
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        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	def 			A							(  self     :   List[str]      ,  lowercase     :   str      ,  lowercase     :   Dict      ,  lowercase     :   Any     ):


				'''simple docstring'''


				return None




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Dict      ,  lowercase     :   List[Any]      ,  lowercase     :   List[Any]      ,  lowercase     :   List[Any]     ):


				'''simple docstring'''


				return None




class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		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 			A							(  self     :   Dict     ):


				'''simple docstring'''


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



	@require_torch
	@slow
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


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



	@require_torch
	@slow
	def 			A							(  self     :   Union[str, 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(lowercase     )     )
							vocab_file.flush()
							_snake_case          =      BertTokenizerFast(vocab_file.name     )

				with TemporaryDirectory() as bert_save_dir:
							_snake_case          =      BertModel(BertConfig(vocab_size=len(lowercase     )     )     )
							model.save_pretrained(lowercase     )
							self._test_export(lowercase      ,  'pt'      ,  12      ,  lowercase     )



	@require_tf
	@slow
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							_snake_case          =      self._test_export(lowercase      ,  'tf'      ,  12      ,  **lowercase     )
							_snake_case          =      quantize(Path(lowercase     )     )

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



	@require_torch
	@slow
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							_snake_case          =      self._test_export(lowercase      ,  'pt'      ,  12      ,  **lowercase     )
							_snake_case          =      quantize(lowercase     )

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



	def 			A							(  self     :   List[Any]      ,  lowercase     :   Any      ,  lowercase     :   Any      ,  lowercase     :   Optional[Any]      ,  lowercase     :   List[Any]=None      ,  **lowercase     :   Tuple     ):


				'''simple docstring'''


				try:
							# Compute path
							with TemporaryDirectory() as tempdir:
										_snake_case          =      Path(lowercase     ).joinpath('model.onnx'     )

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

							# Export
							convert(lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  **lowercase     )

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



	@require_torch
	@require_tokenizers
	@slow
	def 			A							(  self     :   List[str]     ):


				'''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(lowercase      ,  lowercase      ,  'pt'     )



	@require_tf
	@require_tokenizers
	@slow
	def 			A							(  self     :   Dict     ):


				'''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(lowercase      ,  lowercase      ,  'tf'     )



	def 			A							(  self     :   Tuple      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      FeatureExtractionPipeline(lowercase      ,  lowercase     )

				_snake_case          =      ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
				_snake_case     , _snake_case     , _snake_case     , _snake_case          =      infer_shapes(lowercase      ,  lowercase     )

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

				# 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     :   Optional[int]     ):


				'''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()      ,  lowercase      ,  lowercase     )

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

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

				# Parameter should be reordered according to their respective place in the function:
				# (input_ids, token_type_ids, attention_mask)
				self.assertEqual(lowercase      ,  (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()      ,  lowercase      ,  lowercase     )

				# Should have exactly the one arg (all before the one not provided "some_other_args")
				self.assertEqual(len(lowercase     )      ,  1     )
				self.assertEqual(len(lowercase     )      ,  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     :   List[str]     ):


				'''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()     )

def 	a_		(	__lowercase       :     List[Any]			)			->  Tuple:
			_snake_case          =      len(__lowercase			)
			for i in range(length - 1			):
						_snake_case          =      i
						for k in range(i + 1	, __lowercase			):
									if collection[k] < collection[least]:
												_snake_case          =      k
						if least != i:
									_snake_case     , _snake_case          =      (collection[i], collection[least])
			return collection


if __name__ == "__main__":
			_lowerCamelCase	:    str          =      input('''Enter numbers separated by a comma:\n''').strip()
			_lowerCamelCase	:    List[Any]          =      [int(item) for item in user_input.split(''',''')]
			print(selection_sort(unsorted))