



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					:





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						return None







class 				__A					:





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						return None







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





			@require_tf
			@slow
			def        _snake_case    (self				):
						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									self._test_export(__magic_name__							,    """tf"""							,    12							,    **__magic_name__				)





			@require_torch
			@slow
			def        _snake_case    (self				):
						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									self._test_export(__magic_name__							,    """pt"""							,    12							,    **__magic_name__				)





			@require_torch
			@slow
			def        _snake_case    (self				):
						from transformers import BertModel

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

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





			@require_tf
			@slow
			def        _snake_case    (self				):
						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									lowerCamelCase__ :      Dict								=		self._test_export(__magic_name__							,    """tf"""							,    12							,    **__magic_name__				)
									lowerCamelCase__ :      str								=		quantize(Path(__magic_name__				)				)

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





			@require_torch
			@slow
			def        _snake_case    (self				):
						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									lowerCamelCase__ :      Tuple								=		self._test_export(__magic_name__							,    """pt"""							,    12							,    **__magic_name__				)
									lowerCamelCase__ :      Tuple								=		quantize(__magic_name__				)

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





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__=None							,    **__magic_name__				):
						try:
									# Compute path
									with TemporaryDirectory() as tempdir:
												lowerCamelCase__ :      Union[str, Any]								=		Path(__magic_name__				).joinpath("""model.onnx"""				)

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

									# Export
									convert(__magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    **__magic_name__				)

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





			@require_torch
			@require_tokenizers
			@slow
			def        _snake_case    (self				):
						from transformers import BertModel

						lowerCamelCase__ :      Any								=		BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""				)				)
						lowerCamelCase__ :      int								=		BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""				)
						self._test_infer_dynamic_axis(__magic_name__							,    __magic_name__							,    """pt"""				)





			@require_tf
			@require_tokenizers
			@slow
			def        _snake_case    (self				):
						from transformers import TFBertModel

						lowerCamelCase__ :      str								=		TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""				)				)
						lowerCamelCase__ :      int								=		BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""				)
						self._test_infer_dynamic_axis(__magic_name__							,    __magic_name__							,    """tf"""				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      int								=		FeatureExtractionPipeline(__magic_name__							,    __magic_name__				)

						lowerCamelCase__ :      List[Any]								=		["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      List[Any]								=		infer_shapes(__magic_name__							,    __magic_name__				)

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

						# 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        _snake_case    (self				):
						lowerCamelCase__ :      int								=		["""input_ids""", """attention_mask""", """token_type_ids"""]
						lowerCamelCase__ :      str								=		{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
						lowerCamelCase__							,lowerCamelCase__ :      str								=		ensure_valid_input(FuncContiguousArgs()							,    __magic_name__							,    __magic_name__				)

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

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

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

						# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
						lowerCamelCase__							,lowerCamelCase__ :      int								=		ensure_valid_input(FuncNonContiguousArgs()							,    __magic_name__							,    __magic_name__				)

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

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""				)							,    """-test"""				)
						self.assertEqual("""/home/something/my_fake_model-test.onnx"""							,    generated.as_posix()				)








from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

_lowercase     							=						(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowercase     							=						[ord(letter) for letter in string.ascii_lowercase]
_lowercase     							=						{ord(char) for char in VALID_CHARS}

_lowercase     							=						["the", "be", "to", "of", "and", "in", "that", "have"]






def      _A				(UpperCamelCase		:      list[int]      ,			UpperCamelCase		:      tuple[int, ...] )  ->str | None:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		""
			lowerCamelCase__ :      int
			lowerCamelCase__ :      int
			lowerCamelCase__ :      int

			for keychar, cipherchar in zip(cycle(UpperCamelCase )      ,			UpperCamelCase ):
						lowerCamelCase__ :      str								=		cipherchar ^ keychar
						if decodedchar not in VALID_INTS:
									return None
						decoded += chr(UpperCamelCase )

			return decoded






def      _A				(UpperCamelCase		:      list[int] )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      list[str]								=		[]
			for key in product(UpperCamelCase      ,			repeat=3 ):
						lowerCamelCase__ :      Union[str, Any]								=		try_key(UpperCamelCase      ,			UpperCamelCase )
						if encoded is not None:
									possibles.append(UpperCamelCase )
			return possibles






def      _A				(UpperCamelCase		:      list[str]      ,			UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			return [possible for possible in possibles if common_word in possible.lower()]






def      _A				(UpperCamelCase		:      str = "p059_cipher.txt" )  ->int:



			'''simple docstring'''







			lowerCamelCase__ :      list[int]
			lowerCamelCase__ :      list[str]
			lowerCamelCase__ :      str
			lowerCamelCase__ :      str
			lowerCamelCase__ :      str								=		Path(UpperCamelCase ).parent.joinpath(UpperCamelCase ).read_text(encoding="""utf-8""" )

			lowerCamelCase__ :      str								=		[int(UpperCamelCase ) for number in data.strip().split(""",""" )]

			lowerCamelCase__ :      Optional[Any]								=		filter_valid_chars(UpperCamelCase )
			for common_word in COMMON_WORDS:
						lowerCamelCase__ :      Optional[int]								=		filter_common_word(UpperCamelCase      ,			UpperCamelCase )
						if len(UpperCamelCase ) == 1:
									break

			lowerCamelCase__ :      Union[str, Any]								=		possibles[0]
			return sum(ord(UpperCamelCase ) for char in decoded_text )


if __name__ == "__main__":
				print(F'''{solution() = }''')



