





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    RemBertConfig,
							    RemBertForCausalLM,
							    RemBertForMaskedLM,
							    RemBertForMultipleChoice,
							    RemBertForQuestionAnswering,
							    RemBertForSequenceClassification,
							    RemBertForTokenClassification,
							    RemBertModel,
							)
							from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class    __lowercase  :

							"""simple docstring"""


							def __init__(    self				, A_				, A_=13				, A_=7				, A_=True				, A_=True				, A_=True				, A_=True				, A_=99				, A_=32				, A_=18				, A_=43				, A_=5				, A_=4				, A_=37				, A_="gelu"				, A_=0.1				, A_=0.1				, A_=512				, A_=16				, A_=2				, A_=0.02				, A_=3				, A_=4				, A_=None				, )->	Any:
											_SCREAMING_SNAKE_CASE      = parent
											_SCREAMING_SNAKE_CASE      = batch_size
											_SCREAMING_SNAKE_CASE      = seq_length
											_SCREAMING_SNAKE_CASE      = is_training
											_SCREAMING_SNAKE_CASE      = use_input_mask
											_SCREAMING_SNAKE_CASE      = use_token_type_ids
											_SCREAMING_SNAKE_CASE      = use_labels
											_SCREAMING_SNAKE_CASE      = vocab_size
											_SCREAMING_SNAKE_CASE      = hidden_size
											_SCREAMING_SNAKE_CASE      = input_embedding_size
											_SCREAMING_SNAKE_CASE      = output_embedding_size
											_SCREAMING_SNAKE_CASE      = num_hidden_layers
											_SCREAMING_SNAKE_CASE      = num_attention_heads
											_SCREAMING_SNAKE_CASE      = intermediate_size
											_SCREAMING_SNAKE_CASE      = hidden_act
											_SCREAMING_SNAKE_CASE      = hidden_dropout_prob
											_SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE      = max_position_embeddings
											_SCREAMING_SNAKE_CASE      = type_vocab_size
											_SCREAMING_SNAKE_CASE      = type_sequence_label_size
											_SCREAMING_SNAKE_CASE      = initializer_range
											_SCREAMING_SNAKE_CASE      = num_labels
											_SCREAMING_SNAKE_CASE      = num_choices
											_SCREAMING_SNAKE_CASE      = scope





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size	)

											_SCREAMING_SNAKE_CASE      = None
											if self.use_input_mask:
															_SCREAMING_SNAKE_CASE      = random_attention_mask([self.batch_size, self.seq_length]	)

											_SCREAMING_SNAKE_CASE      = None
											if self.use_token_type_ids:
															_SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length]				, self.type_vocab_size	)

											_SCREAMING_SNAKE_CASE      = None
											_SCREAMING_SNAKE_CASE      = None
											_SCREAMING_SNAKE_CASE      = None
											if self.use_labels:
															_SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size]				, self.type_sequence_label_size	)
															_SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length]				, self.num_labels	)
															_SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size]				, self.num_choices	)

											_SCREAMING_SNAKE_CASE      = RemBertConfig(
											    vocab_size=self.vocab_size				, hidden_size=self.hidden_size				, input_embedding_size=self.input_embedding_size				, output_embedding_size=self.output_embedding_size				, num_hidden_layers=self.num_hidden_layers				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, max_position_embeddings=self.max_position_embeddings				, type_vocab_size=self.type_vocab_size				, is_decoder=A_				, initializer_range=self.initializer_range				, )

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





							def 		__magic_name__			(    self	)->	List[Any]:
											(
											    (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,            
											)      = self.prepare_config_and_inputs()

											_SCREAMING_SNAKE_CASE      = True
											_SCREAMING_SNAKE_CASE      = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
											_SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length]				, vocab_size=2	)

