





'''simple docstring'''


import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, 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_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           LevitForImageClassification,
           LevitForImageClassificationWithTeacher,
           LevitModel,
       )
       from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import LevitImageProcessor

class    lowerCAmelCase_		(		__magic_name__		):




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      self.config_class(**self.inputs_dict		)
     self.parent.assertTrue(hasattr(_lowerCAmelCase			,     "hidden_sizes"		)		)
     self.parent.assertTrue(hasattr(_lowerCAmelCase			,     "num_attention_heads"		)		)







class    lowerCAmelCase_		:




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase=13			,     _lowerCAmelCase=64			,     _lowerCAmelCase=3			,     _lowerCAmelCase=3			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1			,     _lowerCAmelCase=16			,     _lowerCAmelCase=[128, 256, 384]			,     _lowerCAmelCase=[4, 6, 8]			,     _lowerCAmelCase=[2, 3, 4]			,     _lowerCAmelCase=[16, 16, 16]			,     _lowerCAmelCase=0			,     _lowerCAmelCase=[2, 2, 2]			,     _lowerCAmelCase=[2, 2, 2]			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=True			,     _lowerCAmelCase=True			,     _lowerCAmelCase=2			,     )    ->							str:
     _lowerCAmelCase				=      parent
     _lowerCAmelCase				=      batch_size
     _lowerCAmelCase				=      image_size
     _lowerCAmelCase				=      num_channels
     _lowerCAmelCase				=      kernel_size
     _lowerCAmelCase				=      stride
     _lowerCAmelCase				=      padding
     _lowerCAmelCase				=      hidden_sizes
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      depths
     _lowerCAmelCase				=      key_dim
     _lowerCAmelCase				=      drop_path_rate
     _lowerCAmelCase				=      patch_size
     _lowerCAmelCase				=      attention_ratio
     _lowerCAmelCase				=      mlp_ratio
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      [
         ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
         ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
     ]
     _lowerCAmelCase				=      is_training
     _lowerCAmelCase				=      use_labels
     _lowerCAmelCase				=      num_labels
     _lowerCAmelCase				=      initializer_range




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

     _lowerCAmelCase				=      None
     if self.use_labels:
         _lowerCAmelCase				=      ids_tensor([self.batch_size]			,     self.num_labels		)

     _lowerCAmelCase				=      self.get_config()
     return config, pixel_values, labels




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     return LevitConfig(
         image_size=self.image_size			,     num_channels=self.num_channels			,     kernel_size=self.kernel_size			,     stride=self.stride			,     padding=self.padding			,     patch_size=self.patch_size			,     hidden_sizes=self.hidden_sizes			,     num_attention_heads=self.num_attention_heads			,     depths=self.depths			,     key_dim=self.key_dim			,     drop_path_rate=self.drop_path_rate			,     mlp_ratio=self.mlp_ratio			,     attention_ratio=self.attention_ratio			,     initializer_range=self.initializer_range			,     down_ops=self.down_ops			,     )




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      LevitModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase		)
     _lowerCAmelCase				=      (self.image_size, self.image_size)
     _lowerCAmelCase			,			_lowerCAmelCase				=      image_size[0], image_size[1]
     for _ in range(4		):
         _lowerCAmelCase				=      floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
         _lowerCAmelCase				=      floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
     self.parent.assertEqual(
         result.last_hidden_state.shape			,     (self.batch_size, ceil(height / 4		) * ceil(width / 4		), self.hidden_sizes[-1])			,     )




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      self.num_labels
     _lowerCAmelCase				=      LevitForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase			,     labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)		)





 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      config_and_inputs
     _lowerCAmelCase				=      {"pixel_values": pixel_values}
     return config, inputs_dict







@require_torch
class    lowerCAmelCase_		(		__magic_name__						,__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							Tuple         =					(
     (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
     if is_torch_available()
     else ()
 )
 __lowerCamelCase						:							Optional[Any]         =					(
     {
         "feature-extraction": LevitModel,
         "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 __lowerCamelCase						:							List[str]         =					False
 __lowerCamelCase						:							Optional[Any]         =					False
 __lowerCamelCase						:							Dict         =					False
 __lowerCamelCase						:							Any         =					False
 __lowerCamelCase						:							Dict         =					False




