
"""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 __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				BioGptTokenizer
			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					Dict	)      -> Dict:







								"""simple docstring"""
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								A_				 =						[
								    "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>",
								]
								A_				 =						dict(zip(_snake_case       ,   range(len(_snake_case	)	)	)	)
								A_				 =						["l o 123", "lo w 1456", "e r</w> 1789", ""]

								A_				 =						os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["vocab_file"]	)
								A_				 =						os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["merges_file"]	)
								with open(self.vocab_file       ,   "w"	) as fp:
													fp.write(json.dumps(_snake_case	)	)
								with open(self.merges_file       ,   "w"	) as fp:
													fp.write("\n".join(_snake_case	)	)




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					List[str]	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						"lower newer"
								A_				 =						"lower newer"
								return input_text, output_text




			def      lowerCamelCase__     (				self						:					List[str]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						BioGptTokenizer(self.vocab_file       ,   self.merges_file	)

								A_				 =						"lower"
								A_				 =						["low", "er</w>"]
								A_				 =						tokenizer.tokenize(_snake_case	)
								self.assertListEqual(_snake_case       ,   _snake_case	)

								A_				 =						tokens + ["<unk>"]
								A_				 =						[14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case	)       ,   _snake_case	)






			@slow
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						BioGptTokenizer.from_pretrained("microsoft/biogpt"	)

								A_				 =						tokenizer.encode("sequence builders"       ,   add_special_tokens=_snake_case	)
								A_				 =						tokenizer.encode("multi-sequence build"       ,   add_special_tokens=_snake_case	)

								A_				 =						tokenizer.build_inputs_with_special_tokens(_snake_case	)
								A_				 =						tokenizer.build_inputs_with_special_tokens(_snake_case       ,   _snake_case	)

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






"""simple docstring"""





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


UpperCamelCase_					:						Tuple				= logging.get_logger(__name__)

UpperCamelCase_					:						Optional[int]				= {
    '''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
    '''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				"markuplm"




			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					Dict=30_522       ,   _snake_case						:					str=768       ,   _snake_case						:					Tuple=12       ,   _snake_case						:					Union[str, Any]=12       ,   _snake_case						:					Union[str, Any]=3_072       ,   _snake_case						:					List[str]="gelu"       ,   _snake_case						:					List[str]=0.1       ,   _snake_case						:					Optional[Any]=0.1       ,   _snake_case						:					Dict=512       ,   _snake_case						:					Union[str, Any]=2       ,   _snake_case						:					Tuple=0.0_2       ,   _snake_case						:					Tuple=1e-12       ,   _snake_case						:					Tuple=0       ,   _snake_case						:					Tuple=0       ,   _snake_case						:					Optional[Any]=2       ,   _snake_case						:					int=256       ,   _snake_case						:					Dict=1_024       ,   _snake_case						:					List[str]=216       ,   _snake_case						:					Optional[int]=1_001       ,   _snake_case						:					Union[str, Any]=32       ,   _snake_case						:					List[str]=50       ,   _snake_case						:					str="absolute"       ,   _snake_case						:					Optional[Any]=True       ,   _snake_case						:					str=None       ,   **_snake_case						:					Optional[Any]       ,   )      -> Optional[Any]:







								"""simple docstring"""
								super().__init__(
								    pad_token_id=_snake_case       ,   bos_token_id=_snake_case       ,   eos_token_id=_snake_case       ,   **_snake_case       ,   )
								A_				 =						vocab_size
								A_				 =						hidden_size
								A_				 =						num_hidden_layers
								A_				 =						num_attention_heads
								A_				 =						hidden_act
								A_				 =						intermediate_size
								A_				 =						hidden_dropout_prob
								A_				 =						attention_probs_dropout_prob
								A_				 =						max_position_embeddings
								A_				 =						type_vocab_size
								A_				 =						initializer_range
								A_				 =						layer_norm_eps
								A_				 =						position_embedding_type
								A_				 =						use_cache
								A_				 =						classifier_dropout
								# additional properties
								A_				 =						max_depth
								A_				 =						max_xpath_tag_unit_embeddings
								A_				 =						max_xpath_subs_unit_embeddings
								A_				 =						tag_pad_id
								A_				 =						subs_pad_id
								A_				 =						xpath_unit_hidden_size




