






import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
				from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import GLPNImageProcessor


class 				__a				(	__A    ):





		'''simple docstring'''



		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.config_class(**self.inputs_dict							)
						self.parent.assertTrue(hasattr(UpperCamelCase__			,     'hidden_sizes'							)							)
						self.parent.assertTrue(hasattr(UpperCamelCase__			,     'num_attention_heads'							)							)
						self.parent.assertTrue(hasattr(UpperCamelCase__			,     'num_encoder_blocks'							)							)






class 				__a				:





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__=13			,     UpperCamelCase__=64			,     UpperCamelCase__=3			,     UpperCamelCase__=4			,     UpperCamelCase__=[2, 2, 2, 2]			,     UpperCamelCase__=[8, 4, 2, 1]			,     UpperCamelCase__=[16, 32, 64, 128]			,     UpperCamelCase__=[1, 4, 8, 16]			,     UpperCamelCase__=[1, 2, 4, 8]			,     UpperCamelCase__=True			,     UpperCamelCase__=True			,     UpperCamelCase__="gelu"			,     UpperCamelCase__=0.1			,     UpperCamelCase__=0.1			,     UpperCamelCase__=0.02			,     UpperCamelCase__=16			,     UpperCamelCase__=3			,     UpperCamelCase__=None			,     ):
						SCREAMING_SNAKE_CASE_ :						Any    				= parent
						SCREAMING_SNAKE_CASE_ :						Tuple    				= batch_size
						SCREAMING_SNAKE_CASE_ :						int    				= image_size
						SCREAMING_SNAKE_CASE_ :						str    				= num_channels
						SCREAMING_SNAKE_CASE_ :						List[str]    				= num_encoder_blocks
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= sr_ratios
						SCREAMING_SNAKE_CASE_ :						int    				= depths
						SCREAMING_SNAKE_CASE_ :						Any    				= hidden_sizes
						SCREAMING_SNAKE_CASE_ :						str    				= downsampling_rates
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= num_attention_heads
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= is_training
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= use_labels
						SCREAMING_SNAKE_CASE_ :						str    				= hidden_act
						SCREAMING_SNAKE_CASE_ :						Any    				= hidden_dropout_prob
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= initializer_range
						SCREAMING_SNAKE_CASE_ :						str    				= decoder_hidden_size
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= num_labels
						SCREAMING_SNAKE_CASE_ :						int    				= scope


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						SCREAMING_SNAKE_CASE_ :						str    				= None
						if self.use_labels:
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= ids_tensor([self.batch_size, self.image_size, self.image_size]			,     self.num_labels							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.get_config()
						return config, pixel_values, labels


		def 	__snake_case      (    self							):
						return GLPNConfig(
						    image_size=self.image_size			,     num_channels=self.num_channels			,     num_encoder_blocks=self.num_encoder_blocks			,     depths=self.depths			,     hidden_sizes=self.hidden_sizes			,     num_attention_heads=self.num_attention_heads			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     initializer_range=self.initializer_range			,     decoder_hidden_size=self.decoder_hidden_size			,     )


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= GLPNModel(config=UpperCamelCase__							)
						model.to(UpperCamelCase__							)
						model.eval()
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= model(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						List[str]    				= self.image_size // (self.downsampling_rates[-1] * 2)
						self.parent.assertEqual(
						    result.last_hidden_state.shape			,     (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.num_labels
						SCREAMING_SNAKE_CASE_ :						Tuple    				= GLPNForDepthEstimation(UpperCamelCase__							)
						model.to(UpperCamelCase__							)
						model.eval()
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= model(UpperCamelCase__							)
						self.parent.assertEqual(result.predicted_depth.shape			,     (self.batch_size, self.image_size, self.image_size)							)
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= model(UpperCamelCase__			,     labels=UpperCamelCase__							)
						self.parent.assertEqual(result.predicted_depth.shape			,     (self.batch_size, self.image_size, self.image_size)							)






