






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__          =						logging.get_logger(__name__)

lowerCAmelCase__          =						{'vocab_file': 'sentencepiece.model'}

lowerCAmelCase__          =						{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

lowerCAmelCase__          =						{
    'google/rembert': 256,
}







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  VOCAB_FILES_NAMES
	_lowerCamelCase              =  PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

	def __init__(      self   :						Dict  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						Tuple=False  ,					UpperCAmelCase_   :						Optional[int]=True  ,					UpperCAmelCase_   :						int=True  ,					UpperCAmelCase_   :						List[Any]="[CLS]"  ,					UpperCAmelCase_   :						Tuple="[SEP]"  ,					UpperCAmelCase_   :						Tuple="[UNK]"  ,					UpperCAmelCase_   :						Optional[int]="[SEP]"  ,					UpperCAmelCase_   :						Any="[PAD]"  ,					UpperCAmelCase_   :						Tuple="[CLS]"  ,					UpperCAmelCase_   :						List[str]="[MASK]"  ,					**UpperCAmelCase_   :						Union[str, Any]  ,					):
			super().__init__(
			    do_lower_case=UpperCAmelCase_  ,					remove_space=UpperCAmelCase_  ,					keep_accents=UpperCAmelCase_  ,					bos_token=UpperCAmelCase_  ,					eos_token=UpperCAmelCase_  ,					unk_token=UpperCAmelCase_  ,					sep_token=UpperCAmelCase_  ,					pad_token=UpperCAmelCase_  ,					cls_token=UpperCAmelCase_  ,					mask_token=UpperCAmelCase_  ,					**UpperCAmelCase_  ,					)

			UpperCamelCase__				:					Tuple					=					do_lower_case
			UpperCamelCase__				:					List[Any]					=					remove_space
			UpperCamelCase__				:					Any					=					keep_accents
			UpperCamelCase__				:					Tuple					=					vocab_file

			UpperCamelCase__				:					Tuple					=					spm.SentencePieceProcessor()
			self.sp_model.Load(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						List[str]):
			return len(self.sp_model)

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			UpperCamelCase__				:					Optional[int]					=					{self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
			vocab.update(self.added_tokens_encoder)
			return vocab

	def __getstate__(      self   :						Dict):
			UpperCamelCase__				:					Union[str, Any]					=					self.__dict__.copy()
			UpperCamelCase__				:					Union[str, Any]					=					None
			return state

	def __setstate__(      self   :						List[str]  ,					UpperCAmelCase_   :						Tuple):
			UpperCamelCase__				:					List[Any]					=					d
			UpperCamelCase__				:					Tuple					=					spm.SentencePieceProcessor()
			self.sp_model.Load(self.vocab_file)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						Optional[int]  ,					UpperCAmelCase_   :						Union[str, Any]=False):
			UpperCamelCase__				:					Dict					=					self.sp_model.EncodeAsPieces(UpperCAmelCase_)
			return pieces

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						Tuple):
			return self.sp_model.PieceToId(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int  ,					UpperCAmelCase_   :						List[str]):
			return self.sp_model.IdToPiece(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					UpperCAmelCase_   :						Union[str, Any]):
			UpperCamelCase__				:					Tuple					=					self.sp_model.decode_pieces(UpperCAmelCase_)
			return out_string

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):
			UpperCamelCase__				:					Dict					=					[self.sep_token_id]
			UpperCamelCase__				:					str					=					[self.cls_token_id]
			if token_ids_a is None:
					return cls + token_ids_a + sep
			return cls + token_ids_a + sep + token_ids_a + sep

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None  ,					UpperCAmelCase_   :						bool = False):

			if already_has_special_tokens:
					if token_ids_a is not None:
							raise ValueError(
							    'You should not supply a second sequence if the provided sequence of '
							    'ids is already formatted with special tokens for the model.')
					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

			if token_ids_a is not None:
					return [1] + ([0] * len(UpperCAmelCase_)) + [1] + ([0] * len(UpperCAmelCase_)) + [1]
			return [1] + ([0] * len(UpperCAmelCase_)) + [1]

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):
			UpperCamelCase__				:					List[str]					=					[self.sep_token_id]
			UpperCamelCase__				:					str					=					[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      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[str] = None):
			if not os.path.isdir(UpperCAmelCase_):
					logger.error('Vocabulary path ({}) should be a directory'.format(UpperCAmelCase_))
					return
			UpperCamelCase__				:					Dict					=					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_):
					copyfile(self.vocab_file  ,					UpperCAmelCase_)

			return (out_vocab_file,)












'''simple docstring'''




import unittest

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


if is_torch_available():
		import torch

		from transformers import AutoModelForImageClassification

if is_vision_available():
		from transformers import AutoImageProcessor







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

	@slow
	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Union[str, Any]					=					AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip')
			UpperCamelCase__				:					List[str]					=					AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip')
			model.to(UpperCAmelCase_)

			from datasets import load_dataset

			UpperCamelCase__				:					Optional[Any]					=					load_dataset('nielsr/rvlcdip-demo')

			UpperCamelCase__				:					int					=					dataset['train'][0]['image'].convert('RGB')

			UpperCamelCase__				:					Union[str, Any]					=					image_processor(UpperCAmelCase_  ,					return_tensors='pt').to(UpperCAmelCase_)

			# forward pass
			with torch.no_grad():
					UpperCamelCase__				:					Optional[Any]					=					model(**UpperCAmelCase_)
					UpperCamelCase__				:					Tuple					=					outputs.logits

			UpperCamelCase__				:					str					=					torch.Size((1, 16))
			self.assertEqual(logits.shape  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					torch.tensor(
			    [-0.41_58, -0.40_92, -0.43_47]  ,					device=UpperCAmelCase_  ,					dtype=torch.float  ,					)
			self.assertTrue(torch.allclose(logits[0, :3]  ,					UpperCAmelCase_  ,					atol=1e-4))




