





'''simple docstring'''




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class  SCREAMING_SNAKE_CASE    (a__					,		unittest.TestCase							):
     lowerCAmelCase					=       MvpTokenizer
     lowerCAmelCase					=       MvpTokenizerFast
     lowerCAmelCase					=       True
     lowerCAmelCase					=       filter_roberta_detectors


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().setUp()
          __A						: int      =       [
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              '\u0120',
              '\u0120l',
              '\u0120n',
              '\u0120lo',
              '\u0120low',
              'er',
              '\u0120lowest',
              '\u0120newer',
              '\u0120wider',
              '<unk>',
          ]
          __A						: Optional[Any]      =       dict(zip(_UpperCAmelCase						,	range(len(_UpperCAmelCase))))
          __A						: int      =       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
          __A						: List[Any]      =       {'unk_token': '<unk>'}

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


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          kwargs.update(self.special_tokens_map)
          return self.tokenizer_class.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          kwargs.update(self.special_tokens_map)
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          return "lower newer", "lower newer"


     @cached_property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return MvpTokenizer.from_pretrained('RUCAIBox/mvp')


     @cached_property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp')


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
          __A						: int      =       [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __A						: Optional[Any]      =       tokenizer(_UpperCAmelCase						,	max_length=len(_UpperCAmelCase)						,	padding=_UpperCAmelCase						,	return_tensors='pt')
               self.assertIsInstance(_UpperCAmelCase						,	_UpperCAmelCase)

               self.assertEqual((2, 9)						,	batch.input_ids.shape)
               self.assertEqual((2, 9)						,	batch.attention_mask.shape)
               __A						: List[str]      =       batch.input_ids.tolist()[0]
               self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)
               # Test that special tokens are reset


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __A						: Dict      =       tokenizer(_UpperCAmelCase						,	padding=_UpperCAmelCase						,	return_tensors='pt')
               # check if input_ids are returned and no labels
               self.assertIn('input_ids'						,	_UpperCAmelCase)
               self.assertIn('attention_mask'						,	_UpperCAmelCase)
               self.assertNotIn('labels'						,	_UpperCAmelCase)
               self.assertNotIn('decoder_attention_mask'						,	_UpperCAmelCase)


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       [
              'Summary of the text.',
              'Another summary.',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __A						: Tuple      =       tokenizer(text_target=_UpperCAmelCase						,	max_length=32						,	padding='max_length'						,	return_tensors='pt')
               self.assertEqual(32						,	targets['input_ids'].shape[1])


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __A						: Optional[Any]      =       tokenizer(
                   ['I am a small frog' * 1024, 'I am a small frog']						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	return_tensors='pt')
               self.assertIsInstance(_UpperCAmelCase						,	_UpperCAmelCase)
               self.assertEqual(batch.input_ids.shape						,	(2, 1024))


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       ['A long paragraph for summarization.']
          __A						: Optional[int]      =       [
              'Summary of the text.',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __A						: Optional[int]      =       tokenizer(_UpperCAmelCase						,	text_target=_UpperCAmelCase						,	return_tensors='pt')
               __A						: int      =       inputs['input_ids']
               __A						: Optional[int]      =       inputs['labels']
               self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
               self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
               self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
               self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                    __A						: Any      =       self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase						,	**_UpperCAmelCase)
                    __A						: str      =       self.tokenizer_class.from_pretrained(_UpperCAmelCase						,	**_UpperCAmelCase)
                    __A						: List[Any]      =       'A, <mask> AllenNLP sentence.'
                    __A						: int      =       tokenizer_r.encode_plus(_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase)
                    __A						: str      =       tokenizer_p.encode_plus(_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase)

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r['token_type_ids'])						,	sum(tokens_p['token_type_ids']))

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])						,	sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])						,	)

                    __A						: int      =       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
                    __A						: Dict      =       tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p['input_ids']						,	[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
                    self.assertSequenceEqual(tokens_r['input_ids']						,	[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

                    self.assertSequenceEqual(
                        _UpperCAmelCase						,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
                    self.assertSequenceEqual(
                        _UpperCAmelCase						,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])






'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       tempfile.mkdtemp()

          # fmt: off
          __A						: Optional[int]      =       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
          # fmt: on
          __A						: Tuple      =       dict(zip(_UpperCAmelCase						,	range(len(_UpperCAmelCase))))
          __A						: Any      =       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
          __A						: List[str]      =       {'unk_token': '<unk>'}

          __A						: int      =       os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES['vocab_file'])
          __A						: Optional[int]      =       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))

