





'''simple docstring'''






import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("""0.8.3"""):
					raise Exception("""requires gluonnlp == 0.8.3""")

if version.parse(mx.__version__) != version.parse("""1.5.0"""):
					raise Exception("""requires mxnet == 1.5.0""")

logging.set_verbosity_info()
lowerCAmelCase       		=      logging.get_logger(__name__)

lowerCAmelCase       		=      """The Nymphenburg Palace is a beautiful palace in Munich!"""






def      __A						(							a_						:  str      ,a_						:  str	):
		lowerCAmelCase     :    List[str]						=   {
		    "attention_cell": "multi_head",
		    "num_layers": 4,
		    "units": 1_0_2_4,
		    "hidden_size": 7_6_8,
		    "max_length": 5_1_2,
		    "num_heads": 8,
		    "scaled": True,
		    "dropout": 0.1,
		    "use_residual": True,
		    "embed_size": 1_0_2_4,
		    "embed_dropout": 0.1,
		    "word_embed": None,
		    "layer_norm_eps": 1e-5,
		    "token_type_vocab_size": 2,
		}

		lowerCAmelCase     :    Dict						=   bort_4_8_768_1024_hparams

		# Let's construct the original Bort model here
		# Taken from official BERT implementation, see:
		# https://github.com/alexa/bort/blob/master/bort/bort.py
		lowerCAmelCase     :    str						=   BERTEncoder(
		    attention_cell=predefined_args["attention_cell"]      ,num_layers=predefined_args["num_layers"]      ,units=predefined_args["units"]      ,hidden_size=predefined_args["hidden_size"]      ,max_length=predefined_args["max_length"]      ,num_heads=predefined_args["num_heads"]      ,scaled=predefined_args["scaled"]      ,dropout=predefined_args["dropout"]      ,output_attention=a_      ,output_all_encodings=a_      ,use_residual=predefined_args["use_residual"]      ,activation=predefined_args.get("activation"      ,"gelu"	)      ,layer_norm_eps=predefined_args.get("layer_norm_eps"      ,a_	)      ,)

		# Vocab information needs to be fetched first
		# It's the same as RoBERTa, so RobertaTokenizer can be used later
		lowerCAmelCase     :    int						=   "openwebtext_ccnews_stories_books_cased"

		# Specify download folder to Gluonnlp's vocab
		lowerCAmelCase     :    int						=   os.path.join(get_home_dir()      ,"models"	)
		lowerCAmelCase     :    List[str]						=   _load_vocab(a_      ,a_      ,a_      ,cls=a_	)

		lowerCAmelCase     :    List[str]						=   nlp.model.BERTModel(
		    a_      ,len(a_	)      ,units=predefined_args["units"]      ,embed_size=predefined_args["embed_size"]      ,embed_dropout=predefined_args["embed_dropout"]      ,word_embed=predefined_args["word_embed"]      ,use_pooler=a_      ,use_token_type_embed=a_      ,token_type_vocab_size=predefined_args["token_type_vocab_size"]      ,use_classifier=a_      ,use_decoder=a_      ,)

		original_bort.load_parameters(a_      ,cast_dtype=a_      ,ignore_extra=a_	)
		lowerCAmelCase     :    Optional[Any]						=   original_bort._collect_params_with_prefix()

		# Build our config 🤗
		lowerCAmelCase     :    Tuple						=   {
		    "architectures": ["BertForMaskedLM"],
		    "attention_probs_dropout_prob": predefined_args["dropout"],
		    "hidden_act": "gelu",
		    "hidden_dropout_prob": predefined_args["dropout"],
		    "hidden_size": predefined_args["embed_size"],
		    "initializer_range": 0.0_2,
		    "intermediate_size": predefined_args["hidden_size"],
		    "layer_norm_eps": predefined_args["layer_norm_eps"],
		    "max_position_embeddings": predefined_args["max_length"],
		    "model_type": "bort",
		    "num_attention_heads": predefined_args["num_heads"],
		    "num_hidden_layers": predefined_args["num_layers"],
		    "pad_token_id": 1,  # 2 = BERT, 1 = RoBERTa
		    "type_vocab_size": 1,  # 2 = BERT, 1 = RoBERTa
		    "vocab_size": len(a_	),
		}

		lowerCAmelCase     :    Optional[Any]						=   BertConfig.from_dict(a_	)
		lowerCAmelCase     :    Tuple						=   BertForMaskedLM(a_	)
		hf_bort_model.eval()

		# Parameter mapping table (Gluonnlp to Transformers)
		# * denotes layer index
		#
		# | Gluon Parameter                                                | Transformers Parameter
		# | -------------------------------------------------------------- | ----------------------
		# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
		# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
		# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
		# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
		# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
		# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
		# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

