





'''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 __a			( _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			Union[str, Any]						   =						BioGptTokenizer
   __UpperCamelCase :			Optional[Any]						   =						False





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      super().setUp()

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

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





   def   UpperCAmelCase__  (       self					:      Any					,lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""lower newer"""
      __SCREAMING_SNAKE_CASE   				=							"""lower newer"""
      return input_text, output_text





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							BioGptTokenizer(self.vocab_file					,self.merges_file     )

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

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





   @slow
   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							BioGptTokenizer.from_pretrained("""microsoft/biogpt"""     )

      __SCREAMING_SNAKE_CASE   				=							tokenizer.encode("""sequence builders"""					,add_special_tokens=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							tokenizer.encode("""multi-sequence build"""					,add_special_tokens=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							tokenizer.build_inputs_with_special_tokens(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							tokenizer.build_inputs_with_special_tokens(lowerCamelCase					,lowerCamelCase     )

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









"""simple docstring"""



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
  from ...onnx.config import PatchingSpec
  from ...tokenization_utils_base import PreTrainedTokenizerBase


__snake_case       =       logging.get_logger(__name__)

__snake_case       =       {
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Tuple				    =    '''longformer'''







   def __init__(      self		,      UpperCamelCase__ = 512		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 1		,      UpperCamelCase__ = 0		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 3_0522		,      UpperCamelCase__ = 768		,      UpperCamelCase__ = 12		,      UpperCamelCase__ = 12		,      UpperCamelCase__ = 3072		,      UpperCamelCase__ = "gelu"		,      UpperCamelCase__ = 0.1		,      UpperCamelCase__ = 0.1		,      UpperCamelCase__ = 512		,      UpperCamelCase__ = 2		,      UpperCamelCase__ = 0.02		,      UpperCamelCase__ = 1e-12		,      UpperCamelCase__ = False		,      **UpperCamelCase__		,      )      ->						str:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__		,      **UpperCamelCase__		)

       snake_case       : int    =       attention_window
       snake_case       : Any    =       sep_token_id
       snake_case       : Dict    =       bos_token_id
       snake_case       : int    =       eos_token_id
       snake_case       : List[str]    =       vocab_size
       snake_case       : Dict    =       hidden_size
       snake_case       : Optional[int]    =       num_hidden_layers
       snake_case       : List[str]    =       num_attention_heads
       snake_case       : Dict    =       hidden_act
       snake_case       : Union[str, Any]    =       intermediate_size
       snake_case       : Optional[int]    =       hidden_dropout_prob
       snake_case       : List[Any]    =       attention_probs_dropout_prob
       snake_case       : Any    =       max_position_embeddings
       snake_case       : Union[str, Any]    =       type_vocab_size
       snake_case       : Union[str, Any]    =       initializer_range
       snake_case       : int    =       layer_norm_eps
       snake_case       : str    =       onnx_export







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__ = "default"		,      UpperCamelCase__ = None		)      ->						List[str]:

       '''simple docstring'''




       super().__init__(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
       snake_case       : int    =       True







   @property
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case       : str    =       {0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case       : List[str]    =       {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("global_attention_mask", dynamic_axis),
           ]		)







   @property
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       snake_case       : str    =       super().outputs
       if self.task == "default":
           snake_case       : str    =       {0: "batch"}
       return outputs







   @property
   def 					lowerCamelCase						(      self		)      ->						float:

       '''simple docstring'''




       return 1e-4







   @property
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       return max(super().default_onnx_opset		,      14		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__ = -1		,      UpperCamelCase__ = -1		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      )      ->						Mapping[str, Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       super().generate_dummy_inputs(
           preprocessor=UpperCamelCase__		,      batch_size=UpperCamelCase__		,      seq_length=UpperCamelCase__		,      is_pair=UpperCamelCase__		,      framework=UpperCamelCase__		)
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       snake_case       : Any    =       torch.zeros_like(inputs["input_ids"]		)
       # make every second token global
       snake_case       : Any    =       1

       return inputs





