


'''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 							__UpperCamelCase				(		lowerCAmelCase_					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  tempfile.mkdtemp()
						__a			:       Dict					    =  8

						# DPR tok
						__a			:       List[Any]					    =  [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						__a			:       int					    =  os.path.join(self.tmpdirname						,				'dpr_tokenizer'							)
						os.makedirs(__a						,				exist_ok=__a							)
						__a			:       List[str]					    =  os.path.join(__a						,				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
						__a			:       Tuple					    =  [
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    '\u0120',
						    '\u0120l',
						    '\u0120n',
						    '\u0120lo',
						    '\u0120low',
						    'er',
						    '\u0120lowest',
						    '\u0120newer',
						    '\u0120wider',
						    '<unk>',
						]
						__a			:       List[str]					    =  dict(zip(__a						,				range(len(__a							)							)							)							)
						__a			:       Dict					    =  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
						__a			:       Any					    =  {'unk_token': '<unk>'}

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  self.get_dummy_dataset()
						__a			:       Dict					    =  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:
											__a			:       Union[str, Any]					    =  dataset
											__a			:       Dict					    =  RagRetriever(
											    __a						,				question_encoder_tokenizer=self.get_dpr_tokenizer()						,				generator_tokenizer=self.get_bart_tokenizer()						,				)
						return retriever




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  self.get_dummy_dataset()
						__a			:       Tuple					    =  RagConfig(
						    retrieval_vector_size=self.retrieval_vector_size						,				question_encoder=DPRConfig().to_dict()						,				generator=BartConfig().to_dict()						,				index_name='custom'						,				)
						if from_disk:
											__a			:       Any					    =  os.path.join(self.tmpdirname						,				'dataset'							)
											__a			:       Union[str, 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
											__a			:       int					    =  RagRetriever(
											    __a						,				question_encoder_tokenizer=self.get_dpr_tokenizer()						,				generator_tokenizer=self.get_bart_tokenizer()						,				)
						else:
											__a			:       List[Any]					    =  RagRetriever(
											    __a						,				question_encoder_tokenizer=self.get_dpr_tokenizer()						,				generator_tokenizer=self.get_bart_tokenizer()						,				index=CustomHFIndex(config.retrieval_vector_size						,				__a							)						,				)
						return retriever




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  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							)

						__a			:       Optional[Any]					    =  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'							)							)

						__a			:       Dict					    =  os.path.join(self.tmpdirname						,				'psgs_w100.tsv.pkl'							)
						__a			:       Union[str, Any]					    =  {sample['id']: [sample['text'], sample['title']] for sample in dataset}
						pickle.dump(__a						,				open(__a						,				'wb'							)							)

