





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      (			SCREAMING_SNAKE_CASE_					,unittest.TestCase ):
       _UpperCAmelCase				:  Dict			   =   MvpTokenizer
       _UpperCAmelCase				:  Optional[Any]			   =   MvpTokenizerFast
       _UpperCAmelCase				:  List[str]			   =   True
       _UpperCAmelCase				:  str			   =   filter_roberta_detectors







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

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







       def 						__lowerCamelCase   (				self					:     Any		, **A					:     Union[str, Any]      )       ->Optional[int]:
          kwargs.update(self.special_tokens_map      )
          return self.tokenizer_class.from_pretrained(self.tmpdirname		, **snake_case__      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, **A					:     Optional[Any]      )       ->List[str]:
          kwargs.update(self.special_tokens_map      )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		, **snake_case__      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Optional[int]      )       ->int:
          return "lower newer", "lower newer"







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







       @cached_property
       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->List[Any]:
          return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''      )







       @require_torch
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->List[str]:
          lowerCamelCase__       :						Union[str, Any]        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
          lowerCamelCase__       :						Optional[Any]        =							[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

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

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







       @require_torch
       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[Any]:
          lowerCamelCase__       :						List[str]        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowerCamelCase__       :						int        =							tokenizer(snake_case__		, padding=snake_case__		, return_tensors='''pt'''      )
             # check if input_ids are returned and no labels
             self.assertIn('''input_ids'''		, snake_case__      )
             self.assertIn('''attention_mask'''		, snake_case__      )
             self.assertNotIn('''labels'''		, snake_case__      )
             self.assertNotIn('''decoder_attention_mask'''		, snake_case__      )







       @require_torch
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->str:
          lowerCamelCase__       :						Optional[int]        =							[
              'Summary of the text.',
              'Another summary.',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowerCamelCase__       :						Tuple        =							tokenizer(text_target=snake_case__		, max_length=3_2		, padding='''max_length'''		, return_tensors='''pt'''      )
             self.assertEqual(3_2		, targets['''input_ids'''].shape[1]      )







       @require_torch
       def 						__lowerCamelCase   (				self					:     str      )       ->str:

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             lowerCamelCase__       :						Optional[Any]        =							tokenizer(
                 ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']		, padding=snake_case__		, truncation=snake_case__		, return_tensors='''pt'''      )
             self.assertIsInstance(snake_case__		, snake_case__      )
             self.assertEqual(batch.input_ids.shape		, (2, 1_0_2_4)      )







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







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

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

                # 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''']      )		, )

                lowerCamelCase__       :						List[Any]        =							tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
                lowerCamelCase__       :						Union[str, Any]        =							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, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )
                self.assertSequenceEqual(tokens_r['''input_ids''']		, [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )

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













from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A :   Any  			=  logging.get_logger(__name__)

_A :   int  			=  {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   "timesformer"







       def __init__(				self					:     List[str]		, A					:     int=2_2_4		, A					:     Optional[Any]=1_6		, A					:     str=3		, A					:     str=8		, A					:     Any=7_6_8		, A					:     Dict=1_2		, A					:     Optional[Any]=1_2		, A					:     Any=3_0_7_2		, A					:     str="gelu"		, A					:     Optional[int]=0.0		, A					:     Union[str, Any]=0.0		, A					:     List[Any]=0.02		, A					:     int=1e-6		, A					:     Tuple=True		, A					:     Any="divided_space_time"		, A					:     Optional[Any]=0		, **A					:     Tuple		, )       ->str:
          super().__init__(**A      )

          lowerCamelCase__       :						Optional[Any]        =							image_size
          lowerCamelCase__       :						int        =							patch_size
          lowerCamelCase__       :						Any        =							num_channels
          lowerCamelCase__       :						Optional[int]        =							num_frames

          lowerCamelCase__       :						Optional[Any]        =							hidden_size
          lowerCamelCase__       :						Optional[int]        =							num_hidden_layers
          lowerCamelCase__       :						Optional[int]        =							num_attention_heads
          lowerCamelCase__       :						Dict        =							intermediate_size
          lowerCamelCase__       :						Optional[Any]        =							hidden_act
          lowerCamelCase__       :						Union[str, Any]        =							hidden_dropout_prob
          lowerCamelCase__       :						Dict        =							attention_probs_dropout_prob
          lowerCamelCase__       :						List[Any]        =							initializer_range
          lowerCamelCase__       :						str        =							layer_norm_eps
          lowerCamelCase__       :						Union[str, Any]        =							qkv_bias

          lowerCamelCase__       :						str        =							attention_type
          lowerCamelCase__       :						List[str]        =							drop_path_rate



