




import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)







def 		_A   (		__A:	int				):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							torch.exp(a__				)
					__magic_name__							:	int    							=							torch.sum(a__							,dim=1				)  # sum of exp(x_i)
					__magic_name__							:	Optional[Any]    							=							torch.sum(x * exp_x							,dim=1				)  # sum of x_i * exp(x_i)
					return torch.log(a__				) - B / A


class    lowerCamelCase    (		nn.Module						):






			'''simple docstring'''





			def __init__( self			:   Tuple       ,						lowerCamelCase_			:   List[str]  )						->   Optional[int]:
								super().__init__()
								__magic_name__							:	Union[str, Any]    							=							config.output_attentions
								__magic_name__							:	int    							=							config.output_hidden_states
								__magic_name__							:	str    							=							nn.ModuleList([BertLayer(__A  ) for _ in range(config.num_hidden_layers  )]  )
								__magic_name__							:	int    							=							nn.ModuleList([BertHighway(__A  ) for _ in range(config.num_hidden_layers  )]  )

								__magic_name__							:	Union[str, Any]    							=							[-1 for _ in range(config.num_hidden_layers  )]

			def 							UpperCAmelCase__       ( self			:   Tuple       ,						lowerCamelCase_			:   Any  )						->   Union[str, Any]:
								if (type(__A  ) is float) or (type(__A  ) is int):
													for i in range(len(self.early_exit_entropy  )  ):
																		__magic_name__							:	Any    							=							x
								else:
													__magic_name__							:	Any    							=							x

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   List[str]  )						->   Union[str, Any]:
								__magic_name__							:	str    							=							pooler.state_dict()
								for highway in self.highway:
													for name, param in highway.pooler.state_dict().items():
																		param.copy_(loaded_model[name]  )


			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   Any=None       ,						lowerCamelCase_			:   Tuple=None       ,						lowerCamelCase_			:   Optional[Any]=None       ,						lowerCamelCase_			:   str=None       ,						)						->   Union[str, Any]:
								__magic_name__							:	Dict    							=							()
								__magic_name__							:	Optional[Any]    							=							()
								__magic_name__							:	int    							=							()
								for i, layer_module in enumerate(self.layer  ):
													if self.output_hidden_states:
																		__magic_name__							:	Any    							=							all_hidden_states + (hidden_states,)

													__magic_name__							:	Dict    							=							layer_module(
													    __A       ,						__A       ,						head_mask[i]       ,						__A       ,						__A  )
													__magic_name__							:	List[str]    							=							layer_outputs[0]

													if self.output_attentions:
																		__magic_name__							:	int    							=							all_attentions + (layer_outputs[1],)

													__magic_name__							:	Optional[Any]    							=							(hidden_states,)
													if self.output_hidden_states:
																		__magic_name__							:	List[str]    							=							current_outputs + (all_hidden_states,)
													if self.output_attentions:
																		__magic_name__							:	List[str]    							=							current_outputs + (all_attentions,)

													__magic_name__							:	Any    							=							self.highway[i](__A  )
													# logits, pooled_output

													if not self.training:
																		__magic_name__							:	str    							=							highway_exit[0]
																		__magic_name__							:	int    							=							entropy(__A  )
																		__magic_name__							:	Dict    							=							highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																		__magic_name__							:	Union[str, Any]    							=							all_highway_exits + (highway_exit,)

																		if highway_entropy < self.early_exit_entropy[i]:
																							__magic_name__							:	List[Any]    							=							(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																							raise HighwayException(__A       ,						i + 1  )
													else:
																		__magic_name__							:	Union[str, Any]    							=							all_highway_exits + (highway_exit,)

        # Add last layer
								if self.output_hidden_states:
													__magic_name__							:	Any    							=							all_hidden_states + (hidden_states,)

								__magic_name__							:	Optional[Any]    							=							(hidden_states,)
								if self.output_hidden_states:
													__magic_name__							:	Union[str, Any]    							=							outputs + (all_hidden_states,)
								if self.output_attentions:
													__magic_name__							:	Dict    							=							outputs + (all_attentions,)

