





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @register_to_config
 def __init__(self    :       List[Any]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       bool = False	,			)	->int:



   '''simple docstring'''



   super().__init__()

   lowerCamelCase__:				Any        =nn.Embedding(UpperCAmelCase_	,			UpperCAmelCase_)

   lowerCamelCase__:				Dict        =nn.Embedding(UpperCAmelCase_	,			UpperCAmelCase_)
   lowerCamelCase__:				Any        =False

   lowerCamelCase__:				List[str]        =nn.Dropout(p=UpperCAmelCase_)

   lowerCamelCase__:				str        =TaConfig(
       vocab_size=UpperCAmelCase_	,			d_model=UpperCAmelCase_	,			num_heads=UpperCAmelCase_	,			d_kv=UpperCAmelCase_	,			d_ff=UpperCAmelCase_	,			dropout_rate=UpperCAmelCase_	,			feed_forward_proj=UpperCAmelCase_	,			is_decoder=UpperCAmelCase_	,			is_encoder_decoder=UpperCAmelCase_	,			)

   lowerCamelCase__:				int        =nn.ModuleList()
   for lyr_num in range(UpperCAmelCase_):
     lowerCamelCase__:				str        =TaBlock(UpperCAmelCase_)
     self.encoders.append(UpperCAmelCase_)

   lowerCamelCase__:				int        =TaLayerNorm(UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =nn.Dropout(p=UpperCAmelCase_)







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



   '''simple docstring'''



   lowerCamelCase__:				Any        =self.token_embedder(UpperCAmelCase_)

   lowerCamelCase__:				Any        =encoder_input_tokens.shape[1]
   lowerCamelCase__:				Union[str, Any]        =torch.arange(UpperCAmelCase_	,			device=encoder_input_tokens.device)
   x += self.position_encoding(UpperCAmelCase_)

   lowerCamelCase__:				Optional[Any]        =self.dropout_pre(UpperCAmelCase_)

   # inverted the attention mask
   lowerCamelCase__:				Optional[int]        =encoder_input_tokens.size()
   lowerCamelCase__:				Optional[Any]        =self.get_extended_attention_mask(UpperCAmelCase_	,			UpperCAmelCase_)

   for lyr in self.encoders:
     lowerCamelCase__:				Optional[int]        =lyr(UpperCAmelCase_	,			UpperCAmelCase_)[0]
   lowerCamelCase__:				int        =self.layer_norm(UpperCAmelCase_)

   return self.dropout_post(UpperCAmelCase_), encoder_inputs_mask








import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      CTRLTokenizer
 lowercase_		=      False
 lowercase_		=      False

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



   '''simple docstring'''



   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   lowerCamelCase__:				Optional[Any]        =["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
   lowerCamelCase__:				Optional[Any]        =dict(zip(UpperCAmelCase_	,			range(len(UpperCAmelCase_))))
   lowerCamelCase__:				Union[str, Any]        =["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
   lowerCamelCase__:				Union[str, Any]        ={"unk_token": "<unk>"}

   lowerCamelCase__:				Union[str, Any]        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["vocab_file"])
   lowerCamelCase__:				Optional[Any]        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["merges_file"])
   with open(self.vocab_file	,			"w"	,			encoding="utf-8") as fp:
     fp.write(json.dumps(UpperCAmelCase_) + "\n")
   with open(self.merges_file	,			"w"	,			encoding="utf-8") as fp:
     fp.write("\n".join(UpperCAmelCase_))

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



   '''simple docstring'''



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

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



   '''simple docstring'''



   lowerCamelCase__:				int        ="adapt react readapt apt"
   lowerCamelCase__:				int        ="adapt react readapt apt"
   return input_text, output_text







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



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =CTRLTokenizer(self.vocab_file	,			self.merges_file	,			**self.special_tokens_map)
   lowerCamelCase__:				Union[str, Any]        ="adapt react readapt apt"
   lowerCamelCase__:				Tuple        ="adapt re@@ a@@ c@@ t re@@ adapt apt".split()
   lowerCamelCase__:				Dict        =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_	,			UpperCAmelCase_)

   lowerCamelCase__:				Optional[int]        =tokens + [tokenizer.unk_token]

   lowerCamelCase__:				List[str]        =[0, 1, 2, 4, 5, 1, 0, 3, 6]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)	,			UpperCAmelCase_)