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    input_mask,
											    sequence_labels,
											    token_labels,
											    choice_labels,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_				, A_				, A_				, A_	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = RemBertModel(config=A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_				, attention_mask=A_				, token_type_ids=A_	)
											_SCREAMING_SNAKE_CASE      = model(A_				, token_type_ids=A_	)
											_SCREAMING_SNAKE_CASE      = model(A_	)
											self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, )->	int:
											_SCREAMING_SNAKE_CASE      = True
											_SCREAMING_SNAKE_CASE      = RemBertModel(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(
											    A_				, attention_mask=A_				, token_type_ids=A_				, encoder_hidden_states=A_				, encoder_attention_mask=A_				, )
											_SCREAMING_SNAKE_CASE      = model(
											    A_				, attention_mask=A_				, token_type_ids=A_				, encoder_hidden_states=A_				, )
											_SCREAMING_SNAKE_CASE      = model(A_				, attention_mask=A_				, token_type_ids=A_	)
											self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, )->	Any:
											_SCREAMING_SNAKE_CASE      = RemBertForCausalLM(config=A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_				, attention_mask=A_				, token_type_ids=A_				, labels=A_	)
											self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.vocab_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_				, A_				, A_				, A_	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = RemBertForMaskedLM(config=A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_				, attention_mask=A_				, token_type_ids=A_				, labels=A_	)
											self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.vocab_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, )->	Any:
											_SCREAMING_SNAKE_CASE      = True
											_SCREAMING_SNAKE_CASE      = True
											_SCREAMING_SNAKE_CASE      = RemBertForCausalLM(config=A_	)
											model.to(A_	)
											model.eval()

											# first forward pass
											_SCREAMING_SNAKE_CASE      = model(
											    A_				, attention_mask=A_				, encoder_hidden_states=A_				, encoder_attention_mask=A_				, use_cache=A_				, )
											_SCREAMING_SNAKE_CASE      = outputs.past_key_values

											# create hypothetical multiple next token and extent to next_input_ids
											_SCREAMING_SNAKE_CASE      = ids_tensor((self.batch_size, 3)				, config.vocab_size	)
											_SCREAMING_SNAKE_CASE      = ids_tensor((self.batch_size, 3)				, vocab_size=2	)

											# append to next input_ids and
											_SCREAMING_SNAKE_CASE      = torch.cat([input_ids, next_tokens]				, dim=-1	)
											_SCREAMING_SNAKE_CASE      = torch.cat([input_mask, next_mask]				, dim=-1	)

											_SCREAMING_SNAKE_CASE      = model(
											    A_				, attention_mask=A_				, encoder_hidden_states=A_				, encoder_attention_mask=A_				, output_hidden_states=A_				, )['hidden_states'][0]
											_SCREAMING_SNAKE_CASE      = model(
											    A_				, attention_mask=A_				, encoder_hidden_states=A_				, encoder_attention_mask=A_				, past_key_values=A_				, output_hidden_states=A_				, )['hidden_states'][0]

											# select random slice
											_SCREAMING_SNAKE_CASE      = ids_tensor((1,)				, output_from_past.shape[-1]	).item()
											_SCREAMING_SNAKE_CASE      = output_from_no_past[:, -3:, random_slice_idx].detach()
											_SCREAMING_SNAKE_CASE      = output_from_past[:, :, random_slice_idx].detach()

