


"""simple docstring"""



import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english







@require_tokenizers
class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: List[Any]													=						RoCBertTokenizer
   __UpperCAmelCase				: List[Any]													=						None
   __UpperCAmelCase				: List[Any]													=						False
   __UpperCAmelCase				: Optional[int]													=						True
   __UpperCAmelCase				: Dict													=						filter_non_english







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       super().setUp()

       snake_case					:   Dict         =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
       snake_case					:   Optional[Any]         =					{}
       snake_case					:   Optional[Any]         =					{}
       for i, value in enumerate(UpperCamelCase__		):
           snake_case					:   int         =					i
           snake_case					:   Optional[int]         =					i
       snake_case					:   List[Any]         =					os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES["vocab_file"]		)
       snake_case					:   Optional[Any]         =					os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES["word_shape_file"]		)
       snake_case					:   str         =					os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES["word_pronunciation_file"]		)
       with open(self.vocab_file		,			"w"		,			encoding="utf-8"		) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)
       with open(self.word_shape_file		,			"w"		,			encoding="utf-8"		) as word_shape_writer:
           json.dump(UpperCamelCase__		,			UpperCamelCase__		,			ensure_ascii=UpperCamelCase__		)
       with open(self.word_pronunciation_file		,			"w"		,			encoding="utf-8"		) as word_pronunciation_writer:
           json.dump(UpperCamelCase__		,			UpperCamelCase__		,			ensure_ascii=UpperCamelCase__		)







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

       '''simple docstring'''




       snake_case					:   Tuple         =					self.tokenizer_class(self.vocab_file		,			self.word_shape_file		,			self.word_pronunciation_file		)

       snake_case					:   Optional[int]         =					tokenizer.tokenize("你好[SEP]你是谁"		)
       self.assertListEqual(UpperCamelCase__		,			["你", "好", "[SEP]", "你", "是", "谁"]		)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)		,			[5, 6, 2, 5, 7, 8]		)
       self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(UpperCamelCase__		)		,			[5, 6, 2, 5, 7, 8]		)
       self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(UpperCamelCase__		)		,			[5, 6, 2, 5, 7, 8]		)







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Any         =					RoCBertBasicTokenizer()

       self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"		)		,			["ah", "\u535A", "\u63A8", "zz"]		)







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					RoCBertBasicTokenizer(do_lower_case=UpperCamelCase__		)

       self.assertListEqual(
           tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "		)		,			["hello", "!", "how", "are", "you", "?"]		)
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)		,			["hello"]		)







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

       '''simple docstring'''




       snake_case					:   Tuple         =					RoCBertBasicTokenizer(do_lower_case=UpperCamelCase__		,			strip_accents=UpperCamelCase__		)

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)		,			["hällo", "!", "how", "are", "you", "?"]		)
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)		,			["h\u00E9llo"]		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Tuple         =					RoCBertBasicTokenizer(do_lower_case=UpperCamelCase__		,			strip_accents=UpperCamelCase__		)

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)		,			["hallo", "!", "how", "are", "you", "?"]		)
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)		,			["hello"]		)







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Dict         =					RoCBertBasicTokenizer(do_lower_case=UpperCamelCase__		)

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)		,			["hallo", "!", "how", "are", "you", "?"]		)
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)		,			["hello"]		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					RoCBertBasicTokenizer(do_lower_case=UpperCamelCase__		)

       self.assertListEqual(
           tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "		)		,			["HeLLo", "!", "how", "Are", "yoU", "?"]		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Dict         =					RoCBertBasicTokenizer(do_lower_case=UpperCamelCase__		,			strip_accents=UpperCamelCase__		)

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)		,			["HäLLo", "!", "how", "Are", "yoU", "?"]		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					RoCBertBasicTokenizer(do_lower_case=UpperCamelCase__		,			strip_accents=UpperCamelCase__		)

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)		,			["HaLLo", "!", "how", "Are", "yoU", "?"]		)







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[Any]         =					RoCBertBasicTokenizer(do_lower_case=UpperCamelCase__		,			never_split=["[UNK]"]		)

       self.assertListEqual(
           tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"		)		,			["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]		)







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

       snake_case					:   List[Any]         =					{}
       for i, token in enumerate(UpperCamelCase__		):
           snake_case					:   int         =					i
       snake_case					:   Union[str, Any]         =					RoCBertWordpieceTokenizer(vocab=UpperCamelCase__		,			unk_token="[UNK]"		)

       self.assertListEqual(tokenizer.tokenize(""		)		,			[]		)

       self.assertListEqual(tokenizer.tokenize("unwanted running"		)		,			["un", "##want", "##ed", "runn", "##ing"]		)

       self.assertListEqual(tokenizer.tokenize("unwantedX running"		)		,			["[UNK]", "runn", "##ing"]		)







