"""simple docstring"""







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 		UpperCamelCase      :



				def  _lowercase					(self    :				Any     ,  _A    :				List[str]     ,  _A    :				List[Any]     ,  _A    :				Optional[int])   ->     Union[str, Any]:
					return None







class 		UpperCamelCase      :



				def  _lowercase					(self    :				List[Any]     ,  _A    :				Optional[Any]     ,  _A    :				Optional[int]     ,  _A    :				List[str]     ,  _A    :				Union[str, Any])   ->     List[Any]:
					return None







class 		UpperCamelCase      (   unittest.TestCase						):
				UpperCAmelCase    :				List[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  _lowercase					(self    :				List[Any])   ->     Any:
					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
						self._test_export(_A     ,  'tf'     ,  12     ,  **_A)



				@require_torch
				@slow
				def  _lowercase					(self    :				int)   ->     Optional[Any]:
					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
						self._test_export(_A     ,  'pt'     ,  12     ,  **_A)



				@require_torch
				@slow
				def  _lowercase					(self    :				Any)   ->     str:
					from transformers import BertModel

					__snake_case      :							Optional[int]						=			['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
					with NamedTemporaryFile(mode='w+t') as vocab_file:
						vocab_file.write('\n'.join(_A))
						vocab_file.flush()
						__snake_case      :							int						=			BertTokenizerFast(vocab_file.name)

					with TemporaryDirectory() as bert_save_dir:
						__snake_case      :							List[Any]						=			BertModel(BertConfig(vocab_size=len(_A)))
						model.save_pretrained(_A)
						self._test_export(_A     ,  'pt'     ,  12     ,  _A)



				@require_tf
				@slow
				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
						__snake_case      :							int						=			self._test_export(_A     ,  'tf'     ,  12     ,  **_A)
						__snake_case      :							List[Any]						=			quantize(Path(_A))

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



				@require_torch
				@slow
				def  _lowercase					(self    :				Dict)   ->     Any:
					for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
						__snake_case      :							Dict						=			self._test_export(_A     ,  'pt'     ,  12     ,  **_A)
						__snake_case      :							Optional[Any]						=			quantize(_A)

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



				def  _lowercase					(self    :				Union[str, Any]     ,  _A    :				str     ,  _A    :				Optional[int]     ,  _A    :				List[Any]     ,  _A    :				List[str]=None     ,  **_A    :				str)   ->     str:
					try:
						# Compute path
						with TemporaryDirectory() as tempdir:
							__snake_case      :							List[Any]						=			Path(_A).joinpath('model.onnx')

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

						# Export
						convert(_A     ,  _A     ,  _A     ,  _A     ,  _A     ,  **_A)

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



				@require_torch
				@require_tokenizers
				@slow
				def  _lowercase					(self    :				Tuple)   ->     int:
					from transformers import BertModel

					__snake_case      :							Any						=			BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'))
					__snake_case      :							Optional[Any]						=			BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random')
					self._test_infer_dynamic_axis(_A     ,  _A     ,  'pt')



				@require_tf
				@require_tokenizers
				@slow
				def  _lowercase					(self    :				str)   ->     Optional[int]:
					from transformers import TFBertModel

					__snake_case      :							List[Any]						=			TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'))
					__snake_case      :							Dict						=			BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random')
					self._test_infer_dynamic_axis(_A     ,  _A     ,  'tf')



				def  _lowercase					(self    :				List[str]     ,  _A    :				Any     ,  _A    :				Any     ,  _A    :				int)   ->     Optional[Any]:
					__snake_case      :							Optional[int]						=			FeatureExtractionPipeline(_A     ,  _A)

					__snake_case      :							List[str]						=			['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
					__snake_case		,    __snake_case		,    __snake_case		,    __snake_case      :							str						=			infer_shapes(_A     ,  _A)

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

					# 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  _lowercase					(self    :				Optional[Any])   ->     List[str]:
					__snake_case      :							Tuple						=			['input_ids', 'attention_mask', 'token_type_ids']
					__snake_case      :							Any						=			{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
					__snake_case		,    __snake_case      :							int						=			ensure_valid_input(FuncContiguousArgs()     ,  _A     ,  _A)