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      LevitModelTester(self		)
     _lowerCAmelCase				=      ConfigTester(self			,     config_class=_lowerCAmelCase			,     has_text_modality=_lowerCAmelCase			,     hidden_size=37		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()




 def 							_snake_case   (	self		)    ->							Any:
     return




 @unittest.skip(reason="Levit does not use inputs_embeds"		)
 def 							_snake_case   (	self		)    ->							Any:
     pass




 @unittest.skip(reason="Levit does not support input and output embeddings"		)
 def 							_snake_case   (	self		)    ->							int:
     pass




 @unittest.skip(reason="Levit does not output attentions"		)
 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     pass




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCAmelCase				=      ["pixel_values"]
         self.assertListEqual(arg_names[:1]			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     def check_hidden_states_output(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		):
         _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.eval()

         with torch.no_grad():
             _lowerCAmelCase				=      model(**self._prepare_for_class(_lowerCAmelCase			,     _lowerCAmelCase		)		)

         _lowerCAmelCase				=      outputs.hidden_states

         _lowerCAmelCase				=      len(self.model_tester.depths		) + 1
         self.assertEqual(len(_lowerCAmelCase		)			,     _lowerCAmelCase		)

         _lowerCAmelCase				=      (self.model_tester.image_size, self.model_tester.image_size)
         _lowerCAmelCase			,			_lowerCAmelCase				=      image_size[0], image_size[1]
         for _ in range(4		):
             _lowerCAmelCase				=      floor(
                 (
                     (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                     / self.model_tester.stride
                 )
                 + 1		)
             _lowerCAmelCase				=      floor(
                 (
                     (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                     / self.model_tester.stride
                 )
                 + 1		)
         # verify the first hidden states (first block)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]		)			,     [
                 height * width,
                 self.model_tester.hidden_sizes[0],
             ]			,     )

     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase				=      True
         check_hidden_states_output(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCAmelCase				=      True

         check_hidden_states_output(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)




 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
 def 							_snake_case   (	self		)    ->							str:
     pass




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=False		)    ->							Dict:
     _lowerCAmelCase				=      super()._prepare_for_class(_lowerCAmelCase			,     _lowerCAmelCase			,     return_labels=_lowerCAmelCase		)

     if return_labels:
         if model_class.__name__ == "LevitForImageClassificationWithTeacher":
             del inputs_dict["labels"]

     return inputs_dict




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Tuple:
     if not self.model_tester.is_training:
         return

     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCAmelCase				=      True

     for model_class in self.all_model_classes:
         # LevitForImageClassificationWithTeacher supports inference-only
         if (
             model_class in get_values(_lowerCAmelCase		)
             or model_class.__name__ == "LevitForImageClassificationWithTeacher"
         ):
             continue
         _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.train()
         _lowerCAmelCase				=      self._prepare_for_class(_lowerCAmelCase			,     _lowerCAmelCase			,     return_labels=_lowerCAmelCase		)
         _lowerCAmelCase				=      model(**_lowerCAmelCase		).loss
         loss.backward()




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
         return

     _lowerCAmelCase				=      False
     _lowerCAmelCase				=      True

     for model_class in self.all_model_classes:
         if model_class in get_values(_lowerCAmelCase		) or not model_class.supports_gradient_checkpointing:
             continue
         # LevitForImageClassificationWithTeacher supports inference-only
         if model_class.__name__ == "LevitForImageClassificationWithTeacher":
             continue
         _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
         model.gradient_checkpointing_enable()
         model.to(_lowerCAmelCase		)
         model.train()
         _lowerCAmelCase				=      self._prepare_for_class(_lowerCAmelCase			,     _lowerCAmelCase			,     return_labels=_lowerCAmelCase		)
         _lowerCAmelCase				=      model(**_lowerCAmelCase		).loss
         loss.backward()




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

     _lowerCAmelCase				=      [
         {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
         {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
         {"title": "regression", "num_labels": 1, "dtype": torch.float},
     ]

     for model_class in self.all_model_classes:
         if (
             model_class
             not in [
                 *get_values(_lowerCAmelCase		),
             ]
             or model_class.__name__ == "LevitForImageClassificationWithTeacher"
         ):
             continue

         for problem_type in problem_types:
             with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}'''		):
                 _lowerCAmelCase				=      problem_type["title"]
                 _lowerCAmelCase				=      problem_type["num_labels"]

