4,
           11,
           16,
           23,
           4,
           11,
           16,
           23,
           6,
           10,
           15,
           21,
           6,
           10,
           15,
           21,
           6,
           10,
           15,
           21,
           6,
           10,
           15,
           21,
       ]

       # Process bit string in chunks, each with 16 32-char words
       for block_words in get_block_words(SCREAMING_SNAKE_CASE__     ):
              _lowerCamelCase       :		Union[str, Any]		=aa
              _lowerCamelCase       :		List[Any]		=ba
              _lowerCamelCase       :		Dict		=ca
              _lowerCamelCase       :		List[Any]		=da

              # Hash current chunk
              for i in range(64     ):
                     if i <= 15:
                            # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                            _lowerCamelCase       :		Optional[Any]		=d ^ (b & (c ^ d))
                            _lowerCamelCase       :		Dict		=i
                     elif i <= 31:
                            # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                            _lowerCamelCase       :		Optional[int]		=c ^ (d & (b ^ c))
                            _lowerCamelCase       :		List[str]		=(5 * i + 1) % 16
                     elif i <= 47:
                            _lowerCamelCase       :		str		=b ^ c ^ d
                            _lowerCamelCase       :		int		=(3 * i + 5) % 16
                     else:
                            _lowerCamelCase       :		Optional[Any]		=c ^ (b | not_aa(SCREAMING_SNAKE_CASE__     ))
                            _lowerCamelCase       :		Dict		=(7 * i) % 16
                     _lowerCamelCase       :		Any		=(f + a + added_consts[i] + block_words[g]) % 2**32
                     _lowerCamelCase       :		str		=d
                     _lowerCamelCase       :		Any		=c
                     _lowerCamelCase       :		Union[str, Any]		=b
                     _lowerCamelCase       :		Dict		=sum_aa(SCREAMING_SNAKE_CASE__		,			left_rotate_aa(SCREAMING_SNAKE_CASE__		,			shift_amounts[i]     )     )

              # Add hashed chunk to running total
              _lowerCamelCase       :		Optional[Any]		=sum_aa(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		List[str]		=sum_aa(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Optional[int]		=sum_aa(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		List[str]		=sum_aa(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       _lowerCamelCase       :		List[Any]		=reformat_hex(SCREAMING_SNAKE_CASE__     ) + reformat_hex(SCREAMING_SNAKE_CASE__     ) + reformat_hex(SCREAMING_SNAKE_CASE__     ) + reformat_hex(SCREAMING_SNAKE_CASE__     )
       return digest


if __name__ == "__main__":
 import doctest

 doctest.testmod()