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

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

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

					# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
					__snake_case		,    __snake_case      :							List[Any]						=			ensure_valid_input(FuncNonContiguousArgs()     ,  _A     ,  _A)

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

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






				def  _lowercase					(self    :				List[Any])   ->     List[str]:
					__snake_case      :							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())






"""simple docstring"""







import itertools
import string
from collections.abc import Generator, Iterable
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Iterable[str]      ,							UpperCAmelCase_   :    int  )							->      Generator[tuple[str, ...], None, None]:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			iter(UpperCAmelCase_  )
	while True:
		__snake_case      :							Optional[int]						=			tuple(itertools.islice(UpperCAmelCase_      ,							UpperCAmelCase_  )  )
		if not chunk:
			return
		yield chunk
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str  )							->      str:

	'''simple docstring'''





	__snake_case      :							Any						=			''.join([c.upper() for c in dirty if c in string.ascii_letters]  )
	__snake_case      :							Union[str, Any]						=			''

	if len(UpperCAmelCase_  ) < 2:
		return dirty

	for i in range(len(UpperCAmelCase_  ) - 1  ):
		clean += dirty[i]

		if dirty[i] == dirty[i + 1]:
			clean += "X"

	clean += dirty[-1]

	if len(UpperCAmelCase_  ) & 1:
		clean += "X"

	return clean
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str  )							->      list[str]:

	'''simple docstring'''





	__snake_case      :							List[str]						=			'ABCDEFGHIKLMNOPQRSTUVWXYZ'
	# we're using a list instead of a '2d' array because it makes the math
	# for setting up the table and doing the actual encoding/decoding simpler
	__snake_case      :							Optional[int]						=			[]

	# copy key chars into the table if they are in `alphabet` ignoring duplicates
	for char in key.upper():
		if char not in table and char in alphabet:
			table.append(UpperCAmelCase_  )

    # fill the rest of the table in with the remaining alphabet chars
	for char in alphabet:
		if char not in table:
			table.append(UpperCAmelCase_  )

	return table
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    str  )							->      str:

	'''simple docstring'''





	__snake_case      :							str						=			generate_table(UpperCAmelCase_  )
	__snake_case      :							Union[str, Any]						=			prepare_input(UpperCAmelCase_  )
	__snake_case      :							Tuple						=			''

	# https://en.wikipedia.org/wiki/Playfair_cipher#Description
	for chara, chara in chunker(UpperCAmelCase_      ,							2  ):
		__snake_case		,    __snake_case      :							Any						=			divmod(table.index(UpperCAmelCase_  )      ,							5  )
		__snake_case		,    __snake_case      :							Tuple						=			divmod(table.index(UpperCAmelCase_  )      ,							5  )

		if rowa == rowa:
			ciphertext += table[rowa * 5 + (cola + 1) % 5]
			ciphertext += table[rowa * 5 + (cola + 1) % 5]
		elif cola == cola:
			ciphertext += table[((rowa + 1) % 5) * 5 + cola]
			ciphertext += table[((rowa + 1) % 5) * 5 + cola]
		else:  # rectangle
			ciphertext += table[rowa * 5 + cola]
			ciphertext += table[rowa * 5 + cola]

	return ciphertext

def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    str  )							->      str:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			generate_table(UpperCAmelCase_  )
	__snake_case      :							Any						=			''

	# https://en.wikipedia.org/wiki/Playfair_cipher#Description
	for chara, chara in chunker(UpperCAmelCase_      ,							2  ):
		__snake_case		,    __snake_case      :							Union[str, Any]						=			divmod(table.index(UpperCAmelCase_  )      ,							5  )
		__snake_case		,    __snake_case      :							Tuple						=			divmod(table.index(UpperCAmelCase_  )      ,							5  )

		if rowa == rowa:
			plaintext += table[rowa * 5 + (cola - 1) % 5]
			plaintext += table[rowa * 5 + (cola - 1) % 5]
		elif cola == cola:
			plaintext += table[((rowa - 1) % 5) * 5 + cola]
			plaintext += table[((rowa - 1) % 5) * 5 + cola]
		else:  # rectangle
			plaintext += table[rowa * 5 + cola]
			plaintext += table[rowa * 5 + cola]

	return plaintext





