



import os

import numpy
import onnx



def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							lowerCAmelCase   =		a.name
							lowerCAmelCase   =		b.name

							lowerCAmelCase   =		''''''
							lowerCAmelCase   =		''''''

							lowerCAmelCase   =		a == b

							lowerCAmelCase   =		name_a
							lowerCAmelCase   =		name_b

							return res



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

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



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							for n in graph_proto.node:
														_node_replace_input_with(_snake_case						, _snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		list(model.graph.initializer							)
							lowerCAmelCase   =		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

														lowerCAmelCase   =		inits[i].name
														lowerCAmelCase   =		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						, _snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		os.path.dirname(_snake_case							)
							lowerCAmelCase   =		os.path.basename(_snake_case							)

							lowerCAmelCase   =		onnx.load(os.path.join(_snake_case						, _snake_case							)							)

							lowerCAmelCase   =		list(model.graph.initializer							)

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

							lowerCAmelCase   =		0

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

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

																												lowerCAmelCase   =		inits[j].data_type
																												lowerCAmelCase   =		numpy.prod(inits[j].dims							)
																												if dtype == 1:
																																			mem_size *= 4
																												elif dtype == 6:
																																			mem_size *= 4
																												elif dtype == 7 or dtype == 11:
																																			mem_size *= 8
																												else:
																																			print('''unexpected data type: '''						, _snake_case							)
																												total_reduced_size += mem_size

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

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

							print('''total reduced size: '''						, total_reduced_size / 1024 / 1024 / 1024						, '''GB'''							)

							lowerCAmelCase   =		sorted(_snake_case							)
							_remove_dup_initializers_from_model(_snake_case						, _snake_case						, _snake_case							)

							lowerCAmelCase   =		'''optimized_''' + model_file_name
							lowerCAmelCase   =		os.path.join(_snake_case						, _snake_case							)
							onnx.save(_snake_case						, _snake_case							)

							return new_model









from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = "arrow"       , **UpperCAmelCase_       , ):
											super().__init__(
											    split=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_       , streaming=UpperCAmelCase_       , **UpperCAmelCase_       , )
											lowerCAmelCase   =		load_from_cache_file
											lowerCAmelCase   =		file_format
											lowerCAmelCase   =		Spark(
											    df=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , working_dir=UpperCAmelCase_       , **UpperCAmelCase_       , )






				def       __snake_case		( self						):
											if self.streaming:
																		return self.builder.as_streaming_dataset(split=self.split						)
											lowerCAmelCase   =		None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
											self.builder.download_and_prepare(
											    download_mode=UpperCAmelCase_       , file_format=self._file_format       , )
											return self.builder.as_dataset(split=self.split						)