								__magic_name__							:	Any    							=							outputs + (all_highway_exits,)
								return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''				,__lowercase				,)
class    lowerCamelCase    (		__lowercase						):






			'''simple docstring'''





			def __init__( self			:   Any       ,						lowerCamelCase_			:   str  )						->   Dict:
								super().__init__(__A  )
								__magic_name__							:	Tuple    							=							config

								__magic_name__							:	Union[str, Any]    							=							BertEmbeddings(__A  )
								__magic_name__							:	Tuple    							=							DeeBertEncoder(__A  )
								__magic_name__							:	List[Any]    							=							BertPooler(__A  )

								self.init_weights()

			def 							UpperCAmelCase__       ( self			:   Dict  )						->   Dict:
								self.encoder.init_highway_pooler(self.pooler  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   Any:
								return self.embeddings.word_embeddings

			def 							UpperCAmelCase__       ( self			:   Tuple       ,						lowerCamelCase_			:   Optional[int]  )						->   Tuple:
								__magic_name__							:	List[str]    							=							value

			def 							UpperCAmelCase__       ( self			:   Tuple       ,						lowerCamelCase_			:   Tuple  )						->   List[str]:
								for layer, heads in heads_to_prune.items():
													self.encoder.layer[layer].attention.prune_heads(__A  )


			@add_start_docstrings_to_model_forward(__A  )
			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[Any]=None       ,						lowerCamelCase_			:   List[Any]=None       ,						lowerCamelCase_			:   int=None       ,						lowerCamelCase_			:   Dict=None       ,						lowerCamelCase_			:   str=None       ,						lowerCamelCase_			:   Optional[int]=None       ,						lowerCamelCase_			:   Optional[int]=None       ,						lowerCamelCase_			:   Union[str, Any]=None       ,						)						->   int:
								if input_ids is not None and inputs_embeds is not None:
													raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''  )
								elif input_ids is not None:
													__magic_name__							:	str    							=							input_ids.size()
								elif inputs_embeds is not None:
													__magic_name__							:	Dict    							=							inputs_embeds.size()[:-1]
								else:
													raise ValueError('''You have to specify either input_ids or inputs_embeds'''  )

								__magic_name__							:	Optional[int]    							=							input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
													__magic_name__							:	Optional[Any]    							=							torch.ones(__A       ,						device=__A  )
								if encoder_attention_mask is None:
													__magic_name__							:	Dict    							=							torch.ones(__A       ,						device=__A  )
								if token_type_ids is None:
													__magic_name__							:	List[Any]    							=							torch.zeros(__A       ,						dtype=torch.long       ,						device=__A  )

								# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
								# ourselves in which case we just need to make it broadcastable to all heads.
								__magic_name__							:	int    							=							self.get_extended_attention_mask(__A       ,						__A       ,						__A  )

								# If a 2D ou 3D attention mask is provided for the cross-attention
								# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
								if encoder_attention_mask.dim() == 3:
													__magic_name__							:	Optional[Any]    							=							encoder_attention_mask[:, None, :, :]
								if encoder_attention_mask.dim() == 2:
													__magic_name__							:	Union[str, Any]    							=							encoder_attention_mask[:, None, None, :]

								__magic_name__							:	str    							=							encoder_extended_attention_mask.to(
								    dtype=next(self.parameters()  ).dtype  )  # fp16 compatibility
								__magic_name__							:	Any    							=							(1.0 - encoder_extended_attention_mask) * -10000.0

								# Prepare head mask if needed
								# 1.0 in head_mask indicate we keep the head
								# attention_probs has shape bsz x n_heads x N x N
								# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
								# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
								__magic_name__							:	Optional[Any]    							=							self.get_head_mask(__A       ,						self.config.num_hidden_layers  )

