


import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowerCamelCase											=       logging.get_logger(__name__)




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  List[str]      ='linear'
      UpperCamelCase__							:  Optional[Any]      ='cosine'
      UpperCamelCase__							:  Union[str, Any]      ='cosine_with_restarts'
      UpperCamelCase__							:  List[Any]      ='polynomial'
      UpperCamelCase__							:  Optional[Any]      ='constant'
      UpperCamelCase__							:  Dict      ='constant_with_warmup'
      UpperCamelCase__							:  int      ='piecewise_constant'




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      int = -1     ):
       '''simple docstring'''





       return LambdaLR(SCREAMING_SNAKE_CASE__		,			lambda SCREAMING_SNAKE_CASE__     : 1		,			last_epoch=SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      int = -1     ):
       '''simple docstring'''





       def lr_lambda(SCREAMING_SNAKE_CASE__					:      int     ):
              if current_step < num_warmup_steps:
                     return float(SCREAMING_SNAKE_CASE__     ) / float(max(1.0		,			SCREAMING_SNAKE_CASE__     )     )
              return 1.0

       return LambdaLR(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			last_epoch=SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      int = -1     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[str]		={}
       _lowerCamelCase       :		int		=step_rules.split(','     )
       for rule_str in rule_list[:-1]:
              _lowerCamelCase	,     _lowerCamelCase       :		str		=rule_str.split(':'     )
              _lowerCamelCase       :		Union[str, Any]		=int(SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Dict		=float(SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		int		=value
       _lowerCamelCase       :		Optional[int]		=float(rule_list[-1]     )

       def create_rules_function(SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      str     ):
              def rule_func(SCREAMING_SNAKE_CASE__					:      int     ) -> float:
                     _lowerCamelCase       :		List[str]		=sorted(rules_dict.keys()     )
                     for i, sorted_step in enumerate(SCREAMING_SNAKE_CASE__     ):
                            if steps < sorted_step:
                                   return rules_dict[sorted_steps[i]]
                     return last_lr_multiple

              return rule_func

       _lowerCamelCase       :		List[str]		=create_rules_function(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       return LambdaLR(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			last_epoch=SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]=-1     ):
       '''simple docstring'''





       def lr_lambda(SCREAMING_SNAKE_CASE__					:      int     ):
              if current_step < num_warmup_steps:
                     return float(SCREAMING_SNAKE_CASE__     ) / float(max(1		,			SCREAMING_SNAKE_CASE__     )     )
              return max(
                  0.0		,			float(num_training_steps - current_step     ) / float(max(1		,			num_training_steps - num_warmup_steps     )     )     )

       return LambdaLR(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      float = 0.5		,			SCREAMING_SNAKE_CASE__					:      int = -1     ):
       '''simple docstring'''





       def lr_lambda(SCREAMING_SNAKE_CASE__					:      Any     ):
              if current_step < num_warmup_steps:
                     return float(SCREAMING_SNAKE_CASE__     ) / float(max(1		,			SCREAMING_SNAKE_CASE__     )     )
              _lowerCamelCase       :		Any		=float(current_step - num_warmup_steps     ) / float(max(1		,			num_training_steps - num_warmup_steps     )     )
              return max(0.0		,			0.5 * (1.0 + math.cos(math.pi * float(SCREAMING_SNAKE_CASE__     ) * 2.0 * progress     ))     )

       return LambdaLR(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      int = 1		,			SCREAMING_SNAKE_CASE__					:      int = -1     ):
       '''simple docstring'''





       def lr_lambda(SCREAMING_SNAKE_CASE__					:      Tuple     ):
              if current_step < num_warmup_steps:
                     return float(SCREAMING_SNAKE_CASE__     ) / float(max(1		,			SCREAMING_SNAKE_CASE__     )     )
              _lowerCamelCase       :		int		=float(current_step - num_warmup_steps     ) / float(max(1		,			num_training_steps - num_warmup_steps     )     )
              if progress >= 1.0:
                     return 0.0
              return max(0.0		,			0.5 * (1.0 + math.cos(math.pi * ((float(SCREAMING_SNAKE_CASE__     ) * progress) % 1.0)     ))     )