		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[str]    				= self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= config_and_inputs
						SCREAMING_SNAKE_CASE_ :						Tuple    				= {'pixel_values': pixel_values}
						return config, inputs_dict






@require_torch
class 				__a				(	__A			,						__A			,						unittest.TestCase    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Union[str, Any]					     =    (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
		UpperCAmelCase__   :					int					     =    (
		    {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
		)

		UpperCAmelCase__   :					Any					     =    False
		UpperCAmelCase__   :					Union[str, Any]					     =    False
		UpperCAmelCase__   :					int					     =    False


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						str    				= GLPNModelTester(self							)
						SCREAMING_SNAKE_CASE_ :						int    				= GLPNConfigTester(self			,     config_class=UpperCamelCase__							)


		def 	__snake_case      (    self							):
						self.config_tester.run_common_tests()


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Tuple    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase__							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase__							)


		@unittest.skip('GLPN does not use inputs_embeds'							)
		def 	__snake_case      (    self							):
						pass


		@unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'							)
		def 	__snake_case      (    self							):
						pass


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_ :						Tuple    				= model_class(UpperCamelCase__							)
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= inspect.signature(model.forward							)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										SCREAMING_SNAKE_CASE_ :						List[str]    				= [*signature.parameters.keys()]

										SCREAMING_SNAKE_CASE_ :						List[Any]    				= ['pixel_values']
										self.assertListEqual(arg_names[:1]			,     UpperCamelCase__							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= True

						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= True
										SCREAMING_SNAKE_CASE_ :						List[str]    				= False
										SCREAMING_SNAKE_CASE_ :						Any    				= True
										SCREAMING_SNAKE_CASE_ :						str    				= model_class(UpperCamelCase__							)
										model.to(UpperCamelCase__							)
										model.eval()
										with torch.no_grad():
														SCREAMING_SNAKE_CASE_ :						int    				= model(**self._prepare_for_class(UpperCamelCase__			,     UpperCamelCase__							)							)
										SCREAMING_SNAKE_CASE_ :						Tuple    				= outputs.attentions

										SCREAMING_SNAKE_CASE_ :						Tuple    				= sum(self.model_tester.depths							)
										self.assertEqual(len(UpperCamelCase__							)			,     UpperCamelCase__							)

										# check that output_attentions also work using config
										del inputs_dict["output_attentions"]
										SCREAMING_SNAKE_CASE_ :						Dict    				= True
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= model_class(UpperCamelCase__							)
										model.to(UpperCamelCase__							)
										model.eval()
										with torch.no_grad():
														SCREAMING_SNAKE_CASE_ :						int    				= model(**self._prepare_for_class(UpperCamelCase__			,     UpperCamelCase__							)							)
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= outputs.attentions

										self.assertEqual(len(UpperCamelCase__							)			,     UpperCamelCase__							)

										# verify the first attentions (first block, first layer)
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= (self.model_tester.image_size // 4) ** 2
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
										self.assertListEqual(
										    list(attentions[0].shape[-3:]							)			,     [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,     )

										# verify the last attentions (last block, last layer)
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= (self.model_tester.image_size // 32) ** 2
										SCREAMING_SNAKE_CASE_ :						Dict    				= (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
										self.assertListEqual(
										    list(attentions[-1].shape[-3:]							)			,     [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]			,     )
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= len(UpperCamelCase__							)

										# Check attention is always last and order is fine
										SCREAMING_SNAKE_CASE_ :						str    				= True
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= True
										SCREAMING_SNAKE_CASE_ :						Any    				= model_class(UpperCamelCase__							)
										model.to(UpperCamelCase__							)
										model.eval()
										with torch.no_grad():
														SCREAMING_SNAKE_CASE_ :						Any    				= model(**self._prepare_for_class(UpperCamelCase__			,     UpperCamelCase__							)							)

										self.assertEqual(out_len + 1			,     len(UpperCamelCase__							)							)