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       self.assertTrue(_is_whitespace(" "		)		)
       self.assertTrue(_is_whitespace("\t"		)		)
       self.assertTrue(_is_whitespace("\r"		)		)
       self.assertTrue(_is_whitespace("\n"		)		)
       self.assertTrue(_is_whitespace("\u00A0"		)		)

       self.assertFalse(_is_whitespace("A"		)		)
       self.assertFalse(_is_whitespace("-"		)		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       self.assertTrue(_is_control("\u0005"		)		)

       self.assertFalse(_is_control("A"		)		)
       self.assertFalse(_is_control(" "		)		)
       self.assertFalse(_is_control("\t"		)		)
       self.assertFalse(_is_control("\r"		)		)







   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       self.assertTrue(_is_punctuation("-"		)		)
       self.assertTrue(_is_punctuation("$"		)		)
       self.assertTrue(_is_punctuation("`"		)		)
       self.assertTrue(_is_punctuation("."		)		)

       self.assertFalse(_is_punctuation("A"		)		)
       self.assertFalse(_is_punctuation(" "		)		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Any         =					self.get_tokenizer()

       # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
       self.assertListEqual([tokenizer.tokenize(UpperCamelCase__		) for t in ["Test", "\xad", "test"]]		,			[["[UNK]"], [], ["[UNK]"]]		)

       if self.test_rust_tokenizer:
           snake_case					:   List[Any]         =					self.get_rust_tokenizer()
           self.assertListEqual(
               [rust_tokenizer.tokenize(UpperCamelCase__		) for t in ["Test", "\xad", "test"]]		,			[["[UNK]"], [], ["[UNK]"]]		)







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'		):
               snake_case					:   Tuple         =					self.rust_tokenizer_class.from_pretrained(UpperCamelCase__		,			**UpperCamelCase__		)

               snake_case					:   Dict         =					F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
               snake_case					:   Tuple         =					tokenizer_r.encode_plus(
                   UpperCamelCase__		,			return_attention_mask=UpperCamelCase__		,			return_token_type_ids=UpperCamelCase__		,			return_offsets_mapping=UpperCamelCase__		,			add_special_tokens=UpperCamelCase__		,			)

               snake_case					:   Union[str, Any]         =					tokenizer_r.do_lower_case if hasattr(UpperCamelCase__		,			"do_lower_case"		) else False
               snake_case					:   str         =					(
                   [
                       ((0, 0), tokenizer_r.cls_token),
                       ((0, 1), "A"),
                       ((1, 2), ","),
                       ((3, 5), "na"),
                       ((5, 6), "##ï"),
                       ((6, 8), "##ve"),
                       ((9, 15), tokenizer_r.mask_token),
                       ((16, 21), "Allen"),
                       ((21, 23), "##NL"),
                       ((23, 24), "##P"),
                       ((25, 33), "sentence"),
                       ((33, 34), "."),
                       ((0, 0), tokenizer_r.sep_token),
                   ]
                   if not do_lower_case
                   else [
                       ((0, 0), tokenizer_r.cls_token),
                       ((0, 1), "a"),
                       ((1, 2), ","),
                       ((3, 8), "naive"),
                       ((9, 15), tokenizer_r.mask_token),
                       ((16, 21), "allen"),
                       ((21, 23), "##nl"),
                       ((23, 24), "##p"),
                       ((25, 33), "sentence"),
                       ((33, 34), "."),
                       ((0, 0), tokenizer_r.sep_token),
                   ]
               )

               self.assertEqual(
                   [e[1] for e in expected_results]		,			tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]		)		)
               self.assertEqual([e[0] for e in expected_results]		,			tokens["offset_mapping"]		)







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Dict         =					["的", "人", "有"]
       snake_case					:   Optional[Any]         =					"".join(UpperCamelCase__		)
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'		):
               snake_case					:   Optional[int]         =					True
               snake_case					:   str         =					self.tokenizer_class.from_pretrained(UpperCamelCase__		,			**UpperCamelCase__		)
               snake_case					:   Tuple         =					self.rust_tokenizer_class.from_pretrained(UpperCamelCase__		,			**UpperCamelCase__		)

               snake_case					:   int         =					tokenizer_p.encode(UpperCamelCase__		,			add_special_tokens=UpperCamelCase__		)
               snake_case					:   Tuple         =					tokenizer_r.encode(UpperCamelCase__		,			add_special_tokens=UpperCamelCase__		)

               snake_case					:   str         =					tokenizer_r.convert_ids_to_tokens(UpperCamelCase__		)
               snake_case					:   int         =					tokenizer_p.convert_ids_to_tokens(UpperCamelCase__		)

