
"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__      :    Optional[Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Dict    							= '▁'

UpperCAmelCase__      :    Optional[int]    							= {'vocab_file': 'sentencepiece.bpe.model'}

UpperCAmelCase__      :    Union[str, Any]    							= {
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

UpperCAmelCase__      :    int    							= {
    'facebook/mbart-large-50-one-to-many-mmt': 1_0_2_4,
}

# fmt: off
UpperCAmelCase__      :    Any    							= ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']



class 	lowerCAmelCase_						(_lowerCAmelCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Tuple					       =   VOCAB_FILES_NAMES
				__UpperCamelCase      :						List[str]					       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase      :						Tuple					       =   PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase      :						Dict					       =   ["input_ids", "attention_mask"]

				__UpperCamelCase      :						List[int]					       =   []
				__UpperCamelCase      :						List[int]					       =   []



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__="</s>"   ,       SCREAMING_SNAKE_CASE__="</s>"   ,       SCREAMING_SNAKE_CASE__="<s>"   ,       SCREAMING_SNAKE_CASE__="<unk>"   ,       SCREAMING_SNAKE_CASE__="<pad>"   ,       SCREAMING_SNAKE_CASE__="<mask>"   ,       SCREAMING_SNAKE_CASE__ = None   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   AddedToken(_lowerCamelCase   ,       lstrip=_lowerCamelCase   ,       rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase   ,       _lowerCamelCase    ) else mask_token

											SCREAMING_SNAKE_CASE__						:       Any           =   {} if sp_model_kwargs is None else sp_model_kwargs

											SCREAMING_SNAKE_CASE__						:       Dict           =   kwargs.get("""additional_special_tokens"""   ,       []    )
											kwargs["additional_special_tokens"] += [
											    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
											]

											super().__init__(
											    src_lang=_lowerCamelCase   ,       tgt_lang=_lowerCamelCase   ,       eos_token=_lowerCamelCase   ,       unk_token=_lowerCamelCase   ,       sep_token=_lowerCamelCase   ,       cls_token=_lowerCamelCase   ,       pad_token=_lowerCamelCase   ,       mask_token=_lowerCamelCase   ,       sp_model_kwargs=self.sp_model_kwargs   ,       **_lowerCamelCase   ,       )

											SCREAMING_SNAKE_CASE__						:       Dict           =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
											self.sp_model.Load(str(_lowerCamelCase    )    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# Mimic fairseq token-to-id alignment for the first 4 token
											SCREAMING_SNAKE_CASE__						:       int           =   {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

											# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
											SCREAMING_SNAKE_CASE__						:       Dict           =   1

											SCREAMING_SNAKE_CASE__						:       int           =   len(self.sp_model    )
											SCREAMING_SNAKE_CASE__						:       str           =   {
											    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowerCamelCase    )
											}
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   {v: k for k, v in self.lang_code_to_id.items()}
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset

											self.fairseq_tokens_to_ids.update(self.lang_code_to_id    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}

											SCREAMING_SNAKE_CASE__						:       Any           =   src_lang if src_lang is not None else """en_XX"""
											SCREAMING_SNAKE_CASE__						:       int           =   self.lang_code_to_id[self._src_lang]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   tgt_lang
											self.set_src_lang_special_tokens(self._src_lang    )



				@property
				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											return len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset + 1  # Plus 1 for the mask token



				@property
				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											return self._src_lang



				@src_lang.setter
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   new_src_lang
											self.set_src_lang_special_tokens(self._src_lang    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.__dict__.copy()
											SCREAMING_SNAKE_CASE__						:       str           =   None
											return state



				def __setstate__(self   ,       SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   d

											# for backward compatibility
											if not hasattr(self   ,       """sp_model_kwargs"""    ):
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   {}

											SCREAMING_SNAKE_CASE__						:       List[str]           =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
											self.sp_model.Load(self.vocab_file    )



				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   {self.convert_ids_to_tokens(_lowerCamelCase    ): i for i in range(self.vocab_size    )}
											vocab.update(self.added_tokens_encoder    )
											return vocab



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[Any]:



											"""simple docstring"""




											return self.sp_model.encode(_lowerCamelCase   ,       out_type=_lowerCamelCase    )



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



											"""simple docstring"""




											if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
											SCREAMING_SNAKE_CASE__						:       int           =   self.sp_model.PieceToId(_lowerCamelCase    )

