





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   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       "ZinengTang/tvlt-base"
										lowerCamelCase__       =       tempfile.mkdtemp()





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return TvltImageProcessor.from_pretrained(self.checkpoint	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return TvltFeatureExtractor.from_pretrained(self.checkpoint	,  **_lowerCAmelCase	)





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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_feature_extractor()

										lowerCamelCase__       =       TvltProcessor(image_processor=_lowerCAmelCase	,  feature_extractor=_lowerCAmelCase	)
										processor.save_pretrained(self.tmpdirname	)
										lowerCamelCase__       =       TvltProcessor.from_pretrained(self.tmpdirname	)

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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_feature_extractor()

										lowerCamelCase__       =       TvltProcessor(image_processor=_lowerCAmelCase	,  feature_extractor=_lowerCAmelCase	)

										lowerCamelCase__       =       np.ones([1_2000]	)

										lowerCamelCase__       =       feature_extractor(_lowerCAmelCase	,  return_tensors="np"	)
										lowerCamelCase__       =       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 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_feature_extractor()

										lowerCamelCase__       =       TvltProcessor(image_processor=_lowerCAmelCase	,  feature_extractor=_lowerCAmelCase	)

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

										lowerCamelCase__       =       image_processor(_lowerCAmelCase	,  return_tensors="np"	)
										lowerCamelCase__       =       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 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_feature_extractor()

										lowerCamelCase__       =       TvltProcessor(image_processor=_lowerCAmelCase	,  feature_extractor=_lowerCAmelCase	)

										lowerCamelCase__       =       np.ones([1_2000]	)
										lowerCamelCase__       =       np.ones([3, 224, 224]	)

										lowerCamelCase__       =       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 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_feature_extractor()

										lowerCamelCase__       =       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"	,  )








from typing import TYPE_CHECKING

from ...utils import _LazyModule


A_												=     {"tokenization_byt5": ["ByT5Tokenizer"]}


if TYPE_CHECKING:
						from .tokenization_byta import ByTaTokenizer
else:
						import sys

						A_												=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

