"""simple docstring"""




import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


__lowerCamelCase							 =     logging.getLogger(__name__)
class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None   )    -> str:
													super().__init__(
													    __UpperCAmelCase						,question_encoder_tokenizer=__UpperCAmelCase						,generator_tokenizer=__UpperCAmelCase						,index=__UpperCAmelCase						,init_retrieval=__UpperCAmelCase						,)
													A__		   =							None





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Tuple:
													logger.info('initializing retrieval'   )

													# initializing a separate process group for retrieval as the default
													# nccl backend doesn't support gather/scatter operations while gloo
													# is too slow to replace nccl for the core gpu communication
													if dist.is_initialized():
																				logger.info('dist initialized'   )
																				# needs to be set manually
																				A__		   =							self._infer_socket_ifname()
																				# avoid clash with the NCCL port
																				A__		   =							str(distributed_port + 1   )
																				A__		   =							dist.new_group(ranks=__UpperCAmelCase						,backend='gloo'   )

													# initialize retriever only on the main worker
													if not dist.is_initialized() or self._is_main():
																				logger.info('dist not initialized / main'   )
																				self.index.init_index()

													# all processes wait untill the retriever is initialized by the main process
													if dist.is_initialized():
																				torch.distributed.barrier(group=self.process_group   )





						def  snake_case__		(				self   )    -> str:
													return dist.get_rank(group=self.process_group   ) == 0





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=torch.floataa   )    -> Dict:
													A__		   =							torch.empty(__UpperCAmelCase						,dtype=__UpperCAmelCase   )
													dist.scatter(__UpperCAmelCase						,src=0						,scatter_list=__UpperCAmelCase						,group=self.process_group   )
													return target_tensor





						def  snake_case__		(				self   )    -> Any:
													A__		   =							psutil.net_if_addrs()
													# a hacky way to deal with varying network interface names
													A__		   =							next((addr for addr in addrs if addr.startswith('e'   ))						,__UpperCAmelCase   )
													return ifname





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple[np.ndarray, List[dict]]:

													# single GPU training
													if not dist.is_initialized():
																				A__   ,     A__		   =							self._main_retrieve(__UpperCAmelCase						,__UpperCAmelCase   )
																				return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__UpperCAmelCase   )

													# distributed training
													A__		   =							dist.get_world_size(group=self.process_group   )

													# gather logic
													A__		   =							None
													if self._is_main():
																				A__		   =							[torch.empty(question_hidden_states.shape						,dtype=torch.floataa   ) for _ in range(__UpperCAmelCase   )]
													dist.gather(torch.tensor(__UpperCAmelCase   )						,dst=0						,gather_list=__UpperCAmelCase						,group=self.process_group   )

													# scatter logic
													A__		   =							question_hidden_states.shape[0]
													A__		   =							[]
													A__		   =							[]
													if self._is_main():
																				assert len(__UpperCAmelCase   ) == world_size
																				A__   ,     A__		   =							self._main_retrieve(torch.cat(__UpperCAmelCase   ).numpy()						,__UpperCAmelCase   )
																				A__   ,     A__		   =							torch.tensor(__UpperCAmelCase   ), torch.tensor(__UpperCAmelCase   )
																				A__		   =							self._chunk_tensor(__UpperCAmelCase						,__UpperCAmelCase   )
																				A__		   =							self._chunk_tensor(__UpperCAmelCase						,__UpperCAmelCase   )
													A__		   =							self._scattered(__UpperCAmelCase						,[n_queries, n_docs]						,target_type=torch.intaa   )
													A__		   =							self._scattered(__UpperCAmelCase						,[n_queries, n_docs, question_hidden_states.shape[1]]   )

													return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__UpperCAmelCase   )






"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> int:
													return None






class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple:
													return None






class 			UpperCamelCase__(	unittest.TestCase       ):
						lowerCAmelCase__     :  Tuple  							=						[
						    # (model_name, model_kwargs)
						    ('bert-base-cased', {}),
						    ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
						]





