





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

class 		SCREAMING_SNAKE_CASE__				(       nn.Module  ):
      A_						:				int
      A_						:				jnp.dtype							=    jnp.floataa


      def    a (self       :      str      ):





         """simple docstring"""



         __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       :      Dict				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   hidden_states.shape
         __snake_case							       =   jax.image.resize(
             a__				,			shape=(batch, height * 2, width * 2, channels)				,			method='''nearest'''				,			)
         __snake_case							       =   self.conv(a__      )
         return hidden_states






class 		SCREAMING_SNAKE_CASE__				(       nn.Module  ):
      A_						:				int
      A_						:				jnp.dtype							=    jnp.floataa


      def    a (self       :      Dict      ):





         """simple docstring"""



         __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       :      Dict				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.conv(a__      )
         return hidden_states






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


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





         """simple docstring"""



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

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

         __snake_case							       =   nn.Dense(a__				,			dtype=self.dtype      )

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

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

         __snake_case							       =   None
         if use_nin_shortcut:
            __snake_case							       =   nn.Conv(
                a__				,			kernel_size=(1, 1)				,			strides=(1, 1)				,			padding='''VALID'''				,			dtype=self.dtype				,			)


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





         """simple docstring"""



         __snake_case							       =   hidden_states
         __snake_case							       =   self.norma(a__      )
         __snake_case							       =   nn.swish(a__      )
         __snake_case							       =   self.conva(a__      )

         __snake_case							       =   self.time_emb_proj(nn.swish(a__      )      )
         __snake_case							       =   jnp.expand_dims(jnp.expand_dims(a__				,			1      )				,			1      )
         __snake_case							       =   hidden_states + temb

         __snake_case							       =   self.norma(a__      )
         __snake_case							       =   nn.swish(a__      )
         __snake_case							       =   self.dropout(a__				,			a__      )
         __snake_case							       =   self.conva(a__      )

         if self.conv_shortcut is not None:
            __snake_case							       =   self.conv_shortcut(a__      )

         return hidden_states + residual








import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


__A									=				get_tests_dir("fixtures/test_sentencepiece_bpe.model")



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			BartphoTokenizer
 lowercase_		=			False
 lowercase_		=			True

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Tuple:



   '''simple docstring'''



   super().setUp()

   lowerCamelCase__:      int			  =["▁This", "▁is", "▁a", "▁t", "est"]
   lowerCamelCase__:      Tuple			  =dict(zip(UpperCAmelCase_       , range(len(UpperCAmelCase_))))
   lowerCamelCase__:      List[Any]			  ={"unk_token": "<unk>"}

   lowerCamelCase__:      Dict			  =os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES["monolingual_vocab_file"])
   with open(self.monolingual_vocab_file       , "w"       , encoding="utf-8") as fp:
     for token in vocab_tokens:
       fp.write(F"""{token} {vocab_tokens[token]}\n""")

   lowerCamelCase__:      Dict			  =BartphoTokenizer(UpperCAmelCase_       , self.monolingual_vocab_file       , **self.special_tokens_map)
   tokenizer.save_pretrained(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , **UpperCAmelCase_				:   Optional[Any])       ->str:



   '''simple docstring'''



   kwargs.update(self.special_tokens_map)
   return BartphoTokenizer.from_pretrained(self.tmpdirname       , **UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   Optional[Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  ="This is a là test"
   lowerCamelCase__:      Optional[Any]			  ="This is a<unk><unk> test"
   return input_text, output_text







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =BartphoTokenizer(UpperCAmelCase_       , self.monolingual_vocab_file       , **self.special_tokens_map)
   lowerCamelCase__:      List[Any]			  ="This is a là test"
   lowerCamelCase__:      Optional[int]			  ="▁This ▁is ▁a ▁l à ▁t est".split()
   lowerCamelCase__:      Optional[int]			  =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   lowerCamelCase__:      Tuple			  =tokens + [tokenizer.unk_token]
   lowerCamelCase__:      List[Any]			  =[4, 5, 6, 3, 3, 7, 8, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)       , UpperCAmelCase_)

