





"""simple docstring"""




import json
import os
from typing import Optional, Tuple

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


UpperCamelCase__     =	logging.get_logger(__name__)

UpperCamelCase__     =	{"vocab_file": "vocab.json"}

UpperCamelCase__     =	{
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

UpperCamelCase__     =	{"mgp-str": 27}

class     __SCREAMING_SNAKE_CASE   (       snake_case_    ):
							snake_case       : List[Any]         =    VOCAB_FILES_NAMES
							snake_case       : List[Any]         =    PRETRAINED_VOCAB_FILES_MAP
							snake_case       : Any         =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase="[GO]"					,	__lowerCAmelCase="[GO]"					,	__lowerCAmelCase="[s]"					,	__lowerCAmelCase="[GO]"					,	**__lowerCAmelCase					):
														super().__init__(
														    unk_token=__lowerCAmelCase					,	bos_token=__lowerCAmelCase					,	eos_token=__lowerCAmelCase					,	pad_token=__lowerCAmelCase					,	**__lowerCAmelCase					,	)

														with open(__lowerCAmelCase					,	encoding="""utf-8"""					) as vocab_handle:
																					UpperCamelCase__			       =     json.load(__lowerCAmelCase					)
														UpperCamelCase__			       =     {v: k for k, v in self.vocab.items()}






							@property
							def 			_lowerCamelCase						(  self					):
														return len(self.vocab					)






							def 			_lowerCamelCase						(  self					):
														return dict(self.vocab					,	**self.added_tokens_encoder					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     []
														for s in text:
																					char_tokens.extend(__lowerCAmelCase					)
														return char_tokens






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														return self.vocab.get(__lowerCAmelCase					,	self.vocab.get(self.unk_token					)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														return self.decoder.get(__lowerCAmelCase					)







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = None					):





														if not os.path.isdir(__lowerCAmelCase					):
																					logger.error("""Vocabulary path ({}) should be a directory""".format(__lowerCAmelCase					)					)
																					return
														UpperCamelCase__			       =     os.path.join(
														    __lowerCAmelCase					,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

														with open(__lowerCAmelCase					,	"""w"""					,	encoding="""utf-8"""					) as f:
																					f.write(json.dumps(self.vocab					,	indent=2					,	sort_keys=__lowerCAmelCase					,	ensure_ascii=__lowerCAmelCase					) + """\n"""					)

														return (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     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """ZinengTang/tvlt-base"""
														UpperCamelCase__			       =     tempfile.mkdtemp()






							def 			_lowerCamelCase						(  self					,	**__lowerCAmelCase					):
														return TvltImageProcessor.from_pretrained(self.checkpoint					,	**__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					,	**__lowerCAmelCase					):
														return TvltFeatureExtractor.from_pretrained(self.checkpoint					,	**__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														shutil.rmtree(self.tmpdirname					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)
														processor.save_pretrained(self.tmpdirname					)
														UpperCamelCase__			       =     TvltProcessor.from_pretrained(self.tmpdirname					)

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






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														UpperCamelCase__			       =     np.ones([12000]					)

														UpperCamelCase__			       =     feature_extractor(__lowerCAmelCase					,	return_tensors="""np"""					)
														UpperCamelCase__			       =     processor(audio=__lowerCAmelCase					,	return_tensors="""np"""					)

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






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														UpperCamelCase__			       =     np.ones([3, 224, 224]					)

														UpperCamelCase__			       =     image_processor(__lowerCAmelCase					,	return_tensors="""np"""					)
														UpperCamelCase__			       =     processor(images=__lowerCAmelCase					,	return_tensors="""np"""					)

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






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														UpperCamelCase__			       =     np.ones([12000]					)
														UpperCamelCase__			       =     np.ones([3, 224, 224]					)

														UpperCamelCase__			       =     processor(audio=__lowerCAmelCase					,	images=__lowerCAmelCase					)

														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(__lowerCAmelCase					):
																					processor()







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														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"""					,	)