								__magic_name__							:	Optional[Any]    							=							self.embeddings(
								    input_ids=__A       ,						position_ids=__A       ,						token_type_ids=__A       ,						inputs_embeds=__A  )
								__magic_name__							:	Any    							=							self.encoder(
								    __A       ,						attention_mask=__A       ,						head_mask=__A       ,						encoder_hidden_states=__A       ,						encoder_attention_mask=__A       ,						)
								__magic_name__							:	Union[str, Any]    							=							encoder_outputs[0]
								__magic_name__							:	Any    							=							self.pooler(__A  )

								__magic_name__							:	Union[str, Any]    							=							(
								    sequence_output,
								    pooled_output,
								) + encoder_outputs[
								    1:
								]  # add hidden_states and attentions if they are here
								return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class    lowerCamelCase    (		__lowercase						):






			'''simple docstring'''





			def __init__( self			:   str       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[Any]  )						->   Dict:
								__magic_name__							:	str    							=							message
								__magic_name__							:	List[Any]    							=							exit_layer  # start from 1!






class    lowerCamelCase    (		nn.Module						):






			'''simple docstring'''





			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Tuple  )						->   int:
								super().__init__()
								__magic_name__							:	Tuple    							=							BertPooler(__A  )
								__magic_name__							:	Union[str, Any]    							=							nn.Dropout(config.hidden_dropout_prob  )
								__magic_name__							:	List[str]    							=							nn.Linear(config.hidden_size       ,						config.num_labels  )


			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   List[Any]  )						->   int:
								# Pooler
								__magic_name__							:	int    							=							encoder_outputs[0]
								__magic_name__							:	List[str]    							=							self.pooler(__A  )
								# "return" pooler_output

								# BertModel
								__magic_name__							:	Union[str, Any]    							=							(pooler_input, pooler_output) + encoder_outputs[1:]
								# "return" bmodel_output

								# Dropout and classification
								__magic_name__							:	Tuple    							=							bmodel_output[1]

								__magic_name__							:	int    							=							self.dropout(__A  )
								__magic_name__							:	Any    							=							self.classifier(__A  )

								return logits, pooled_output






@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '''				,__lowercase				,)
class    lowerCamelCase    (		__lowercase						):






			'''simple docstring'''





			def __init__( self			:   Optional[Any]       ,						lowerCamelCase_			:   Dict  )						->   Union[str, Any]:
								super().__init__(__A  )
								__magic_name__							:	Tuple    							=							config.num_labels
								__magic_name__							:	List[Any]    							=							config.num_hidden_layers

								__magic_name__							:	Tuple    							=							DeeBertModel(__A  )
								__magic_name__							:	List[str]    							=							nn.Dropout(config.hidden_dropout_prob  )
								__magic_name__							:	Union[str, Any]    							=							nn.Linear(config.hidden_size       ,						self.config.num_labels  )

								self.init_weights()


			@add_start_docstrings_to_model_forward(__A  )
			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   str=None       ,						lowerCamelCase_			:   str=None       ,						lowerCamelCase_			:   Optional[Any]=None       ,						lowerCamelCase_			:   str=None       ,						lowerCamelCase_			:   Optional[Any]=None       ,						lowerCamelCase_			:   List[Any]=None       ,						lowerCamelCase_			:   str=None       ,						lowerCamelCase_			:   Any=-1       ,						lowerCamelCase_			:   int=False       ,						)						->   Tuple:
								__magic_name__							:	int    							=							self.num_layers
								try:
													__magic_name__							:	List[str]    							=							self.bert(
													    __A       ,						attention_mask=__A       ,						token_type_ids=__A       ,						position_ids=__A       ,						head_mask=__A       ,						inputs_embeds=__A       ,						)
													# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

