




import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 			__lowerCamelCase	(   _UpperCamelCase     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ 							=				RobertaTokenizer
    snake_case__ 							=				RobertaTokenizerFast
    snake_case__ 							=				True
    snake_case__ 							=				{'cls_token': '<s>'}


    def        a				(	self					:					Tuple       )				->	Optional[Any]:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowerCAmelCase__          =			[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         lowerCAmelCase__          =			dict(zip(_UpperCAmelCase       ,     range(len(_UpperCAmelCase       )       )       )       )
         lowerCAmelCase__          =			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         lowerCAmelCase__          =			{'unk_token': '<unk>'}

         lowerCAmelCase__          =			os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES["vocab_file"]       )
         lowerCAmelCase__          =			os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES["merges_file"]       )
         with open(self.vocab_file       ,     "w"       ,     encoding="utf-8"       ) as fp:
              fp.write(json.dumps(_UpperCAmelCase       ) + "\n"       )
         with open(self.merges_file       ,     "w"       ,     encoding="utf-8"       ) as fp:
              fp.write("\n".join(_UpperCAmelCase       )       )


    def        a				(	self					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[int]:
         kwargs.update(self.special_tokens_map       )
         return self.tokenizer_class.from_pretrained(self.tmpdirname       ,     **_UpperCAmelCase       )


    def        a				(	self					:					Optional[Any]       ,     **SCREAMING_SNAKE_CASE__					:					List[str]       )				->	List[str]:
         kwargs.update(self.special_tokens_map       )
         return RobertaTokenizerFast.from_pretrained(self.tmpdirname       ,     **_UpperCAmelCase       )


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	Tuple:
         lowerCAmelCase__          =			'lower newer'
         lowerCAmelCase__          =			'lower newer'
         return input_text, output_text


    def        a				(	self					:					Optional[Any]       )				->	Tuple:
         lowerCAmelCase__          =			self.tokenizer_class(self.vocab_file       ,     self.merges_file       ,     **self.special_tokens_map       )
         lowerCAmelCase__          =			'lower newer'
         lowerCAmelCase__          =			['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
         lowerCAmelCase__          =			tokenizer.tokenize(_UpperCAmelCase       )  # , add_prefix_space=True)
         self.assertListEqual(_UpperCAmelCase       ,     _UpperCAmelCase       )

         lowerCAmelCase__          =			tokens + [tokenizer.unk_token]
         lowerCAmelCase__          =			[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase       )       ,     _UpperCAmelCase       )


    def        a				(	self					:					List[Any]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.get_tokenizer()

         self.assertListEqual(tokenizer.encode("Hello world!"       ,     add_special_tokens=_UpperCAmelCase       )       ,     [0, 31_414, 232, 328, 2]       )
         self.assertListEqual(
             tokenizer.encode("Hello world! cécé herlolip 418"       ,     add_special_tokens=_UpperCAmelCase       )       ,     [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]       ,     )


    @slow
    def        a				(	self					:					int       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.tokenizer_class.from_pretrained("roberta-base"       )

         lowerCAmelCase__          =			tokenizer.encode("sequence builders"       ,     add_special_tokens=_UpperCAmelCase       )
         lowerCAmelCase__          =			tokenizer.encode("multi-sequence build"       ,     add_special_tokens=_UpperCAmelCase       )

         lowerCAmelCase__          =			tokenizer.encode(
             "sequence builders"       ,     add_special_tokens=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       )
         lowerCAmelCase__          =			tokenizer.encode(
             "sequence builders"       ,     "multi-sequence build"       ,     add_special_tokens=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       )

         lowerCAmelCase__          =			tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase       )
         lowerCAmelCase__          =			tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase       ,     _UpperCAmelCase       )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode


    def        a				(	self					:					List[str]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			'Encode this sequence.'
         lowerCAmelCase__          =			tokenizer.byte_encoder[' '.encode("utf-8"       )[0]]