          __A						: str      =       {
              'do_resize': True,
              'size': 20,
              'do_center_crop': True,
              'crop_size': 18,
              'do_normalize': True,
              'image_mean': [0.48145466, 0.4578275, 0.40821073],
              'image_std': [0.26862954, 0.26130258, 0.27577711],
          }
          __A						: Union[str, Any]      =       os.path.join(self.tmpdirname						,	_UpperCAmelCase)
          with open(self.image_processor_file						,	'w'						,	encoding='utf-8') as fp:
               json.dump(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return CLIPTokenizer.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return CLIPImageProcessor.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       [np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta)]

          __A						: Optional[int]      =       [Image.fromarray(np.moveaxis(_UpperCAmelCase						,	0						,	-1)) for x in image_inputs]

          return image_inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: str      =       self.get_tokenizer()
          __A						: int      =       self.get_rust_tokenizer()
          __A						: Any      =       self.get_image_processor()

          __A						: Union[str, Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          processor_slow.save_pretrained(self.tmpdirname)
          __A						: Dict      =       CLIPProcessor.from_pretrained(self.tmpdirname						,	use_fast=_UpperCAmelCase)

          __A						: Union[str, Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          processor_fast.save_pretrained(self.tmpdirname)
          __A						: Union[str, Any]      =       CLIPProcessor.from_pretrained(self.tmpdirname)

          self.assertEqual(processor_slow.tokenizer.get_vocab()						,	tokenizer_slow.get_vocab())
          self.assertEqual(processor_fast.tokenizer.get_vocab()						,	tokenizer_fast.get_vocab())
          self.assertEqual(tokenizer_slow.get_vocab()						,	tokenizer_fast.get_vocab())
          self.assertIsInstance(processor_slow.tokenizer						,	_UpperCAmelCase)
          self.assertIsInstance(processor_fast.tokenizer						,	_UpperCAmelCase)

          self.assertEqual(processor_slow.image_processor.to_json_string()						,	image_processor.to_json_string())
          self.assertEqual(processor_fast.image_processor.to_json_string()						,	image_processor.to_json_string())
          self.assertIsInstance(processor_slow.image_processor						,	_UpperCAmelCase)
          self.assertIsInstance(processor_fast.image_processor						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       CLIPProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          __A						: Dict      =       self.get_tokenizer(bos_token='(BOS)'						,	eos_token='(EOS)')
          __A						: Any      =       self.get_image_processor(do_normalize=_UpperCAmelCase						,	padding_value=1.0)

          __A						: Tuple      =       CLIPProcessor.from_pretrained(
              self.tmpdirname						,	bos_token='(BOS)'						,	eos_token='(EOS)'						,	do_normalize=_UpperCAmelCase						,	padding_value=1.0)

          self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.tokenizer						,	_UpperCAmelCase)

          self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       self.get_image_processor()
          __A						: List[Any]      =       self.get_tokenizer()

          __A						: List[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: List[str]      =       self.prepare_image_inputs()

          __A						: Dict      =       image_processor(_UpperCAmelCase						,	return_tensors='np')
          __A						: str      =       processor(images=_UpperCAmelCase						,	return_tensors='np')

          for key in input_image_proc.keys():
               self.assertAlmostEqual(input_image_proc[key].sum()						,	input_processor[key].sum()						,	delta=1e-2)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.get_image_processor()
          __A						: List[str]      =       self.get_tokenizer()

          __A						: Optional[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Dict      =       'lower newer'

          __A						: List[Any]      =       processor(text=_UpperCAmelCase)

          __A						: Optional[int]      =       tokenizer(_UpperCAmelCase)

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]						,	encoded_processor[key])


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       self.get_image_processor()
          __A						: Optional[int]      =       self.get_tokenizer()

          __A						: Optional[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: int      =       'lower newer'
          __A						: Any      =       self.prepare_image_inputs()

          __A						: Tuple      =       processor(text=_UpperCAmelCase						,	images=_UpperCAmelCase)

          self.assertListEqual(list(inputs.keys())						,	['input_ids', 'attention_mask', 'pixel_values'])

          # test if it raises when no input is passed
          with pytest.raises(_UpperCAmelCase):
               processor()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       self.get_image_processor()
          __A						: str      =       self.get_tokenizer()

          __A						: Optional[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Dict      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __A						: str      =       processor.batch_decode(_UpperCAmelCase)
          __A						: Optional[Any]      =       tokenizer.batch_decode(_UpperCAmelCase)

          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       self.get_image_processor()
          __A						: int      =       self.get_tokenizer()

          __A						: Any      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Any      =       'lower newer'
          __A						: Any      =       self.prepare_image_inputs()

          __A						: Any      =       processor(text=_UpperCAmelCase						,	images=_UpperCAmelCase)

          self.assertListEqual(list(inputs.keys())						,	processor.model_input_names)