






import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase     :							List[str]    =get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')





@require_sentencepiece
@require_tokenizers
class       __a  (   _UpperCamelCase	,       unittest.TestCase	):
 _lowerCAmelCase	:	Union[str, Any]				=			PegasusTokenizer
 _lowerCAmelCase	:	List[Any]				=			PegasusTokenizerFast
 _lowerCAmelCase	:	Union[str, Any]				=			True
 _lowerCAmelCase	:	int				=			True







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      super().setUp()

      # We have a SentencePiece fixture for testing
      UpperCamelCase__      :       Union[str, Any]	    =  PegasusTokenizer(_SCREAMING_SNAKE_CASE						)
      tokenizer.save_pretrained(self.tmpdirname						)







 @cached_property
 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      return PegasusTokenizer.from_pretrained("google/pegasus-large"						)







 def  __lowercase (		self :       Optional[int]							,       **SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      return PegasusTokenizer.from_pretrained(self.tmpdirname							,       **_SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       Optional[Any]						):


      '''simple docstring'''






      return ("This is a test", "This is a test")







 def  __lowercase (		self :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  "</s>"
      UpperCamelCase__      :       List[str]	    =  1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE						)							,       _SCREAMING_SNAKE_CASE						)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE						)							,       _SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  list(self.get_tokenizer().get_vocab().keys()						)

      self.assertEqual(vocab_keys[0]							,       "<pad>"						)
      self.assertEqual(vocab_keys[1]							,       "</s>"						)
      self.assertEqual(vocab_keys[-1]							,       "v"						)
      self.assertEqual(len(_SCREAMING_SNAKE_CASE						)							,       11_03						)







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


      '''simple docstring'''






      self.assertEqual(self.get_tokenizer().vocab_size							,       11_03						)







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname						)
      UpperCamelCase__      :       Tuple	    =  self.tokenizer_class.from_pretrained(self.tmpdirname						)
      UpperCamelCase__      :       Optional[Any]	    =  (
          "Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
          " </s> <pad> <pad> <pad>"
      )
      UpperCamelCase__      :       Any	    =  rust_tokenizer([raw_input_str]							,       return_tensors=_SCREAMING_SNAKE_CASE							,       add_special_tokens=_SCREAMING_SNAKE_CASE						).input_ids[0]
      UpperCamelCase__      :       Optional[int]	    =  py_tokenizer([raw_input_str]							,       return_tensors=_SCREAMING_SNAKE_CASE							,       add_special_tokens=_SCREAMING_SNAKE_CASE						).input_ids[0]
      self.assertListEqual(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Union[str, Any]	    =  self._large_tokenizer
      # <mask_1> masks whole sentence while <mask_2> masks single word
      UpperCamelCase__      :       int	    =  "<mask_1> To ensure a <mask_2> flow of bank resolutions."
      UpperCamelCase__      :       Dict	    =  [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
      UpperCamelCase__      :       List[Any]	    =  tokenizer([raw_input_str]							,       return_tensors=_SCREAMING_SNAKE_CASE						).input_ids[0]
      self.assertListEqual(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       str						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  self._large_tokenizer
      # The tracebacks for the following asserts are **better** without messages or self.assertEqual
      assert tokenizer.vocab_size == 9_61_03
      assert tokenizer.pad_token_id == 0
      assert tokenizer.eos_token_id == 1
      assert tokenizer.offset == 1_03
      assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
      assert tokenizer.unk_token == "<unk>"
      assert tokenizer.model_max_length == 10_24
      UpperCamelCase__      :       Tuple	    =  "To ensure a smooth flow of bank resolutions."
      UpperCamelCase__      :       str	    =  [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
      UpperCamelCase__      :       List[str]	    =  tokenizer([raw_input_str]							,       return_tensors=_SCREAMING_SNAKE_CASE						).input_ids[0]
      self.assertListEqual(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE						)
      assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]						) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







 @require_torch
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  ["This is going to be way too long." * 1_50, "short example"]
      UpperCamelCase__      :       Optional[int]	    =  ["not super long but more than 5 tokens", "tiny"]
      UpperCamelCase__      :       Dict	    =  self._large_tokenizer(_SCREAMING_SNAKE_CASE							,       padding=_SCREAMING_SNAKE_CASE							,       truncation=_SCREAMING_SNAKE_CASE							,       return_tensors="pt"						)
      UpperCamelCase__      :       int	    =  self._large_tokenizer(
          text_target=_SCREAMING_SNAKE_CASE							,       max_length=5							,       padding=_SCREAMING_SNAKE_CASE							,       truncation=_SCREAMING_SNAKE_CASE							,       return_tensors="pt"						)

      assert batch.input_ids.shape == (2, 10_24)
      assert batch.attention_mask.shape == (2, 10_24)
      assert targets["input_ids"].shape == (2, 5)
      assert len(_SCREAMING_SNAKE_CASE						) == 2  # input_ids, attention_mask.







