





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  BlenderbotSmallTokenizer
       SCREAMING_SNAKE_CASE_      : int      =  False







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Dict:
         super().setUp()

         _lowercase							= ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
         _lowercase							= dict(zip(__A						,range(len(__A  )  )  )  )

         _lowercase							= ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
         _lowercase							= {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

         _lowercase							= os.path.join(self.tmpdirname						,VOCAB_FILES_NAMES['vocab_file']  )
         _lowercase							= 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(__A  ) + '\n'  )
         with open(self.merges_file						,'w'						,encoding='utf-8'  ) as fp:
           fp.write('\n'.join(__A  )  )







       def    __UpperCAmelCase (    self	:	Dict						,**__A	:	str  )						->					str:
         kwargs.update(self.special_tokens_map  )
         return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname						,**__A  )







       def    __UpperCAmelCase (    self	:	int						,__A	:	Any  )						->					Union[str, Any]:
         _lowercase							= 'adapt act apte'
         _lowercase							= 'adapt act apte'
         return input_text, output_text







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Optional[int]:
         _lowercase							= BlenderbotSmallTokenizer(self.vocab_file						,self.merges_file						,**self.special_tokens_map  )
         _lowercase							= 'adapt act apte'
         _lowercase							= ['adapt', 'act', 'ap@@', 'te']
         _lowercase							= tokenizer.tokenize(__A  )
         self.assertListEqual(__A						,__A  )

         _lowercase							= [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

         _lowercase							= [0, 1, 2, 3, 4, 5]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__A  )						,__A  )







       def    __UpperCAmelCase (    self	:	Dict  )						->					Dict:
         _lowercase							= BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
         assert tok('sam'  ).input_ids == [1384]
         _lowercase							= 'I am a small frog.'
         _lowercase							= tok([src_text]						,padding=__A						,truncation=__A  )['input_ids']
         _lowercase							= tok.batch_decode(__A						,skip_special_tokens=__A						,clean_up_tokenization_spaces=__A  )[0]
         assert src_text != decoded  # I wish it did!
         assert decoded == "i am a small frog ."







       def    __UpperCAmelCase (    self	:	List[str]  )						->					str:
         _lowercase							= BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
         _lowercase							= 'I am a small frog .'
         _lowercase							= '.'
         _lowercase							= tok(__A  )['input_ids']
         _lowercase							= tok(__A  )['input_ids']

         assert encoded[-1] == encoded_dot[0]






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
      import torch

      from transformers.generation import DisjunctiveConstraint





@require_torch
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[Any]:
         # For consistency across different places the DisjunctiveConstraint is called,
         # dc.token_ids is a list of integers. It is also initialized only by integers.

         _lowercase							= [[1, 2, 4], [1, 2, 3, 4]]
         _lowercase							= DisjunctiveConstraint(__A  )
         self.assertTrue(isinstance(dc.token_ids						,__A  )  )

         with self.assertRaises(__A  ):
           DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]  )  )

         with self.assertRaises(__A  ):
           DisjunctiveConstraint([torch.LongTensor([1, 2, 4]  ), torch.LongTensor([1, 2, 3, 4, 5]  )]  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					str:
         # We can't have constraints that are complete subsets of another. This leads to a preverse
         # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
         # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
         # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
         # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
         _lowercase							= [[1, 2], [1, 2, 3, 4]]

         with self.assertRaises(__A  ):
           DisjunctiveConstraint(__A  )  # fails here







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[Any]:
         _lowercase							= [[1, 2, 3], [1, 2, 4]]

         _lowercase							= DisjunctiveConstraint(__A  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         _lowercase							= stepped is True and completed is False and reset is False
         self.assertTrue(__A  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         _lowercase							= stepped is True and completed is False and reset is False
         self.assertTrue(__A  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(3  )
         _lowercase							= stepped is True and completed is True and reset is False
         self.assertTrue(__A  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.current_seq == [1, 2, 3]  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Any:
         _lowercase							= [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

         _lowercase							= DisjunctiveConstraint(__A  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(4  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2, 4]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(5  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.current_seq == [1, 2, 4, 5]  )

         dc.reset()

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.remaining() == 3  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.remaining() == 2  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(5  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.remaining() == 0  )
         self.assertTrue(dc.current_seq == [1, 2, 5]  )