






'''simple docstring'''




import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
		import faiss







@require_faiss
class    A_	(a_							):
							"""simple docstring"""




							def 							_A	(		self			:Dict				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					int   =       tempfile.mkdtemp()
										snake_case_				:					Union[str, Any]   =       8

										# DPR tok
										snake_case_				:					str   =       [
										    "[UNK]",
										    "[CLS]",
										    "[SEP]",
										    "[PAD]",
										    "[MASK]",
										    "want",
										    "##want",
										    "##ed",
										    "wa",
										    "un",
										    "runn",
										    "##ing",
										    ",",
										    "low",
										    "lowest",
										]
										snake_case_				:					List[Any]   =       os.path.join(self.tmpdirname       ,							"dpr_tokenizer"				)
										os.makedirs(lowerCAmelCase__       ,							exist_ok=lowerCAmelCase__				)
										snake_case_				:					Tuple   =       os.path.join(lowerCAmelCase__       ,							DPR_VOCAB_FILES_NAMES["vocab_file"]				)
										with open(self.vocab_file       ,							"w"       ,							encoding="utf-8"				) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)

										# BART tok
										snake_case_				:					Optional[int]   =       [
										    "l",
										    "o",
										    "w",
										    "e",
										    "r",
										    "s",
										    "t",
										    "i",
										    "d",
										    "n",
										    "\u0120",
										    "\u0120l",
										    "\u0120n",
										    "\u0120lo",
										    "\u0120low",
										    "er",
										    "\u0120lowest",
										    "\u0120newer",
										    "\u0120wider",
										    "<unk>",
										]
										snake_case_				:					str   =       dict(zip(lowerCAmelCase__       ,							range(len(lowerCAmelCase__				)				)				)				)
										snake_case_				:					Any   =       ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
										snake_case_				:					Tuple   =       {"unk_token": "<unk>"}

										snake_case_				:					str   =       os.path.join(self.tmpdirname       ,							"bart_tokenizer"				)
										os.makedirs(lowerCAmelCase__       ,							exist_ok=lowerCAmelCase__				)
										snake_case_				:					Any   =       os.path.join(lowerCAmelCase__       ,							BART_VOCAB_FILES_NAMES["vocab_file"]				)
										snake_case_				:					List[Any]   =       os.path.join(lowerCAmelCase__       ,							BART_VOCAB_FILES_NAMES["merges_file"]				)
										with open(self.vocab_file       ,							"w"       ,							encoding="utf-8"				) as fp:
													fp.write(json.dumps(lowerCAmelCase__				) + "\n"				)
										with open(self.merges_file       ,							"w"       ,							encoding="utf-8"				) as fp:
													fp.write("\n".join(lowerCAmelCase__				)				)

							def 							_A	(		self			:Union[str, Any]				)				->  DPRQuestionEncoderTokenizer:


										'''simple docstring'''




										return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,							"dpr_tokenizer"				)				)

							def 							_A	(		self			:List[str]				)				->  DPRContextEncoderTokenizer:


										'''simple docstring'''




										return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,							"dpr_tokenizer"				)				)

							def 							_A	(		self			:Union[str, Any]				)				->  BartTokenizer:


										'''simple docstring'''




										return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,							"bart_tokenizer"				)				)

							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										shutil.rmtree(self.tmpdirname				)

							def 							_A	(		self			:Union[str, Any]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Dict   =       Dataset.from_dict(
										    {
										        "id": ["0", "1"],
										        "text": ["foo", "bar"],
										        "title": ["Foo", "Bar"],
										        "embeddings": [np.ones(self.retrieval_vector_size				), 2 * np.ones(self.retrieval_vector_size				)],
										    }				)
										dataset.add_faiss_index("embeddings"       ,							string_factory="Flat"       ,							metric_type=faiss.METRIC_INNER_PRODUCT				)
										return dataset

							def 							_A	(		self			:int				)				->  int:


										'''simple docstring'''




										snake_case_				:					str   =       self.get_dummy_dataset()
										snake_case_				:					str   =       RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size       ,							question_encoder=DPRConfig().to_dict()       ,							generator=BartConfig().to_dict()       ,							)
										with patch("transformers.models.rag.retrieval_rag.load_dataset"				) as mock_load_dataset:
													snake_case_				:					List[Any]   =       dataset
													snake_case_				:					Optional[Any]   =       RagRetriever(
													    lowerCAmelCase__       ,							question_encoder_tokenizer=self.get_dpr_tokenizer()       ,							generator_tokenizer=self.get_bart_tokenizer()       ,							)
										return retriever

