



'''simple docstring'''





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  CTRLTokenizer
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase													=    ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
    __lowerCamelCase													=    dict(zip(__UpperCAmelCase	,				range(len(__UpperCAmelCase				)				)				)				)
    __lowerCamelCase													=    ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
    __lowerCamelCase													=    {'''unk_token''': '''<unk>'''}

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




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    kwargs.update(self.special_tokens_map				)
    return CTRLTokenizer.from_pretrained(self.tmpdirname	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    '''adapt react readapt apt'''
    __lowerCamelCase													=    '''adapt react readapt apt'''
    return input_text, output_text




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    CTRLTokenizer(self.vocab_file	,				self.merges_file	,				**self.special_tokens_map				)
    __lowerCamelCase													=    '''adapt react readapt apt'''
    __lowerCamelCase													=    '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
    __lowerCamelCase													=    tokenizer.tokenize(__UpperCAmelCase				)
    self.assertListEqual(__UpperCAmelCase	,				__UpperCAmelCase				)

    __lowerCamelCase													=    tokens + [tokenizer.unk_token]

    __lowerCamelCase													=    [0, 1, 2, 4, 5, 1, 0, 3, 6]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)	,				__UpperCAmelCase				)











import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Any ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any							):
   if isinstance(_UpperCamelCase ,_UpperCamelCase							):
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length, 2) ,_UpperCamelCase							)
   else:
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length) ,_UpperCamelCase							)

   for i, tensor in enumerate(_UpperCamelCase							):
      if padding_side == "right":
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]
      else:
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]

   return out_tensor.tolist()





def 		a__			(		_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    ord(_UpperCamelCase							)
   if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
      return True
   __lowerCamelCase													=    unicodedata.category(_UpperCamelCase							)
   if cat.startswith('''P'''							):
      return True
   return False





@dataclass
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  -1_0_0
 lowerCAmelCase__       					=  "pt"




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    '''label''' if '''label''' in features[0].keys() else '''labels'''
    __lowerCamelCase													=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __lowerCamelCase													=    self.tokenizer.pad(
        __UpperCAmelCase	,				padding=self.padding	,				max_length=self.max_length	,				pad_to_multiple_of=self.pad_to_multiple_of	,				return_tensors='''pt''' if labels is None else None	,				)

    if labels is None:
       return batch

    __lowerCamelCase													=    torch.tensor(batch['''entity_ids''']				).shape[1]
    __lowerCamelCase													=    self.tokenizer.padding_side
    if padding_side == "right":
       __lowerCamelCase													=    [
           list(__UpperCAmelCase				) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) for label in labels
       ]
    else:
       __lowerCamelCase													=    [
           [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) + list(__UpperCAmelCase				) for label in labels
       ]

    __lowerCamelCase													=    [feature['''ner_tags'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				-1	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    [feature['''original_entity_spans'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				(-1, -1)	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    {k: torch.tensor(__UpperCAmelCase	,				dtype=torch.intaa				) for k, v in batch.items()}

    return batch