													__magic_name__							:	List[str]    							=							outputs[1]

													__magic_name__							:	Optional[Any]    							=							self.dropout(__A  )
													__magic_name__							:	Dict    							=							self.classifier(__A  )
													__magic_name__							:	Tuple    							=							(logits,) + outputs[2:]  # add hidden states and attention if they are here
								except HighwayException as e:
													__magic_name__							:	str    							=							e.message
													__magic_name__							:	Dict    							=							e.exit_layer
													__magic_name__							:	Union[str, Any]    							=							outputs[0]

								if not self.training:
													__magic_name__							:	Dict    							=							entropy(__A  )
													__magic_name__							:	Dict    							=							[]
													__magic_name__							:	Any    							=							[]
								if labels is not None:
													if self.num_labels == 1:
																		#  We are doing regression
																		__magic_name__							:	int    							=							MSELoss()
																		__magic_name__							:	Dict    							=							loss_fct(logits.view(-1  )       ,						labels.view(-1  )  )
													else:
																		__magic_name__							:	List[Any]    							=							CrossEntropyLoss()
																		__magic_name__							:	Dict    							=							loss_fct(logits.view(-1       ,						self.num_labels  )       ,						labels.view(-1  )  )

													# work with highway exits
													__magic_name__							:	str    							=							[]
													for highway_exit in outputs[-1]:
																		__magic_name__							:	List[Any]    							=							highway_exit[0]
																		if not self.training:
																							highway_logits_all.append(__A  )
																							highway_entropy.append(highway_exit[2]  )
																		if self.num_labels == 1:
																							#  We are doing regression
																							__magic_name__							:	str    							=							MSELoss()
																							__magic_name__							:	Dict    							=							loss_fct(highway_logits.view(-1  )       ,						labels.view(-1  )  )
																		else:
																							__magic_name__							:	Dict    							=							CrossEntropyLoss()
																							__magic_name__							:	str    							=							loss_fct(highway_logits.view(-1       ,						self.num_labels  )       ,						labels.view(-1  )  )
																		highway_losses.append(__A  )

													if train_highway:
																		__magic_name__							:	Optional[int]    							=							(sum(highway_losses[:-1]  ),) + outputs
																		# exclude the final highway, of course
													else:
																		__magic_name__							:	str    							=							(loss,) + outputs
								if not self.training:
													__magic_name__							:	Union[str, Any]    							=							outputs + ((original_entropy, highway_entropy), exit_layer)
													if output_layer >= 0:
																		__magic_name__							:	Any    							=							(
																		    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																		)  # use the highway of the last layer

								return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)









import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=CanineTokenizer
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   List[str]:
								super().setUp()
								__magic_name__							:	Optional[int]    							=							CanineTokenizer()
								tokenizer.save_pretrained(self.tmpdirname  )

			@cached_property
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Tuple:
								return CanineTokenizer.from_pretrained('''google/canine-s'''  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						**lowerCamelCase_			:   Optional[int]  )						->   CanineTokenizer:
								__magic_name__							:	int    							=							self.tokenizer_class.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							1024
								return tokenizer

			@require_torch
			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								__magic_name__							:	List[Any]    							=							self.canine_tokenizer
								__magic_name__							:	Any    							=							['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
								# fmt: off
								__magic_name__							:	Optional[int]    							=							[57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
								# fmt: on
								__magic_name__							:	List[Any]    							=							tokenizer(lowerCamelCase_       ,						padding=lowerCamelCase_       ,						return_tensors='''pt'''  )
								self.assertIsInstance(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Tuple    							=							list(batch.input_ids.numpy()[0]  )

								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								self.assertEqual((2, 39)       ,						batch.input_ids.shape  )
								self.assertEqual((2, 39)       ,						batch.attention_mask.shape  )

			@require_torch
			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Optional[Any]:
								__magic_name__							:	Any    							=							self.canine_tokenizer
								__magic_name__							:	Dict    							=							['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
								__magic_name__							:	Dict    							=							tokenizer(lowerCamelCase_       ,						padding=lowerCamelCase_       ,						return_tensors='''pt'''  )
								# check if input_ids, attention_mask and token_type_ids are returned
								self.assertIn('''input_ids'''       ,						lowerCamelCase_  )
								self.assertIn('''attention_mask'''       ,						lowerCamelCase_  )
								self.assertIn('''token_type_ids'''       ,						lowerCamelCase_  )