                 _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
                 model.to(_lowerCAmelCase		)
                 model.train()

                 _lowerCAmelCase				=      self._prepare_for_class(_lowerCAmelCase			,     _lowerCAmelCase			,     return_labels=_lowerCAmelCase		)

                 if problem_type["num_labels"] > 1:
                     _lowerCAmelCase				=      inputs["labels"].unsqueeze(1		).repeat(1			,     problem_type["num_labels"]		)

                 _lowerCAmelCase				=      inputs["labels"].to(problem_type["dtype"]		)

                 # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                 # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                 # they have the same size." which is a symptom something in wrong for the regression problem.
                 # See https://github.com/huggingface/transformers/issues/11780
                 with warnings.catch_warnings(record=_lowerCAmelCase		) as warning_list:
                     _lowerCAmelCase				=      model(**_lowerCAmelCase		).loss
                 for w in warning_list:
                     if "Using a target size that is different to the input size" in str(w.message		):
                         raise ValueError(
                             f'''Something is going wrong in the regression problem: intercepted {w.message}'''		)

                 loss.backward()





 @slow
 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase				=      LevitModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)







def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
    return image





@require_torch
@require_vision
class    lowerCAmelCase_		(		unittest.TestCase		):




 @cached_property
 def 							_snake_case   (	self		)    ->							Dict:
     return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)





 @slow
 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
         _lowerCAmelCase		)

     _lowerCAmelCase				=      self.default_image_processor
     _lowerCAmelCase				=      prepare_img()
     _lowerCAmelCase				=      image_processor(images=_lowerCAmelCase			,     return_tensors="pt"		).to(_lowerCAmelCase		)

     # forward pass
     with torch.no_grad():
         _lowerCAmelCase				=      model(**_lowerCAmelCase		)

     # verify the logits
     _lowerCAmelCase				=      torch.Size((1, 1000)		)
     self.assertEqual(outputs.logits.shape			,     _lowerCAmelCase		)

     _lowerCAmelCase				=      torch.tensor([1.0448, -0.3745, -1.8317]		).to(_lowerCAmelCase		)

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









'''simple docstring'''


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

import sentencepiece as spm

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


if is_torch_available():
       import torch


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE	 =							{"vocab_file": "spiece.model"}

_SCREAMING_SNAKE_CASE	 =							{
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

_SCREAMING_SNAKE_CASE	 =							{
    "AI-Sweden/gpt-sw3-126m": 20_48,
    "AI-Sweden/gpt-sw3-350m": 20_48,
    "AI-Sweden/gpt-sw3-1.6b": 20_48,
    "AI-Sweden/gpt-sw3-6.7b": 20_48,
    "AI-Sweden/gpt-sw3-20b": 20_48,
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					VOCAB_FILES_NAMES
 __lowerCamelCase						:							List[Any]         =					PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase						:							Tuple         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase						:							List[str]         =					["input_ids", "attention_mask"]




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase=False			,     _lowerCAmelCase=False			,     _lowerCAmelCase=False			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							None:
     _lowerCAmelCase				=      {} if sp_model_kwargs is None else sp_model_kwargs

     _lowerCAmelCase				=      kwargs.get("name_or_path"		)
     if name_or_path is None:
         logger.warning(
             "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
             " you are testing the model, this can safely be ignored"		)
         _lowerCAmelCase				=      "None"

     # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
     _lowerCAmelCase				=      "<|endoftext|>" if eos_token is None else eos_token
     _lowerCAmelCase				=      "<unk>" if unk_token is None else unk_token
     if "gpt-sw3-7b" in name_or_path:
         _lowerCAmelCase				=      unk_token if pad_token is None else pad_token
         _lowerCAmelCase				=      eos_token if bos_token is None else bos_token
     else:
         _lowerCAmelCase				=      "<pad>" if pad_token is None else pad_token
         _lowerCAmelCase				=      "<s>" if bos_token is None else bos_token

     super().__init__(
         do_lower_case=_lowerCAmelCase			,     remove_space=_lowerCAmelCase			,     keep_accents=_lowerCAmelCase			,     bos_token=_lowerCAmelCase			,     eos_token=_lowerCAmelCase			,     unk_token=_lowerCAmelCase			,     pad_token=_lowerCAmelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **_lowerCAmelCase			,     )

