





'''simple docstring'''

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


a        =						logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class __a (   _snake_case			):
   __UpperCamelCase   : bool	      =	field(default=_snake_case,   metadata={'help': 'Whether to use SortishSampler or not.'}			)
   __UpperCamelCase   : bool	      =	field(
       default=_snake_case,   metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
   __UpperCamelCase   : Optional[int]	      =	field(
       default=_snake_case,   metadata={
           'help': (
               'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `max_length` value of the model configuration.'
           )
       },   )
   __UpperCamelCase   : Optional[int]	      =	field(
       default=_snake_case,   metadata={
           'help': (
               'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `num_beams` value of the model configuration.'
           )
       },   )
   __UpperCamelCase   : Optional[Union[str, Path, GenerationConfig]]	      =	field(
       default=_snake_case,   metadata={
           'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
       },   )





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       super().to_dict()
      for k, v in d.items():
         if isinstance(lowerCamelCase				,lowerCamelCase    ):
            __SCREAMING_SNAKE_CASE      							=       v.to_dict()
      return d












'''simple docstring'''

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







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[Any]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       hidden_states.shape
      __SCREAMING_SNAKE_CASE      							=       jax.image.resize(
          lowerCamelCase				,shape=(batch, height * 2, width * 2, channels)				,method="""nearest"""				,)
      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(2, 2)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : int	      =	None
   __UpperCamelCase   : float	      =	0.0
   __UpperCamelCase   : bool	      =	None
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.in_channels if self.out_channels is None else self.out_channels

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       nn.Dense(lowerCamelCase				,dtype=self.dtype    )

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Dropout(self.dropout_prob    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

      __SCREAMING_SNAKE_CASE      							=       None
      if use_nin_shortcut:
         __SCREAMING_SNAKE_CASE      							=       nn.Conv(
             lowerCamelCase				,kernel_size=(1, 1)				,strides=(1, 1)				,padding="""VALID"""				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			Union[str, Any]=True    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       hidden_states
      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       self.time_emb_proj(nn.swish(lowerCamelCase    )    )
      __SCREAMING_SNAKE_CASE      							=       jnp.expand_dims(jnp.expand_dims(lowerCamelCase				,1    )				,1    )
      __SCREAMING_SNAKE_CASE      							=       hidden_states + temb

      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.dropout(lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      if self.conv_shortcut is not None:
         __SCREAMING_SNAKE_CASE      							=       self.conv_shortcut(lowerCamelCase    )

      return hidden_states + residual





