






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 				_A			:
    """simple docstring"""

    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Tuple      )      ->							List[str]:
           return None




class 				_A			:
    """simple docstring"""

    def _a							(				self      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     Any      )      ->							List[str]:
           return None




class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							Tuple				= [
        # (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      :     str      )      ->							str:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  self._test_export(__SCREAMING_SNAKE_CASE ,     """tf""" ,     12 ,     **__SCREAMING_SNAKE_CASE      )



    @require_torch
    @slow
    def _a							(				self      :     Union[str, Any]      )      ->							Any:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  self._test_export(__SCREAMING_SNAKE_CASE ,     """pt""" ,     12 ,     **__SCREAMING_SNAKE_CASE      )



    @require_torch
    @slow
    def _a							(				self      :     Dict      )      ->							Tuple:
           from transformers import BertModel

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

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



    @require_tf
    @slow
    def _a							(				self      :     Any      )      ->							List[str]:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  __UpperCAmelCase     				=self._test_export(__SCREAMING_SNAKE_CASE ,     """tf""" ,     12 ,     **__SCREAMING_SNAKE_CASE      )
                  __UpperCAmelCase     				=quantize(Path(__SCREAMING_SNAKE_CASE      )      )

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



    @require_torch
    @slow
    def _a							(				self      :     Any      )      ->							Tuple:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                  __UpperCAmelCase     				=self._test_export(__SCREAMING_SNAKE_CASE ,     """pt""" ,     12 ,     **__SCREAMING_SNAKE_CASE      )
                  __UpperCAmelCase     				=quantize(__SCREAMING_SNAKE_CASE      )

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



    def _a							(				self      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=None ,     **__SCREAMING_SNAKE_CASE      :     Optional[int]      )      ->							Any:
           try:
                  # Compute path
                  with TemporaryDirectory() as tempdir:
                         __UpperCAmelCase     				=Path(__SCREAMING_SNAKE_CASE      ).joinpath("""model.onnx"""      )

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

                  # Export
                  convert(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )

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



    @require_torch
    @require_tokenizers
    @slow
    def _a							(				self      :     Optional[Any]      )      ->							Any:
           from transformers import BertModel

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



    @require_tf
    @require_tokenizers
    @slow
    def _a							(				self      :     List[str]      )      ->							Any:
           from transformers import TFBertModel

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



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Optional[int]      )      ->							Optional[int]:
           __UpperCAmelCase     				=FeatureExtractionPipeline(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=infer_shapes(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

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

           # 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      :     str      )      ->							Optional[Any]:
           __UpperCAmelCase     				=["""input_ids""", """attention_mask""", """token_type_ids"""]
           __UpperCAmelCase     				={"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
           __UpperCAmelCase				, __UpperCAmelCase     				=ensure_valid_input(FuncContiguousArgs() ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

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

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

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

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

           # Should have exactly the one arg (all before the one not provided "some_other_args")
           self.assertEqual(len(__SCREAMING_SNAKE_CASE      ) ,     1      )
           self.assertEqual(len(__SCREAMING_SNAKE_CASE      ) ,     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      :     Tuple      )      ->							List[Any]:
           __UpperCAmelCase     				=generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""      ) ,     """-test"""      )
           self.assertEqual("""/home/something/my_fake_model-test.onnx""" ,     generated.as_posix()      )









'''simple docstring'''





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''tokenizer''']
 A__				= '''AutoImageProcessor'''
 A__				= '''AutoTokenizer'''
 def __init__(   self						:							List[str]      ,     __a						:							Optional[int]=None      ,     __a						:							Any=None      ,     **__a						:							Optional[int]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   str													=    None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'      ,     __a      ,     )
             __snake_case :   Tuple													=    kwargs.pop('feature_extractor'  )

       __snake_case :   Union[str, Any]													=    image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(__a      ,     __a  )
       __snake_case :   Any													=    self.image_processor
       __snake_case :   int													=    False
 def __call__(   self						:							str      ,     *__a						:							List[str]      ,     **__a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       # For backward compatibility
       if self._in_target_context_manager:
             return self.current_processor(*__a      ,     **__a  )

