


import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

lowerCAmelCase__      		= {
    'artists_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
    },
    'genres_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
    },
    'lyrics_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
    },
}

lowerCAmelCase__      		= {
    'jukebox': 5_12,
}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_LYRIC_TOKENS_SIZES
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=["v3", "v2", "v2"] ,						__lowerCamelCase=5_1_2 ,						__lowerCamelCase=5 ,						__lowerCamelCase="<|endoftext|>" ,						**__lowerCamelCase ,						)			->				int:
            _A      :       Tuple				    = AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase ,						__lowerCamelCase) else unk_token
            super().__init__(
                unk_token=__lowerCamelCase ,						n_genres=__lowerCamelCase ,						version=__lowerCamelCase ,						max_n_lyric_tokens=__lowerCamelCase ,						**__lowerCamelCase ,						)
            _A      :       List[str]				    = version
            _A      :       Dict				    = max_n_lyric_tokens
            _A      :       str				    = n_genres

            with open(__lowerCamelCase ,						encoding="utf-8") as vocab_handle:
                   _A      :       List[str]				    = json.load(__lowerCamelCase)

            with open(__lowerCamelCase ,						encoding="utf-8") as vocab_handle:
                   _A      :       str				    = json.load(__lowerCamelCase)

            with open(__lowerCamelCase ,						encoding="utf-8") as vocab_handle:
                   _A      :       Optional[Any]				    = json.load(__lowerCamelCase)

            _A      :       int				    = r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"
            # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
            if len(self.lyrics_encoder) == 7_9:
                   _A      :       Tuple				    = oov.replace(r"\-'" ,						r"\-+'")

            _A      :       Any				    = regex.compile(__lowerCamelCase)
            _A      :       Dict				    = {v: k for k, v in self.artists_encoder.items()}
            _A      :       Optional[int]				    = {v: k for k, v in self.genres_encoder.items()}
            _A      :       Optional[Any]				    = {v: k for k, v in self.lyrics_encoder.items()}


     @property
     def  _lowerCamelCase (						self)			->				str:
            return len(self.artists_encoder) + len(self.genres_encoder) + len(self.lyrics_encoder)


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            return dict(self.artists_encoder ,						self.genres_encoder ,						self.lyrics_encoder)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Optional[Any]:
            _A      :       str				    = [self.artists_encoder.get(__lowerCamelCase ,						0) for artist in list_artists]
            for genres in range(len(__lowerCamelCase)):
                   _A      :       Dict				    = [self.genres_encoder.get(__lowerCamelCase ,						0) for genre in list_genres[genres]]
                   _A      :       Dict				    = list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]))

            _A      :       int				    = [[self.lyrics_encoder.get(__lowerCamelCase ,						0) for character in list_lyrics[0]], [], []]
            return artists_id, list_genres, lyric_ids


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            return list(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            _A    ,			_A    ,			_A      :       int				    = self.prepare_for_tokenization(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)
            _A      :       Any				    = self._tokenize(__lowerCamelCase)
            return artist, genre, lyrics


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = False)			->				Tuple[str, str, str, Dict[str, Any]]:
            for idx in range(len(self.version)):
                   if self.version[idx] == "v3":
                          _A      :       List[str]				    = artists[idx].lower()
                          _A      :       List[str]				    = [genres[idx].lower()]
                   else:
                          _A      :       List[Any]				    = self._normalize(artists[idx]) + ".v2"
                          _A      :       List[str]				    = [
                              self._normalize(__lowerCamelCase) + ".v2" for genre in genres[idx].split("_")
                          ]  # split is for the full dictionary with combined genres

            if self.version[0] == "v2":
                   _A      :       Dict				    = regex.compile(r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+")
                   _A      :       List[Any]				    = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"
                   _A      :       Optional[Any]				    = {vocab[index]: index + 1 for index in range(len(__lowerCamelCase))}
                   _A      :       str				    = 0
                   _A      :       Tuple				    = len(__lowerCamelCase) + 1
                   _A      :       Union[str, Any]				    = self.vocab
                   _A      :       str				    = {v: k for k, v in self.vocab.items()}
                   _A      :       Tuple				    = ""
            else:
                   _A      :       int				    = regex.compile(r"[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+")

            _A      :       Tuple				    = self._run_strip_accents(__lowerCamelCase)
            _A      :       str				    = lyrics.replace("\\" ,						"\n")
            _A      :       Optional[int]				    = self.out_of_vocab.sub("" ,						__lowerCamelCase), [], []
            return artists, genres, lyrics


