




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 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Union[str, Any]					,     lowerCAmelCase		:						Dict	)	->       Tuple:







						"""simple docstring"""







						return None






class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Any					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						Any	)	->       Any:







						"""simple docstring"""







						return None






class    SCREAMING_SNAKE_CASE_				(       unittest.TestCase   ):



	"""simple docstring"""


	__magic_name__   :       Dict      	=		[
	    # (model_name, model_kwargs)
	    ('bert-base-cased', {}),
	    ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
	]






	@require_tf
	@slow
	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Dict:







						"""simple docstring"""







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






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







						"""simple docstring"""







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






	@require_torch
	@slow
	def 					lowerCamelCase__     (					self		:						str	)	->       Any:







						"""simple docstring"""







						from transformers import BertModel

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

						with TemporaryDirectory() as bert_save_dir:
											__UpperCamelCase					:	Union[str, Any]             =      BertModel(BertConfig(vocab_size=len(lowerCAmelCase	)	)	)
											model.save_pretrained(lowerCAmelCase	)
											self._test_export(lowerCAmelCase					,     """pt"""					,     12					,     lowerCAmelCase	)






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







						"""simple docstring"""







						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											__UpperCamelCase					:	Dict             =      self._test_export(lowerCAmelCase					,     """tf"""					,     12					,     **lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      quantize(Path(lowerCAmelCase	)	)

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






	@require_torch
	@slow
	def 					lowerCamelCase__     (					self		:						Dict	)	->       Any:







						"""simple docstring"""







						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											__UpperCamelCase					:	List[Any]             =      self._test_export(lowerCAmelCase					,     """pt"""					,     12					,     **lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      quantize(lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]=None					,     **lowerCAmelCase		:						Dict	)	->       List[str]:







						"""simple docstring"""







						try:
											# Compute path
											with TemporaryDirectory() as tempdir:
																__UpperCamelCase					:	str             =      Path(lowerCAmelCase	).joinpath("""model.onnx"""	)

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

											# Export
											convert(lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     **lowerCAmelCase	)

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






	@require_torch
	@require_tokenizers
	@slow
	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       Optional[int]:







						"""simple docstring"""







						from transformers import BertModel

						__UpperCamelCase					:	List[str]             =      BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""	)	)
						__UpperCamelCase					:	Any             =      BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""	)
						self._test_infer_dynamic_axis(lowerCAmelCase					,     lowerCAmelCase					,     """pt"""	)






	@require_tf
	@require_tokenizers
	@slow
	def 					lowerCamelCase__     (					self		:						int	)	->       int:







						"""simple docstring"""







						from transformers import TFBertModel

						__UpperCamelCase					:	int             =      TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""	)	)
						__UpperCamelCase					:	Dict             =      BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""	)
						self._test_infer_dynamic_axis(lowerCAmelCase					,     lowerCAmelCase					,     """tf"""	)






	def 					lowerCamelCase__     (					self		:						Dict					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						Tuple					,     lowerCAmelCase		:						Dict	)	->       Any:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      FeatureExtractionPipeline(lowerCAmelCase					,     lowerCAmelCase	)

						__UpperCamelCase					:	List[str]             =      ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
						__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Any             =      infer_shapes(lowerCAmelCase					,     lowerCAmelCase	)

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

						# 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 					lowerCamelCase__     (					self		:						Optional[int]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, Any]             =      ["""input_ids""", """attention_mask""", """token_type_ids"""]
						__UpperCamelCase					:	Any             =      {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
						__UpperCamelCase      ,			__UpperCamelCase					:	Any             =      ensure_valid_input(FuncContiguousArgs()					,     lowerCAmelCase					,     lowerCAmelCase	)

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

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

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

						# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
						__UpperCamelCase      ,			__UpperCamelCase					:	Any             =      ensure_valid_input(FuncNonContiguousArgs()					,     lowerCAmelCase					,     lowerCAmelCase	)

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

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






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       Union[str, Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, 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 collections import defaultdict





def 				A__ (snake_case   :		int				)     ->						int:
					__UpperCamelCase					:	Optional[int]             =      1
					__UpperCamelCase					:	Tuple             =      True
					for v in tree[start]:
										if v not in visited:
															ret += dfs(snake_case				)
					if ret % 2 == 0:
										cuts.append(snake_case				)
					return ret





def 				A__ ()     ->						str:
					dfs(1				)


if __name__ == "__main__":
	a__     ,							a__		     =     10, 9
	a__		     =     defaultdict(list)
	a__		     =     {}
	a__		     =     []
	a__		     =     0
	a__		     =     [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
	for u, v in edges:
		tree[u].append(v)
		tree[v].append(u)
	even_tree()
	print(len(cuts) - 1)