			@require_torch
			def 							UpperCAmelCase__       ( self			:   int  )						->   Optional[int]:
								__magic_name__							:	int    							=							self.canine_tokenizer
								__magic_name__							:	Dict    							=							[
								    '''What\'s the weater?''',
								    '''It\'s about 25 degrees.''',
								]
								__magic_name__							:	List[Any]    							=							tokenizer(
								    text_target=lowerCamelCase_       ,						max_length=32       ,						padding='''max_length'''       ,						truncation=lowerCamelCase_       ,						return_tensors='''pt'''  )
								self.assertEqual(32       ,						targets['''input_ids'''].shape[1]  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Dict:
								# safety check on max_len default value so we are sure the test works
								__magic_name__							:	str    							=							self.get_tokenizers()
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		self.assertNotEqual(tokenizer.model_max_length       ,						42  )

        # Now let's start the test
								__magic_name__							:	Union[str, Any]    							=							self.get_tokenizers()
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		# Isolate this from the other tests because we save additional tokens/etc
																		__magic_name__							:	str    							=							tempfile.mkdtemp()

																		__magic_name__							:	Any    							=							''' He is very happy, UNwant\u00E9d,running'''
																		__magic_name__							:	str    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		tokenizer.save_pretrained(lowerCamelCase_  )

																		__magic_name__							:	str    							=							tokenizer.__class__.from_pretrained(lowerCamelCase_  )
																		__magic_name__							:	Tuple    							=							after_tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

																		shutil.rmtree(lowerCamelCase_  )

								__magic_name__							:	Any    							=							self.get_tokenizers(model_max_length=42  )
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		# Isolate this from the other tests because we save additional tokens/etc
																		__magic_name__							:	Any    							=							tempfile.mkdtemp()

																		__magic_name__							:	Any    							=							''' He is very happy, UNwant\u00E9d,running'''

																		__magic_name__							:	str    							=							tokenizer.additional_special_tokens

																		# We can add a new special token for Canine as follows:
																		__magic_name__							:	str    							=							chr(0XE007  )
																		additional_special_tokens.append(lowerCamelCase_  )
																		tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}  )
																		__magic_name__							:	Tuple    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		tokenizer.save_pretrained(lowerCamelCase_  )

																		__magic_name__							:	List[Any]    							=							tokenizer.__class__.from_pretrained(lowerCamelCase_  )
																		__magic_name__							:	Optional[int]    							=							after_tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )
																		self.assertIn(lowerCamelCase_       ,						after_tokenizer.additional_special_tokens  )
																		self.assertEqual(after_tokenizer.model_max_length       ,						42  )

																		__magic_name__							:	List[Any]    							=							tokenizer.__class__.from_pretrained(lowerCamelCase_       ,						model_max_length=43  )
																		self.assertEqual(tokenizer.model_max_length       ,						43  )

																		shutil.rmtree(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   Optional[int]:
								__magic_name__							:	Any    							=							self.get_tokenizers(do_lower_case=lowerCamelCase_  )
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		__magic_name__							,    __magic_name__							:	str    							=							self.get_clean_sequence(lowerCamelCase_  )

																		# a special token for Canine can be defined as follows:
																		__magic_name__							:	Optional[Any]    							=							0XE005
																		__magic_name__							:	Optional[Any]    							=							chr(lowerCamelCase_  )

																		tokenizer.add_special_tokens({'''cls_token''': special_token}  )
																		__magic_name__							:	Optional[int]    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		self.assertEqual(len(lowerCamelCase_  )       ,						1  )

																		__magic_name__							:	Union[str, Any]    							=							tokenizer.decode(ids + encoded_special_token       ,						clean_up_tokenization_spaces=lowerCamelCase_  )
																		__magic_name__							:	str    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )

																		__magic_name__							:	Optional[Any]    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		__magic_name__							:	Union[str, Any]    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		self.assertEqual(lowerCamelCase_       ,						input_encoded + special_token_id  )

