
import copy
import re





class 	_lowerCAmelCase					:
				_lowercase					='''hp'''
				_lowercase					={}
				_lowercase					=None




				@classmethod
				def      __a						(					cls				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[Any]:
							lowerCAmelCase_						=	prefix
							lowerCAmelCase_						=	defaults
							cls.build_naming_info()




				@staticmethod
				def      __a						(					_UpperCamelCase				,				_UpperCamelCase     )				->      List[str]:






							if len(_UpperCamelCase     ) == 0:
										return ""
							lowerCAmelCase_						=	None
							if any(char.isdigit() for char in word     ):
										raise Exception(f"""Parameters should not contain numbers: \'{word}\' contains a number"""     )
							if word in info["short_word"]:
										return info["short_word"][word]
							for prefix_len in range(1				,				len(_UpperCamelCase     ) + 1     ):
										lowerCAmelCase_						=	word[:prefix_len]
										if prefix in info["reverse_short_word"]:
													continue
										else:
													lowerCAmelCase_						=	prefix
													break

							if short_word is None:
										# Paranoid fallback
										def int_to_alphabetic(_UpperCamelCase     ):
													lowerCAmelCase_						=	''''''
													while integer != 0:
																lowerCAmelCase_						=	chr(ord("A"     ) + integer % 10     ) + s
																integer //= 10
													return s

										lowerCAmelCase_						=	0
										while True:
													lowerCAmelCase_						=	word + '''#''' + int_to_alphabetic(_UpperCamelCase     )
													if sword in info["reverse_short_word"]:
																continue
													else:
																lowerCAmelCase_						=	sword
																break

							lowerCAmelCase_						=	short_word
							lowerCAmelCase_						=	word
							return short_word




				@staticmethod
				def      __a						(					_UpperCamelCase				,				_UpperCamelCase     )				->      int:
							lowerCAmelCase_						=	param_name.split("_"     )

							lowerCAmelCase_						=	[TrialShortNamer.shortname_for_word(_UpperCamelCase				,				_UpperCamelCase     ) for word in words]

							# We try to create a separatorless short name, but if there is a collision we have to fallback
							# to a separated short name
							lowerCAmelCase_						=	['''''', '''_''']

							for separator in separators:
										lowerCAmelCase_						=	separator.join(_UpperCamelCase     )
										if shortname not in info["reverse_short_param"]:
													lowerCAmelCase_						=	shortname
													lowerCAmelCase_						=	param_name
													return shortname

							return param_name




				@staticmethod
				def      __a						(					_UpperCamelCase				,				_UpperCamelCase     )				->      Any:
							lowerCAmelCase_						=	TrialShortNamer.shortname_for_key(_UpperCamelCase				,				_UpperCamelCase     )
							lowerCAmelCase_						=	short_name
							lowerCAmelCase_						=	param_name




				@classmethod
				def      __a						(					cls     )				->      Union[str, Any]:






							if cls.NAMING_INFO is not None:
										return

							lowerCAmelCase_						=	{
							    '''short_word''': {},
							    '''reverse_short_word''': {},
							    '''short_param''': {},
							    '''reverse_short_param''': {},
							}

							lowerCAmelCase_						=	list(cls.DEFAULTS.keys()     )

							for k in field_keys:
										cls.add_new_param_name(_UpperCamelCase				,				_UpperCamelCase     )

							lowerCAmelCase_						=	info




				@classmethod
				def      __a						(					cls				,				_UpperCamelCase     )				->      Tuple:
							cls.build_naming_info()
							assert cls.PREFIX is not None
							lowerCAmelCase_						=	[copy.copy(cls.PREFIX     )]

							for k, v in params.items():
										if k not in cls.DEFAULTS:
													raise Exception(f"""You should provide a default value for the param name {k} with value {v}"""     )
										if v == cls.DEFAULTS[k]:
													# The default value is not added to the name
													continue

										lowerCAmelCase_						=	cls.NAMING_INFO['''short_param'''][k]

										if isinstance(_UpperCamelCase				,				_UpperCamelCase     ):
													lowerCAmelCase_						=	1 if v else 0

										lowerCAmelCase_						=	'''''' if isinstance(_UpperCamelCase				,				(int, float)     ) else '''-'''
										lowerCAmelCase_						=	f"""{key}{sep}{v}"""
										name.append(_UpperCamelCase     )

							return "_".join(_UpperCamelCase     )




