
'''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 	__lowercase				:

			def        UpperCAmelCase__ (self			,   A			,   A			,   A      ):
								return None

class 	__lowercase				:

			def        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A      ):
								return None

class 	__lowercase				(  unittest.TestCase    ):
			lowerCamelCase			:		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        UpperCAmelCase__ (self      ):
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(A			,   '''tf'''			,   1_2			,   **A      )

			@require_torch
			@slow
			def        UpperCAmelCase__ (self      ):
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(A			,   '''pt'''			,   1_2			,   **A      )

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

								lowerCamelCase_					:      Dict     =		['''[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()
													lowerCamelCase_					:      List[str]     =		BertTokenizerFast(vocab_file.name      )

								with TemporaryDirectory() as bert_save_dir:
													lowerCamelCase_					:      Any     =		BertModel(BertConfig(vocab_size=len(A      )      )      )
													model.save_pretrained(A      )
													self._test_export(A			,   '''pt'''			,   1_2			,   A      )

			@require_tf
			@slow
			def        UpperCAmelCase__ (self      ):
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													lowerCamelCase_					:      Tuple     =		self._test_export(A			,   '''tf'''			,   1_2			,   **A      )
													lowerCamelCase_					:      int     =		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        UpperCAmelCase__ (self      ):
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													lowerCamelCase_					:      Any     =		self._test_export(A			,   '''pt'''			,   1_2			,   **A      )
													lowerCamelCase_					:      List[str]     =		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        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A=None			,   **A      ):
								try:
													# Compute path
													with TemporaryDirectory() as tempdir:
																		lowerCamelCase_					:      Union[str, 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        UpperCAmelCase__ (self      ):
								from transformers import BertModel

								lowerCamelCase_					:      Dict     =		BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''      )      )
								lowerCamelCase_					:      Dict     =		BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''      )
								self._test_infer_dynamic_axis(A			,   A			,   '''pt'''      )

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

								lowerCamelCase_					:      Optional[Any]     =		TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''      )      )
								lowerCamelCase_					:      Optional[int]     =		BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''      )
								self._test_infer_dynamic_axis(A			,   A			,   '''tf'''      )

			def        UpperCAmelCase__ (self			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Tuple     =		FeatureExtractionPipeline(A			,   A      )

								lowerCamelCase_					:      List[str]     =		['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
								lowerCamelCase_					:      Optional[int]     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		['''input_ids''', '''attention_mask''', '''token_type_ids''']
								lowerCamelCase_					:      List[str]     =		{'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
								lowerCamelCase_					:      Optional[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)
								lowerCamelCase_					:      Optional[int]     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      int     =		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'''


from __future__ import annotations




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if (direction == 1 and array[indexa] > array[indexa]) or (
					    direction == 0 and array[indexa] < array[indexa]
					):
										lowerCamelCase_,      lowerCamelCase_					:      str     =		array[indexa], array[indexa]




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if length > 1:
										lowerCamelCase_					:      int     =		int(length / 2				)
										for i in range(_lowercase			,       low + middle				):
															comp_and_swap(_lowercase			,       _lowercase			,       i + middle			,       _lowercase				)
										bitonic_merge(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)
										bitonic_merge(_lowercase			,       low + middle			,       _lowercase			,       _lowercase				)




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if length > 1:
										lowerCamelCase_					:      Dict     =		int(length / 2				)
										bitonic_sort(_lowercase			,       _lowercase			,       _lowercase			,       1				)
										bitonic_sort(_lowercase			,       low + middle			,       _lowercase			,       0				)
										bitonic_merge(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)


if __name__ == "__main__":
					__lowercase							:   Any					=     input('''Enter numbers separated by a comma:\n''').strip()
					__lowercase							:   Tuple					=     [int(item.strip()) for item in user_input.split(''',''')]

					bitonic_sort(unsorted, 0, len(unsorted), 1)
					print('''\nSorted array in ascending order is: ''', end='''''')
					print(*unsorted, sep=''', ''')

					bitonic_merge(unsorted, 0, len(unsorted), 0)
					print('''Sorted array in descending order is: ''', end='''''')
					print(*unsorted, sep=''', ''')