		# Helper function to convert MXNET Arrays to PyTorch
		def to_torch(a_						:  Optional[Any]	) -> nn.Parameter:
				return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()	)	)

		# Check param shapes and map new HF param back
		def check_and_map_params(a_						:  Any      ,a_						:  Optional[Any]	):
				lowerCAmelCase     :    Any						=   hf_param.shape

				lowerCAmelCase     :    Any						=   to_torch(params[gluon_param]	)
				lowerCAmelCase     :    Tuple						=   gluon_param.shape

				assert (
				    shape_hf == shape_gluon
				), f'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

				return gluon_param

		lowerCAmelCase     :    Optional[int]						=   check_and_map_params(
		    hf_bort_model.bert.embeddings.word_embeddings.weight      ,"word_embed.0.weight"	)
		lowerCAmelCase     :    str						=   check_and_map_params(
		    hf_bort_model.bert.embeddings.position_embeddings.weight      ,"encoder.position_weight"	)
		lowerCAmelCase     :    List[str]						=   check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.bias      ,"encoder.layer_norm.beta"	)
		lowerCAmelCase     :    Dict						=   check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.weight      ,"encoder.layer_norm.gamma"	)

		# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
		lowerCAmelCase     :    Optional[Any]						=   torch.zeros_like(
		    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data	)

		for i in range(hf_bort_config.num_hidden_layers	):
				lowerCAmelCase     :    BertLayer						=   hf_bort_model.bert.encoder.layer[i]

				# self attention
				lowerCAmelCase     :    BertSelfAttention						=   layer.attention.self

				lowerCAmelCase     :    List[str]						=   check_and_map_params(
				    self_attn.key.bias.data      ,f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''	)

				lowerCAmelCase     :    Union[str, Any]						=   check_and_map_params(
				    self_attn.key.weight.data      ,f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''	)
				lowerCAmelCase     :    Tuple						=   check_and_map_params(
				    self_attn.query.bias.data      ,f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''	)
				lowerCAmelCase     :    Dict						=   check_and_map_params(
				    self_attn.query.weight.data      ,f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''	)
				lowerCAmelCase     :    Optional[Any]						=   check_and_map_params(
				    self_attn.value.bias.data      ,f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''	)
				lowerCAmelCase     :    int						=   check_and_map_params(
				    self_attn.value.weight.data      ,f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''	)

				# self attention output
				lowerCAmelCase     :    BertSelfOutput						=   layer.attention.output

				lowerCAmelCase     :    str						=   check_and_map_params(
				    self_output.dense.bias      ,f'''encoder.transformer_cells.{i}.proj.bias'''	)
				lowerCAmelCase     :    List[Any]						=   check_and_map_params(
				    self_output.dense.weight      ,f'''encoder.transformer_cells.{i}.proj.weight'''	)
				lowerCAmelCase     :    str						=   check_and_map_params(
				    self_output.LayerNorm.bias      ,f'''encoder.transformer_cells.{i}.layer_norm.beta'''	)
				lowerCAmelCase     :    List[str]						=   check_and_map_params(
				    self_output.LayerNorm.weight      ,f'''encoder.transformer_cells.{i}.layer_norm.gamma'''	)

				# intermediate
				lowerCAmelCase     :    BertIntermediate						=   layer.intermediate

				lowerCAmelCase     :    int						=   check_and_map_params(
				    intermediate.dense.bias      ,f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''	)
				lowerCAmelCase     :    Union[str, Any]						=   check_and_map_params(
				    intermediate.dense.weight      ,f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''	)

				# output
				lowerCAmelCase     :    BertOutput						=   layer.output

				lowerCAmelCase     :    Tuple						=   check_and_map_params(
				    bert_output.dense.bias      ,f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''	)
				lowerCAmelCase     :    int						=   check_and_map_params(
				    bert_output.dense.weight      ,f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''	)
				lowerCAmelCase     :    str						=   check_and_map_params(
				    bert_output.LayerNorm.bias      ,f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''	)
				lowerCAmelCase     :    Union[str, Any]						=   check_and_map_params(
				    bert_output.LayerNorm.weight      ,f'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma'''	)

		# Save space and energy 🎄
		hf_bort_model.half()

		# Compare output of both models
		lowerCAmelCase     :    Union[str, Any]						=   RobertaTokenizer.from_pretrained("roberta-base"	)

		lowerCAmelCase     :    Union[str, Any]						=   tokenizer.encode_plus(a_	)["input_ids"]

		# Get gluon output
		lowerCAmelCase     :    List[Any]						=   mx.nd.array([input_ids]	)
		lowerCAmelCase     :    int						=   original_bort(inputs=a_      ,token_types=[]	)