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  1
						__a			:       Union[str, Any]					    =  self.get_dummy_canonical_hf_index_retriever()
						__a			:       Optional[Any]					    =  np.array(
						    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]						,				dtype=np.floataa							)
						__a    ,				__a    ,				__a			:       Union[str, Any]					    =  retriever.retrieve(__a						,				n_docs=__a							)
						self.assertEqual(retrieved_doc_embeds.shape						,				(2, n_docs, self.retrieval_vector_size)							)
						self.assertEqual(len(__a							)						,				2							)
						self.assertEqual(sorted(doc_dicts[0]							)						,				['embeddings', 'id', 'text', 'title']							)
						self.assertEqual(len(doc_dicts[0]['id']							)						,				__a							)
						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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  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:
																__a			:       Union[str, Any]					    =  self.get_dummy_dataset()
																retriever.save_pretrained(__a							)
																__a			:       List[str]					    =  RagRetriever.from_pretrained(__a							)
																self.assertIsInstance(__a						,				__a							)
																__a			:       Any					    =  np.array(
																    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]						,				dtype=np.floataa							)
																__a			:       Optional[Any]					    =  retriever.retrieve(__a						,				n_docs=1							)
																self.assertTrue(out is not None							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  1
						__a			:       Union[str, Any]					    =  self.get_dummy_custom_hf_index_retriever(from_disk=__a							)
						__a			:       Dict					    =  np.array(
						    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]						,				dtype=np.floataa							)
						__a    ,				__a    ,				__a			:       str					    =  retriever.retrieve(__a						,				n_docs=__a							)
						self.assertEqual(retrieved_doc_embeds.shape						,				(2, n_docs, self.retrieval_vector_size)							)
						self.assertEqual(len(__a							)						,				2							)
						self.assertEqual(sorted(doc_dicts[0]							)						,				['embeddings', 'id', 'text', 'title']							)
						self.assertEqual(len(doc_dicts[0]['id']							)						,				__a							)
						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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  1
						__a			:       List[str]					    =  self.get_dummy_custom_hf_index_retriever(from_disk=__a							)
						__a			:       int					    =  np.array(
						    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]						,				dtype=np.floataa							)
						__a    ,				__a    ,				__a			:       Any					    =  retriever.retrieve(__a						,				n_docs=__a							)
						self.assertEqual(retrieved_doc_embeds.shape						,				(2, n_docs, self.retrieval_vector_size)							)
						self.assertEqual(len(__a							)						,				2							)
						self.assertEqual(sorted(doc_dicts[0]							)						,				['embeddings', 'id', 'text', 'title']							)
						self.assertEqual(len(doc_dicts[0]['id']							)						,				__a							)
						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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  1
						__a			:       str					    =  self.get_dummy_legacy_index_retriever()
						__a			:       int					    =  np.array(
						    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]						,				dtype=np.floataa							)
						__a    ,				__a    ,				__a			:       int					    =  retriever.retrieve(__a						,				n_docs=__a							)
						self.assertEqual(retrieved_doc_embeds.shape						,				(2, n_docs, self.retrieval_vector_size)							)
						self.assertEqual(len(__a							)						,				2							)
						self.assertEqual(sorted(doc_dicts[0]							)						,				['text', 'title']							)
						self.assertEqual(len(doc_dicts[0]['text']							)						,				__a							)
						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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						import torch

						__a			:       Optional[int]					    =  1
						__a			:       Any					    =  self.get_dummy_canonical_hf_index_retriever()
						__a			:       Optional[int]					    =  [[5, 7], [10, 11]]
						__a			:       str					    =  np.array(
						    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]						,				dtype=np.floataa							)
						__a			:       List[str]					    =  retriever(__a						,				__a						,				prefix=retriever.config.generator.prefix						,				n_docs=__a							)
						__a    ,				__a    ,				__a			:       Dict					    =  (
						    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(__a						,				__a							)
						self.assertIsInstance(__a						,				__a							)
						self.assertIsInstance(__a						,				np.ndarray							)

						__a			:       int					    =  retriever(
						    __a						,				__a						,				prefix=retriever.config.generator.prefix						,				n_docs=__a						,				return_tensors='pt'						,				)
						__a    ,				__a    ,				__a    ,				__a			:       List[str]					    =  (  # 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(__a						,				torch.Tensor							)
						self.assertIsInstance(__a						,				torch.Tensor							)
						self.assertIsInstance(__a						,				torch.Tensor							)





	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  self.get_dpr_ctx_encoder_tokenizer()
						__a			:       Optional[int]					    =  1
						__a			:       Union[str, Any]					    =  self.get_dummy_custom_hf_index_retriever(from_disk=__a							)
						retriever.set_ctx_encoder_tokenizer(__a							)

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

						self.assertEqual(
						    len(__a							)						,				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')							)						,				__a							)  # check for doc token related keys in dictionary.