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Union[str, Any]:
            _A      :       Any				    = unicodedata.normalize("NFD" ,						__lowerCamelCase)
            _A      :       Tuple				    = []
            for char in text:
                   _A      :       Optional[int]				    = unicodedata.category(__lowerCamelCase)
                   if cat == "Mn":
                          continue
                   output.append(__lowerCamelCase)
            return "".join(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            _A      :       Tuple				    = (
                [chr(__lowerCamelCase) for i in range(ord("a") ,						ord("z") + 1)]
                + [chr(__lowerCamelCase) for i in range(ord("A") ,						ord("Z") + 1)]
                + [chr(__lowerCamelCase) for i in range(ord("0") ,						ord("9") + 1)]
                + ["."]
            )
            _A      :       Dict				    = frozenset(__lowerCamelCase)
            _A      :       Any				    = re.compile(r"_+")
            _A      :       Dict				    = "".join([c if c in accepted else "_" for c in text.lower()])
            _A      :       Optional[int]				    = pattern.sub("_" ,						__lowerCamelCase).strip("_")
            return text


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            return " ".join(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = False)			->				Dict:
            # Convert to TensorType
            if not isinstance(__lowerCamelCase ,						__lowerCamelCase):
                   _A      :       Optional[Any]				    = TensorType(__lowerCamelCase)

            # Get a function reference for the correct framework
            if tensor_type == TensorType.TENSORFLOW:
                   if not is_tf_available():
                          raise ImportError(
                              "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.")
                   import tensorflow as tf

                   _A      :       Optional[Any]				    = tf.constant
                   _A      :       str				    = tf.is_tensor
            elif tensor_type == TensorType.PYTORCH:
                   if not is_torch_available():
                          raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
                   import torch

                   _A      :       int				    = torch.tensor
                   _A      :       str				    = torch.is_tensor
            elif tensor_type == TensorType.JAX:
                   if not is_flax_available():
                          raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed.")
                   import jax.numpy as jnp  # noqa: F811

                   _A      :       List[Any]				    = jnp.array
                   _A      :       int				    = _is_jax
            else:
                   _A      :       Optional[Any]				    = np.asarray
                   _A      :       List[str]				    = _is_numpy

            # Do the tensor conversion in batch

            try:
                   if prepend_batch_axis:
                          _A      :       Any				    = [inputs]

                   if not is_tensor(__lowerCamelCase):
                          _A      :       List[str]				    = as_tensor(__lowerCamelCase)
            except:  # noqa E722
                   raise ValueError(
                       "Unable to create tensor, you should probably activate truncation and/or padding "
                       "with 'padding=True' 'truncation=True' to have batched tensors with the same length.")

            return inputs


     def __call__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase="" ,						__lowerCamelCase="pt")			->				BatchEncoding:
            _A      :       List[Any]				    = [0, 0, 0]
            _A      :       str				    = [artist] * len(self.version)
            _A      :       List[str]				    = [genres] * len(self.version)

            _A    ,			_A    ,			_A      :       Union[str, Any]				    = self.tokenize(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)
            _A    ,			_A    ,			_A      :       Tuple				    = self._convert_token_to_id(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)

            _A      :       Dict				    = [-INFINITY] * len(full_tokens[-1])
            _A      :       Dict				    = [
                self.convert_to_tensors(
                    [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]] ,						tensor_type=__lowerCamelCase)
                for i in range(len(self.version))
            ]
            return BatchEncoding({"input_ids": input_ids, "attention_masks": attention_masks})


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            if not os.path.isdir(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory")
                   return

            _A      :       str				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["artists_file"])
            with open(__lowerCamelCase ,						"w" ,						encoding="utf-8") as f:
                   f.write(json.dumps(self.artists_encoder ,						ensure_ascii=__lowerCamelCase))

            _A      :       Union[str, Any]				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["genres_file"])
            with open(__lowerCamelCase ,						"w" ,						encoding="utf-8") as f:
                   f.write(json.dumps(self.genres_encoder ,						ensure_ascii=__lowerCamelCase))

            _A      :       Dict				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["lyrics_file"])
            with open(__lowerCamelCase ,						"w" ,						encoding="utf-8") as f:
                   f.write(json.dumps(self.lyrics_encoder ,						ensure_ascii=__lowerCamelCase))

            return (artists_file, genres_file, lyrics_file)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Optional[int]:
            _A      :       List[Any]				    = self.artists_decoder.get(__lowerCamelCase)
            _A      :       Union[str, Any]				    = [self.genres_decoder.get(__lowerCamelCase) for genre in genres_index]
            _A      :       Optional[int]				    = [self.lyrics_decoder.get(__lowerCamelCase) for character in lyric_index]
            return artist, genres, lyrics




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())