		# Get Transformer output (save and reload model again)
		hf_bort_model.save_pretrained(a_	)
		lowerCAmelCase     :    List[str]						=   BertModel.from_pretrained(a_	)
		hf_bort_model.eval()

		lowerCAmelCase     :    Optional[int]						=   tokenizer.encode_plus(a_      ,return_tensors="pt"	)
		lowerCAmelCase     :    str						=   hf_bort_model(**a_	)[0]

		lowerCAmelCase     :    List[Any]						=   output_gluon[0].asnumpy()
		lowerCAmelCase     :    int						=   output_hf[0].detach().numpy()

		lowerCAmelCase     :    Dict						=   np.max(np.abs(hf_layer - gluon_layer	)	).item()
		lowerCAmelCase     :    Dict						=   np.allclose(a_      ,a_      ,atol=1e-3	)

		if success:
				print("✔️ Both model do output the same tensors"	)
		else:
				print("❌ Both model do **NOT** output the same tensors"	)
				print("Absolute difference is:"      ,a_	)


if __name__ == "__main__":
					lowerCAmelCase       		=      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--bort_checkpoint_path""", default=None, type=str, required=True, help="""Path the official Bort params file."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					lowerCAmelCase       		=      parser.parse_args()
					convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf

					from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self		,      a_	):
			for model_result in results.values():
					for batch_size, sequence_length in zip(model_result["bs"]		,      model_result["ss"]	):
							lowerCAmelCase     :    Tuple						=   model_result["result"][batch_size][sequence_length]
							self.assertIsNotNone(a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      eager_mode=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    Union[str, Any]						=   "sgugger/tiny-distilbert-classification"
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      only_pretrain_model=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   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	):
			lowerCAmelCase     :    Union[str, Any]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    str						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   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	):
			lowerCAmelCase     :    List[str]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      eager_mode=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    int						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    Tuple						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    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	):
			lowerCAmelCase     :    Tuple						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   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	):
			lowerCAmelCase     :    List[Any]						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    Union[str, Any]						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmark(a_		,      [config]	)
			lowerCAmelCase     :    int						=   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	):
			lowerCAmelCase     :    str						=   "patrickvonplaten/t5-tiny-random"
			lowerCAmelCase     :    Tuple						=   AutoConfig.from_pretrained(a_	)
			lowerCAmelCase     :    Dict						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=a_		,      )
			lowerCAmelCase     :    List[str]						=   TensorFlowBenchmark(a_		,      configs=[config]	)
			lowerCAmelCase     :    List[Any]						=   benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result	)
			self.check_results_dict_not_empty(results.memory_inference_result	)



	@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"	)	) == 0		,      "Cannot do xla on CPU."	)
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   "sshleifer/tiny-gpt2"
			lowerCAmelCase     :    List[Any]						=   TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]		,      training=a_		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      use_xla=a_		,      multi_process=a_		,      )
			lowerCAmelCase     :    Any						=   TensorFlowBenchmark(a_	)
			lowerCAmelCase     :    str						=   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	):
			lowerCAmelCase     :    Tuple						=   "sshleifer/tiny-gpt2"
			with tempfile.TemporaryDirectory() as tmp_dir:
					lowerCAmelCase     :    Optional[int]						=   TensorFlowBenchmarkArguments(
					    models=[MODEL_ID]		,      inference=a_		,      save_to_csv=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      inference_time_csv_file=os.path.join(a_		,      "inf_time.csv"	)		,      inference_memory_csv_file=os.path.join(a_		,      "inf_mem.csv"	)		,      env_info_csv_file=os.path.join(a_		,      "env.csv"	)		,      multi_process=a_		,      )
					lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmark(a_	)
					benchmark.run()
					self.assertTrue(Path(os.path.join(a_		,      "inf_time.csv"	)	).exists()	)
					self.assertTrue(Path(os.path.join(a_		,      "inf_mem.csv"	)	).exists()	)
					self.assertTrue(Path(os.path.join(a_		,      "env.csv"	)	).exists()	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   "sshleifer/tiny-gpt2"

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

			with tempfile.TemporaryDirectory() as tmp_dir:
					lowerCAmelCase     :    int						=   TensorFlowBenchmarkArguments(
					    models=[MODEL_ID]		,      inference=a_		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      log_filename=os.path.join(a_		,      "log.txt"	)		,      log_print=a_		,      trace_memory_line_by_line=a_		,      eager_mode=a_		,      multi_process=a_		,      )
					lowerCAmelCase     :    Optional[Any]						=   TensorFlowBenchmark(a_	)
					lowerCAmelCase     :    Tuple						=   benchmark.run()
					_check_summary_is_not_empty(result.inference_summary	)
					self.assertTrue(Path(os.path.join(a_		,      "log.txt"	)	).exists()	)