     _lowerCAmelCase				=      do_lower_case
     _lowerCAmelCase				=      remove_space
     _lowerCAmelCase				=      keep_accents
     _lowerCAmelCase				=      vocab_file

     _lowerCAmelCase				=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(_lowerCAmelCase		)

     # Used for whitespace normalization in input texts
     # fmt : off
     _lowerCAmelCase				=      {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
     # fmt : on

     # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
     _lowerCAmelCase				=      re.compile(
         f'''[{''.join(map(_lowerCAmelCase			,     list(range(0			,     9		)		) + list(range(11			,     32		)		) + list(range(127			,     160		)		) + [160, 173, 8203]		)		)}]'''		)




 def __getstate__(	self		)    ->							Any:
     _lowerCAmelCase				=      self.__dict__.copy()
     _lowerCAmelCase				=      None
     return state




 def __setstate__(	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      d

     # for backward compatibility
     if not hasattr(self			,     "sp_model_kwargs"		):
         _lowerCAmelCase				=      {}

     _lowerCAmelCase				=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(self.vocab_file		)




 @property
 # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
 def 							_snake_case   (	self		)    ->							int:
     return len(self.sp_model		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      self.non_printing_characters_re.sub(""			,     _lowerCAmelCase		)

     # Normalize whitespaces
     _lowerCAmelCase				=      "".join([char if char not in self.whitespaces else " " for char in text]		)

     # NFC Unicode normalization
     _lowerCAmelCase				=      unicodedata.normalize("NFC"			,     _lowerCAmelCase		)
     return text




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[str]:
     _lowerCAmelCase				=      self.preprocess_text(_lowerCAmelCase		)
     return self.sp_model.encode(_lowerCAmelCase			,     out_type=_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							int:
     return self.sp_model.PieceToId(_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     return self.sp_model.IdToPiece(_lowerCAmelCase		)




 @staticmethod
 def 							_snake_case   (	_lowerCAmelCase		)    ->							str:
     return out_string




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      []
     _lowerCAmelCase				=      ""
     _lowerCAmelCase				=      False
     for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
             # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
             if not prev_is_special:
                 out_string += " "

             out_string += self.sp_model.decode(_lowerCAmelCase		) + token
             _lowerCAmelCase				=      True
             _lowerCAmelCase				=      []
         else:
             current_sub_tokens.append(_lowerCAmelCase		)
             _lowerCAmelCase				=      False
     out_string += self.sp_model.decode(_lowerCAmelCase		)

     return out_string




 def 							_snake_case   (	self		)    ->							Dict[str, int]:
     _lowerCAmelCase				=      {self.convert_ids_to_tokens(_lowerCAmelCase		): i for i in range(self.vocab_size		)}
     vocab.update(self.added_tokens_encoder		)
     return vocab




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

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

     return (out_vocab_file,)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False		)    ->							Union[List[int], List[List[int]], "torch.Tensor"]:

     if isinstance(_lowerCAmelCase			,     _lowerCAmelCase		):
         _lowerCAmelCase				=      self.preprocess_text(_lowerCAmelCase		)
         _lowerCAmelCase				=      self.sp_model.encode(_lowerCAmelCase		)
     else:
         _lowerCAmelCase				=      [self.preprocess_text(_lowerCAmelCase		) for t in text]
         _lowerCAmelCase				=      self.sp_model.encode(_lowerCAmelCase		)

     if return_tensors is True or return_tensors == "pt":
         _lowerCAmelCase				=      torch.tensor(_lowerCAmelCase		)

     return token_ids




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     return self.sp_model.decode(_lowerCAmelCase		)





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[int]:
     _lowerCAmelCase				=      [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
     _lowerCAmelCase				=      (
         f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(_lowerCAmelCase		) + f'''{self.bos_token}Bot:'''
     )
     return self.encode(text=_lowerCAmelCase		)


