


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 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Dict:




											'''simple docstring'''

											return None






class     UpperCamelCase:


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Tuple:




											'''simple docstring'''

											return None






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


					@require_tf
					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Optional[Any]:




											'''simple docstring'''

											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(SCREAMING_SNAKE_CASE     ,    "tf"     ,    1_2     ,    **SCREAMING_SNAKE_CASE						)


					@require_torch
					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Union[str, Any]:




											'''simple docstring'''

											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(SCREAMING_SNAKE_CASE     ,    "pt"     ,    1_2     ,    **SCREAMING_SNAKE_CASE						)


					@require_torch
					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Optional[Any]:




											'''simple docstring'''

											from transformers import BertModel

											__snake_case				=		["[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()
																	__snake_case				=		BertTokenizerFast(vocab_file.name						)

											with TemporaryDirectory() as bert_save_dir:
																	__snake_case				=		BertModel(BertConfig(vocab_size=len(SCREAMING_SNAKE_CASE						)						)						)
																	model.save_pretrained(SCREAMING_SNAKE_CASE						)
																	self._test_export(SCREAMING_SNAKE_CASE     ,    "pt"     ,    1_2     ,    SCREAMING_SNAKE_CASE						)


					@require_tf
					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	str:




											'''simple docstring'''

											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	__snake_case				=		self._test_export(SCREAMING_SNAKE_CASE     ,    "tf"     ,    1_2     ,    **SCREAMING_SNAKE_CASE						)
																	__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	str:




											'''simple docstring'''

											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	__snake_case				=		self._test_export(SCREAMING_SNAKE_CASE     ,    "pt"     ,    1_2     ,    **SCREAMING_SNAKE_CASE						)
																	__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		str=None     ,    **SCREAMING_SNAKE_CASE	:		Dict						)			->	Optional[int]:




											'''simple docstring'''

											try:
																	# Compute path
																	with TemporaryDirectory() as tempdir:
																							__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											from transformers import BertModel

											__snake_case				=		BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"						)						)
											__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Optional[int]:




											'''simple docstring'''

											from transformers import TFBertModel

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		FeatureExtractionPipeline(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

											__snake_case				=		["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
											__snake_case		,					__snake_case		,					__snake_case		,					__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Any:




											'''simple docstring'''

											__snake_case				=		["input_ids", "attention_mask", "token_type_ids"]
											__snake_case				=		{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
											__snake_case		,					__snake_case				=		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)
											__snake_case		,					__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	int:




											'''simple docstring'''

											__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()						)









import itertools
import string
from collections.abc import Generator, Iterable





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Generator[tuple[str, ...], None, None]:


						'''simple docstring'''






						__snake_case				=		iter(_lowerCAmelCase )
						while True:
												__snake_case				=		tuple(itertools.islice(_lowerCAmelCase      ,	_lowerCAmelCase ) )
												if not chunk:
																		return
												yield chunk





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		"".join([c.upper() for c in dirty if c in string.ascii_letters] )
						__snake_case				=		""

						if len(_lowerCAmelCase ) < 2:
												return dirty

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

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

						clean += dirty[-1]

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

						return clean





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	list[str]:


						'''simple docstring'''






						__snake_case				=		"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				=		[]

						# 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(_lowerCAmelCase )

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

						return table





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		generate_table(_lowerCAmelCase )
						__snake_case				=		prepare_input(_lowerCAmelCase )
						__snake_case				=		""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowerCAmelCase      ,	2 ):
												__snake_case		,					__snake_case				=		divmod(table.index(_lowerCAmelCase )      ,	5 )
												__snake_case		,					__snake_case				=		divmod(table.index(_lowerCAmelCase )      ,	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 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		generate_table(_lowerCAmelCase )
						__snake_case				=		""

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