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:bool				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       self.get_dummy_dataset()
										snake_case_				:					str   =       RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size       ,							question_encoder=DPRConfig().to_dict()       ,							generator=BartConfig().to_dict()       ,							index_name="custom"       ,							)
										if from_disk:
													snake_case_				:					Any   =       os.path.join(self.tmpdirname       ,							"dataset"				)
													snake_case_				:					Any   =       os.path.join(self.tmpdirname       ,							"index.faiss"				)
													dataset.get_index("embeddings"				).save(os.path.join(self.tmpdirname       ,							"index.faiss"				)				)
													dataset.drop_index("embeddings"				)
													dataset.save_to_disk(os.path.join(self.tmpdirname       ,							"dataset"				)				)
													del dataset
													snake_case_				:					int   =       RagRetriever(
													    lowerCAmelCase__       ,							question_encoder_tokenizer=self.get_dpr_tokenizer()       ,							generator_tokenizer=self.get_bart_tokenizer()       ,							)
										else:
													snake_case_				:					str   =       RagRetriever(
													    lowerCAmelCase__       ,							question_encoder_tokenizer=self.get_dpr_tokenizer()       ,							generator_tokenizer=self.get_bart_tokenizer()       ,							index=CustomHFIndex(config.retrieval_vector_size       ,							lowerCAmelCase__				)       ,							)
										return retriever

							def 							_A	(		self			:Optional[Any]				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       Dataset.from_dict(
										    {
										        "id": ["0", "1"],
										        "text": ["foo", "bar"],
										        "title": ["Foo", "Bar"],
										        "embeddings": [np.ones(self.retrieval_vector_size + 1				), 2 * np.ones(self.retrieval_vector_size + 1				)],
										    }				)
										dataset.add_faiss_index("embeddings"       ,							string_factory="Flat"       ,							metric_type=faiss.METRIC_INNER_PRODUCT				)

										snake_case_				:					str   =       os.path.join(self.tmpdirname       ,							"hf_bert_base.hnswSQ8_correct_phi_128.c_index"				)
										dataset.save_faiss_index("embeddings"       ,							index_file_name + ".index.dpr"				)
										pickle.dump(dataset["id"]       ,							open(index_file_name + ".index_meta.dpr"       ,							"wb"				)				)

										snake_case_				:					List[str]   =       os.path.join(self.tmpdirname       ,							"psgs_w100.tsv.pkl"				)
										snake_case_				:					int   =       {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
										pickle.dump(lowerCAmelCase__       ,							open(lowerCAmelCase__       ,							"wb"				)				)

										snake_case_				:					str   =       RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size       ,							question_encoder=DPRConfig().to_dict()       ,							generator=BartConfig().to_dict()       ,							index_name="legacy"       ,							index_path=self.tmpdirname       ,							)
										snake_case_				:					Union[str, Any]   =       RagRetriever(
										    lowerCAmelCase__       ,							question_encoder_tokenizer=self.get_dpr_tokenizer()       ,							generator_tokenizer=self.get_bart_tokenizer()				)
										return retriever

							def 							_A	(		self			:int				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       1
										snake_case_				:					List[str]   =       self.get_dummy_canonical_hf_index_retriever()
										snake_case_				:					Optional[int]   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_,	snake_case_,	snake_case_				:					Union[str, Any]   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=lowerCAmelCase__				)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										self.assertEqual(sorted(doc_dicts[0]				)       ,							["embeddings", "id", "text", "title"]				)
										self.assertEqual(len(doc_dicts[0]["id"]				)       ,							lowerCAmelCase__				)
										self.assertEqual(doc_dicts[0]["id"][0]       ,							"1"				)  # max inner product is reached with second doc
										self.assertEqual(doc_dicts[1]["id"][0]       ,							"0"				)  # max inner product is reached with first doc
										self.assertListEqual(doc_ids.tolist()       ,							[[1], [0]]				)

							def 							_A	(		self			:List[Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       self.get_dummy_canonical_hf_index_retriever()
										with tempfile.TemporaryDirectory() as tmp_dirname:
													with patch("transformers.models.rag.retrieval_rag.load_dataset"				) as mock_load_dataset:
																snake_case_				:					Optional[int]   =       self.get_dummy_dataset()
																retriever.save_pretrained(lowerCAmelCase__				)
																snake_case_				:					Optional[Any]   =       RagRetriever.from_pretrained(lowerCAmelCase__				)
																self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
																snake_case_				:					Tuple   =       np.array(
																    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
																snake_case_				:					str   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=1				)
																self.assertTrue(out is not None				)

							def 							_A	(		self			:int				)				->  Any:


