



import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 				__snake_case						(     UpperCamelCase_						):




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       str):

									with open(A_				,				encoding='''utf-8''') as input_file:
											lowerCAmelCase_			:     List[Any]									= re.compile(r'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)''')
											lowerCAmelCase_			:     Any									= input_file.read()
											lowerCAmelCase_			:     int									= regexp.search(A_)

									return match




							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       str):

									with open(A_				,				encoding='''utf-8''') as input_file:
											lowerCAmelCase_			:     int									= re.compile(r'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'''				,				re.DOTALL)
											lowerCAmelCase_			:     Dict									= input_file.read()
											# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
											lowerCAmelCase_			:     Optional[Any]									= regexp.finditer(A_)

									lowerCAmelCase_			:     str									= [match for match in matches if match is not None and match.group(1) is not None]
									return matches[0] if matches else None




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     int									= Path('''./datasets''')
									lowerCAmelCase_			:     str									= list(dataset_paths.absolute().glob('''**/*.py'''))

									for dataset in dataset_files:
											if self._no_encoding_on_file_open(str(A_)):
													raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}""")







							def    UpperCAmelCase__			(       self     :       Dict):
									lowerCAmelCase_			:     Tuple									= Path('''./datasets''')
									lowerCAmelCase_			:     Union[str, Any]									= list(dataset_paths.absolute().glob('''**/*.py'''))

									for dataset in dataset_files:
											if self._no_print_statements(str(A_)):
													raise AssertionError(F"""print statement found in {dataset}. Use datasets.logger/logging instead.""")









import os

import numpy
import onnx







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			a.name
		__lowercase=			b.name

		__lowercase=			''
		__lowercase=			''

		__lowercase=			a == b

		__lowercase=			name_a
		__lowercase=			name_b

		return res







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		for i, input_name in enumerate(node_proto.input	):
				if input_name == name:
						node_proto.input.insert(lowercase__   , lowercase__	)
						node_proto.input.pop(i + 1	)

		if node_proto.op_type == "If":
				_graph_replace_input_with(node_proto.attribute[0].g   , lowercase__   , lowercase__	)
				_graph_replace_input_with(node_proto.attribute[1].g   , lowercase__   , lowercase__	)
		if node_proto.op_type == "Loop":
				_graph_replace_input_with(node_proto.attribute[0].g   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	str:

		'''simple docstring'''







		for n in graph_proto.node:
				_node_replace_input_with(lowercase__   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		__lowercase=			list(model.graph.initializer	)
		__lowercase=			list(model_without_ext.graph.initializer	)
		for i, ref_i in ind_to_replace:
				assert inits_with_data[i].name == inits[i].name
				assert inits_with_data[ref_i].name == inits[ref_i].name
				assert i > ref_i

				__lowercase=			inits[i].name
				__lowercase=			inits[ref_i].name

				model_without_ext.graph.initializer.remove(inits[i]	)

				# for n in model.graph.node:
				_graph_replace_input_with(model_without_ext.graph   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__	)						->	Dict:

		'''simple docstring'''







		__lowercase=			os.path.dirname(lowercase__	)
		__lowercase=			os.path.basename(lowercase__	)

		__lowercase=			onnx.load(os.path.join(lowercase__   , lowercase__	)	)

		__lowercase=			list(model.graph.initializer	)

		__lowercase=			set()
		__lowercase=			{}
		__lowercase=			[]

		__lowercase=			0

		for i in range(len(lowercase__	)	):
				if i in dup_set:
						continue

				for j in range(i + 1   , len(lowercase__	)	):
						if j in dup_set:
								continue
						if _is_equal_tensor_proto(inits[i]   , inits[j]	):
								dup_set.add(lowercase__	)
								dup_set.add(lowercase__	)

								__lowercase=			inits[j].data_type
								__lowercase=			numpy.prod(inits[j].dims	)
								if dtype == 1:
										mem_size *= 4
								elif dtype == 6:
										mem_size *= 4
								elif dtype == 7 or dtype == 1_1:
										mem_size *= 8
								else:
										print('unexpected data type: '   , lowercase__	)
								total_reduced_size += mem_size

								__lowercase=			inits[i].name
								__lowercase=			inits[j].name

								if name_i in dup_map:
										dup_map[name_i].append(lowercase__	)
								else:
										__lowercase=			[name_j]
								ind_to_replace.append((j, i)	)

		print('total reduced size: '   , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4   , 'GB'	)

		__lowercase=			sorted(lowercase__	)
		_remove_dup_initializers_from_model(lowercase__   , lowercase__   , lowercase__	)

		__lowercase=			'optimized_' + model_file_name
		__lowercase=			os.path.join(lowercase__   , lowercase__	)
		onnx.save(lowercase__   , lowercase__	)

		return new_model