										SCREAMING_SNAKE_CASE_ :						List[str]    				= outputs.attentions

										self.assertEqual(len(UpperCamelCase__							)			,     UpperCamelCase__							)
										# verify the first attentions (first block, first layer)
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= (self.model_tester.image_size // 4) ** 2
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
										self.assertListEqual(
										    list(self_attentions[0].shape[-3:]							)			,     [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,     )


		def 	__snake_case      (    self							):
						def check_hidden_states_output(UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= model_class(UpperCamelCase__							)
										model.to(UpperCamelCase__							)
										model.eval()

										with torch.no_grad():
														SCREAMING_SNAKE_CASE_ :						str    				= model(**self._prepare_for_class(UpperCamelCase__			,     UpperCamelCase__							)							)

										SCREAMING_SNAKE_CASE_ :						str    				= outputs.hidden_states

										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.model_tester.num_encoder_blocks
										self.assertEqual(len(UpperCamelCase__							)			,     UpperCamelCase__							)

										# verify the first hidden states (first block)
										self.assertListEqual(
										    list(hidden_states[0].shape[-3:]							)			,     [
										        self.model_tester.hidden_sizes[0],
										        self.model_tester.image_size // 4,
										        self.model_tester.image_size // 4,
										    ]			,     )

						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_ :						List[str]    				= True
										check_hidden_states_output(UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										SCREAMING_SNAKE_CASE_ :						Any    				= True

										check_hidden_states_output(UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							)


		def 	__snake_case      (    self							):
						if not self.model_tester.is_training:
										return

						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE_ :						Dict    				= True

						for model_class in self.all_model_classes:
										if model_class in get_values(UpperCamelCase__							):
														continue
										# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
										# this can then be incorporated into _prepare_for_class in test_modeling_common.py
										if model_class.__name__ == "GLPNForDepthEstimation":
														SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Tuple    				= inputs_dict['pixel_values'].shape
														SCREAMING_SNAKE_CASE_ :						Tuple    				= torch.zeros(
														    [self.model_tester.batch_size, height, width]			,     device=UpperCamelCase__							).long()
										SCREAMING_SNAKE_CASE_ :						Dict    				= model_class(UpperCamelCase__							)
										model.to(UpperCamelCase__							)
										model.train()
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self._prepare_for_class(UpperCamelCase__			,     UpperCamelCase__			,     return_labels=UpperCamelCase__							)
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= model(**UpperCamelCase__							).loss
										loss.backward()






		@slow
		def 	__snake_case      (    self							):
						for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_ :						List[str]    				= GLPNModel.from_pretrained(UpperCamelCase__							)
										self.assertIsNotNone(UpperCamelCase__							)






def        _lowerCamelCase(      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						str    				= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
				return image


@require_torch
@require_vision
@slow
class 				__a				(	unittest.TestCase    ):





		'''simple docstring'''



		@slow
		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						int    				= GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= prepare_img()
						SCREAMING_SNAKE_CASE_ :						str    				= image_processor(images=UpperCamelCase__			,     return_tensors='pt'							).to(UpperCamelCase__							)

						# forward pass
						with torch.no_grad():
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= model(**UpperCamelCase__							)

						# verify the predicted depth
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= torch.Size([1, 480, 640]							)
						self.assertEqual(outputs.predicted_depth.shape			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= torch.tensor(
						    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]							).to(UpperCamelCase__							)

						self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]			,     UpperCamelCase__			,     atol=1E-4							)							)







import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation

A										=      logging.get_logger(__name__)

A										=      {'vocab_file': 'tokenizer.model'}

A										=      {
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
A										=      {
    'hf-internal-testing/llama-tokenizer': 2048,
}
A										=      '▁'

A			,	A										=      '[INST]', '[/INST]'
A			,	A										=      '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
A										=      'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'


class 				__a				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Optional[int]					     =    VOCAB_FILES_NAMES
		UpperCAmelCase__   :					Dict					     =    PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__   :					int					     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__   :					Union[str, Any]					     =    ["""input_ids""", """attention_mask"""]


