



'''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     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Tuple		=			BlenderbotSmallTokenizer
 __lowerCamelCase       :			List[str]		=			False



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      super().setUp()

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

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

      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    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    a__    (self					,  **A			)       ->	Optional[int]:


      """simple docstring"""



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



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


      """simple docstring"""



      _a           =    '''adapt act apte'''
      _a           =    '''adapt act apte'''
      return input_text, output_text



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    BlenderbotSmallTokenizer(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map			)
      _a           =    '''adapt act apte'''
      _a           =    ['''adapt''', '''act''', '''ap@@''', '''te''']
      _a           =    tokenizer.tokenize(A			)
      self.assertListEqual(A					,  A			)

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

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



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


      """simple docstring"""



      _a           =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''			)
      assert tok('''sam'''			).input_ids == [1_384]
      _a           =    '''I am a small frog.'''
      _a           =    tok([src_text]					,  padding=A					,  truncation=A			)['''input_ids''']
      _a           =    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    a__    (self			)       ->	int:


      """simple docstring"""



      _a           =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''			)
      _a           =    '''I am a small frog .'''
      _a           =    '''.'''
      _a           =    tok(A			)['''input_ids''']
      _a           =    tok(A			)['''input_ids''']

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








'''simple docstring'''





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    min_resolution
      _a           =    max_resolution
      _a           =    do_resize
      _a           =    size
      _a           =    do_normalize
      _a           =    image_mean
      _a           =    image_std



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


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

      _a           =    self.image_processing_class.from_dict(self.image_processor_dict					,  size=42			)
      self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  Image.Image			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  numpify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  np.ndarray			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  torchify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  torch.Tensor			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



