import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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


__lowerCamelCase    :				List[Any]							     =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

__lowerCamelCase    :				List[str]							     =	{
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

__lowerCamelCase    :				Tuple							     =	{
    '''allenai/longformer-base-4096''': 4096,
    '''allenai/longformer-large-4096''': 4096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 			__SCREAMING_SNAKE_CASE						(      )							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   (
      list(range(ord("""!"""			)				,  ord("""~"""			) + 1			)			) + list(range(ord("""¡"""			)				,  ord("""¬"""			) + 1			)			) + list(range(ord("""®"""			)				,  ord("""ÿ"""			) + 1			)			)
  )
  SCREAMING_SNAKE_CASE__    							=   bs[:]
  SCREAMING_SNAKE_CASE__    							=   0
  for b in range(2**8			):
    if b not in bs:
      bs.append(__UpperCamelCase			)
      cs.append(2**8 + n			)
      n += 1
  SCREAMING_SNAKE_CASE__    							=   [chr(__UpperCamelCase			) for n in cs]
  return dict(zip(__UpperCamelCase				,  __UpperCamelCase			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   set()
  SCREAMING_SNAKE_CASE__    							=   word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)			)
    SCREAMING_SNAKE_CASE__    							=   char
  return pairs







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							["input_ids", "attention_mask"]




   def __init__(					self    :       str ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Dict="replace" ,			_lowercase    :       Tuple="<s>" ,			_lowercase    :       List[str]="</s>" ,			_lowercase    :       List[str]="</s>" ,			_lowercase    :       int="<s>" ,			_lowercase    :       Optional[int]="<unk>" ,			_lowercase    :       Optional[Any]="<pad>" ,			_lowercase    :       List[Any]="<mask>" ,			_lowercase    :       Union[str, Any]=False ,			**_lowercase    :       Optional[Any] ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else bos_token
     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else eos_token
     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else sep_token
     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else cls_token
     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else unk_token
     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else mask_token

     super().__init__(
         errors=_lowercase ,			bos_token=_lowercase ,			eos_token=_lowercase ,			unk_token=_lowercase ,			sep_token=_lowercase ,			cls_token=_lowercase ,			pad_token=_lowercase ,			mask_token=_lowercase ,			add_prefix_space=_lowercase ,			**_lowercase ,			)

     with open(_lowercase ,			encoding="""utf-8"""   ) as vocab_handle:
       SCREAMING_SNAKE_CASE__    							=   json.load(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   {v: k for k, v in self.encoder.items()}
     SCREAMING_SNAKE_CASE__    							=   errors  # how to handle errors in decoding
     SCREAMING_SNAKE_CASE__    							=   bytes_to_unicode()
     SCREAMING_SNAKE_CASE__    							=   {v: k for k, v in self.byte_encoder.items()}
     with open(_lowercase ,			encoding="""utf-8"""   ) as merges_handle:
       SCREAMING_SNAKE_CASE__    							=   merges_handle.read().split("""\n"""   )[1:-1]
     SCREAMING_SNAKE_CASE__    							=   [tuple(merge.split()   ) for merge in bpe_merges]
     SCREAMING_SNAKE_CASE__    							=   dict(zip(_lowercase ,			range(len(_lowercase   )   )   )   )
     SCREAMING_SNAKE_CASE__    							=   {}
     SCREAMING_SNAKE_CASE__    							=   add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     SCREAMING_SNAKE_CASE__    							=   re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""   )




   @property
   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     return len(self.encoder   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     return dict(self.encoder ,			**self.added_tokens_encoder   )




   def 			__a		(					self    :       Tuple ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     if token in self.cache:
       return self.cache[token]
     SCREAMING_SNAKE_CASE__    							=   tuple(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   get_pairs(_lowercase   )

     if not pairs:
       return token

     while True:
       SCREAMING_SNAKE_CASE__    							=   min(_lowercase ,			key=lambda _lowercase   : self.bpe_ranks.get(_lowercase ,			float("""inf"""   )   )   )
       if bigram not in self.bpe_ranks:
         break
       SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   bigram
       SCREAMING_SNAKE_CASE__    							=   []
       SCREAMING_SNAKE_CASE__    							=   0
       while i < len(_lowercase   ):
         try:
           SCREAMING_SNAKE_CASE__    							=   word.index(_lowercase ,			_lowercase   )
         except ValueError:
           new_word.extend(word[i:]   )
           break
         else:
           new_word.extend(word[i:j]   )
           SCREAMING_SNAKE_CASE__    							=   j