		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__="<unk>"			,     UpperCamelCase__="<s>"			,     UpperCamelCase__="</s>"			,     UpperCamelCase__=None			,     UpperCamelCase__ = None			,     UpperCamelCase__=True			,     UpperCamelCase__=False			,     UpperCamelCase__=False			,     UpperCamelCase__=True			,     **UpperCamelCase__			,     ):
						SCREAMING_SNAKE_CASE_ :						str    				= {} if sp_model_kwargs is None else sp_model_kwargs
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= AddedToken(UpperCamelCase__			,     lstrip=UpperCamelCase__			,     rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__			,     UpperCamelCase__							) else bos_token
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= AddedToken(UpperCamelCase__			,     lstrip=UpperCamelCase__			,     rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__			,     UpperCamelCase__							) else eos_token
						SCREAMING_SNAKE_CASE_ :						str    				= AddedToken(UpperCamelCase__			,     lstrip=UpperCamelCase__			,     rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__			,     UpperCamelCase__							) else unk_token
						SCREAMING_SNAKE_CASE_ :						int    				= AddedToken(UpperCamelCase__			,     lstrip=UpperCamelCase__			,     rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__			,     UpperCamelCase__							) else pad_token
						super().__init__(
						    bos_token=UpperCamelCase__			,     eos_token=UpperCamelCase__			,     unk_token=UpperCamelCase__			,     pad_token=UpperCamelCase__			,     add_bos_token=UpperCamelCase__			,     add_eos_token=UpperCamelCase__			,     sp_model_kwargs=self.sp_model_kwargs			,     clean_up_tokenization_spaces=UpperCamelCase__			,     legacy=UpperCamelCase__			,     **UpperCamelCase__			,     )
						if legacy:
										logger.warning_once(
										    F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
										    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'							)
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= legacy
						SCREAMING_SNAKE_CASE_ :						Dict    				= vocab_file
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= add_bos_token
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= add_eos_token
						SCREAMING_SNAKE_CASE_ :						Any    				= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(UpperCamelCase__							)


		def __getstate__(    self							):
						SCREAMING_SNAKE_CASE_ :						List[str]    				= self.__dict__.copy()
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= None
						SCREAMING_SNAKE_CASE_ :						Any    				= self.sp_model.serialized_model_proto()
						return state


		def __setstate__(    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						str    				= d
						SCREAMING_SNAKE_CASE_ :						str    				= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)


		@property
		def 	__snake_case      (    self							):
						return self.sp_model.get_piece_size()


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Tuple    				= {self.convert_ids_to_tokens(UpperCamelCase__							): i for i in range(self.vocab_size							)}
						vocab.update(self.added_tokens_encoder							)
						return vocab


		def 	__snake_case      (    self			,     UpperCamelCase__			,     **UpperCamelCase__							):
						# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
						# the beginning of the text
						if not self.legacy:
										SCREAMING_SNAKE_CASE_ :						List[str]    				= SPIECE_UNDERLINE + text.replace(UpperCamelCase__			,     ' '							)
						return super().tokenize(UpperCamelCase__			,     **UpperCamelCase__							)


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						if not self.legacy:
										SCREAMING_SNAKE_CASE_ :						Any    				= text.startswith(UpperCamelCase__							)
										if is_first:
														SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= text[1:]

						SCREAMING_SNAKE_CASE_ :						Tuple    				= self.sp_model.encode(UpperCamelCase__			,     out_type=UpperCamelCase__							)

						if not self.legacy and not is_first and not text.startswith(' '							) and tokens[0].startswith(UpperCamelCase__							):
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= ([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
						return tokens


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						return self.sp_model.piece_to_id(UpperCamelCase__							)


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.sp_model.IdToPiece(UpperCamelCase__							)
						return token