       __snake_case :   List[Any]													=    kwargs.pop('images'      ,     __a  )
       __snake_case :   Any													=    kwargs.pop('text'      ,     __a  )
       if len(__a  ) > 0:
             __snake_case :   List[Any]													=    args[0]
             __snake_case :   Dict													=    args[1:]

       if images is None and text is None:
             raise ValueError('You need to specify either an `images` or `text` input to process.'  )

       if images is not None:
             __snake_case :   int													=    self.image_processor(__a      ,     *__a      ,     **__a  )
       if text is not None:
             __snake_case :   Tuple													=    self.tokenizer(__a      ,     **__a  )

       if text is None:
             return inputs
       elif images is None:
             return encodings
       else:
             __snake_case :   List[str]													=    encodings['input_ids']
             return inputs
 def A_   (   self						:							str      ,     *__a						:							Union[str, Any]      ,     **__a						:							Optional[Any]  )      ->   List[str]:







       '''simple docstring'''
       return self.tokenizer.batch_decode(*__a      ,     **__a  )
 def A_   (   self						:							Any      ,     *__a						:							List[Any]      ,     **__a						:							Dict  )      ->   List[Any]:







       '''simple docstring'''
       return self.tokenizer.decode(*__a      ,     **__a  )
 @contextmanager
 def A_   (   self						:							str  )      ->   List[Any]:







       '''simple docstring'''
       warnings.warn(
           '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
           'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
           'your images inputs, or in a separate call.'  )
       __snake_case :   Union[str, Any]													=    True
       __snake_case :   List[Any]													=    self.tokenizer
       yield
       __snake_case :   str													=    self.image_processor
       __snake_case :   Tuple													=    False
 def A_   (   self						:							List[str]      ,     __a						:							Tuple      ,     __a						:							Union[str, Any]=False      ,     __a						:							str=None  )      ->   List[str]:







       '''simple docstring'''
       if added_vocab is None:
             __snake_case :   List[Any]													=    self.tokenizer.get_added_vocab()

       __snake_case :   Tuple													=    {}

       while tokens:
             __snake_case :   Optional[Any]													=    re.search(r'<s_(.*?)>'      ,     __a      ,     re.IGNORECASE  )
             if start_token is None:
                   break
             __snake_case :   Any													=    start_token.group(1  )
             __snake_case :   Dict													=    re.search(rf'''</s_{key}>'''      ,     __a      ,     re.IGNORECASE  )
             __snake_case :   List[str]													=    start_token.group()
             if end_token is None:
                   __snake_case :   List[Any]													=    tokens.replace(__a      ,     ''  )
             else:
                   __snake_case :   int													=    end_token.group()
                   __snake_case :   Optional[int]													=    re.escape(__a  )
                   __snake_case :   List[str]													=    re.escape(__a  )
                   __snake_case :   Optional[int]													=    re.search(f'''{start_token_escaped}(.*?){end_token_escaped}'''      ,     __a      ,     re.IGNORECASE  )
                   if content is not None:
                         __snake_case :   Tuple													=    content.group(1  ).strip()
                         if r"<s_" in content and r"</s_" in content:  # non-leaf node
                               __snake_case :   str													=    self.tokenajson(__a      ,     is_inner_value=__a      ,     added_vocab=__a  )
                               if value:
                                     if len(__a  ) == 1:
                                           __snake_case :   int													=    value[0]
                                     __snake_case :   Union[str, Any]													=    value
                         else:  # leaf nodes
                               __snake_case :   Any													=    []
                               for leaf in content.split(r'<sep/>'  ):
                                     __snake_case :   str													=    leaf.strip()
                                     if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                           __snake_case :   Any													=    leaf[1:-2]  # for categorical special tokens
                                     output[key].append(__a  )
                               if len(output[key]  ) == 1:
                                     __snake_case :   int													=    output[key][0]

                   __snake_case :   Union[str, Any]													=    tokens[tokens.find(__a  ) + len(__a  ) :].strip()
                   if tokens[:6] == r"<sep/>":  # non-leaf nodes
                         return [output] + self.tokenajson(tokens[6:]      ,     is_inner_value=__a      ,     added_vocab=__a  )

       if len(__a  ):
             return [output] if is_inner_value else output
       else:
             return [] if is_inner_value else {"text_sequence": tokens}
 @property
 def A_   (   self						:							Optional[int]  )      ->   Any:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,     __a      ,     )
       return self.image_processor_class





 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[int]:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,     __a      ,     )
       return self.image_processor