										'''simple docstring'''




										snake_case_				:					str   =       1
										snake_case_				:					Union[str, Any]   =       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_,	snake_case_,	snake_case_				:					Dict   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=lowerCAmelCase__				)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										self.assertEqual(sorted(doc_dicts[0]				)       ,							["embeddings", "id", "text", "title"]				)
										self.assertEqual(len(doc_dicts[0]["id"]				)       ,							lowerCAmelCase__				)
										self.assertEqual(doc_dicts[0]["id"][0]       ,							"1"				)  # max inner product is reached with second doc
										self.assertEqual(doc_dicts[1]["id"][0]       ,							"0"				)  # max inner product is reached with first doc
										self.assertListEqual(doc_ids.tolist()       ,							[[1], [0]]				)

							def 							_A	(		self			:Optional[int]				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase__				)
										with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(lowerCAmelCase__				)
													snake_case_				:					Union[str, Any]   =       RagRetriever.from_pretrained(lowerCAmelCase__				)
													self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
													snake_case_				:					int   =       np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
													snake_case_				:					Any   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=1				)
													self.assertTrue(out is not None				)

							def 							_A	(		self			:str				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       1
										snake_case_				:					Any   =       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase__				)
										snake_case_				:					int   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_,	snake_case_,	snake_case_				:					int   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=lowerCAmelCase__				)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										self.assertEqual(sorted(doc_dicts[0]				)       ,							["embeddings", "id", "text", "title"]				)
										self.assertEqual(len(doc_dicts[0]["id"]				)       ,							lowerCAmelCase__				)
										self.assertEqual(doc_dicts[0]["id"][0]       ,							"1"				)  # max inner product is reached with second doc
										self.assertEqual(doc_dicts[1]["id"][0]       ,							"0"				)  # max inner product is reached with first doc
										self.assertListEqual(doc_ids.tolist()       ,							[[1], [0]]				)

							def 							_A	(		self			:List[Any]				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Any   =       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase__				)
										with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(lowerCAmelCase__				)
													snake_case_				:					Dict   =       RagRetriever.from_pretrained(lowerCAmelCase__				)
													self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
													snake_case_				:					Any   =       np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
													snake_case_				:					int   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=1				)
													self.assertTrue(out is not None				)

							def 							_A	(		self			:int				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       1
										snake_case_				:					Dict   =       self.get_dummy_legacy_index_retriever()
										snake_case_				:					Optional[Any]   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_,	snake_case_,	snake_case_				:					Optional[Any]   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=lowerCAmelCase__				)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										self.assertEqual(sorted(doc_dicts[0]				)       ,							["text", "title"]				)
										self.assertEqual(len(doc_dicts[0]["text"]				)       ,							lowerCAmelCase__				)
										self.assertEqual(doc_dicts[0]["text"][0]       ,							"bar"				)  # max inner product is reached with second doc
										self.assertEqual(doc_dicts[1]["text"][0]       ,							"foo"				)  # max inner product is reached with first doc
										self.assertListEqual(doc_ids.tolist()       ,							[[1], [0]]				)

							def 							_A	(		self			:List[Any]				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       self.get_dummy_legacy_index_retriever()
										with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(lowerCAmelCase__				)
													snake_case_				:					Optional[Any]   =       RagRetriever.from_pretrained(lowerCAmelCase__				)
													self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
													snake_case_				:					List[Any]   =       np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
													snake_case_				:					List[Any]   =       retriever.retrieve(lowerCAmelCase__       ,							n_docs=1				)
													self.assertTrue(out is not None				)

							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def 							_A	(		self			:List[str]				)				->  Dict:


										'''simple docstring'''




										import torch

										snake_case_				:					Optional[Any]   =       1
										snake_case_				:					Dict   =       self.get_dummy_canonical_hf_index_retriever()
										snake_case_				:					str   =       [[5, 7], [10, 11]]
										snake_case_				:					List[Any]   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_				:					List[Any]   =       retriever(lowerCAmelCase__       ,							lowerCAmelCase__       ,							prefix=retriever.config.generator.prefix       ,							n_docs=lowerCAmelCase__				)
										snake_case_,	snake_case_,	snake_case_				:					Union[str, Any]   =       (
										    out["context_input_ids"],
										    out["context_attention_mask"],
										    out["retrieved_doc_embeds"],
										)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
										self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)
										self.assertIsInstance(lowerCAmelCase__       ,							np.ndarray				)