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						List[str]    				= []
						SCREAMING_SNAKE_CASE_ :						int    				= ''
						SCREAMING_SNAKE_CASE_ :						List[str]    				= False
						for i, token in enumerate(UpperCamelCase__							):
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
														if not prev_is_special and i != 0:
																		out_string += " "
														out_string += self.sp_model.decode(UpperCamelCase__							) + token
														SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= True
														SCREAMING_SNAKE_CASE_ :						str    				= []
										else:
														current_sub_tokens.append(UpperCamelCase__							)
														SCREAMING_SNAKE_CASE_ :						int    				= False
						out_string += self.sp_model.decode(UpperCamelCase__							)
						return out_string


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__ = None							):
						if not os.path.isdir(UpperCamelCase__							):
										logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
										return
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= os.path.join(
						    UpperCamelCase__			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

						if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCamelCase__							) and os.path.isfile(self.vocab_file							):
										copyfile(self.vocab_file			,     UpperCamelCase__							)
						elif not os.path.isfile(self.vocab_file							):
										with open(UpperCamelCase__			,     'wb'							) as fi:
														SCREAMING_SNAKE_CASE_ :						Dict    				= self.sp_model.serialized_model_proto()
														fi.write(UpperCamelCase__							)

						return (out_vocab_file,)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__=None							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= [self.bos_token_id] if self.add_bos_token else []
						SCREAMING_SNAKE_CASE_ :						List[str]    				= [self.eos_token_id] if self.add_eos_token else []

						SCREAMING_SNAKE_CASE_ :						int    				= bos_token_id + token_ids_a + eos_token_id

						if token_ids_a is not None:
										SCREAMING_SNAKE_CASE_ :						int    				= output + bos_token_id + token_ids_a + eos_token_id

						return output


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__ = None			,     UpperCamelCase__ = False							):
						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=UpperCamelCase__			,     token_ids_a=UpperCamelCase__			,     already_has_special_tokens=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= [1] if self.add_bos_token else []
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= [1] if self.add_eos_token else []

						if token_ids_a is None:
										return bos_token_id + ([0] * len(UpperCamelCase__							)) + eos_token_id
						return (
						    bos_token_id
						    + ([0] * len(UpperCamelCase__							))
						    + eos_token_id
						    + bos_token_id
						    + ([0] * len(UpperCamelCase__							))
						    + eos_token_id
						)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__ = None							):
						SCREAMING_SNAKE_CASE_ :						Any    				= [self.bos_token_id] if self.add_bos_token else []
						SCREAMING_SNAKE_CASE_ :						Tuple    				= [self.eos_token_id] if self.add_eos_token else []

						SCREAMING_SNAKE_CASE_ :						Any    				= [0] * len(bos_token_id + token_ids_a + eos_token_id							)

						if token_ids_a is not None:
										output += [1] * len(bos_token_id + token_ids_a + eos_token_id							)

						return output






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						int    				= list(conversation.iter_texts()							)
						if not all([is_user for is_user, msg in dialogue[::2]]							) or not all(
						    [not is_user for is_user, msg in dialogue[1::2]]							):
										raise ValueError(
										    'The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'							)

						SCREAMING_SNAKE_CASE_ :						List[int]    				= []
						if len(conversation.past_user_inputs							) > 0:
										if not conversation.past_user_inputs[0].startswith(UpperCamelCase__							) or E_SYS not in conversation.past_user_inputs[0]:
														SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= (
														    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
														)
						elif not dialogue[0][1].startswith(UpperCamelCase__							) or E_SYS not in dialogue[0][1]:
										SCREAMING_SNAKE_CASE_ :						Tuple    				= (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

						dialog_tokens += sum(
						    [
						        [self.bos_token_id]
						        + self.encode(
						            F'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''			,     add_special_tokens=UpperCamelCase__							)
						        + [self.eos_token_id]
						        for prompt, answer in zip(dialogue[::2]			,     dialogue[1::2]							)
						    ]			,     []			,     )
						if not (dialogue[-1][0]):
										raise ValueError(F'''Last message must be from user, got {dialogue[-1]['role']}'''							)
						dialog_tokens += [self.bos_token_id] + self.encode(
						    F'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''			,     add_special_tokens=UpperCamelCase__							)
						return dialog_tokens