






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    __lowercase   :







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         return None


class    __lowercase   :







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         return None


class    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					[
         # (model_name, model_kwargs)
         ("bert-base-cased", {}),
         ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
     ]




     @require_tf
     @slow
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



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




     @require_torch
     @slow
     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



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




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


         """simple docstring"""



         from transformers import BertModel

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

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




     @require_tf
     @slow
     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



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

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




     @require_torch
     @slow
     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



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

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




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      int=None			,						**UpperCamelCase_							:      Any					):


         """simple docstring"""



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

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

             # Export
             convert(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_			,						**UpperCamelCase_					)

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




     @require_torch
     @require_tokenizers
     @slow
     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         from transformers import BertModel

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




     @require_tf
     @require_tokenizers
     @slow
     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         from transformers import TFBertModel

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




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



         __A       =						FeatureExtractionPipeline(UpperCamelCase_			,						UpperCamelCase_					)

         __A       =						["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
         __A     ,						__A     ,						__A     ,						__A       =						infer_shapes(UpperCamelCase_			,						UpperCamelCase_					)

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

         # 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 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



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

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

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

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

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

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

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





     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



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












def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   str:





    """simple docstring"""

    __A       =						""""""
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   dict[str, str]:





    """simple docstring"""

    __A       =						[chr(i + 6_5     ) for i in range(2_6     )]
    # Remove duplicate characters from key
    __A       =						remove_duplicates(key.upper()     )
    __A       =						len(__lowercase     )
    # First fill cipher with key characters
    __A       =						{alphabet[i]: char for i, char in enumerate(__lowercase     )}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(__lowercase     )      ,					2_6     ):
        __A       =						alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            __A       =						alphabet[i - offset]
        __A       =						char
    return cipher_alphabet



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      dict[str, str]     )    ->   str:





    """simple docstring"""

    return "".join(cipher_map.get(__lowercase      ,					__lowercase     ) for ch in message.upper()     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      dict[str, str]     )    ->   str:





    """simple docstring"""

    __A       =						{v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(__lowercase      ,					__lowercase     ) for ch in message.upper()     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





    """simple docstring"""

    __A       =						input("""Enter message to encode or decode: """     ).strip()
    __A       =						input("""Enter keyword: """     ).strip()
    __A       =						input("""Encipher or decipher? E/D:"""     ).strip()[0].lower()
    try:
        __A       =						{"""e""": encipher, """d""": decipher}[option]
    except KeyError:
        raise KeyError("""invalid input option"""     )
    __A       =						create_cipher_map(__lowercase     )
    print(func(__lowercase      ,					__lowercase     )     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




