

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   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Optional[int]										=    MvpTokenizer
      UpperCamelCase_      :		Union[str, Any]										=    MvpTokenizerFast
      UpperCamelCase_      :		Optional[Any]										=    True
      UpperCamelCase_      :		Union[str, Any]										=    filter_roberta_detectors



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

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



      def 						UpperCamelCase_		(      self				:    str      , **A__				:    int		)   ->					Optional[int]:
        kwargs.update(self.special_tokens_map		)
        return self.tokenizer_class.from_pretrained(self.tmpdirname      , **_lowerCAmelCase		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , **A__				:    Any		)   ->					Dict:
        kwargs.update(self.special_tokens_map		)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      , **_lowerCAmelCase		)



      def 						UpperCamelCase_		(      self				:    str      , A__				:    str		)   ->					Dict:
        return "lower newer", "lower newer"



      @cached_property
      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Optional[Any]:
        return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''		)



      @cached_property
      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Tuple:
        return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''		)



      @require_torch
      def 						UpperCamelCase_		(      self				:    str		)   ->					Dict:
        _snake_case						=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        _snake_case						=							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          _snake_case						=							tokenizer(_lowerCAmelCase      , max_length=len(_lowerCAmelCase		)      , padding=_lowerCAmelCase      , return_tensors='''pt'''		)
          self.assertIsInstance(_lowerCAmelCase      , _lowerCAmelCase		)

          self.assertEqual((2, 9)      , batch.input_ids.shape		)
          self.assertEqual((2, 9)      , batch.attention_mask.shape		)
          _snake_case						=							batch.input_ids.tolist()[0]
          self.assertListEqual(_lowerCAmelCase      , _lowerCAmelCase		)
          # Test that special tokens are reset



      @require_torch
      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Dict:
        _snake_case						=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          _snake_case						=							tokenizer(_lowerCAmelCase      , padding=_lowerCAmelCase      , return_tensors='''pt'''		)
          # check if input_ids are returned and no labels
          self.assertIn('''input_ids'''      , _lowerCAmelCase		)
          self.assertIn('''attention_mask'''      , _lowerCAmelCase		)
          self.assertNotIn('''labels'''      , _lowerCAmelCase		)
          self.assertNotIn('''decoder_attention_mask'''      , _lowerCAmelCase		)



      @require_torch
      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Tuple:
        _snake_case						=							[
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          _snake_case						=							tokenizer(text_target=_lowerCAmelCase      , max_length=32      , padding='''max_length'''      , return_tensors='''pt'''		)
          self.assertEqual(32      , targets['''input_ids'''].shape[1]		)



      @require_torch
      def 						UpperCamelCase_		(      self				:    int		)   ->					List[Any]:

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



      @require_torch
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[int]:
        _snake_case						=							['''A long paragraph for summarization.''']
        _snake_case						=							[
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          _snake_case						=							tokenizer(_lowerCAmelCase      , text_target=_lowerCAmelCase      , return_tensors='''pt'''		)
          _snake_case						=							inputs['''input_ids''']
          _snake_case						=							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 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[Any]:
        pass







      def 						UpperCamelCase_		(      self				:    Any		)   ->					Tuple:

        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
            _snake_case						=							self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase      , **_lowerCAmelCase		)
            _snake_case						=							self.tokenizer_class.from_pretrained(_lowerCAmelCase      , **_lowerCAmelCase		)
            _snake_case						=							'''A, <mask> AllenNLP sentence.'''
            _snake_case						=							tokenizer_r.encode_plus(_lowerCAmelCase      , add_special_tokens=_lowerCAmelCase      , return_token_type_ids=_lowerCAmelCase		)
            _snake_case						=							tokenizer_p.encode_plus(_lowerCAmelCase      , add_special_tokens=_lowerCAmelCase      , return_token_type_ids=_lowerCAmelCase		)

            # 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''']		)      , )

            _snake_case						=							tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
            _snake_case						=							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, 50264, 3823, 487, 21992, 3645, 4, 2]		)
            self.assertSequenceEqual(tokens_r['''input_ids''']      , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

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




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_video_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import VivitImageProcessor



class   lowercase_   (  unittest.TestCase					):



      def __init__(      self				:    Union[str, Any]      , A__				:    int      , A__				:    List[str]=7      , A__				:    Tuple=3      , A__				:    List[str]=10      , A__				:    Optional[int]=18      , A__				:    int=30      , A__				:    Tuple=400      , A__				:    Dict=True      , A__				:    str=None      , A__				:    str=True      , A__				:    List[str]=[0.5, 0.5, 0.5]      , A__				:    int=[0.5, 0.5, 0.5]      , A__				:    List[Any]=None      , )   ->					int:
        _snake_case						=							size if size is not None else {'''shortest_edge''': 18}
        _snake_case						=							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							num_channels
        _snake_case						=							num_frames
        _snake_case						=							image_size
        _snake_case						=							min_resolution
        _snake_case						=							max_resolution
        _snake_case						=							do_resize
        _snake_case						=							size
        _snake_case						=							do_normalize
        _snake_case						=							image_mean
        _snake_case						=							image_std
        _snake_case						=							crop_size







      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Union[str, Any]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Tuple										=    VivitImageProcessor if is_vision_available() else None



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
        _snake_case						=							VivitImageProcessingTester(self		)



      @property
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Optional[Any]:
        _snake_case						=							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__      , '''do_center_crop'''		)		)
        self.assertTrue(hasattr(A__      , '''size'''		)		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					List[Any]:
        _snake_case						=							self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size      , {'''shortest_edge''': 18}		)
        self.assertEqual(image_processor.crop_size      , {'''height''': 18, '''width''': 18}		)

        _snake_case						=							self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84		)
        self.assertEqual(image_processor.size      , {'''shortest_edge''': 42}		)
        self.assertEqual(image_processor.crop_size      , {'''height''': 84, '''width''': 84}		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[Any]:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PIL videos
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , Image.Image		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )



      def 						UpperCamelCase_		(      self				:    Any		)   ->					List[str]:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__      , numpify=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , np.ndarray		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )







      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					int:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__      , torchify=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , torch.Tensor		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

