

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_camembert import CamembertTokenizer
else:
					SCREAMING_SNAKE_CASE_            = None


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE_            = {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE_            = {
    'camembert-base': 512,
}

SCREAMING_SNAKE_CASE_            = '▁'

class 			a					(		lowerCamelCase_			):
						_lowercase        =     VOCAB_FILES_NAMES
						_lowercase        =     PRETRAINED_VOCAB_FILES_MAP
						_lowercase        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase        =     ["""input_ids""", """attention_mask"""]
						_lowercase        =     CamembertTokenizer


						def __init__(    self  ,   A_=None  ,   A_=None  ,   A_="<s>"  ,   A_="</s>"  ,   A_="</s>"  ,   A_="<s>"  ,   A_="<unk>"  ,   A_="<pad>"  ,   A_="<mask>"  ,   A_=["<s>NOTUSED", "</s>NOTUSED"]  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      AddedToken(_UpperCAmelCase  ,   lstrip=_UpperCAmelCase  ,   rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase  ,   _UpperCAmelCase		) else mask_token

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

										_UpperCAmelCase    :						List[str]							=      vocab_file
										_UpperCAmelCase    :						str							=      False if not self.vocab_file else True


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      [self.sep_token_id]
										_UpperCAmelCase    :						Any							=      [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     _UpperCAmelCase			(    self  ,   A_  ,   A_ = 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(_UpperCAmelCase		):
														logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
														return
										_UpperCAmelCase    :						Optional[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		):
														copyfile(self.vocab_file  ,   _UpperCAmelCase		)

										return (out_vocab_file,)





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
					from transformers import TvltImageProcessor

if is_speech_available():
					from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class 			a					(		unittest.TestCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      "ZinengTang/tvlt-base"
										_UpperCAmelCase    :						int							=      tempfile.mkdtemp()


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										return TvltImageProcessor.from_pretrained(self.checkpoint  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										return TvltFeatureExtractor.from_pretrained(self.checkpoint  ,   **A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										shutil.rmtree(self.tmpdirname		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.get_image_processor()
										_UpperCAmelCase    :						Optional[int]							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Tuple							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)
										processor.save_pretrained(self.tmpdirname		)
										_UpperCAmelCase    :						str							=      TvltProcessor.from_pretrained(self.tmpdirname		)

										self.assertIsInstance(processor.feature_extractor  ,   A_		)
										self.assertIsInstance(processor.image_processor  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Tuple							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						List[str]							=      np.ones([12000]		)

										_UpperCAmelCase    :						int							=      feature_extractor(A_  ,   return_tensors="np"		)
										_UpperCAmelCase    :						int							=      processor(audio=A_  ,   return_tensors="np"		)

										for key in audio_dict.keys():
														self.assertAlmostEqual(audio_dict[key].sum()  ,   input_processor[key].sum()  ,   delta=1e-2		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Optional[Any]							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      np.ones([3, 224, 224]		)

										_UpperCAmelCase    :						Tuple							=      image_processor(A_  ,   return_tensors="np"		)
										_UpperCAmelCase    :						List[str]							=      processor(images=A_  ,   return_tensors="np"		)

										for key in image_dict.keys():
														self.assertAlmostEqual(image_dict[key].sum()  ,   input_processor[key].sum()  ,   delta=1e-2		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_image_processor()
										_UpperCAmelCase    :						Any							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Dict							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						str							=      np.ones([12000]		)
										_UpperCAmelCase    :						Optional[Any]							=      np.ones([3, 224, 224]		)

										_UpperCAmelCase    :						List[Any]							=      processor(audio=A_  ,   images=A_		)

										self.assertListEqual(list(inputs.keys()		)  ,   ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]		)

										# test if it raises when no input is passed
										with pytest.raises(A_		):
														processor()





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						str							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										self.assertListEqual(
										    processor.model_input_names  ,   image_processor.model_input_names + feature_extractor.model_input_names  ,   msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"  ,   )


