






import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Any					=XLMTokenizer
       __lowerCAmelCase    :		Union[str, Any]					=False





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          super().setUp()

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

          _lowercase              =os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES['vocab_file'])
          _lowercase              =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 				UpperCamelCase__						(				self	:Optional[int],      snake_case	:List[Any]):



          """simple docstring"""

          _lowercase              ='lower newer'
          _lowercase              ='lower newer'
          return input_text, output_text





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =XLMTokenizer(self.vocab_file,      self.merges_file)

          _lowercase              ='lower'
          _lowercase              =['low', 'er</w>']
          _lowercase              =tokenizer.tokenize(snake_case)
          self.assertListEqual(snake_case,      snake_case)

          _lowercase              =tokens + ['<unk>']
          _lowercase              =[14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case),      snake_case)





       @slow
       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          _lowercase              =XLMTokenizer.from_pretrained('xlm-mlm-en-2048')

          _lowercase              =tokenizer.encode('sequence builders',      add_special_tokens=snake_case)
          _lowercase              =tokenizer.encode('multi-sequence build',      add_special_tokens=snake_case)

          _lowercase              =tokenizer.build_inputs_with_special_tokens(snake_case)
          _lowercase              =tokenizer.build_inputs_with_special_tokens(snake_case,      snake_case)

          assert encoded_sentence == [0] + text + [1]
          assert encoded_pair == [0] + text + [1] + text_a + [1]










import warnings

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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[str]					='''mvp'''
       __lowerCAmelCase    :		List[Any]					=['''past_key_values''']
       __lowerCAmelCase    :		Optional[Any]					={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





       def __init__(				self	:Any,      snake_case	:Any=5_0267,      snake_case	:List[str]=1024,      snake_case	:List[str]=12,      snake_case	:List[Any]=4096,      snake_case	:List[Any]=16,      snake_case	:Optional[int]=12,      snake_case	:Optional[int]=4096,      snake_case	:List[str]=16,      snake_case	:Optional[Any]=0.0,      snake_case	:Tuple=0.0,      snake_case	:str="gelu",      snake_case	:int=1024,      snake_case	:Any=0.1,      snake_case	:Dict=0.0,      snake_case	:Optional[Any]=0.0,      snake_case	:int=0.0_2,      snake_case	:Union[str, Any]=0.0,      snake_case	:Tuple=False,      snake_case	:Optional[int]=True,      snake_case	:Any=1,      snake_case	:Union[str, Any]=0,      snake_case	:Optional[Any]=2,      snake_case	:Union[str, Any]=True,      snake_case	:Tuple=2,      snake_case	:Any=2,      snake_case	:List[Any]=False,      snake_case	:Optional[int]=100,      snake_case	:Union[str, Any]=800,      **snake_case	:Tuple,      ):



          """simple docstring"""

          _lowercase              =vocab_size
          _lowercase              =max_position_embeddings
          _lowercase              =d_model
          _lowercase              =encoder_ffn_dim
          _lowercase              =encoder_layers
          _lowercase              =encoder_attention_heads
          _lowercase              =decoder_ffn_dim
          _lowercase              =decoder_layers
          _lowercase              =decoder_attention_heads
          _lowercase              =dropout
          _lowercase              =attention_dropout
          _lowercase              =activation_dropout
          _lowercase              =activation_function
          _lowercase              =init_std
          _lowercase              =encoder_layerdrop
          _lowercase              =decoder_layerdrop
          _lowercase              =classifier_dropout
          _lowercase              =use_cache
          _lowercase              =encoder_layers
          _lowercase              =scale_embedding  # scale factor will be sqrt(d_model) if True
          _lowercase              =use_prompt
          _lowercase              =prompt_length
          _lowercase              =prompt_mid_dim

          super().__init__(
              pad_token_id=snake_case,      bos_token_id=snake_case,      eos_token_id=snake_case,      is_encoder_decoder=snake_case,      decoder_start_token_id=snake_case,      forced_eos_token_id=snake_case,      **snake_case,      )

          if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',      snake_case):
             _lowercase              =self.bos_token_id
             warnings.warn(
                 f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                 'The config can simply be saved and uploaded again to be fixed.')


