



"""simple docstring"""





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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_barthez import BarthezTokenizer
else:
			lowerCAmelCase_     =	None

lowerCAmelCase_     =	logging.get_logger(__name__)

lowerCAmelCase_     =	{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_     =	{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase_     =	{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

lowerCAmelCase_     =	'▁'




class  _snake_case		(    lowerCAmelCase__ ):







				"""simple docstring"""






				a			=						VOCAB_FILES_NAMES
				a			=						PRETRAINED_VOCAB_FILES_MAP
				a			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a			=						["input_ids", "attention_mask"]
				a			=						BarthezTokenizer





				def __init__(      self							:							int      ,    _A							:							List[str]=None      ,    _A							:							Dict=None      ,    _A							:							str="<s>"      ,    _A							:							Tuple="</s>"      ,    _A							:							Any="</s>"      ,    _A							:							List[Any]="<s>"      ,    _A							:							Optional[Any]="<unk>"      ,    _A							:							List[Any]="<pad>"      ,    _A							:							Union[str, Any]="<mask>"      ,    **_A							:							List[str]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    AddedToken(_SCREAMING_SNAKE_CASE      ,    lstrip=_SCREAMING_SNAKE_CASE      ,    rstrip=_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE) else mask_token

											super().__init__(
											    _SCREAMING_SNAKE_CASE      ,    tokenizer_file=_SCREAMING_SNAKE_CASE      ,    bos_token=_SCREAMING_SNAKE_CASE      ,    eos_token=_SCREAMING_SNAKE_CASE      ,    unk_token=_SCREAMING_SNAKE_CASE      ,    sep_token=_SCREAMING_SNAKE_CASE      ,    cls_token=_SCREAMING_SNAKE_CASE      ,    pad_token=_SCREAMING_SNAKE_CASE      ,    mask_token=_SCREAMING_SNAKE_CASE      ,    **_SCREAMING_SNAKE_CASE      ,    )

											_SCREAMING_SNAKE_CASE		:						Dict			  =    vocab_file
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    False if not self.vocab_file else True





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							Any      ,    _A							:							List[str] = None):



											"""simple docstring"""







											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [self.cls_token_id]
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Optional[Any]      ,    _A							:							Dict = None):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    [self.sep_token_id]
											_SCREAMING_SNAKE_CASE		:						Dict			  =    [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 + sep + token_ids_a + sep) * [0]





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							str      ,    _A							:							Union[str, Any] = None):



											"""simple docstring"""







											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																		    """tokenizer.""")

											if not os.path.isdir(_SCREAMING_SNAKE_CASE):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
																		return
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    os.path.join(
											    _SCREAMING_SNAKE_CASE      ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

											if os.path.abspath(self.vocab_file) != os.path.abspath(_SCREAMING_SNAKE_CASE):
																		copyfile(self.vocab_file      ,    _SCREAMING_SNAKE_CASE)

											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  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				@slow
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											_SCREAMING_SNAKE_CASE		:						str			  =    AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											model.to(_A)

											from datasets import load_dataset

											_SCREAMING_SNAKE_CASE		:						Any			  =    load_dataset("""nielsr/rvlcdip-demo""")

											_SCREAMING_SNAKE_CASE		:						Any			  =    dataset["""train"""][0]["""image"""].convert("""RGB""")

											_SCREAMING_SNAKE_CASE		:						str			  =    image_processor(_A      ,    return_tensors="""pt""").to(_A)

											# forward pass
											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						Any			  =    model(**_A)
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    outputs.logits

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.Size((1, 1_6))
											self.assertEqual(logits.shape      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.tensor(
											    [-0.4_158, -0.4_092, -0.4_347]      ,    device=_A      ,    dtype=torch.float      ,    )
											self.assertTrue(torch.allclose(logits[0, :3]      ,    _A      ,    atol=1e-4))


