
'''simple docstring'''

import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

     from transformers import (
         MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         MODEL_MAPPING,
         DeiTForImageClassification,
         DeiTForImageClassificationWithTeacher,
         DeiTForMaskedImageModeling,
         DeiTModel,
     )
     from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import DeiTImageProcessor




class    lowerCAmelCase       :


 def __init__(    self  : str			,							a__  : Tuple			,							a__  : Dict=13			,							a__  : Tuple=30			,							a__  : Optional[int]=2			,							a__  : List[str]=3			,							a__  : Optional[Any]=True			,							a__  : List[Any]=True			,							a__  : Optional[Any]=32			,							a__  : Optional[Any]=5			,							a__  : Optional[int]=4			,							a__  : List[str]=37			,							a__  : Union[str, Any]="gelu"			,							a__  : str=0.1			,							a__  : Optional[Any]=0.1			,							a__  : Optional[Any]=10			,							a__  : Optional[Any]=0.02			,							a__  : Dict=3			,							a__  : int=None			,							a__  : Optional[Any]=2			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	parent
  lowerCAmelCase__     :   int     		=	batch_size
  lowerCAmelCase__     :   Optional[int]     		=	image_size
  lowerCAmelCase__     :   Dict     		=	patch_size
  lowerCAmelCase__     :   str     		=	num_channels
  lowerCAmelCase__     :   List[Any]     		=	is_training
  lowerCAmelCase__     :   Optional[int]     		=	use_labels
  lowerCAmelCase__     :   str     		=	hidden_size
  lowerCAmelCase__     :   str     		=	num_hidden_layers
  lowerCAmelCase__     :   Tuple     		=	num_attention_heads
  lowerCAmelCase__     :   List[str]     		=	intermediate_size
  lowerCAmelCase__     :   int     		=	hidden_act
  lowerCAmelCase__     :   Optional[int]     		=	hidden_dropout_prob
  lowerCAmelCase__     :   int     		=	attention_probs_dropout_prob
  lowerCAmelCase__     :   Tuple     		=	type_sequence_label_size
  lowerCAmelCase__     :   Tuple     		=	initializer_range
  lowerCAmelCase__     :   str     		=	scope
  lowerCAmelCase__     :   int     		=	encoder_stride

  # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
  lowerCAmelCase__     :   List[str]     		=	(image_size // patch_size) ** 2
  lowerCAmelCase__     :   Union[str, Any]     		=	num_patches + 2


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

  lowerCAmelCase__     :   int     		=	None
  if self.use_labels:
   lowerCAmelCase__     :   Any     		=	ids_tensor([self.batch_size]			,							self.type_sequence_label_size    )

  lowerCAmelCase__     :   str     		=	self.get_config()

  return config, pixel_values, labels


 def    _A	(    self  : str    ):
  '''simple docstring'''

  return DeiTConfig(
      image_size=self.image_size			,							patch_size=self.patch_size			,							num_channels=self.num_channels			,							hidden_size=self.hidden_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			,							is_decoder=a__			,							initializer_range=self.initializer_range			,							encoder_stride=self.encoder_stride			,							)


 def    _A	(    self  : List[Any]			,							a__  : Any			,							a__  : Union[str, Any]			,							a__  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	DeiTModel(config=a__    )
  model.to(a__    )
  model.eval()
  lowerCAmelCase__     :   str     		=	model(a__    )
  self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)    )


 def    _A	(    self  : Tuple			,							a__  : Tuple			,							a__  : int			,							a__  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	DeiTForMaskedImageModeling(config=a__    )
  model.to(a__    )
  model.eval()
  lowerCAmelCase__     :   Tuple     		=	model(a__    )
  self.parent.assertEqual(
      result.reconstruction.shape			,							(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

  # test greyscale images
  lowerCAmelCase__     :   str     		=	1
  lowerCAmelCase__     :   Optional[Any]     		=	DeiTForMaskedImageModeling(a__    )
  model.to(a__    )
  model.eval()

  lowerCAmelCase__     :   Union[str, Any]     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
  lowerCAmelCase__     :   Any     		=	model(a__    )
  self.parent.assertEqual(result.reconstruction.shape			,							(self.batch_size, 1, self.image_size, self.image_size)    )