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
													with NamedTemporaryFile(mode='w+t'   ) as vocab_file:
																				vocab_file.write('\n'.join(__UpperCAmelCase   )   )
																				vocab_file.flush()
																				A__		   =							BertTokenizerFast(vocab_file.name   )

													with TemporaryDirectory() as bert_save_dir:
																				A__		   =							BertModel(BertConfig(vocab_size=len(__UpperCAmelCase   )   )   )
																				model.save_pretrained(__UpperCAmelCase   )
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,__UpperCAmelCase   )





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(Path(__UpperCAmelCase   )   )

																				# Ensure the actual quantized model is not bigger than the original one
																				if quantized_path.stat().st_size >= Path(__UpperCAmelCase   ).stat().st_size:
																											self.fail('Quantized model is bigger than initial ONNX model'   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> str:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(__UpperCAmelCase   )

																				# Ensure the actual quantized model is not bigger than the original one
																				if quantized_path.stat().st_size >= Path(__UpperCAmelCase   ).stat().st_size:
																											self.fail('Quantized model is bigger than initial ONNX model'   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None						,**__UpperCAmelCase   )    -> Union[str, Any]:
													try:
																				# Compute path
																				with TemporaryDirectory() as tempdir:
																											A__		   =							Path(__UpperCAmelCase   ).joinpath('model.onnx'   )

																				# Remove folder if exists
																				if path.parent.exists():
																											path.parent.rmdir()

																				# Export
																				convert(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,**__UpperCAmelCase   )

																				return path
													except Exception as e:
																				self.fail(__UpperCAmelCase   )





						@require_torch
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'pt'   )





						@require_tf
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import TFBertModel

													A__		   =							TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'tf'   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													A__		   =							FeatureExtractionPipeline(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
													A__   ,     A__   ,     A__   ,     A__		   =							infer_shapes(__UpperCAmelCase						,__UpperCAmelCase   )

													# Assert all variables are present
													self.assertEqual(len(__UpperCAmelCase   )						,len(__UpperCAmelCase   )   )
													self.assertTrue(all(var_name in shapes for var_name in variable_names   )   )
													self.assertSequenceEqual(variable_names[:3]						,__UpperCAmelCase   )
													self.assertSequenceEqual(variable_names[3:]						,__UpperCAmelCase   )

													# Assert inputs are {0: batch, 1: sequence}
													for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
																				self.assertDictEqual(shapes[var_name]						,{0: 'batch', 1: 'sequence'}   )

													# Assert outputs are {0: batch, 1: sequence} and {0: batch}
													self.assertDictEqual(shapes['output_0']						,{0: 'batch', 1: 'sequence'}   )
													self.assertDictEqual(shapes['output_1']						,{0: 'batch'}   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							['input_ids', 'attention_mask', 'token_type_ids']
													A__		   =							{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
													A__   ,     A__		   =							ensure_valid_input(FuncContiguousArgs()						,__UpperCAmelCase						,__UpperCAmelCase   )

													# Should have exactly the same number of args (all are valid)
													self.assertEqual(len(__UpperCAmelCase   )						,3   )

													# Should have exactly the same input names
													self.assertEqual(set(__UpperCAmelCase   )						,set(__UpperCAmelCase   )   )

													# Parameter should be reordered according to their respective place in the function:
													# (input_ids, token_type_ids, attention_mask)
													self.assertEqual(__UpperCAmelCase						,(tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])   )

													# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
													A__   ,     A__		   =							ensure_valid_input(FuncNonContiguousArgs()						,__UpperCAmelCase						,__UpperCAmelCase   )

													# Should have exactly the one arg (all before the one not provided "some_other_args")
													self.assertEqual(len(__UpperCAmelCase   )						,1   )
													self.assertEqual(len(__UpperCAmelCase   )						,1   )

													# Should have only "input_ids"
													self.assertEqual(inputs_args[0]						,tokens['input_ids']   )
													self.assertEqual(ordered_input_names[0]						,'input_ids'   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							generate_identified_filename(Path('/home/something/my_fake_model.onnx'   )						,'-test'   )
													self.assertEqual('/home/something/my_fake_model-test.onnx'						,generated.as_posix()   )





