

'''simple docstring'''





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    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								='''ZinengTang/tvlt-base'''
										lowercase   :       Optional[int]								=tempfile.mkdtemp()







				def    lowerCamelCase_  (					self    :		Any		,				**UpperCAmelCase__    :		List[Any]      ):
										'''simple docstring'''
										return TvltImageProcessor.from_pretrained(self.checkpoint		,				**_A      )







				def    lowerCamelCase_  (					self    :		Any		,				**UpperCAmelCase__    :		str      ):
										'''simple docstring'''
										return TvltFeatureExtractor.from_pretrained(self.checkpoint		,				**_A      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										shutil.rmtree(self.tmpdirname      )







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Tuple								=self.get_image_processor()
										lowercase   :       Dict								=self.get_feature_extractor()

										lowercase   :       Union[str, Any]								=TvltProcessor(image_processor=_A		,				feature_extractor=_A      )
										processor.save_pretrained(self.tmpdirname      )
										lowercase   :       int								=TvltProcessor.from_pretrained(self.tmpdirname      )

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







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.get_image_processor()
										lowercase   :       str								=self.get_feature_extractor()

										lowercase   :       int								=TvltProcessor(image_processor=_A		,				feature_extractor=_A      )

										lowercase   :       Optional[int]								=np.ones([12000]      )

										lowercase   :       Any								=feature_extractor(_A		,				return_tensors='''np'''      )
										lowercase   :       Tuple								=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    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.get_image_processor()
										lowercase   :       Optional[int]								=self.get_feature_extractor()

										lowercase   :       Union[str, Any]								=TvltProcessor(image_processor=_A		,				feature_extractor=_A      )

										lowercase   :       Tuple								=np.ones([3, 224, 224]      )

										lowercase   :       str								=image_processor(_A		,				return_tensors='''np'''      )
										lowercase   :       Optional[Any]								=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    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       Tuple								=self.get_image_processor()
										lowercase   :       List[Any]								=self.get_feature_extractor()

										lowercase   :       Any								=TvltProcessor(image_processor=_A		,				feature_extractor=_A      )

										lowercase   :       List[Any]								=np.ones([12000]      )
										lowercase   :       Tuple								=np.ones([3, 224, 224]      )

										lowercase   :       List[str]								=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    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       Tuple								=self.get_image_processor()
										lowercase   :       Any								=self.get_feature_extractor()

										lowercase   :       Optional[Any]								=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'''		,				)








'''simple docstring'''





import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						BioGptTokenizer
				lowerCamelCase_							=						False







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowercase   :       List[str]								=[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''w</w>''',
										    '''r</w>''',
										    '''t</w>''',
										    '''lo''',
										    '''low''',
										    '''er</w>''',
										    '''low</w>''',
										    '''lowest</w>''',
										    '''newer</w>''',
										    '''wider</w>''',
										    '''<unk>''',
										]
										lowercase   :       Any								=dict(zip(UpperCAmelCase__		,				range(len(UpperCAmelCase__      )      )      )      )
										lowercase   :       Union[str, Any]								=['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

										lowercase   :       Optional[int]								=os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''vocab_file''']      )
										lowercase   :       Union[str, Any]								=os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''merges_file''']      )
										with open(self.vocab_file		,				'''w'''      ) as fp:
																fp.write(json.dumps(UpperCAmelCase__      )      )
										with open(self.merges_file		,				'''w'''      ) as fp:
																fp.write('''\n'''.join(UpperCAmelCase__      )      )







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Dict								='''lower newer'''
										lowercase   :       str								='''lower newer'''
										return input_text, output_text







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=BioGptTokenizer(self.vocab_file		,				self.merges_file      )

										lowercase   :       Any								='''lower'''
										lowercase   :       int								=['''low''', '''er</w>''']
										lowercase   :       Optional[Any]								=tokenizer.tokenize(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       Optional[int]								=tokens + ['''<unk>''']
										lowercase   :       Any								=[14, 15, 20]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__      )		,				UpperCAmelCase__      )







				@slow
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Dict								=BioGptTokenizer.from_pretrained('''microsoft/biogpt'''      )

										lowercase   :       List[str]								=tokenizer.encode('''sequence builders'''		,				add_special_tokens=UpperCAmelCase__      )
										lowercase   :       Optional[int]								=tokenizer.encode('''multi-sequence build'''		,				add_special_tokens=UpperCAmelCase__      )

										lowercase   :       str								=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__		,				UpperCAmelCase__      )

										self.assertTrue(encoded_sentence == [2] + text      )
										self.assertTrue(encoded_pair == [2] + text + [2] + text_a      )





