"""simple docstring"""






import os

import numpy
import onnx



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							a.name
				UpperCAmelCase__             =							b.name

				UpperCAmelCase__             =							''
				UpperCAmelCase__             =							''

				UpperCAmelCase__             =							a == b

				UpperCAmelCase__             =							name_a
				UpperCAmelCase__             =							name_b

				return res



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

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



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							list(model.graph.initializer       )
				UpperCAmelCase__             =							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

								UpperCAmelCase__             =							inits[i].name
								UpperCAmelCase__             =							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					,			lowerCamelCase					,			lowerCamelCase       )







def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							os.path.dirname(lowerCamelCase       )
				UpperCAmelCase__             =							os.path.basename(lowerCamelCase       )

				UpperCAmelCase__             =							onnx.load(os.path.join(lowerCamelCase					,			lowerCamelCase       )       )

				UpperCAmelCase__             =							list(model.graph.initializer       )

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

				UpperCAmelCase__             =							0

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

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

																UpperCAmelCase__             =							inits[j].data_type
																UpperCAmelCase__             =							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: '					,			lowerCamelCase       )
																total_reduced_size += mem_size

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

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCamelCase       )
																else:
																				UpperCAmelCase__             =							[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'       )

				UpperCAmelCase__             =							sorted(lowerCamelCase       )
				_remove_dup_initializers_from_model(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

				UpperCAmelCase__             =							'optimized_' + model_file_name
				UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			lowerCamelCase       )
				onnx.save(lowerCamelCase					,			lowerCamelCase       )

				return new_model







from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class        _lowercase      (			A__       ):






				'''simple docstring'''



				def __init__(       self			:int			,			lowerCAmelCase__			:NestedDataStructureLike[PathLike]			,			lowerCAmelCase__			:Optional[NamedSplit] = None			,			lowerCAmelCase__			:Optional[Features] = None			,			lowerCAmelCase__			:str = None			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:Optional[int] = None			,			**lowerCAmelCase__			:Optional[int]			,			)		->			Tuple:
										super().__init__(
										    lowerCAmelCase__			,			split=lowerCAmelCase__			,			features=lowerCAmelCase__			,			cache_dir=lowerCAmelCase__			,			keep_in_memory=lowerCAmelCase__			,			streaming=lowerCAmelCase__			,			num_proc=lowerCAmelCase__			,			**lowerCAmelCase__			,			)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		path_or_paths if isinstance(lowerCAmelCase__			,			lowerCAmelCase__	) else {self.split: path_or_paths}
										__SCREAMING_SNAKE_CASE						:     int					=		Text(
										    cache_dir=lowerCAmelCase__			,			data_files=lowerCAmelCase__			,			features=lowerCAmelCase__			,			**lowerCAmelCase__			,			)



				def 		__magic_name__(       self			:Dict	)		->			Tuple:
										# Build iterable dataset
										if self.streaming:
																__SCREAMING_SNAKE_CASE						:     int					=		self.builder.as_streaming_dataset(split=self.split	)
										# Build regular (map-style) dataset
										else:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		None
																__SCREAMING_SNAKE_CASE						:     str					=		None
																__SCREAMING_SNAKE_CASE						:     Dict					=		None
																__SCREAMING_SNAKE_CASE						:     Tuple					=		None

																self.builder.download_and_prepare(
																    download_config=lowerCAmelCase__			,			download_mode=lowerCAmelCase__			,			verification_mode=lowerCAmelCase__			,			base_path=lowerCAmelCase__			,			num_proc=self.num_proc			,			)
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		self.builder.as_dataset(
																    split=self.split			,			verification_mode=lowerCAmelCase__			,			in_memory=self.keep_in_memory	)
										return dataset