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : Tuple			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.type_sequence_label_size
  lowerCAmelCase__     :   Dict     		=	DeiTForImageClassification(a__    )
  model.to(a__    )
  model.eval()
  lowerCAmelCase__     :   int     		=	model(a__			,							labels=a__    )
  self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)    )

  # test greyscale images
  lowerCAmelCase__     :   Union[str, Any]     		=	1
  lowerCAmelCase__     :   Dict     		=	DeiTForImageClassification(a__    )
  model.to(a__    )
  model.eval()

  lowerCAmelCase__     :   str     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
  lowerCAmelCase__     :   str     		=	model(a__			,							labels=a__    )
  self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)    )


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase__
  )		,     (
      lowerCAmelCase__
  )		,     (
      lowerCAmelCase__
  )		,     
  )     :   Any     		=	config_and_inputs
  lowerCAmelCase__     :   Optional[Any]     		=	{"pixel_values": pixel_values}
  return config, inputs_dict




@require_torch
class    lowerCAmelCase       (     UpperCamelCase_    ,  UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				List[str]	=			(
     (
         DeiTModel,
         DeiTForImageClassification,
         DeiTForImageClassificationWithTeacher,
         DeiTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 A_				:				Union[str, Any]	=			(
     {
         """feature-extraction""": DeiTModel,
         """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 A_				:				str	=			False
 A_				:				str	=			False
 A_				:				Optional[int]	=			False


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	DeiTModelTester(self    )
  lowerCAmelCase__     :   Optional[Any]     		=	ConfigTester(self			,							config_class=a__			,							has_text_modality=a__			,							hidden_size=37    )


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  self.config_tester.run_common_tests()


 @unittest.skip(reason="DeiT does not use inputs_embeds"    )
 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  pass


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   Any     		=	self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   lowerCAmelCase__     :   List[str]     		=	model_class(a__    )
   self.assertIsInstance(model.get_input_embeddings()			,							(nn.Module)    )
   lowerCAmelCase__     :   Dict     		=	model.get_output_embeddings()
   self.assertTrue(x is None or isinstance(a__			,							nn.Linear    )    )


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   str     		=	self.model_tester.prepare_config_and_inputs_for_common()

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

   lowerCAmelCase__     :   Optional[int]     		=	["pixel_values"]
   self.assertListEqual(arg_names[:1]			,							a__    )


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*a__    )


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_image_modeling(*a__    )


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*a__    )


 def    _A	(    self  : Union[str, Any]			,							a__  : Optional[int]			,							a__  : str			,							a__  : Tuple=False    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	super()._prepare_for_class(a__			,							a__			,							return_labels=a__    )

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

  return inputs_dict


 def    _A	(    self  : int    ):
  '''simple docstring'''

  if not self.model_tester.is_training:
   return

  lowerCAmelCase__		,     lowerCAmelCase__     :   Any     		=	self.model_tester.prepare_config_and_inputs_for_common()
  lowerCAmelCase__     :   Dict     		=	True

  for model_class in self.all_model_classes:
   # DeiTForImageClassificationWithTeacher supports inference-only
   if (
       model_class in get_values(a__    )
       or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
   ):
    continue
   lowerCAmelCase__     :   Union[str, Any]     		=	model_class(a__    )
   model.to(a__    )
   model.train()
   lowerCAmelCase__     :   Tuple     		=	self._prepare_for_class(a__			,							a__			,							return_labels=a__    )
   lowerCAmelCase__     :   Tuple     		=	model(**a__    ).loss
   loss.backward()


