


import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowerCamelCase_       :       Any      						=  logging.getLogger(__name__)






class 				__A    (	__UpperCamelCase      ):






 """simple docstring"""




 def __init__(		self			,   __A=-1						) ->					int:
     # in NER datasets, the last column is usually reserved for NER label
     a					=label_idx


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					List[Any]:
     if isinstance(_lowerCAmelCase			,   _lowerCAmelCase						):
         a					=mode.value
     a					=os.path.join(_lowerCAmelCase			,   f'''{mode}.txt'''						)
     a					=1
     a					=[]
     with open(_lowerCAmelCase			,   encoding='''utf-8'''						) as f:
         a					=[]
         a					=[]
         for line in f:
             if line.startswith('''-DOCSTART-'''						) or line == "" or line == "\n":
                 if words:
                     examples.append(InputExample(guid=f'''{mode}-{guid_index}'''			,   words=_lowerCAmelCase			,   labels=_lowerCAmelCase						)						)
                     guid_index += 1
                     a					=[]
                     a					=[]
             else:
                 a					=line.split(''' '''						)
                 words.append(splits[0]						)
                 if len(_lowerCAmelCase						) > 1:
                     labels.append(splits[self.label_idx].replace('''\n'''			,   ''''''						)						)
                 else:
                     # Examples could have no label for mode = "test"
                     labels.append('''O'''						)
         if words:
             examples.append(InputExample(guid=f'''{mode}-{guid_index}'''			,   words=_lowerCAmelCase			,   labels=_lowerCAmelCase						)						)
     return examples


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Optional[Any]:
     a					=0
     for line in test_input_reader:
         if line.startswith('''-DOCSTART-'''						) or line == "" or line == "\n":
             writer.write(_lowerCAmelCase						)
             if not preds_list[example_id]:
                 example_id += 1
         elif preds_list[example_id]:
             a					=line.split()[0] + """ """ + preds_list[example_id].pop(0						) + """\n"""
             writer.write(_lowerCAmelCase						)
         else:
             logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''			,   line.split()[0]						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					str:
     if path:
         with open(_lowerCAmelCase			,   '''r'''						) as f:
             a					=f.read().splitlines()
         if "O" not in labels:
             a					=["""O"""] + labels
         return labels
     else:
         return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class 				__A    (	__UpperCamelCase      ):






 """simple docstring"""




 def __init__(		self						) ->					Any:
     # in CONLL2003 dataset chunk column is second-to-last
     super().__init__(label_idx=-2						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[Any]:
     if path:
         with open(_lowerCAmelCase			,   '''r'''						) as f:
             a					=f.read().splitlines()
         if "O" not in labels:
             a					=["""O"""] + labels
         return labels
     else:
         return [
             "O",
             "B-ADVP",
             "B-INTJ",
             "B-LST",
             "B-PRT",
             "B-NP",
             "B-SBAR",
             "B-VP",
             "B-ADJP",
             "B-CONJP",
             "B-PP",
             "I-ADVP",
             "I-INTJ",
             "I-LST",
             "I-PRT",
             "I-NP",
             "I-SBAR",
             "I-VP",
             "I-ADJP",
             "I-CONJP",
             "I-PP",
         ]






class 				__A    (	__UpperCamelCase      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					Union[str, Any]:
     if isinstance(_lowerCAmelCase			,   _lowerCAmelCase						):
         a					=mode.value
     a					=os.path.join(_lowerCAmelCase			,   f'''{mode}.txt'''						)
     a					=1
     a					=[]

     with open(_lowerCAmelCase			,   encoding='''utf-8'''						) as f:
         for sentence in parse_incr(_lowerCAmelCase						):
             a					=[]
             a					=[]
             for token in sentence:
                 words.append(token['''form''']						)
                 labels.append(token['''upos''']						)
             assert len(_lowerCAmelCase						) == len(_lowerCAmelCase						)
             if words:
                 examples.append(InputExample(guid=f'''{mode}-{guid_index}'''			,   words=_lowerCAmelCase			,   labels=_lowerCAmelCase						)						)
                 guid_index += 1
     return examples


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					str:
     a					=0
     for sentence in parse_incr(_lowerCAmelCase						):
         a					=preds_list[example_id]
         a					=""""""
         for token in sentence:
             out += f'''{token["form"]} ({token["upos"]}|{s_p.pop(0						)}) '''
         out += "\n"
         writer.write(_lowerCAmelCase						)
         example_id += 1





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Optional[int]:
     if path:
         with open(_lowerCAmelCase			,   '''r'''						) as f:
             return f.read().splitlines()
     else:
         return [
             "ADJ",
             "ADP",
             "ADV",
             "AUX",
             "CCONJ",
             "DET",
             "INTJ",
             "NOUN",
             "NUM",
             "PART",
             "PRON",
             "PROPN",
             "PUNCT",
             "SCONJ",
             "SYM",
             "VERB",
             "X",
         ]



"""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						)