




'''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 							_a				(			SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				Union[str, Any]        =						MvpTokenizer
       A						:				Union[str, Any]        =						MvpTokenizerFast
       A						:				Tuple        =						True
       A						:				List[Any]        =						filter_roberta_detectors







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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

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







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return "lower newer", "lower newer"







       @cached_property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return MvpTokenizer.from_pretrained('RUCAIBox/mvp'  )







       @cached_property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'  )







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             SCREAMING_SNAKE_CASE   :      List[str]    					=						[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tokenizer(A,							max_length=len(A  ),							padding=A,							return_tensors='pt'  )
                   self.assertIsInstance(A,							A  )

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







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer(
                       ['I am a small frog' * 1_024, 'I am a small frog'],							padding=A,							truncation=A,							return_tensors='pt'  )
                   self.assertIsInstance(A,							A  )
                   self.assertEqual(batch.input_ids.shape,							(2, 1_024)  )







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						['A long paragraph for summarization.']
             SCREAMING_SNAKE_CASE   :      Dict    					=						[
                 'Summary of the text.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer(A,							text_target=A,							return_tensors='pt'  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						inputs['input_ids']
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						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  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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

                         # 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']  ),							)

                         SCREAMING_SNAKE_CASE   :      int    					=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )
                         self.assertSequenceEqual(tokens_r['input_ids'],							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__						=				{
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__						=				[
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		lowerCamelCase__						=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