         if word[i] == first and i < len(_lowercase   ) - 1 and word[i + 1] == second:
           new_word.append(first + second   )
           i += 2
         else:
           new_word.append(word[i]   )
           i += 1
       SCREAMING_SNAKE_CASE__    							=   tuple(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   new_word
       if len(_lowercase   ) == 1:
         break
       else:
         SCREAMING_SNAKE_CASE__    							=   get_pairs(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   """ """.join(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   word
     return word




   def 			__a		(					self    :       Any ,			_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   []
     for token in re.findall(self.pat ,			_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   """""".join(
           self.byte_encoder[b] for b in token.encode("""utf-8"""   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowercase   ).split(""" """   )   )
     return bpe_tokens




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return self.encoder.get(_lowercase ,			self.encoder.get(self.unk_token   )   )




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Optional[int]   ):



     """simple docstring"""






     return self.decoder.get(_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """""".join(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8""" ,			errors=self.errors   )
     return text




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       str ,			_lowercase    :       Optional[str] = None   ):



     """simple docstring"""






     if not os.path.isdir(_lowercase   ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
       return
     SCREAMING_SNAKE_CASE__    							=   os.path.join(
         _lowercase ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
     SCREAMING_SNAKE_CASE__    							=   os.path.join(
         _lowercase ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]   )

     with open(_lowercase ,			"""w""" ,			encoding="""utf-8"""   ) as f:
       f.write(json.dumps(self.encoder ,			indent=2 ,			sort_keys=_lowercase ,			ensure_ascii=_lowercase   ) + """\n"""   )

     SCREAMING_SNAKE_CASE__    							=   0
     with open(_lowercase ,			"""w""" ,			encoding="""utf-8"""   ) as writer:
       writer.write("""#version: 0.2\n"""   )
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,			key=lambda _lowercase   : kv[1]   ):
         if index != token_index:
           logger.warning(
               f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
               """ Please check that the tokenizer is not corrupted!"""   )
           SCREAMING_SNAKE_CASE__    							=   token_index
         writer.write(""" """.join(_lowercase   ) + """\n"""   )
         index += 1

     return vocab_file, merge_file




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None   ):



     """simple docstring"""






     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     SCREAMING_SNAKE_CASE__    							=   [self.cls_token_id]
     SCREAMING_SNAKE_CASE__    							=   [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep




   def 			__a		(					self    :       Any ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None ,			_lowercase    :       bool = False   ):



     """simple docstring"""






     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowercase ,			token_ids_a=_lowercase ,			already_has_special_tokens=_lowercase   )

     if token_ids_a is None:
       return [1] + ([0] * len(_lowercase   )) + [1]
     return [1] + ([0] * len(_lowercase   )) + [1, 1] + ([0] * len(_lowercase   )) + [1]




   def 			__a		(					self    :       Tuple ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   [self.sep_token_id]
     SCREAMING_SNAKE_CASE__    							=   [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       str ,			_lowercase    :       Tuple=False ,			**_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""add_prefix_space""" ,			self.add_prefix_space   )
     if (is_split_into_words or add_prefix_space) and (len(_lowercase   ) > 0 and not text[0].isspace()):
       SCREAMING_SNAKE_CASE__    							=   """ """ + text
     return (text, kwargs)

import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow







class __snake_case       :




   def 			__a		(					self    :       str ,			_lowercase    :       str ,			_lowercase    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return None







class __snake_case       :




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[str] ,			_lowercase    :       str ,			_lowercase    :       List[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     return None







class __snake_case       (		unittest.TestCase	):
   lowerCAmelCase_														=							[
       # (model_name, model_kwargs)
       ("bert-base-cased", {}),
       ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
   ]




   @require_tf
   @slow
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       self._test_export(_lowercase ,			"""tf""" ,			12 ,			**_lowercase   )




   @require_torch
   @slow
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       self._test_export(_lowercase ,			"""pt""" ,			12 ,			**_lowercase   )




   @require_torch
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     from transformers import BertModel

