




import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 							UpperCamelCase_       (					UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = LayoutLMTokenizer
    UpperCAmelCase__				    = LayoutLMTokenizerFast
    UpperCAmelCase__				    = True
    UpperCAmelCase__				    = True


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Tuple:





           '''simple docstring'''

           super().setUp()

           A__ 					=       [
               '''[UNK]''',
               '''[CLS]''',
               '''[SEP]''',
               '''want''',
               '''##want''',
               '''##ed''',
               '''wa''',
               '''un''',
               '''runn''',
               '''##ing''',
               ''',''',
               '''low''',
               '''lowest''',
           ]
           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					**UpperCAmelCase__ :      List[str])		->Optional[Any]:





           '''simple docstring'''

           return LayoutLMTokenizer.from_pretrained(self.tmpdirname   ,					**UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      List[str])		->Tuple:





           '''simple docstring'''

           A__ 					=       '''UNwant\u00E9d,running'''
           A__ 					=       '''unwanted, running'''
           return input_text, output_text


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->List[Any]:





           '''simple docstring'''

           A__ 					=       self.tokenizer_class(self.vocab_file)

           A__ 					=       tokenizer.tokenize('''UNwant\u00E9d,running''')
           self.assertListEqual(UpperCAmelCase__   ,					['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''])
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__)   ,					[7, 4, 5, 10, 8, 9])





    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->int:





           '''simple docstring'''

           pass










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


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Optional[Any]								={
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   "pegasus"
		__snake_case      :       List[str]     =   ["past_key_values"]
		__snake_case      :       Union[str, Any]     =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







		def __init__(					self       ,A__=50265       ,A__=1024       ,A__=12       ,A__=4096       ,A__=16       ,A__=12       ,A__=4096       ,A__=16       ,A__=0.0       ,A__=0.0       ,A__=True       ,A__=True       ,A__="gelu"       ,A__=1024       ,A__=0.1       ,A__=0.0       ,A__=0.0       ,A__=0.02       ,A__=0       ,A__=False       ,A__=0       ,A__=1       ,A__=1       ,**A__       ,):
						_A	:				List[str]   =      vocab_size
						_A	:				List[Any]   =      max_position_embeddings
						_A	:				Union[str, Any]   =      d_model
						_A	:				Union[str, Any]   =      encoder_ffn_dim
						_A	:				Union[str, Any]   =      encoder_layers
						_A	:				int   =      encoder_attention_heads
						_A	:				List[Any]   =      decoder_ffn_dim
						_A	:				Optional[int]   =      decoder_layers
						_A	:				Tuple   =      decoder_attention_heads
						_A	:				List[str]   =      dropout
						_A	:				Tuple   =      attention_dropout
						_A	:				Union[str, Any]   =      activation_dropout
						_A	:				int   =      activation_function
						_A	:				Union[str, Any]   =      init_std
						_A	:				Any   =      encoder_layerdrop
						_A	:				int   =      decoder_layerdrop
						_A	:				Tuple   =      use_cache
						_A	:				Dict   =      encoder_layers
						_A	:				List[str]   =      scale_embedding  # scale factor will be sqrt(d_model) if True
						super().__init__(
						    pad_token_id=A__       ,eos_token_id=A__       ,is_encoder_decoder=A__       ,decoder_start_token_id=A__       ,forced_eos_token_id=A__       ,**A__       ,)







		@property
		def  A__      (					self		):
						return self.encoder_attention_heads







		@property
		def  A__      (					self		):
						return self.d_model



