


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






      def 			lowerCamelCase			(   self		:	Dict					)   ->		str:
             """simple docstring"""




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

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






      def 			lowerCamelCase			(   self		:	Any     ,  **lowercase_		:	Optional[Any]					)   ->		Optional[Any]:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Any     ,  **lowercase_		:	int					)   ->		str:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	Optional[Any]					)   ->		List[Any]:
             """simple docstring"""




             return "lower newer", "lower newer"






      @cached_property
      def 			lowerCamelCase			(   self		:	Tuple					)   ->		int:
             """simple docstring"""




             return MvpTokenizer.from_pretrained('RUCAIBox/mvp'					)






      @cached_property
      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Any:
             """simple docstring"""




             return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'					)






      @require_torch
      def 			lowerCamelCase			(   self		:	Any					)   ->		Union[str, Any]:
             """simple docstring"""




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

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    _lowerCamelCase			:						List[str]            =tokenizer(lowercase_     ,  max_length=len(lowercase_					)     ,  padding=lowercase_     ,  return_tensors='pt'					)
                    self.assertIsInstance(lowercase_     ,  lowercase_					)

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






      @require_torch
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




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






      @require_torch
      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Dict:
             """simple docstring"""




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






      @require_torch
      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		List[Any]:
             """simple docstring"""




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






      @require_torch
      def 			lowerCamelCase			(   self		:	int					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =['A long paragraph for summarization.']
             _lowerCamelCase			:						Optional[Any]            =[
                 'Summary of the text.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    _lowerCamelCase			:						Any            =tokenizer(lowercase_     ,  text_target=lowercase_     ,  return_tensors='pt'					)
                    _lowerCamelCase			:						List[str]            =inputs['input_ids']
                    _lowerCamelCase			:						Dict            =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		:	Any					)   ->		str:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	int					)   ->		Optional[Any]:
             """simple docstring"""




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

                           # 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			:						str            =tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
                           _lowerCamelCase			:						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(
                               lowercase_     ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
                           self.assertSequenceEqual(
                               lowercase_     ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)






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


lowerCamelCase		   =			logging.get_logger(__name__)

lowerCamelCase		   =			{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    Union[str, Any] ='vit_msn'






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	List[str]=768     ,  lowercase_		:	Optional[int]=12     ,  lowercase_		:	List[str]=12     ,  lowercase_		:	List[Any]=3072     ,  lowercase_		:	int="gelu"     ,  lowercase_		:	List[Any]=0.0     ,  lowercase_		:	Optional[int]=0.0     ,  lowercase_		:	Dict=0.02     ,  lowercase_		:	Any=1E-06     ,  lowercase_		:	Union[str, Any]=224     ,  lowercase_		:	Optional[int]=16     ,  lowercase_		:	List[Any]=3     ,  lowercase_		:	Any=True     ,  **lowercase_		:	Tuple     ,  )   ->		Dict:
             """simple docstring"""




             super().__init__(**lowercase_					)

             _lowerCamelCase			:						Dict            =hidden_size
             _lowerCamelCase			:						Any            =num_hidden_layers
             _lowerCamelCase			:						Union[str, Any]            =num_attention_heads
             _lowerCamelCase			:						List[Any]            =intermediate_size
             _lowerCamelCase			:						Tuple            =hidden_act
             _lowerCamelCase			:						Any            =hidden_dropout_prob
             _lowerCamelCase			:						Dict            =attention_probs_dropout_prob
             _lowerCamelCase			:						Any            =initializer_range
             _lowerCamelCase			:						List[Any]            =layer_norm_eps
             _lowerCamelCase			:						Optional[int]            =image_size
             _lowerCamelCase			:						Optional[int]            =patch_size
             _lowerCamelCase			:						List[Any]            =num_channels
             _lowerCamelCase			:						Optional[Any]            =qkv_bias