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   str     		=	self.model_tester.prepare_config_and_inputs_for_common()
  if not self.model_tester.is_training:
   return

  lowerCAmelCase__     :   Optional[int]     		=	False
  lowerCAmelCase__     :   Any     		=	True

  for model_class in self.all_model_classes:
   if model_class in get_values(a__    ) or not model_class.supports_gradient_checkpointing:
    continue
   # DeiTForImageClassificationWithTeacher supports inference-only
   if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
    continue
   lowerCAmelCase__     :   str     		=	model_class(a__    )
   model.gradient_checkpointing_enable()
   model.to(a__    )
   model.train()
   lowerCAmelCase__     :   Tuple     		=	self._prepare_for_class(a__			,							a__			,							return_labels=a__    )
   lowerCAmelCase__     :   Optional[int]     		=	model(**a__    ).loss
   loss.backward()


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   int     		=	self.model_tester.prepare_config_and_inputs_for_common()

  lowerCAmelCase__     :   Any     		=	[
      {"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(a__    ),
           *get_values(a__    ),
       ]
       or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
   ):
    continue

   for problem_type in problem_types:
    with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''    ):
     lowerCAmelCase__     :   Tuple     		=	problem_type["title"]
     lowerCAmelCase__     :   int     		=	problem_type["num_labels"]

     lowerCAmelCase__     :   List[Any]     		=	model_class(a__    )
     model.to(a__    )
     model.train()

     lowerCAmelCase__     :   Optional[Any]     		=	self._prepare_for_class(a__			,							a__			,							return_labels=a__    )

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

     lowerCAmelCase__     :   Union[str, Any]     		=	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=a__    ) as warning_list:
      lowerCAmelCase__     :   Union[str, Any]     		=	model(**a__    ).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    _A	(    self  : Any    ):
  '''simple docstring'''

  for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   lowerCAmelCase__     :   List[str]     		=	DeiTModel.from_pretrained(a__    )
   self.assertIsNotNone(a__    )



def        UpperCAmelCase_ (  ):







 """simple docstring"""


 lowerCAmelCase__     :   Union[str, Any]     		=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
 return image







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


 @cached_property
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  return (
      DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"    )
      if is_vision_available()
      else None
  )


 @slow
 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"    ).to(
      a__    )

  lowerCAmelCase__     :   Optional[Any]     		=	self.default_image_processor
  lowerCAmelCase__     :   Tuple     		=	prepare_img()
  lowerCAmelCase__     :   List[Any]     		=	image_processor(images=a__			,							return_tensors="pt"    ).to(a__    )

  # forward pass
  with torch.no_grad():
   lowerCAmelCase__     :   int     		=	model(**a__    )

  # verify the logits
  lowerCAmelCase__     :   List[Any]     		=	torch.Size((1, 1000)    )
  self.assertEqual(outputs.logits.shape			,							a__    )

  lowerCAmelCase__     :   Dict     		=	torch.tensor([-1.0266, 0.1912, -1.2861]    ).to(a__    )

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


 @slow
 @require_accelerate
 @require_torch_gpu
 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	DeiTModel.from_pretrained(
      "facebook/deit-base-distilled-patch16-224"			,							torch_dtype=torch.floataa			,							device_map="auto"    )
  lowerCAmelCase__     :   int     		=	self.default_image_processor

  lowerCAmelCase__     :   Union[str, Any]     		=	prepare_img()
  lowerCAmelCase__     :   Union[str, Any]     		=	image_processor(images=a__			,							return_tensors="pt"    )
  lowerCAmelCase__     :   Dict     		=	inputs.pixel_values.to(a__    )

  # forward pass to make sure inference works in fp16
  with torch.no_grad():
   lowerCAmelCase__     :   List[str]     		=	model(a__    )







'''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


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

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""",
    }
}

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       (     UpperCamelCase_  ):
 A_				:				Dict	=			VOCAB_FILES_NAMES
 A_				:				Optional[int]	=			PRETRAINED_VOCAB_FILES_MAP
 A_				:				Optional[Any]	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A_				:				Union[str, Any]	=			["""input_ids""", """attention_mask"""]


 def __init__(    self  : List[str]			,							a__  : Any			,							a__  : Dict=False			,							a__  : List[Any]=False			,							a__  : Dict=False			,							a__  : Any=None			,							a__  : Tuple=None			,							a__  : str=None			,							a__  : Optional[int]=None			,							a__  : Optional[Dict[str, Any]] = None			,							**a__  : Union[str, Any]			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	{} if sp_model_kwargs is None else sp_model_kwargs

  lowerCAmelCase__     :   List[str]     		=	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__     :   Dict     		=	"None"

