






import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				__a				(	__A			,						unittest.TestCase    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Union[str, Any]					     =    MPNetTokenizer
		UpperCAmelCase__   :					Optional[Any]					     =    MPNetTokenizerFast
		UpperCAmelCase__   :					Optional[int]					     =    True
		UpperCAmelCase__   :					Optional[Any]					     =    True


		def 	__snake_case      (    self							):
						super().setUp()

						SCREAMING_SNAKE_CASE_ :						Any    				= [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE_ :						Dict    				= os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['vocab_file']							)
						with open(self.vocab_file			,     'w'			,     encoding='utf-8'							) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Any    				= 'UNwant\u00E9d,running'
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= 'unwanted, running'
						return input_text, output_text


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.tokenizer_class(self.vocab_file							)

						SCREAMING_SNAKE_CASE_ :						Any    				= tokenizer.tokenize('UNwant\u00E9d,running'							)
						self.assertListEqual(UpperCamelCase__			,     ['un', '##want', '##ed', ',', 'runn', '##ing']							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__							)			,     [9, 6, 7, 12, 10, 11]							)






		@slow
		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.tokenizer_class.from_pretrained('microsoft/mpnet-base'							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= tokenizer.encode('sequence builders'			,     add_special_tokens=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= tokenizer.encode('multi-sequence build'			,     add_special_tokens=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Tuple    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						str    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__			,     UpperCamelCase__							)

						assert encoded_sentence == [0] + text + [2]
						assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A										=      logging.get_logger(__name__)

A										=      {
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}


class 				__a				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					str					     =    """tvlt"""


		def __init__(    self			,     UpperCamelCase__=224			,     UpperCamelCase__=2048			,     UpperCamelCase__=128			,     UpperCamelCase__=[16, 16]			,     UpperCamelCase__=[16, 16]			,     UpperCamelCase__=3			,     UpperCamelCase__=1			,     UpperCamelCase__=8			,     UpperCamelCase__=768			,     UpperCamelCase__=12			,     UpperCamelCase__=12			,     UpperCamelCase__=3072			,     UpperCamelCase__="gelu"			,     UpperCamelCase__=0.0			,     UpperCamelCase__=0.0			,     UpperCamelCase__=0.02			,     UpperCamelCase__=1E-6			,     UpperCamelCase__=True			,     UpperCamelCase__=False			,     UpperCamelCase__=16			,     UpperCamelCase__=512			,     UpperCamelCase__=8			,     UpperCamelCase__=2048			,     UpperCamelCase__=0.75			,     UpperCamelCase__=0.15			,     UpperCamelCase__="frame-level"			,     UpperCamelCase__=True			,     UpperCamelCase__=True			,     UpperCamelCase__="classification"			,     **UpperCamelCase__			,     ):
						super().__init__(**UpperCamelCase__							)

						if audio_mask_type not in ("frame-level", "patch_level"):
										raise ValueError(
										    'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
										    F'''got {audio_mask_type}'''							)

						SCREAMING_SNAKE_CASE_ :						Tuple    				= image_size
						SCREAMING_SNAKE_CASE_ :						int    				= spectrogram_length
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= frequency_length
						SCREAMING_SNAKE_CASE_ :						Any    				= image_patch_size
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= audio_patch_size
						SCREAMING_SNAKE_CASE_ :						int    				= num_image_channels
						SCREAMING_SNAKE_CASE_ :						Any    				= num_audio_channels
						SCREAMING_SNAKE_CASE_ :						Any    				= num_frames

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= hidden_size
						SCREAMING_SNAKE_CASE_ :						str    				= num_hidden_layers
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= num_attention_heads
						SCREAMING_SNAKE_CASE_ :						str    				= intermediate_size
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= hidden_act
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= hidden_dropout_prob
						SCREAMING_SNAKE_CASE_ :						int    				= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_ :						Dict    				= initializer_range
						SCREAMING_SNAKE_CASE_ :						Dict    				= layer_norm_eps
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= qkv_bias
						SCREAMING_SNAKE_CASE_ :						List[str]    				= use_mean_pooling

						SCREAMING_SNAKE_CASE_ :						str    				= decoder_num_attention_heads
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= decoder_hidden_size
						SCREAMING_SNAKE_CASE_ :						Any    				= decoder_num_hidden_layers
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= decoder_intermediate_size
						SCREAMING_SNAKE_CASE_ :						List[str]    				= pixel_mask_ratio
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= audio_mask_ratio
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= audio_mask_type

						SCREAMING_SNAKE_CASE_ :						Any    				= task_matching
						SCREAMING_SNAKE_CASE_ :						Any    				= task_mae
						SCREAMING_SNAKE_CASE_ :						int    				= loss_type