import os

import numpy
import onnx





def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    a.name
					SCREAMING_SNAKE_CASE           =    b.name

					SCREAMING_SNAKE_CASE           =    ""
					SCREAMING_SNAKE_CASE           =    ""

					SCREAMING_SNAKE_CASE           =    a == b

					SCREAMING_SNAKE_CASE           =    name_a
					SCREAMING_SNAKE_CASE           =    name_b

					return res





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Any					):
					for i, input_name in enumerate(node_proto.input					):
										if input_name == name:
															node_proto.input.insert(UpperCAmelCase__ ,	UpperCAmelCase__					)
															node_proto.input.pop(i + 1					)

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Any					):
					for n in graph_proto.node:
										_node_replace_input_with(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Any					):
					SCREAMING_SNAKE_CASE           =    list(model.graph.initializer					)
					SCREAMING_SNAKE_CASE           =    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

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    os.path.dirname(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    os.path.basename(UpperCAmelCase__					)

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

					SCREAMING_SNAKE_CASE           =    list(model.graph.initializer					)

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

					SCREAMING_SNAKE_CASE           =    0

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

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

																				SCREAMING_SNAKE_CASE           =    inits[j].data_type
																				SCREAMING_SNAKE_CASE           =    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: " ,	UpperCAmelCase__					)
																				total_reduced_size += mem_size

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

																				if name_i in dup_map:
																									dup_map[name_i].append(UpperCAmelCase__					)
																				else:
																									SCREAMING_SNAKE_CASE           =    [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"					)

					SCREAMING_SNAKE_CASE           =    sorted(UpperCAmelCase__					)
					_remove_dup_initializers_from_model(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    "optimized_" + model_file_name
					SCREAMING_SNAKE_CASE           =    os.path.join(UpperCAmelCase__ ,	UpperCAmelCase__					)
					onnx.save(UpperCAmelCase__ ,	UpperCAmelCase__					)

					return new_model





import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)


class   lowercase      :
							lowercase__  :    str									=							None





@experimental
def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Any					):
					if ParallelBackendConfig.backend_name is None:
										return _map_with_multiprocessing_pool(
										    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					return _map_with_joblib(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    num_proc if num_proc <= len(UpperCAmelCase__					) else len(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    []  # We organize the splits ourselve (contiguous splits)
					for index in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) // num_proc
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) % num_proc
										SCREAMING_SNAKE_CASE           =    div * index + min(UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    start + div + (1 if index < mod else 0)
										split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)					)

					if len(UpperCAmelCase__					) != sum(len(i[1]					) for i in split_kwds					):
										raise ValueError(
										    F"Error dividing inputs iterable among processes. "
										    F"Total number of objects {len(UpperCAmelCase__					)}, "
										    F"length: {sum(len(i[1]					) for i in split_kwds					)}"					)

					logger.info(
					    F"Spawning {num_proc} processes for {len(UpperCAmelCase__					)} objects in slices of {[len(i[1]					) for i in split_kwds]}"					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    None, None
					if not disable_tqdm:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    (RLock(),), tqdm.set_lock
					with Pool(UpperCAmelCase__ ,	initargs=UpperCAmelCase__ ,	initializer=UpperCAmelCase__					) as pool:
										SCREAMING_SNAKE_CASE           =    pool.map(UpperCAmelCase__ ,	UpperCAmelCase__					)
					logger.info(F"Finished {num_proc} processes"					)
					SCREAMING_SNAKE_CASE           =    [obj for proc_res in mapped for obj in proc_res]
					logger.info(F"Unpacked {len(UpperCAmelCase__					)} objects"					)

					return mapped





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Any					):
					# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
					# and it requires monkey-patching joblib internal classes which is subject to change
					import joblib

					with joblib.parallel_backend(ParallelBackendConfig.backend_name ,	n_jobs=UpperCAmelCase__					):
										return joblib.Parallel()(
										    joblib.delayed(UpperCAmelCase__					)((function, obj, types, None, True, None)					) for obj in iterable					)





@experimental
@contextlib.contextmanager
def 		__lowerCamelCase							(UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    backend_name

					if backend_name == "spark":
										from joblibspark import register_spark

										register_spark()

										# TODO: call create_cache_and_write_probe if "download" in steps
										# TODO: raise NotImplementedError when Dataset.map etc is called

					try:
										yield
					finally:
										SCREAMING_SNAKE_CASE           =    None