											self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(A_				, A_				, atol=1e-3	)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_				, A_				, A_				, A_	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = RemBertForQuestionAnswering(config=A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(
											    A_				, attention_mask=A_				, token_type_ids=A_				, start_positions=A_				, end_positions=A_				, )
											self.parent.assertEqual(result.start_logits.shape				, (self.batch_size, self.seq_length)	)
											self.parent.assertEqual(result.end_logits.shape				, (self.batch_size, self.seq_length)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_				, A_				, A_				, A_	)->	Any:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = RemBertForSequenceClassification(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_				, attention_mask=A_				, token_type_ids=A_				, labels=A_	)
											self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_				, A_				, A_				, A_	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = RemBertForTokenClassification(config=A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_				, attention_mask=A_				, token_type_ids=A_				, labels=A_	)
											self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.num_labels)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_				, A_				, A_				, A_	)->	Any:
											_SCREAMING_SNAKE_CASE      = self.num_choices
											_SCREAMING_SNAKE_CASE      = RemBertForMultipleChoice(config=A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = input_ids.unsqueeze(1	).expand(-1				, self.num_choices				, -1	).contiguous()
											_SCREAMING_SNAKE_CASE      = token_type_ids.unsqueeze(1	).expand(-1				, self.num_choices				, -1	).contiguous()
											_SCREAMING_SNAKE_CASE      = input_mask.unsqueeze(1	).expand(-1				, self.num_choices				, -1	).contiguous()
											_SCREAMING_SNAKE_CASE      = model(
											    A_				, attention_mask=A_				, token_type_ids=A_				, labels=A_				, )
											self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_choices)	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
											(
											    (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,            
											)      = config_and_inputs
											_SCREAMING_SNAKE_CASE      = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
											return config, inputs_dict







@require_torch
class    __lowercase  (       UpperCamelCase      ,     UpperCamelCase      ,     unittest.TestCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					List[Any]        =						(
							    (
							        RemBertModel,
							        RemBertForMaskedLM,
							        RemBertForCausalLM,
							        RemBertForMultipleChoice,
							        RemBertForQuestionAnswering,
							        RemBertForSequenceClassification,
							        RemBertForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							SCREAMING_SNAKE_CASE   :					List[Any]        =						(RemBertForCausalLM,) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE   :					int        =						(
							    {
							        "feature-extraction": RemBertModel,
							        "fill-mask": RemBertForMaskedLM,
							        "question-answering": RemBertForQuestionAnswering,
							        "text-classification": RemBertForSequenceClassification,
							        "text-generation": RemBertForCausalLM,
							        "token-classification": RemBertForTokenClassification,
							        "zero-shot": RemBertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = RemBertModelTester(self	)
											_SCREAMING_SNAKE_CASE      = ConfigTester(self				, config_class=A_				, hidden_size=37	)





							def 		__magic_name__			(    self	)->	Optional[int]:
											self.config_tester.run_common_tests()





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
															_SCREAMING_SNAKE_CASE      = type
															self.model_tester.create_and_check_model(*A_	)





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*A_	)





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*A_	)





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_decoder_model_past_large_inputs(*A_	)





							def 		__magic_name__			(    self	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*A_	)





							def 		__magic_name__			(    self	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*A_	)





							def 		__magic_name__			(    self	)->	List[Any]:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_model_as_decoder(*A_	)





							def 		__magic_name__			(    self	)->	str:
											# This regression test was failing with PyTorch < 1.3
											(
											    (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,      (
											    _SCREAMING_SNAKE_CASE      
											)      ,            
											)      = self.model_tester.prepare_config_and_inputs_for_decoder()

											_SCREAMING_SNAKE_CASE      = None

											self.model_tester.create_and_check_model_as_decoder(
											    A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, )





							@slow
							def 		__magic_name__			(    self	)->	Optional[int]:
											for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE      = RemBertModel.from_pretrained(A_	)
															self.assertIsNotNone(A_	)







@require_torch
class    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							@slow
							def 		__magic_name__			(    self	)->	str:
											# Test exact values at the last hidden layer
											_SCREAMING_SNAKE_CASE      = RemBertModel.from_pretrained('google/rembert'	)
											_SCREAMING_SNAKE_CASE      = torch.tensor([[312, 56498, 313, 2125, 313]]	)
											_SCREAMING_SNAKE_CASE      = torch.tensor([[0, 0, 0, 1, 1]]	)
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(A_				, token_type_ids=A_				, output_hidden_states=A_	)

											_SCREAMING_SNAKE_CASE      = 1152

											_SCREAMING_SNAKE_CASE      = torch.Size((1, 5, hidden_size)	)
											self.assertEqual(output['last_hidden_state'].shape				, A_	)