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											if index in self.fairseq_ids_to_tokens:
																		return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   []
											SCREAMING_SNAKE_CASE__						:       Tuple           =   """"""
											SCREAMING_SNAKE_CASE__						:       Dict           =   False
											for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																									if not prev_is_special:
																																out_string += " "
																									out_string += self.sp_model.decode(_lowerCamelCase    ) + token
																									SCREAMING_SNAKE_CASE__						:       Any           =   True
																									SCREAMING_SNAKE_CASE__						:       List[str]           =   []
																		else:
																									current_sub_tokens.append(_lowerCamelCase    )
																									SCREAMING_SNAKE_CASE__						:       int           =   False
											out_string += self.sp_model.decode(_lowerCamelCase    )
											return out_string.strip()



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		int:



											"""simple docstring"""




											if not os.path.isdir(_lowerCamelCase    ):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
																		return
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   os.path.join(
											    _lowerCamelCase   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

											if os.path.abspath(self.vocab_file    ) != os.path.abspath(_lowerCamelCase    ) and os.path.isfile(self.vocab_file    ):
																		copyfile(self.vocab_file   ,       _lowerCamelCase    )
											elif not os.path.isfile(self.vocab_file    ):
																		with open(_lowerCamelCase   ,       """wb"""    ) as fi:
																									SCREAMING_SNAKE_CASE__						:       Any           =   self.sp_model.serialized_model_proto()
																									fi.write(_lowerCamelCase    )

											return (out_vocab_file,)



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = False    )					->		Optional[int]:



											"""simple docstring"""




											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=_lowerCamelCase   ,       token_ids_a=_lowerCamelCase   ,       already_has_special_tokens=_lowerCamelCase    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   [1] * len(self.prefix_tokens    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   [1] * len(self.suffix_tokens    )
											if token_ids_a is None:
																		return prefix_ones + ([0] * len(_lowerCamelCase    )) + suffix_ones
											return prefix_ones + ([0] * len(_lowerCamelCase    )) + ([0] * len(_lowerCamelCase    )) + suffix_ones



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		Any:



											"""simple docstring"""




											if token_ids_a is None:
																		return self.prefix_tokens + token_ids_a + self.suffix_tokens
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											if src_lang is None or tgt_lang is None:
																		raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""    )
											SCREAMING_SNAKE_CASE__						:       Any           =   src_lang
											SCREAMING_SNAKE_CASE__						:       str           =   self(_lowerCamelCase   ,       add_special_tokens=_lowerCamelCase   ,       return_tensors=_lowerCamelCase   ,       **_lowerCamelCase    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.convert_tokens_to_ids(_lowerCamelCase    )
											SCREAMING_SNAKE_CASE__						:       int           =   tgt_lang_id
											return inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = "en_XX"   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = "ro_RO"   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   src_lang
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tgt_lang
											return super().prepare_seqaseq_batch(_lowerCamelCase   ,       _lowerCamelCase   ,       **_lowerCamelCase    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											return self.set_src_lang_special_tokens(self.src_lang    )



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



											"""simple docstring"""




											return self.set_tgt_lang_special_tokens(self.tgt_lang    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.lang_code_to_id[src_lang]
											SCREAMING_SNAKE_CASE__						:       Dict           =   [self.cur_lang_code_id]
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [self.eos_token_id]



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.lang_code_to_id[tgt_lang]
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [self.cur_lang_code_id]
											SCREAMING_SNAKE_CASE__						:       str           =   [self.eos_token_id]












"""simple docstring"""


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 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     for model_result in results.values():
       for batch_size, sequence_length in zip(model_result['''bs'''],       model_result['''ss''']  ):
         __A									= model_result['''result'''][batch_size][sequence_length]
         self.assertIsNotNone(_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sgugger/tiny-distilbert-classification'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       only_pretrain_model=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       torchscript=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       fpaa=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     # set architectures equal to `None`
     __A									= None
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       fpaa=_lowerCamelCase,       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tinier_bart'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= '''sshleifer/tinier_bart'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     with tempfile.TemporaryDirectory() as tmp_dir:
       __A									= 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									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''

     def _check_summary_is_not_empty(_lowerCamelCase :     List[Any]  ):
       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									= 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									= PyTorchBenchmark(_lowerCamelCase  )
       __A									= 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()  )