import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
    from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            for model_result in results.values():
                   for batch_size, sequence_length in zip(model_result["bs"] ,						model_result["ss"]):
                          _A      :       Optional[int]				    = model_result["result"][batch_size][sequence_length]
                          self.assertIsNotNone(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Optional[int]				    = "sshleifer/tiny-gpt2"
            _A      :       int				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       List[str]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Optional[Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       int				    = "sgugger/tiny-distilbert-classification"
            _A      :       str				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						only_pretrain_model=__lowerCamelCase ,						)
            _A      :       Dict				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Union[str, Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Tuple				    = "sshleifer/tiny-gpt2"
            _A      :       Optional[Any]				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						torchscript=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Union[str, Any]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       List[str]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     @unittest.skipIf(torch_device == "cpu" ,						"Cant do half precision")
     def  _lowerCamelCase (						self)			->				int:
            _A      :       Any				    = "sshleifer/tiny-gpt2"
            _A      :       Tuple				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						fpaa=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Any				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Union[str, Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       Union[str, Any]				    = "sshleifer/tiny-gpt2"
            _A      :       Any				    = AutoConfig.from_pretrained(__lowerCamelCase)
            # set architectures equal to `None`
            _A      :       Dict				    = None
            _A      :       Any				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Union[str, Any]				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       int				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       List[Any]				    = "sshleifer/tiny-gpt2"
            _A      :       int				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Optional[Any]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       int				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     @unittest.skipIf(torch_device == "cpu" ,						"Can't do half precision")
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Any				    = "sshleifer/tiny-gpt2"
            _A      :       Tuple				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						fpaa=__lowerCamelCase ,						multi_process=__lowerCamelCase ,						)
            _A      :       List[Any]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Tuple				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     def  _lowerCamelCase (						self)			->				str:
            _A      :       List[str]				    = "sshleifer/tiny-gpt2"
            _A      :       Union[str, Any]				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Any				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Optional[Any]				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Tuple				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Tuple				    = "sshleifer/tinier_bart"
            _A      :       Optional[Any]				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Optional[int]				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Dict				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Optional[int]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				str:
            _A      :       List[Any]				    = "sshleifer/tiny-gpt2"
            _A      :       Optional[Any]				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Optional[Any]				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       List[str]				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Dict				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       int				    = "sshleifer/tinier_bart"
            _A      :       str				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Tuple				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Tuple				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Union[str, Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       List[str]				    = "sshleifer/tiny-gpt2"
            with tempfile.TemporaryDirectory() as tmp_dir:
                   _A      :       Optional[Any]				    = PyTorchBenchmarkArguments(
                       models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						save_to_csv=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						inference_time_csv_file=os.path.join(__lowerCamelCase ,						"inf_time.csv") ,						train_memory_csv_file=os.path.join(__lowerCamelCase ,						"train_mem.csv") ,						inference_memory_csv_file=os.path.join(__lowerCamelCase ,						"inf_mem.csv") ,						train_time_csv_file=os.path.join(__lowerCamelCase ,						"train_time.csv") ,						env_info_csv_file=os.path.join(__lowerCamelCase ,						"env.csv") ,						multi_process=__lowerCamelCase ,						)
                   _A      :       Tuple				    = PyTorchBenchmark(__lowerCamelCase)
                   benchmark.run()
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"inf_time.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"train_time.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"inf_mem.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"train_mem.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"env.csv")).exists())


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Dict				    = "sshleifer/tiny-gpt2"

            def _check_summary_is_not_empty(__lowerCamelCase):
                   self.assertTrue(hasattr(__lowerCamelCase ,						"sequential"))
                   self.assertTrue(hasattr(__lowerCamelCase ,						"cumulative"))
                   self.assertTrue(hasattr(__lowerCamelCase ,						"current"))
                   self.assertTrue(hasattr(__lowerCamelCase ,						"total"))

            with tempfile.TemporaryDirectory() as tmp_dir:
                   _A      :       Union[str, Any]				    = PyTorchBenchmarkArguments(
                       models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						log_filename=os.path.join(__lowerCamelCase ,						"log.txt") ,						log_print=__lowerCamelCase ,						trace_memory_line_by_line=__lowerCamelCase ,						multi_process=__lowerCamelCase ,						)
                   _A      :       Optional[int]				    = PyTorchBenchmark(__lowerCamelCase)
                   _A      :       Dict				    = benchmark.run()
                   _check_summary_is_not_empty(result.inference_summary)
                   _check_summary_is_not_empty(result.train_summary)
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"log.txt")).exists())
