



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  lowerCamelCase_	(      lowerCAmelCase__	:	List[Any]			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	List[str]				)       ->							Union[str, Any]:

							'''simple docstring'''






							if isinstance(lowerCAmelCase__			,	lowerCAmelCase__				):
														A      						=    np.full((len(lowerCAmelCase__				), sequence_length, 2)			,	lowerCAmelCase__				)
							else:
														A      						=    np.full((len(lowerCAmelCase__				), sequence_length)			,	lowerCAmelCase__				)

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

							return out_tensor.tolist()






def  lowerCamelCase_	(      lowerCAmelCase__	:	int				)       ->							str:

							'''simple docstring'''






							A      						=    ord(lowerCAmelCase__				)
							if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
														return True
							A      						=    unicodedata.category(lowerCAmelCase__				)
							if cat.startswith('P'				):
														return True
							return False



@dataclass
class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     PreTrainedTokenizerBase
				A_   :     Union[bool, str, PaddingStrategy]								=     True
				A_   :     Optional[int]								=     None
				A_   :     Optional[int]								=     None
				A_   :     int								=     -1_0_0
				A_   :     str								=     "pt"


				def __UpperCamelCase						(							self      :  int						, __UpperCamelCase      :  Optional[Any]       )		->     int:
											import torch

											A      						=    'label' if 'label' in features[0].keys() else 'labels'
											A      						=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
											A      						=    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

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

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

											return batch




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       ):
				A_   :     int								=     CTRLTokenizer
				A_   :     List[str]								=     False
				A_   :     str								=     False


				def __UpperCamelCase						(							self      :  Any       )		->     Tuple:
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											A      						=    ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
											A      						=    dict(zip(__UpperCamelCase						, range(len(__UpperCamelCase       )       )       )       )
											A      						=    ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
											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(__UpperCamelCase       ) + '\n'       )
											with open(self.merges_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write('\n'.join(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Optional[Any]						, **__UpperCamelCase      :  List[str]       )		->     List[Any]:
											kwargs.update(self.special_tokens_map       )
											return CTRLTokenizer.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int						, __UpperCamelCase      :  Optional[int]       )		->     Optional[Any]:
											A      						=    'adapt react readapt apt'
											A      						=    'adapt react readapt apt'
											return input_text, output_text


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[int]:
											A      						=    CTRLTokenizer(self.vocab_file						, self.merges_file						, **self.special_tokens_map       )
											A      						=    'adapt react readapt apt'
											A      						=    'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
											A      						=    tokenizer.tokenize(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )

											A      						=    tokens + [tokenizer.unk_token]

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