										snake_case_				:					Any   =       retriever(
										    lowerCAmelCase__       ,							lowerCAmelCase__       ,							prefix=retriever.config.generator.prefix       ,							n_docs=lowerCAmelCase__       ,							return_tensors="pt"       ,							)
										snake_case_,	snake_case_,	snake_case_,	snake_case_				:					Union[str, Any]   =       (  # noqa: F841
										    out["context_input_ids"],
										    out["context_attention_mask"],
										    out["retrieved_doc_embeds"],
										    out["doc_ids"],
										)
										self.assertEqual(retrieved_doc_embeds.shape       ,							(2, n_docs, self.retrieval_vector_size)				)
										self.assertIsInstance(lowerCAmelCase__       ,							torch.Tensor				)
										self.assertIsInstance(lowerCAmelCase__       ,							torch.Tensor				)
										self.assertIsInstance(lowerCAmelCase__       ,							torch.Tensor				)






							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def 							_A	(		self			:List[str]				)				->  str:


										'''simple docstring'''




										snake_case_				:					str   =       self.get_dpr_ctx_encoder_tokenizer()
										snake_case_				:					int   =       1
										snake_case_				:					Any   =       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase__				)
										retriever.set_ctx_encoder_tokenizer(lowerCAmelCase__				)

										snake_case_				:					Optional[Any]   =       [[5, 7], [10, 11]]
										snake_case_				:					Optional[int]   =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,							dtype=np.floataa				)
										snake_case_				:					str   =       retriever(lowerCAmelCase__       ,							lowerCAmelCase__       ,							prefix=retriever.config.generator.prefix       ,							n_docs=lowerCAmelCase__				)

										self.assertEqual(
										    len(lowerCAmelCase__				)       ,							6				)  # check whether the retriever output consist of 6 attributes including tokenized docs
										self.assertEqual(
										    all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")				)       ,							lowerCAmelCase__				)  # check for doc token related keys in dictionary.












'''simple docstring'''




import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCamelCase   :					str   							=					'''.'''

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__lowerCamelCase   :					Tuple   							=					[
    '''Assert''',
    '''AssignVariableOp''',
    '''EmptyTensorList''',
    '''MergeV2Checkpoints''',
    '''ReadVariableOp''',
    '''ResourceGather''',
    '''RestoreV2''',
    '''SaveV2''',
    '''ShardedFilename''',
    '''StatefulPartitionedCall''',
    '''StaticRegexFullMatch''',
    '''VarHandleOp''',
]

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       SavedModel()
			snake_case_				:					Dict   =       []

			with open(os.path.join(__magic_name__     ,"utils"     ,"tf_ops"     ,"onnx.json"						)						) as f:
						snake_case_				:					Dict   =       json.load(__magic_name__						)["opsets"]

			for i in range(1     ,opset + 1						):
						onnx_ops.extend(onnx_opsets[str(__magic_name__						)]						)

			with open(__magic_name__     ,"rb"						) as f:
						saved_model.ParseFromString(f.read()						)

			snake_case_				:					Tuple   =       set()

			# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
			for meta_graph in saved_model.meta_graphs:
						# Add operations in the graph definition
						model_op_names.update(node.op for node in meta_graph.graph_def.node						)

						# Go through the functions in the graph definition
						for func in meta_graph.graph_def.library.function:
									# Add operations in each function
									model_op_names.update(node.op for node in func.node_def						)

    # Convert to list, sorted if you want
			snake_case_				:					str   =       sorted(__magic_name__						)
			snake_case_				:					Optional[int]   =       []

			for op in model_op_names:
						if op not in onnx_ops and op not in INTERNAL_OPS:
									incompatible_ops.append(__magic_name__						)

			if strict and len(__magic_name__						) > 0:
						raise Exception(F'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops						)
			elif len(__magic_name__						) > 0:
						print(F'''Found the following incompatible ops for the opset {opset}:'''						)
						print(*__magic_name__     ,sep="\n"						)
			else:
						print(F'''The saved model {saved_model_path} can properly be converted with ONNX.'''						)


if __name__ == "__main__":
		__lowerCamelCase   :					Any   							=					argparse.ArgumentParser()
		parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
		parser.add_argument(
		    '''--opset''', default=12, type=int, help='''The ONNX opset against which the model has to be tested.'''
		)
		parser.add_argument(
		    '''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
		)
		parser.add_argument(
		    '''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
		)
		__lowerCamelCase   :					Dict   							=					parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)