																		__magic_name__							:	Any    							=							tokenizer.decode(lowerCamelCase_       ,						skip_special_tokens=lowerCamelCase_  )
																		self.assertTrue(special_token not in decoded  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   int:
								__magic_name__							:	str    							=							self.get_tokenizers(do_lower_case=lowerCamelCase_  )
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		__magic_name__							:	Tuple    							=							chr(0XE005  )
																		__magic_name__							:	Optional[int]    							=							chr(0XE006  )

																		# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
																		tokenizer.add_tokens([SPECIAL_TOKEN_1]       ,						special_tokens=lowerCamelCase_  )
																		# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
																		# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
																		tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]}  )

																		__magic_name__							:	List[str]    							=							tokenizer.tokenize(lowerCamelCase_  )
																		__magic_name__							:	Optional[int]    							=							tokenizer.tokenize(lowerCamelCase_  )

																		self.assertEqual(len(lowerCamelCase_  )       ,						1  )
																		self.assertEqual(len(lowerCamelCase_  )       ,						1  )
																		self.assertEqual(token_a[0]       ,						lowerCamelCase_  )
																		self.assertEqual(token_a[0]       ,						lowerCamelCase_  )

			@require_tokenizers
			def 							UpperCAmelCase__       ( self			:   str  )						->   List[str]:
								__magic_name__							:	List[Any]    							=							self.get_tokenizers(do_lower_case=lowerCamelCase_  )
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		# a special token for Canine can be defined as follows:
																		__magic_name__							:	str    							=							0XE006
																		__magic_name__							:	Optional[int]    							=							chr(lowerCamelCase_  )

																		__magic_name__							:	Optional[Any]    							=							AddedToken(lowerCamelCase_       ,						lstrip=lowerCamelCase_  )
																		tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]}  )

																		with tempfile.TemporaryDirectory() as tmp_dir_name:
																							tokenizer.save_pretrained(lowerCamelCase_  )
																							tokenizer.from_pretrained(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[Any]:
								__magic_name__							:	Union[str, Any]    							=							[]
								if self.test_slow_tokenizer:
													tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

								if self.test_rust_tokenizer:
													tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

								for tokenizer_class, tokenizer_utils in tokenizer_list:
													with tempfile.TemporaryDirectory() as tmp_dir:
																		tokenizer_utils.save_pretrained(lowerCamelCase_  )

																		with open(os.path.join(lowerCamelCase_       ,						'''special_tokens_map.json'''  )       ,						encoding='''utf-8'''  ) as json_file:
																							__magic_name__							:	List[Any]    							=							json.load(lowerCamelCase_  )

																		with open(os.path.join(lowerCamelCase_       ,						'''tokenizer_config.json'''  )       ,						encoding='''utf-8'''  ) as json_file:
																							__magic_name__							:	int    							=							json.load(lowerCamelCase_  )

																		# a special token for Canine can be defined as follows:
																		__magic_name__							:	List[str]    							=							0XE006
																		__magic_name__							:	List[str]    							=							chr(lowerCamelCase_  )

																		__magic_name__							:	int    							=							[new_token_a]
																		__magic_name__							:	str    							=							[new_token_a]

																		with open(os.path.join(lowerCamelCase_       ,						'''special_tokens_map.json'''  )       ,						'''w'''       ,						encoding='''utf-8'''  ) as outfile:
																							json.dump(lowerCamelCase_       ,						lowerCamelCase_  )
																		with open(os.path.join(lowerCamelCase_       ,						'''tokenizer_config.json'''  )       ,						'''w'''       ,						encoding='''utf-8'''  ) as outfile:
																							json.dump(lowerCamelCase_       ,						lowerCamelCase_  )

																		# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																		# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																		# "special_tokens_map.json" files
																		__magic_name__							:	int    							=							tokenizer_class.from_pretrained(lowerCamelCase_       ,						extra_ids=0  )
																		self.assertIn(lowerCamelCase_       ,						tokenizer_without_change_in_init.additional_special_tokens  )
																		# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																		self.assertEqual(
																		    [new_token_a]       ,						tokenizer_without_change_in_init.convert_ids_to_tokens(
																		        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]  )  )       ,						)

