



"""simple docstring"""







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


UpperCAmelCase:  Optional[int]			     =				logging.get_logger(__name__)

UpperCAmelCase:  Any			     =				{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       str						       =  "tvlt"






	def __init__(     self				,UpperCAmelCase_=2_24				,UpperCAmelCase_=20_48				,UpperCAmelCase_=1_28				,UpperCAmelCase_=[16, 16]				,UpperCAmelCase_=[16, 16]				,UpperCAmelCase_=3				,UpperCAmelCase_=1				,UpperCAmelCase_=8				,UpperCAmelCase_=7_68				,UpperCAmelCase_=12				,UpperCAmelCase_=12				,UpperCAmelCase_=30_72				,UpperCAmelCase_="gelu"				,UpperCAmelCase_=0.0				,UpperCAmelCase_=0.0				,UpperCAmelCase_=0.02				,UpperCAmelCase_=1E-6				,UpperCAmelCase_=True				,UpperCAmelCase_=False				,UpperCAmelCase_=16				,UpperCAmelCase_=5_12				,UpperCAmelCase_=8				,UpperCAmelCase_=20_48				,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}"""   )

			_lowercase				: List[Any]     =   image_size
			_lowercase				: Optional[int]     =   spectrogram_length
			_lowercase				: Dict     =   frequency_length
			_lowercase				: Dict     =   image_patch_size
			_lowercase				: Any     =   audio_patch_size
			_lowercase				: Tuple     =   num_image_channels
			_lowercase				: Union[str, Any]     =   num_audio_channels
			_lowercase				: Optional[Any]     =   num_frames

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

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

			_lowercase				: Optional[Any]     =   task_matching
			_lowercase				: List[str]     =   task_mae
			_lowercase				: Tuple     =   loss_type











"""simple docstring"""

import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

@require_tokenizers
class     A_				(   unittest.TestCase	):
				UpperCAmelCase__										=					EsmTokenizer







				def      _snake_case       (				self				:    List[str]						)					->       Dict:
										super().setUp()
										__magic_name__      =  tempfile.mkdtemp()
										# fmt: off
										__magic_name__      =  ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
										# fmt: on
										__magic_name__      =  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				:    Optional[Any]    ,     **__lowerCamelCase				:    str						)					->       List[PreTrainedTokenizerBase]:
										return [self.get_tokenizer(**__lowerCamelCase						)]







				def      _snake_case       (				self				:    Dict    ,     **__lowerCamelCase				:    int						)					->       PreTrainedTokenizer:
										return self.tokenizer_class.from_pretrained(self.tmpdirname    ,     **__lowerCamelCase						)







				def      _snake_case       (				self				:    str						)					->       Optional[int]:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  tokenizer.tokenize("LAGVS"						)
										self.assertListEqual(__lowerCamelCase    ,     ["L", "A", "G", "V", "S"]						)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase						)    ,     [4, 5, 6, 7, 8]						)







				def      _snake_case       (				self				:    int						)					->       Dict:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  "LAGVS"
										self.assertListEqual(tokenizer.encode(__lowerCamelCase						)    ,     [0, 4, 5, 6, 7, 8, 2]						)







				def      _snake_case       (				self				:    List[Any]						)					->       Tuple:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  ["LAGVS", "WCB"]
										__magic_name__      =  tokenizer(__lowerCamelCase    ,     padding=__lowerCamelCase						)["input_ids"]

										self.assertListEqual(__lowerCamelCase    ,     [[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]]						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       List[str]:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  ["LAGVS", "WCB"]
										__magic_name__      =  tokenizer(__lowerCamelCase    ,     padding=__lowerCamelCase						)["input_ids"]

										self.assertListEqual(__lowerCamelCase    ,     [[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]]						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Dict:
										__magic_name__      =  self.get_tokenizers(fast=__lowerCamelCase						)
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
																						__magic_name__      =  "<unk>"
																						__magic_name__      =  "<mask>"

																						__magic_name__      =  tokenizer.tokenize(__lowerCamelCase						)
																						__magic_name__      =  tokenizer.tokenize(__lowerCamelCase						)

																						self.assertEqual(len(__lowerCamelCase						)    ,     1						)
																						self.assertEqual(len(__lowerCamelCase						)    ,     1						)
																						self.assertEqual(token_a[0]    ,     __lowerCamelCase						)
																						self.assertEqual(token_a[0]    ,     __lowerCamelCase						)



