






'''simple docstring'''






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							):
   _snake_case        =							BlenderbotSmallTokenizer
   _snake_case        =							False







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      super().setUp()

      __snake_case				:      List[Any]     							= ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
      __snake_case				:      Optional[Any]     							= dict(zip(__a					,   range(len(__a))))

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

      __snake_case				:      Optional[Any]     							= os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file'])
      __snake_case				:      Dict     							= 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     SCREAMING_SNAKE_CASE__					(self					,   **__a)			->      Tuple:
      """simple docstring"""






      kwargs.update(self.special_tokens_map)
      return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      int:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= 'adapt act apte'
      __snake_case				:      str     							= 'adapt act apte'
      return input_text, output_text







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= BlenderbotSmallTokenizer(self.vocab_file					,   self.merges_file					,   **self.special_tokens_map)
      __snake_case				:      Dict     							= 'adapt act apte'
      __snake_case				:      List[str]     							= ['adapt', 'act', 'ap@@', 'te']
      __snake_case				:      int     							= tokenizer.tokenize(__a)
      self.assertListEqual(__a					,   __a)

      __snake_case				:      Tuple     							= [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

      __snake_case				:      str     							= [0, 1, 2, 3, 4, 5]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__a)					,   __a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      __snake_case				:      Dict     							= BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M')
      assert tok('sam').input_ids == [1_3_8_4]
      __snake_case				:      int     							= 'I am a small frog.'
      __snake_case				:      Optional[Any]     							= tok([src_text]					,   padding=__a					,   truncation=__a)['input_ids']
      __snake_case				:      int     							= 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     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Any     							= BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M')
      __snake_case				:      str     							= 'I am a small frog .'
      __snake_case				:      Tuple     							= '.'
      __snake_case				:      int     							= tok(__a)['input_ids']
      __snake_case				:      Optional[Any]     							= tok(__a)['input_ids']

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







'''simple docstring'''






import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
      import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin




@require_torch
class a_    (    unittest.TestCase		,      UpperCamelCase_							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[str]     							= load_tool('text-to-speech')
      self.tool.setup()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Dict     							= self.tool('hey')
      __snake_case				:      List[Any]     							= result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]					,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485])					,   ))







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Any     							= self.tool('hey')
      __snake_case				:      Any     							= result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]					,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485])					,   ))