				@classmethod
				def      __a						(					cls				,				_UpperCamelCase     )				->      Union[str, Any]:
							lowerCAmelCase_						=	repr[len(cls.PREFIX     ) + 1 :]
							if repr == "":
										lowerCAmelCase_						=	[]
							else:
										lowerCAmelCase_						=	repr.split("_"     )

							lowerCAmelCase_						=	{}

							for value in values:
										if "-" in value:
													lowerCAmelCase_						=	value.split("-"     )
										else:
													lowerCAmelCase_						=	re.sub("[0-9.]"				,				""				,				_UpperCamelCase     )
													lowerCAmelCase_						=	float(re.sub("[^0-9.]"				,				""				,				_UpperCamelCase     )     )

										lowerCAmelCase_						=	cls.NAMING_INFO['''reverse_short_param'''][p_k]

										lowerCAmelCase_						=	p_v

							for k in cls.DEFAULTS:
										if k not in parameters:
													lowerCAmelCase_						=	cls.DEFAULTS[k]

							return parameters





'''simple docstring'''




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						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Union[str, Any]					=						[]
         A       :   Union[str, Any]					=						[]

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

             A       :   Optional[Any]					=						FlaxResnetBlockaD(
                 in_channels=SCREAMING_SNAKE_CASE						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)

             A       :   Optional[int]					=						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(SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						resnets
         A       :   Union[str, Any]					=						attentions

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





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						()

         for resnet, attn in zip(self.resnets						,			self.attentions	):
             A       :   int					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Dict					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         if self.add_downsample:
             A       :   Optional[Any]					=						self.downsamplers_a(SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

         return hidden_states, output_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						[]

         for i in range(self.num_layers	):
             A       :   Optional[Any]					=						self.in_channels if i == 0 else self.out_channels

             A       :   List[str]					=						FlaxResnetBlockaD(
                 in_channels=SCREAMING_SNAKE_CASE						,			out_channels=self.out_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
             resnets.append(SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						resnets

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





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Optional[Any]:






         """simple docstring"""






         A       :   str					=						()

         for resnet in self.resnets:
             A       :   Optional[int]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             output_states += (hidden_states,)

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

         return hidden_states, output_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Optional[Any]					=						[]
         A       :   Optional[int]					=						[]

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

             A       :   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(SCREAMING_SNAKE_CASE	)

             A       :   int					=						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(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						resnets
         A       :   Optional[Any]					=						attentions

         if self.add_upsample:
             A       :   Optional[int]					=						FlaxUpsampleaD(self.out_channels						,			dtype=self.dtype	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				Optional[int]:






         """simple docstring"""






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

             A       :   Union[str, Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Tuple					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         if self.add_upsample:
             A       :   Dict					=						self.upsamplers_a(SCREAMING_SNAKE_CASE	)

         return hidden_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		True
     __magic_name__						=		jnp.floataa





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   int					=						[]

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

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

         A       :   List[Any]					=						resnets

         if self.add_upsample:
             A       :   Optional[Any]					=						FlaxUpsampleaD(self.out_channels						,			dtype=self.dtype	)





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






         """simple docstring"""






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

             A       :   Optional[Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         if self.add_upsample:
             A       :   List[str]					=						self.upsamplers_a(SCREAMING_SNAKE_CASE	)

         return hidden_states





class  A				(							nn.Module						):
     __magic_name__						=		42
     __magic_name__						=		0.0
     __magic_name__						=		1
     __magic_name__						=		1
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		jnp.floataa





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






         """simple docstring"""






         A       :   str					=						[
             FlaxResnetBlockaD(
                 in_channels=self.in_channels						,			out_channels=self.in_channels						,			dropout_prob=self.dropout						,			dtype=self.dtype						,			)
         ]

         A       :   List[Any]					=						[]

         for _ in range(self.num_layers	):
             A       :   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(SCREAMING_SNAKE_CASE	)

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

         A       :   List[str]					=						resnets
         A       :   List[str]					=						attentions





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






         """simple docstring"""






         A       :   Optional[Any]					=						self.resnets[0](SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         for attn, resnet in zip(self.attentions						,			self.resnets[1:]	):
             A       :   Optional[int]					=						attn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)
             A       :   Union[str, Any]					=						resnet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			deterministic=SCREAMING_SNAKE_CASE	)

         return hidden_states




