


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"""




 __lowerCAmelCase			 =  MvpTokenizer
 __lowerCAmelCase			 =  MvpTokenizerFast
 __lowerCAmelCase			 =  True
 __lowerCAmelCase			 =  filter_roberta_detectors


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

     a					=os.path.join(self.tmpdirname			,   VOCAB_FILES_NAMES['''vocab_file''']						)
     a					=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 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Optional[Any]:
     kwargs.update(self.special_tokens_map						)
     return self.tokenizer_class.from_pretrained(self.tmpdirname			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Any:
     kwargs.update(self.special_tokens_map						)
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					int:
     return "lower newer", "lower newer"


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


 @cached_property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''						)


 @require_torch
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     a					=[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a					=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						)
         a					=batch.input_ids.tolist()[0]
         self.assertListEqual(__A			,   __A						)
         # Test that special tokens are reset


 @require_torch
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a					=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 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=[
         '''Summary of the text.''',
         '''Another summary.''',
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a					=tokenizer(text_target=__A			,   max_length=32			,   padding='''max_length'''			,   return_tensors='''pt'''						)
         self.assertEqual(32			,   targets['''input_ids'''].shape[1]						)


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


 @require_torch
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a					=['''A long paragraph for summarization.''']
     a					=[
         '''Summary of the text.''',
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         a					=tokenizer(__A			,   text_target=__A			,   return_tensors='''pt'''						)
         a					=inputs['''input_ids''']
         a					=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						) ->					List[str]:
     pass





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
             a					=self.rust_tokenizer_class.from_pretrained(__A			,   **__A						)
             a					=self.tokenizer_class.from_pretrained(__A			,   **__A						)
             a					='''A, <mask> AllenNLP sentence.'''
             a					=tokenizer_r.encode_plus(__A			,   add_special_tokens=__A			,   return_token_type_ids=__A						)
             a					=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''']						)			,   )

             a					=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']						)
             a					=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(
                 __A			,   ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']						)
             self.assertSequenceEqual(
                 __A			,   ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']						)



"""simple docstring"""







import os
import sys
import unittest


lowerCamelCase_       :       Tuple      						=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCamelCase_       :       Dict      						=  os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
lowerCamelCase_       :       Dict      						=  os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")






class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=get_test_to_tester_mapping(__A						)
     a					=get_test_to_tester_mapping(__A						)

     a					={'''BertModelTest''': '''BertModelTester'''}

     a					={
         '''BlipModelTest''': '''BlipModelTester''',
         '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
         '''BlipTextModelTest''': '''BlipTextModelTester''',
         '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
         '''BlipVQAModelTest''': '''BlipVQAModelTester''',
         '''BlipVisionModelTest''': '''BlipVisionModelTester''',
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=get_model_to_test_mapping(__A						)
     a					=get_model_to_test_mapping(__A						)

     a					={
         '''BertForMaskedLM''': ['''BertModelTest'''],
         '''BertForMultipleChoice''': ['''BertModelTest'''],
         '''BertForNextSentencePrediction''': ['''BertModelTest'''],
         '''BertForPreTraining''': ['''BertModelTest'''],
         '''BertForQuestionAnswering''': ['''BertModelTest'''],
         '''BertForSequenceClassification''': ['''BertModelTest'''],
         '''BertForTokenClassification''': ['''BertModelTest'''],
         '''BertLMHeadModel''': ['''BertModelTest'''],
         '''BertModel''': ['''BertModelTest'''],
     }

     a					={
         '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
         '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
         '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
         '''BlipModel''': ['''BlipModelTest'''],
         '''BlipTextModel''': ['''BlipTextModelTest'''],
         '''BlipVisionModel''': ['''BlipVisionModelTest'''],
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=get_model_to_tester_mapping(__A						)
     a					=get_model_to_tester_mapping(__A						)

     a					={
         '''BertForMaskedLM''': ['''BertModelTester'''],
         '''BertForMultipleChoice''': ['''BertModelTester'''],
         '''BertForNextSentencePrediction''': ['''BertModelTester'''],
         '''BertForPreTraining''': ['''BertModelTester'''],
         '''BertForQuestionAnswering''': ['''BertModelTester'''],
         '''BertForSequenceClassification''': ['''BertModelTester'''],
         '''BertForTokenClassification''': ['''BertModelTester'''],
         '''BertLMHeadModel''': ['''BertModelTester'''],
         '''BertModel''': ['''BertModelTester'''],
     }

     a					={
         '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
         '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
         '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
         '''BlipModel''': ['''BlipModelTester'''],
         '''BlipTextModel''': ['''BlipTextModelTester'''],
         '''BlipVisionModel''': ['''BlipVisionModelTester'''],
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)