																		__magic_name__							:	Optional[int]    							=							0XE007
																		__magic_name__							:	List[str]    							=							chr(lowerCamelCase_  )
																		# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																		__magic_name__							:	List[str]    							=							[AddedToken(lowerCamelCase_       ,						lstrip=lowerCamelCase_  )]
																		__magic_name__							:	str    							=							tokenizer_class.from_pretrained(
																		    lowerCamelCase_       ,						additional_special_tokens=lowerCamelCase_       ,						extra_ids=0  )

																		self.assertIn(lowerCamelCase_       ,						tokenizer.additional_special_tokens  )
																		# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
																		self.assertEqual(
																		    [new_token_a]       ,						tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]  )  )  )

			@require_tokenizers
			def 							UpperCAmelCase__       ( self			:   Any  )						->   List[str]:
								__magic_name__							:	Optional[int]    							=							self.get_tokenizers(do_lower_case=lowerCamelCase_  )
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		__magic_name__							:	Union[str, Any]    							=							'''hello world'''
																		if self.space_between_special_tokens:
																							__magic_name__							:	List[Any]    							=							'''[CLS] hello world [SEP]'''
																		else:
																							__magic_name__							:	List[str]    							=							input
																		__magic_name__							:	Dict    							=							tokenizer.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  )
																		__magic_name__							:	Union[str, Any]    							=							tokenizer.decode(lowerCamelCase_       ,						spaces_between_special_tokens=self.space_between_special_tokens  )
																		self.assertIn(lowerCamelCase_       ,						[output, output.lower()]  )

			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Optional[Any]:
								__magic_name__							:	Dict    							=							self.get_tokenizers()
								for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
																		__magic_name__							:	str    							=							[
																		    '''bos_token''',
																		    '''eos_token''',
																		    '''unk_token''',
																		    '''sep_token''',
																		    '''pad_token''',
																		    '''cls_token''',
																		    '''mask_token''',
																		]

								__magic_name__							:	Any    							=							'''a'''
								__magic_name__							:	List[str]    							=							ord(lowerCamelCase_  )

								for attr in attributes_list:
													setattr(lowerCamelCase_       ,						attr + '''_id'''       ,						lowerCamelCase_  )
													self.assertEqual(getattr(lowerCamelCase_       ,						lowerCamelCase_  )       ,						lowerCamelCase_  )
													self.assertEqual(getattr(lowerCamelCase_       ,						attr + '''_id'''  )       ,						lowerCamelCase_  )

													setattr(lowerCamelCase_       ,						attr + '''_id'''       ,						lowerCamelCase_  )
													self.assertEqual(getattr(lowerCamelCase_       ,						lowerCamelCase_  )       ,						lowerCamelCase_  )
													self.assertEqual(getattr(lowerCamelCase_       ,						attr + '''_id'''  )       ,						lowerCamelCase_  )

								setattr(lowerCamelCase_       ,						'''additional_special_tokens_ids'''       ,						[]  )
								self.assertListEqual(getattr(lowerCamelCase_       ,						'''additional_special_tokens'''  )       ,						[]  )
								self.assertListEqual(getattr(lowerCamelCase_       ,						'''additional_special_tokens_ids'''  )       ,						[]  )

								__magic_name__							:	Any    							=							0XE006
								__magic_name__							:	str    							=							chr(lowerCamelCase_  )
								setattr(lowerCamelCase_       ,						'''additional_special_tokens_ids'''       ,						[additional_special_token_id]  )
								self.assertListEqual(getattr(lowerCamelCase_       ,						'''additional_special_tokens'''  )       ,						[additional_special_token]  )
								self.assertListEqual(getattr(lowerCamelCase_       ,						'''additional_special_tokens_ids'''  )       ,						[additional_special_token_id]  )

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Union[str, Any]:
								pass

			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Optional[int]:
								pass

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Any:
								pass

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Optional[Any]:
								pass

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   str:
								pass

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Optional[Any]:
								pass

			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Optional[Any]:
								pass


			def 							UpperCAmelCase__       ( self			:   Any  )						->   Union[str, Any]:
								pass



