





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
       import tensorflow as tf

if is_tensorflow_text_available():
       from transformers.models.bert import TFBertTokenizer


lowercase    : Optional[Any]    	=  ["""bert-base-uncased""", """bert-base-cased"""]
lowercase    : Union[str, Any]    	=  """hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():

       class  __A(						tf.keras.Model ):

            def __init__(      self,       A      ):





                   """simple docstring"""

                   super().__init__()
                   _UpperCamelCase              =						tokenizer
                   _UpperCamelCase              =						AutoConfig.from_pretrained(A      )
                   _UpperCamelCase              =						TFAutoModel.from_config(A      )



            def 	_UpperCamelCase     (      self,       A      ):





                   """simple docstring"""

                   _UpperCamelCase              =						self.tokenizer(A      )
                   _UpperCamelCase              =						self.bert(**A      )
                   return out["pooler_output"]

@require_tf
@require_tensorflow_text
class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            super().setUp()

            _UpperCamelCase              =						[
                BertTokenizer.from_pretrained(A      ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
            ]  # repeat for when fast_bert_tokenizer=false
            _UpperCamelCase              =						[TFBertTokenizer.from_pretrained(A      ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
                TFBertTokenizer.from_pretrained(A,       use_fast_bert_tokenizer=A      )
                for checkpoint in TOKENIZER_CHECKPOINTS
            ]
            assert len(self.tokenizers      ) == len(self.tf_tokenizers      )

            _UpperCamelCase              =						[
                '''This is a straightforward English test sentence.''',
                '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
                '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
                '''And some much more rare Chinese: 齉 堃 齉堃''',
                '''Je vais aussi écrire en français pour tester les accents''',
                '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
            ]
            _UpperCamelCase              =						list(zip(self.test_sentences,       self.test_sentences[::-1]      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            for tokenizer, tf_tokenizer in zip(self.tokenizers,       self.tf_tokenizers      ):
                   for test_inputs in (self.test_sentences, self.paired_sentences):
                          _UpperCamelCase              =						tokenizer(A,       return_tensors='''tf''',       padding='''longest'''      )
                          _UpperCamelCase              =						tf_tokenizer(A      )

                          for key in python_outputs.keys():
                                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape      )      )
                                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key],       tf.intaa      ) == tf_outputs[key]      )      )

     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            for tf_tokenizer in self.tf_tokenizers:
                   _UpperCamelCase              =						tf_tokenizer(self.paired_sentences      )
                   _UpperCamelCase              =						tf_tokenizer(
                       text=[sentence[0] for sentence in self.paired_sentences],       text_pair=[sentence[1] for sentence in self.paired_sentences],       )
                   for key in merged_outputs.keys():
                          self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key],       tf.intaa      ) == separated_outputs[key]      )      )

     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            for tf_tokenizer in self.tf_tokenizers:
                   _UpperCamelCase              =						tf.function(A      )
                   for test_inputs in (self.test_sentences, self.paired_sentences):
                          _UpperCamelCase              =						tf.constant(A      )
                          _UpperCamelCase              =						compiled_tokenizer(A      )
                          _UpperCamelCase              =						tf_tokenizer(A      )

                          for key in eager_outputs.keys():
                                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]      )      )



     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            for tf_tokenizer in self.tf_tokenizers:
                   _UpperCamelCase              =						ModelToSave(tokenizer=A      )
                   _UpperCamelCase              =						tf.convert_to_tensor(self.test_sentences      )
                   _UpperCamelCase              =						model(A      )  # Build model with some sample inputs
                   with TemporaryDirectory() as tempdir:
                          _UpperCamelCase              =						Path(A      ) / '''saved.model'''
                          model.save(A      )
                          _UpperCamelCase              =						tf.keras.models.load_model(A      )
                   _UpperCamelCase              =						loaded_model(A      )
                   # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
                   self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output      )      ),       1E-5      )













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

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


lowercase    : Optional[Any]    	=  logging.getLogger(__name__)

class  __A(						__UpperCAmelCase ):

     def __init__(      self,       A=-1      ):





            """simple docstring"""

            _UpperCamelCase              =						label_idx

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            if isinstance(A,       A      ):
                   _UpperCamelCase              =						mode.value
            _UpperCamelCase              =						os.path.join(A,       F'''{mode}.txt'''      )
            _UpperCamelCase              =						1
            _UpperCamelCase              =						[]
            with open(A,       encoding='''utf-8'''      ) as f:
                   _UpperCamelCase              =						[]
                   _UpperCamelCase              =						[]
                   for line in f:
                          if line.startswith('''-DOCSTART-'''      ) or line == "" or line == "\n":
                                 if words:
                                        examples.append(InputExample(guid=F'''{mode}-{guid_index}''',       words=A,       labels=A      )      )
                                        guid_index += 1
                                        _UpperCamelCase              =						[]
                                        _UpperCamelCase              =						[]
                          else:
                                 _UpperCamelCase              =						line.split(''' '''      )
                                 words.append(splits[0]      )
                                 if len(A      ) > 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=A,       labels=A      )      )
            return examples

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						0
            for line in test_input_reader:
                   if line.startswith('''-DOCSTART-'''      ) or line == "" or line == "\n":
                          writer.write(A      )
                          if not preds_list[example_id]:
                                 example_id += 1
                   elif preds_list[example_id]:
                          _UpperCamelCase              =						line.split()[0] + ''' ''' + preds_list[example_id].pop(0      ) + '''\n'''
                          writer.write(A      )
                   else:
                          logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''',       line.split()[0]      )



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if path:
                   with open(A,       '''r'''      ) as f:
                          _UpperCamelCase              =						f.read().splitlines()
                   if "O" not in labels:
                          _UpperCamelCase              =						['''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 ):

     def __init__(      self      ):





            """simple docstring"""

            super().__init__(label_idx=-2      )



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if path:
                   with open(A,       '''r'''      ) as f:
                          _UpperCamelCase              =						f.read().splitlines()
                   if "O" not in labels:
                          _UpperCamelCase              =						['''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 ):

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            if isinstance(A,       A      ):
                   _UpperCamelCase              =						mode.value
            _UpperCamelCase              =						os.path.join(A,       F'''{mode}.txt'''      )
            _UpperCamelCase              =						1
            _UpperCamelCase              =						[]

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

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						0
            for sentence in parse_incr(A      ):
                   _UpperCamelCase              =						preds_list[example_id]
                   _UpperCamelCase              =						''''''
                   for token in sentence:
                          out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0      )}) '''
                   out += "\n"
                   writer.write(A      )
                   example_id += 1



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if path:
                   with open(A,       '''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",
                   ]