               # it is expected that each Chinese character is not preceded by "##"
               self.assertListEqual(UpperCamelCase__		,			UpperCamelCase__		)
               self.assertListEqual(UpperCamelCase__		,			UpperCamelCase__		)

               snake_case					:   Optional[int]         =					False
               snake_case					:   int         =					self.rust_tokenizer_class.from_pretrained(UpperCamelCase__		,			**UpperCamelCase__		)
               snake_case					:   List[Any]         =					self.tokenizer_class.from_pretrained(UpperCamelCase__		,			**UpperCamelCase__		)

               snake_case					:   str         =					tokenizer_r.encode(UpperCamelCase__		,			add_special_tokens=UpperCamelCase__		)
               snake_case					:   Optional[Any]         =					tokenizer_p.encode(UpperCamelCase__		,			add_special_tokens=UpperCamelCase__		)

               snake_case					:   Dict         =					tokenizer_r.convert_ids_to_tokens(UpperCamelCase__		)
               snake_case					:   int         =					tokenizer_p.convert_ids_to_tokens(UpperCamelCase__		)

               # it is expected that only the first Chinese character is not preceded by "##".
               snake_case					:   Any         =					[
                   F'##{token}' if idx != 0 else token for idx, token in enumerate(UpperCamelCase__		)
               ]
               self.assertListEqual(UpperCamelCase__		,			UpperCamelCase__		)
               self.assertListEqual(UpperCamelCase__		,			UpperCamelCase__		)







   @slow
   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   Dict         =					self.tokenizer_class(self.vocab_file		,			self.word_shape_file		,			self.word_pronunciation_file		)

       snake_case					:   int         =					tokenizer.encode("你好"		,			add_special_tokens=UpperCamelCase__		)
       snake_case					:   Dict         =					tokenizer.encode("你是谁"		,			add_special_tokens=UpperCamelCase__		)

       snake_case					:   Union[str, Any]         =					tokenizer.build_inputs_with_special_tokens(UpperCamelCase__		)
       snake_case					:   Any         =					tokenizer.build_inputs_with_special_tokens(UpperCamelCase__		,			UpperCamelCase__		)

       assert encoded_sentence == [1] + text + [2]
       assert encoded_pair == [1] + text + [2] + text_a + [2]







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   int         =					self.get_tokenizers(do_lower_case=UpperCamelCase__		)
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'		):
               snake_case					:   Optional[Any]         =					"你好，你是谁"
               snake_case					:   List[Any]         =					tokenizer.tokenize(UpperCamelCase__		)
               snake_case					:   Dict         =					tokenizer.convert_tokens_to_ids(UpperCamelCase__		)
               snake_case					:   Union[str, Any]         =					tokenizer.convert_tokens_to_shape_ids(UpperCamelCase__		)
               snake_case					:   Optional[int]         =					tokenizer.convert_tokens_to_pronunciation_ids(UpperCamelCase__		)
               snake_case					:   int         =					tokenizer.prepare_for_model(
                   UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			add_special_tokens=UpperCamelCase__		)

               snake_case					:   Optional[int]         =					tokenizer.encode_plus(UpperCamelCase__		,			add_special_tokens=UpperCamelCase__		)

               self.assertEqual(UpperCamelCase__		,			UpperCamelCase__		)









"""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 _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       for model_result in results.values():
           for batch_size, sequence_length in zip(model_result["bs"]		,			model_result["ss"]		):
               snake_case					:   Optional[int]         =					model_result["result"][batch_size][sequence_length]
               self.assertIsNotNone(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   List[Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Dict         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   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		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   str         =					"sgugger/tiny-distilbert-classification"
       snake_case					:   int         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			only_pretrain_model=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   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		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   List[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   Tuple         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			torchscript=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   int         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   int         =					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		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   int         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			fpaa=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[Any]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   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		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					"sshleifer/tiny-gpt2"
       snake_case					:   int         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       # set architectures equal to `None`
       snake_case					:   str         =					None
       snake_case					:   List[Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   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:

       '''simple docstring'''




       snake_case					:   Tuple         =					"sshleifer/tiny-gpt2"
       snake_case					:   str         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[str]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   Dict         =					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		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   Optional[Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			fpaa=UpperCamelCase__		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Optional[int]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   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		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Tuple         =					"sshleifer/tiny-gpt2"
       snake_case					:   List[Any]         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   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		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					"sshleifer/tinier_bart"
       snake_case					:   List[str]         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   Optional[int]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   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		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   str         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   List[str]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Optional[int]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   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		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   str         =					"sshleifer/tinier_bart"
       snake_case					:   Union[str, Any]         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   Any         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   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		)			->	Any:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					"sshleifer/tiny-gpt2"

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

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