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

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

  lowerCAmelCase__     :   Dict     		=	do_lower_case
  lowerCAmelCase__     :   Union[str, Any]     		=	remove_space
  lowerCAmelCase__     :   Any     		=	keep_accents
  lowerCAmelCase__     :   List[str]     		=	vocab_file

  lowerCAmelCase__     :   Dict     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(a__    )

  # Used for whitespace normalization in input texts
  # fmt : off
  lowerCAmelCase__     :   List[str]     		=	{" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
  # fmt : on

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


 def __getstate__(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.__dict__.copy()
  lowerCAmelCase__     :   List[str]     		=	None
  return state


 def __setstate__(    self  : List[Any]			,							a__  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	d

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

  lowerCAmelCase__     :   str     		=	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    _A	(    self  : str    ):
  '''simple docstring'''

  return len(self.sp_model    )


 def    _A	(    self  : List[str]			,							a__  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.non_printing_characters_re.sub(""			,							a__    )

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

  # NFC Unicode normalization
  lowerCAmelCase__     :   Tuple     		=	unicodedata.normalize("NFC"			,							a__    )
  return text


 def    _A	(    self  : str			,							a__  : str			,							**a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	self.preprocess_text(a__    )
  return self.sp_model.encode(a__			,							out_type=a__    )


 def    _A	(    self  : List[str]			,							a__  : str    ):
  '''simple docstring'''

  return self.sp_model.PieceToId(a__    )


 def    _A	(    self  : Optional[int]			,							a__  : int    ):
  '''simple docstring'''

  return self.sp_model.IdToPiece(a__    )


 @staticmethod
 def    _A	(    a__  : str    ):
  '''simple docstring'''

  return out_string


 def    _A	(    self  : int			,							a__  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	[]
  lowerCAmelCase__     :   Tuple     		=	""
  lowerCAmelCase__     :   str     		=	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(a__    ) + token
    lowerCAmelCase__     :   Union[str, Any]     		=	True
    lowerCAmelCase__     :   Dict     		=	[]
   else:
    current_sub_tokens.append(a__    )
    lowerCAmelCase__     :   Dict     		=	False
  out_string += self.sp_model.decode(a__    )

  return out_string


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	{self.convert_ids_to_tokens(a__    ): i for i in range(self.vocab_size    )}
  vocab.update(self.added_tokens_encoder    )
  return vocab


 def    _A	(    self  : str			,							a__  : str			,							a__  : Optional[str] = None    ):
  '''simple docstring'''

  if not os.path.isdir(a__    ):
   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
   return
  lowerCAmelCase__     :   List[str]     		=	os.path.join(
      a__			,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

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

  return (out_vocab_file,)


 def    _A	(    self  : List[Any]			,							a__  : Union[str, List[str]]			,							a__  : Union[str, bool] = False    ):
  '''simple docstring'''

  if isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   int     		=	self.preprocess_text(a__    )
   lowerCAmelCase__     :   Dict     		=	self.sp_model.encode(a__    )
  else:
   lowerCAmelCase__     :   Union[str, Any]     		=	[self.preprocess_text(a__    ) for t in text]
   lowerCAmelCase__     :   Optional[int]     		=	self.sp_model.encode(a__    )

  if return_tensors is True or return_tensors == "pt":
   lowerCAmelCase__     :   Union[str, Any]     		=	torch.tensor(a__    )

  return token_ids


 def    _A	(    self  : Dict			,							a__  : Union[int, List[int]]    ):
  '''simple docstring'''

  return self.sp_model.decode(a__    )


 def    _A	(    self  : int			,							a__  : "Conversation"    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
  lowerCAmelCase__     :   List[str]     		=	(
      F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(a__    ) + F'''{self.bos_token}Bot:'''
  )
  return self.encode(text=a__    )