 @slow
 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  {"input_ids": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=_SCREAMING_SNAKE_CASE							,       model_name="google/bigbird-pegasus-large-arxiv"							,       revision="ba85d0851d708441f91440d509690f1ab6353415"							,       )







@require_sentencepiece
@require_tokenizers
class       __a  (   _UpperCamelCase	,       unittest.TestCase	):
 _lowerCAmelCase	:	int				=			PegasusTokenizer
 _lowerCAmelCase	:	List[str]				=			PegasusTokenizerFast
 _lowerCAmelCase	:	Dict				=			True
 _lowerCAmelCase	:	Tuple				=			True







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      super().setUp()

      # We have a SentencePiece fixture for testing
      UpperCamelCase__      :       int	    =  PegasusTokenizer(_SCREAMING_SNAKE_CASE							,       offset=0							,       mask_token_sent=_SCREAMING_SNAKE_CASE							,       mask_token="[MASK]"						)
      tokenizer.save_pretrained(self.tmpdirname						)







 @cached_property
 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"						)







 def  __lowercase (		self :       Union[str, Any]							,       **SCREAMING_SNAKE_CASE :       Dict						):


      '''simple docstring'''






      return PegasusTokenizer.from_pretrained(self.tmpdirname							,       **_SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[str]							,       SCREAMING_SNAKE_CASE :       Dict						):


      '''simple docstring'''






      return ("This is a test", "This is a test")







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Union[str, Any]	    =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname						)
      UpperCamelCase__      :       Any	    =  self.tokenizer_class.from_pretrained(self.tmpdirname						)
      UpperCamelCase__      :       Tuple	    =  (
          "Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
          " <pad> <pad> <pad>"
      )
      UpperCamelCase__      :       List[str]	    =  rust_tokenizer([raw_input_str]							,       return_tensors=_SCREAMING_SNAKE_CASE							,       add_special_tokens=_SCREAMING_SNAKE_CASE						).input_ids[0]
      UpperCamelCase__      :       List[str]	    =  py_tokenizer([raw_input_str]							,       return_tensors=_SCREAMING_SNAKE_CASE							,       add_special_tokens=_SCREAMING_SNAKE_CASE						).input_ids[0]
      self.assertListEqual(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE						)







 @require_torch
 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  ["This is going to be way too long." * 10_00, "short example"]
      UpperCamelCase__      :       Union[str, Any]	    =  ["not super long but more than 5 tokens", "tiny"]
      UpperCamelCase__      :       Dict	    =  self._large_tokenizer(_SCREAMING_SNAKE_CASE							,       padding=_SCREAMING_SNAKE_CASE							,       truncation=_SCREAMING_SNAKE_CASE							,       return_tensors="pt"						)
      UpperCamelCase__      :       Dict	    =  self._large_tokenizer(
          text_target=_SCREAMING_SNAKE_CASE							,       max_length=5							,       padding=_SCREAMING_SNAKE_CASE							,       truncation=_SCREAMING_SNAKE_CASE							,       return_tensors="pt"						)

      assert batch.input_ids.shape == (2, 40_96)
      assert batch.attention_mask.shape == (2, 40_96)
      assert targets["input_ids"].shape == (2, 5)
      assert len(_SCREAMING_SNAKE_CASE						) == 2  # input_ids, attention_mask.







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  (
          "This is an example string that is used to test the original TF implementation against the HF"
          " implementation"
      )

      UpperCamelCase__      :       int	    =  self._large_tokenizer(_SCREAMING_SNAKE_CASE						).input_ids

      self.assertListEqual(
          _SCREAMING_SNAKE_CASE							,       [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]							,       )







import unittest

import numpy as np

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

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 GLPNImageProcessor





class       __a  (   unittest.TestCase	):







 def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[int]							,       SCREAMING_SNAKE_CASE :       List[Any]=7							,       SCREAMING_SNAKE_CASE :       str=3							,       SCREAMING_SNAKE_CASE :       Dict=18							,       SCREAMING_SNAKE_CASE :       str=30							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=4_00							,       SCREAMING_SNAKE_CASE :       int=True							,       SCREAMING_SNAKE_CASE :       Any=32							,       SCREAMING_SNAKE_CASE :       int=True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  parent
      UpperCamelCase__      :       Dict	    =  batch_size
      UpperCamelCase__      :       Optional[Any]	    =  num_channels
      UpperCamelCase__      :       int	    =  image_size
      UpperCamelCase__      :       Dict	    =  min_resolution
      UpperCamelCase__      :       List[str]	    =  max_resolution
      UpperCamelCase__      :       Union[str, Any]	    =  do_resize
      UpperCamelCase__      :       Optional[Any]	    =  size_divisor
      UpperCamelCase__      :       int	    =  do_rescale







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


      '''simple docstring'''






      return {
          "do_resize": self.do_resize,
          "size_divisor": self.size_divisor,
          "do_rescale": self.do_rescale,
      }







@require_torch
@require_vision
class       __a  (   A__	,       unittest.TestCase	):
 _lowerCAmelCase	:	List[Any]				=			GLPNImageProcessor if is_vision_available() else None







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  GLPNImageProcessingTester(self						)







 @property
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_resize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "size_divisor"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "resample"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_rescale"						)						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      pass







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






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

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       List[Any]	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__      :       Optional[Any]	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       numpify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       np.ndarray						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       str	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






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

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       Dict	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)