											_SCREAMING_SNAKE_CASE      = torch.tensor(
											    [
											        [
											            [0.0754, -0.2022, 0.1904],
											            [-0.3354, -0.3692, -0.4791],
											            [-0.2314, -0.6729, -0.0749],
											            [-0.0396, -0.3105, -0.4234],
											            [-0.1571, -0.0525, 0.5353],
											        ]
											    ]	)

											# Running on the original tf implementation gives slightly different results here.
											# Not clear why this variations is present
											# TODO: Find reason for discrepancy
											# expected_original_implementation = [[
											#     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
											#     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
											#     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
											#     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
											#     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
											# ]]

											self.assertTrue(torch.allclose(output['last_hidden_state'][:, :, :3]				, A_				, atol=1e-4	)	)




import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

snake_case			:      Any 							=   logging.get_logger(__name__)

snake_case			:      str 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

snake_case			:      Optional[int] 							=   {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case			:      Tuple 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Any 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = (
				    list(range(ord('!'							)   ,ord('~'							) + 1							)							) + list(range(ord('¡'							)   ,ord('¬'							) + 1							)							) + list(range(ord('®'							)   ,ord('ÿ'							) + 1							)							)
				)
				_SCREAMING_SNAKE_CASE      = bs[:]
				_SCREAMING_SNAKE_CASE      = 0
				for b in range(2**8							):
								if b not in bs:
												bs.append(UpperCAmelCase__							)
												cs.append(2**8 + n							)
												n += 1
				_SCREAMING_SNAKE_CASE      = [chr(UpperCAmelCase__							) for n in cs]
				return dict(zip(UpperCAmelCase__   ,UpperCAmelCase__							)							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = set()
				_SCREAMING_SNAKE_CASE      = word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)							)
								_SCREAMING_SNAKE_CASE      = char
				return pairs



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					int        =						VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE   :					List[Any]        =						PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE   :					Any        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						["input_ids", "attention_mask", "token_type_ids"]





							def __init__(    self				, A_				, A_				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, A_=False				, **A_				, )->	List[str]:
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else bos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else eos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else sep_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else cls_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else unk_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else mask_token

											super().__init__(
											    errors=A_				, bos_token=A_				, eos_token=A_				, unk_token=A_				, sep_token=A_				, cls_token=A_				, pad_token=A_				, mask_token=A_				, add_prefix_space=A_				, add_bos_token=A_				, **A_				, )
											_SCREAMING_SNAKE_CASE      = add_bos_token

											with open(A_				, encoding='utf-8'	) as vocab_handle:
															_SCREAMING_SNAKE_CASE      = json.load(A_	)
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.encoder.items()}
											_SCREAMING_SNAKE_CASE      = errors  # how to handle errors in decoding
											_SCREAMING_SNAKE_CASE      = bytes_to_unicode()
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.byte_encoder.items()}
											with open(A_				, encoding='utf-8'	) as merges_handle:
															_SCREAMING_SNAKE_CASE      = merges_handle.read().split('\n'	)[1:-1]
											_SCREAMING_SNAKE_CASE      = [tuple(merge.split()	) for merge in bpe_merges]
											_SCREAMING_SNAKE_CASE      = dict(zip(A_				, range(len(A_	)	)	)	)
											_SCREAMING_SNAKE_CASE      = {}
											_SCREAMING_SNAKE_CASE      = add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											_SCREAMING_SNAKE_CASE      = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)