     SCREAMING_SNAKE_CASE__    							=   ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
     with NamedTemporaryFile(mode="""w+t"""   ) as vocab_file:
       vocab_file.write("""\n""".join(_lowercase   )   )
       vocab_file.flush()
       SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast(vocab_file.name   )

     with TemporaryDirectory() as bert_save_dir:
       SCREAMING_SNAKE_CASE__    							=   BertModel(BertConfig(vocab_size=len(_lowercase   )   )   )
       model.save_pretrained(_lowercase   )
       self._test_export(_lowercase ,			"""pt""" ,			12 ,			_lowercase   )




   @require_tf
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""tf""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   quantize(Path(_lowercase   )   )

       # Ensure the actual quantized model is not bigger than the original one
       if quantized_path.stat().st_size >= Path(_lowercase   ).stat().st_size:
         self.fail("""Quantized model is bigger than initial ONNX model"""   )




   @require_torch
   @slow
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""pt""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   quantize(_lowercase   )

       # Ensure the actual quantized model is not bigger than the original one
       if quantized_path.stat().st_size >= Path(_lowercase   ).stat().st_size:
         self.fail("""Quantized model is bigger than initial ONNX model"""   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional[Any] ,			_lowercase    :       str=None ,			**_lowercase    :       List[str]   ):



     """simple docstring"""






     try:
       # Compute path
       with TemporaryDirectory() as tempdir:
         SCREAMING_SNAKE_CASE__    							=   Path(_lowercase   ).joinpath("""model.onnx"""   )

       # Remove folder if exists
       if path.parent.exists():
         path.parent.rmdir()

       # Export
       convert(_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			**_lowercase   )

       return path
     except Exception as e:
       self.fail(_lowercase   )




   @require_torch
   @require_tokenizers
   @slow
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     from transformers import BertModel

     SCREAMING_SNAKE_CASE__    							=   BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""   )   )
     SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""   )
     self._test_infer_dynamic_axis(_lowercase ,			_lowercase ,			"""pt"""   )




   @require_tf
   @require_tokenizers
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     from transformers import TFBertModel

     SCREAMING_SNAKE_CASE__    							=   TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""   )   )
     SCREAMING_SNAKE_CASE__    							=   BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""   )
     self._test_infer_dynamic_axis(_lowercase ,			_lowercase ,			"""tf"""   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   FeatureExtractionPipeline(_lowercase ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   infer_shapes(_lowercase ,			_lowercase   )

     # Assert all variables are present
     self.assertEqual(len(_lowercase   ) ,			len(_lowercase   )   )
     self.assertTrue(all(var_name in shapes for var_name in variable_names   )   )
     self.assertSequenceEqual(variable_names[:3] ,			_lowercase   )
     self.assertSequenceEqual(variable_names[3:] ,			_lowercase   )

     # Assert inputs are {0: batch, 1: sequence}
     for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
       self.assertDictEqual(shapes[var_name] ,			{0: """batch""", 1: """sequence"""}   )

     # Assert outputs are {0: batch, 1: sequence} and {0: batch}
     self.assertDictEqual(shapes["""output_0"""] ,			{0: """batch""", 1: """sequence"""}   )
     self.assertDictEqual(shapes["""output_1"""] ,			{0: """batch"""}   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ["""input_ids""", """attention_mask""", """token_type_ids"""]
     SCREAMING_SNAKE_CASE__    							=   {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   ensure_valid_input(FuncContiguousArgs() ,			_lowercase ,			_lowercase   )

     # Should have exactly the same number of args (all are valid)
     self.assertEqual(len(_lowercase   ) ,			3   )

     # Should have exactly the same input names
     self.assertEqual(set(_lowercase   ) ,			set(_lowercase   )   )

     # Parameter should be reordered according to their respective place in the function:
     # (input_ids, token_type_ids, attention_mask)
     self.assertEqual(_lowercase ,			(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""])   )

     # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   ensure_valid_input(FuncNonContiguousArgs() ,			_lowercase ,			_lowercase   )

     # Should have exactly the one arg (all before the one not provided "some_other_args")
     self.assertEqual(len(_lowercase   ) ,			1   )
     self.assertEqual(len(_lowercase   ) ,			1   )

     # Should have only "input_ids"
     self.assertEqual(inputs_args[0] ,			tokens["""input_ids"""]   )
     self.assertEqual(ordered_input_names[0] ,			"""input_ids"""   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""   ) ,			"""-test"""   )
     self.assertEqual("""/home/something/my_fake_model-test.onnx""" ,			generated.as_posix()   )