       return LambdaLR(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      Tuple		,			SCREAMING_SNAKE_CASE__					:      Union[str, Any]=1e-7		,			SCREAMING_SNAKE_CASE__					:      List[str]=1.0		,			SCREAMING_SNAKE_CASE__					:      List[Any]=-1     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=optimizer.defaults['lr']
       if not (lr_init > lr_end):
              raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''     )

       def lr_lambda(SCREAMING_SNAKE_CASE__					:      int     ):
              if current_step < num_warmup_steps:
                     return float(SCREAMING_SNAKE_CASE__     ) / float(max(1		,			SCREAMING_SNAKE_CASE__     )     )
              elif current_step > num_training_steps:
                     return lr_end / lr_init  # as LambdaLR multiplies by lr_init
              else:
                     _lowerCamelCase       :		List[Any]		=lr_init - lr_end
                     _lowerCamelCase       :		Optional[Any]		=num_training_steps - num_warmup_steps
                     _lowerCamelCase       :		List[Any]		=1 - (current_step - num_warmup_steps) / decay_steps
                     _lowerCamelCase       :		str		=lr_range * pct_remaining**power + lr_end
                     return decay / lr_init  # as LambdaLR multiplies by lr_init

       return LambdaLR(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )


lowerCamelCase											=       {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, SchedulerType]		,			SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      Optional[str] = None		,			SCREAMING_SNAKE_CASE__					:      Optional[int] = None		,			SCREAMING_SNAKE_CASE__					:      Optional[int] = None		,			SCREAMING_SNAKE_CASE__					:      int = 1		,			SCREAMING_SNAKE_CASE__					:      float = 1.0		,			SCREAMING_SNAKE_CASE__					:      int = -1		,			):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[int]		=SchedulerType(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		int		=TYPE_TO_SCHEDULER_FUNCTION[name]
       if name == SchedulerType.CONSTANT:
              return schedule_func(SCREAMING_SNAKE_CASE__		,			last_epoch=SCREAMING_SNAKE_CASE__     )

       if name == SchedulerType.PIECEWISE_CONSTANT:
              return schedule_func(SCREAMING_SNAKE_CASE__		,			step_rules=SCREAMING_SNAKE_CASE__		,			last_epoch=SCREAMING_SNAKE_CASE__     )

       # All other schedulers require `num_warmup_steps`
       if num_warmup_steps is None:
              raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.'''     )

       if name == SchedulerType.CONSTANT_WITH_WARMUP:
              return schedule_func(SCREAMING_SNAKE_CASE__		,			num_warmup_steps=SCREAMING_SNAKE_CASE__		,			last_epoch=SCREAMING_SNAKE_CASE__     )

       # All other schedulers require `num_training_steps`
       if num_training_steps is None:
              raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.'''     )

       if name == SchedulerType.COSINE_WITH_RESTARTS:
              return schedule_func(
                  SCREAMING_SNAKE_CASE__		,			num_warmup_steps=SCREAMING_SNAKE_CASE__		,			num_training_steps=SCREAMING_SNAKE_CASE__		,			num_cycles=SCREAMING_SNAKE_CASE__		,			last_epoch=SCREAMING_SNAKE_CASE__		,			)

       if name == SchedulerType.POLYNOMIAL:
              return schedule_func(
                  SCREAMING_SNAKE_CASE__		,			num_warmup_steps=SCREAMING_SNAKE_CASE__		,			num_training_steps=SCREAMING_SNAKE_CASE__		,			power=SCREAMING_SNAKE_CASE__		,			last_epoch=SCREAMING_SNAKE_CASE__		,			)

       return schedule_func(
           SCREAMING_SNAKE_CASE__		,			num_warmup_steps=SCREAMING_SNAKE_CASE__		,			num_training_steps=SCREAMING_SNAKE_CASE__		,			last_epoch=SCREAMING_SNAKE_CASE__     )






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