							@property
							# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
							def 		__magic_name__			(    self	)->	int:
											return len(self.encoder	)





							def 		__magic_name__			(    self	)->	Tuple:
											return dict(self.encoder				, **self.added_tokens_encoder	)





							def 		__magic_name__			(    self				, A_	)->	Optional[int]:
											if token in self.cache:
															return self.cache[token]
											_SCREAMING_SNAKE_CASE      = tuple(A_	)
											_SCREAMING_SNAKE_CASE      = get_pairs(A_	)

											if not pairs:
															return token

											while True:
															_SCREAMING_SNAKE_CASE      = min(A_				, key=lambda A_	: self.bpe_ranks.get(A_				, float('inf'	)	)	)
															if bigram not in self.bpe_ranks:
																			break
															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = bigram
															_SCREAMING_SNAKE_CASE      = []
															_SCREAMING_SNAKE_CASE      = 0
															while i < len(A_	):
																			try:
																							_SCREAMING_SNAKE_CASE      = word.index(A_				, A_	)
																			except ValueError:
																							new_word.extend(word[i:]	)
																							break
																			else:
																							new_word.extend(word[i:j]	)
																							_SCREAMING_SNAKE_CASE      = j

																			if word[i] == first and i < len(A_	) - 1 and word[i + 1] == second:
																							new_word.append(first + second	)
																							i += 2
																			else:
																							new_word.append(word[i]	)
																							i += 1
															_SCREAMING_SNAKE_CASE      = tuple(A_	)
															_SCREAMING_SNAKE_CASE      = new_word
															if len(A_	) == 1:
																			break
															else:
																			_SCREAMING_SNAKE_CASE      = get_pairs(A_	)
											_SCREAMING_SNAKE_CASE      = ' '.join(A_	)
											_SCREAMING_SNAKE_CASE      = word
											return word





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep





							def 		__magic_name__			(    self				, A_				, A_ = None				, A_ = False	)->	List[int]:
											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=A_				, token_ids_a=A_				, already_has_special_tokens=A_	)

											if token_ids_a is None:
															return [1] + ([0] * len(A_	)) + [1]
											return [1] + ([0] * len(A_	)) + [1] + ([0] * len(A_	)) + [1]





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep	) * [0]
											return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = []
											for token in re.findall(self.pat				, A_	):
															_SCREAMING_SNAKE_CASE      = ''.join(
															    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_	).split(' '	)	)
											return bpe_tokens





							def 		__magic_name__			(    self				, A_	)->	Optional[Any]:
											return self.encoder.get(A_				, self.encoder.get(self.unk_token	)	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											return self.decoder.get(A_	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = ''.join(A_	)
											_SCREAMING_SNAKE_CASE      = bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'				, errors=self.errors	)
											return text





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
											if not os.path.isdir(A_	):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
															return
											_SCREAMING_SNAKE_CASE      = os.path.join(
											    A_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
											_SCREAMING_SNAKE_CASE      = os.path.join(
											    A_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

											with open(A_				, 'w'				, encoding='utf-8'	) as f:
															f.write(json.dumps(self.encoder				, indent=2				, sort_keys=A_				, ensure_ascii=A_	) + '\n'	)

											_SCREAMING_SNAKE_CASE      = 0
											with open(A_				, 'w'				, encoding='utf-8'	) as writer:
															writer.write('#version: 0.2\n'	)
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				, key=lambda A_	: kv[1]	):
																			if index != token_index:
																							logger.warning(
																							    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																							    ' Please check that the tokenizer is not corrupted!'	)
																							_SCREAMING_SNAKE_CASE      = token_index
																			writer.write(' '.join(A_	) + '\n'	)
																			index += 1

											return vocab_file, merge_file





							def 		__magic_name__			(    self				, A_				, A_=False				, **A_	)->	str:
											_SCREAMING_SNAKE_CASE      = kwargs.pop('add_prefix_space'				, self.add_prefix_space	)
											if (is_split_into_words or add_prefix_space) and (len(A_	) > 0 and not text[0].isspace()):
															_SCREAMING_SNAKE_CASE      = ' ' + text
											return (text, kwargs)





							def 		__magic_name__			(    self				, A_	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = []
											for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(A_				, add_special_tokens=A_	) + [self.eos_token_id]	)
											if len(A_	) > self.model_max_length:
															_SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
											return input_ids
