'''simple docstring'''

import os

import numpy
import onnx



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=a.name
						_lowerCAmelCase										=b.name

						_lowerCAmelCase										=''
						_lowerCAmelCase										=''

						_lowerCAmelCase										=a == b

						_lowerCAmelCase										=name_a
						_lowerCAmelCase										=name_b

						return res



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






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

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



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






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



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_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			,      a__			,      a__      )



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=os.path.dirname(a__      )
						_lowerCAmelCase										=os.path.basename(a__      )

						_lowerCAmelCase										=onnx.load(os.path.join(a__			,      a__      )      )

						_lowerCAmelCase										=list(model.graph.initializer      )

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

						_lowerCAmelCase										=0

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

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

																								_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 == 1_1:
																														mem_size *= 8
																								else:
																														print('unexpected data type: '			,      a__      )
																								total_reduced_size += mem_size

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

																								if name_i in dup_map:
																														dup_map[name_i].append(a__      )
																								else:
																														_lowerCAmelCase										=[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'      )

						_lowerCAmelCase										=sorted(a__      )
						_remove_dup_initializers_from_model(a__			,      a__			,      a__      )

						_lowerCAmelCase										='optimized_' + model_file_name
						_lowerCAmelCase										=os.path.join(a__			,      a__      )
						onnx.save(a__			,      a__      )

						return new_model






'''simple docstring'''

from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