         # Testing encoder arguments
         lowerCAmelCase__          =			tokenizer.encode(_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       )
         lowerCAmelCase__          =			tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
         self.assertNotEqual(_UpperCAmelCase       ,     _UpperCAmelCase       )

         lowerCAmelCase__          =			tokenizer.encode(_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       )
         lowerCAmelCase__          =			tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
         self.assertEqual(_UpperCAmelCase       ,     _UpperCAmelCase       )

         tokenizer.add_special_tokens({"bos_token": "<s>"}       )
         lowerCAmelCase__          =			tokenizer.encode(_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       )
         lowerCAmelCase__          =			tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
         self.assertNotEqual(_UpperCAmelCase       ,     _UpperCAmelCase       )

         # Testing spaces after special tokens
         lowerCAmelCase__          =			'<mask>'
         tokenizer.add_special_tokens(
             {"mask_token": AddedToken(_UpperCAmelCase       ,     lstrip=_UpperCAmelCase       ,     rstrip=_UpperCAmelCase       )}       )  # mask token has a left space
         lowerCAmelCase__          =			tokenizer.convert_tokens_to_ids(_UpperCAmelCase       )

         lowerCAmelCase__          =			'Encode <mask> sequence'
         lowerCAmelCase__          =			'Encode <mask>sequence'

         lowerCAmelCase__          =			tokenizer.encode(_UpperCAmelCase       )
         lowerCAmelCase__          =			encoded.index(_UpperCAmelCase       )
         lowerCAmelCase__          =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
         self.assertEqual(_UpperCAmelCase       ,     _UpperCAmelCase       )

         lowerCAmelCase__          =			tokenizer.encode(_UpperCAmelCase       )
         lowerCAmelCase__          =			encoded.index(_UpperCAmelCase       )
         lowerCAmelCase__          =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
         self.assertNotEqual(_UpperCAmelCase       ,     _UpperCAmelCase       )


    def        a				(	self					:					Dict       )				->	Any:
         pass


    def        a				(	self					:					Optional[Any]       )				->	List[str]:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
                   lowerCAmelCase__          =			self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase       ,     **_UpperCAmelCase       )
                   lowerCAmelCase__          =			self.tokenizer_class.from_pretrained(_UpperCAmelCase       ,     **_UpperCAmelCase       )
                   lowerCAmelCase__          =			'A, <mask> AllenNLP sentence.'
                   lowerCAmelCase__          =			tokenizer_r.encode_plus(_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       ,     return_token_type_ids=_UpperCAmelCase       )
                   lowerCAmelCase__          =			tokenizer_p.encode_plus(_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       ,     return_token_type_ids=_UpperCAmelCase       )

                   # token_type_ids should put 0 everywhere
                   self.assertEqual(sum(tokens_r["token_type_ids"]       )       ,     sum(tokens_p["token_type_ids"]       )       )

                   # attention_mask should put 1 everywhere, so sum over length should be 1
                   self.assertEqual(
                       sum(tokens_r["attention_mask"]       ) / len(tokens_r["attention_mask"]       )       ,     sum(tokens_p["attention_mask"]       ) / len(tokens_p["attention_mask"]       )       ,     )

                   lowerCAmelCase__          =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]       )
                   lowerCAmelCase__          =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]       )

                   # Rust correctly handles the space before the mask while python doesnt
                   self.assertSequenceEqual(tokens_p["input_ids"]       ,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )
                   self.assertSequenceEqual(tokens_r["input_ids"]       ,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )

                   self.assertSequenceEqual(
                       _UpperCAmelCase       ,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]       )
                   self.assertSequenceEqual(
                       _UpperCAmelCase       ,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]       )


    def        a				(	self					:					Tuple       )				->	Dict:
         for trim_offsets, add_prefix_space in itertools.product([True, False]       ,     repeat=2       ):
              lowerCAmelCase__          =			self.rust_tokenizer_class.from_pretrained(
                  self.tmpdirname       ,     use_fast=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       ,     trim_offsets=_UpperCAmelCase       )

              lowerCAmelCase__          =			json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
              lowerCAmelCase__          =			json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()       )

              self.assertEqual(pre_tokenizer_state["add_prefix_space"]       ,     _UpperCAmelCase       )

              self.assertEqual(post_processor_state["add_prefix_space"]       ,     _UpperCAmelCase       )
              self.assertEqual(post_processor_state["trim_offsets"]       ,     _UpperCAmelCase       )


    def        a				(	self					:					Union[str, Any]       )				->	List[Any]:
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
         # `trim_offsets`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
                   lowerCAmelCase__          =			'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                   lowerCAmelCase__          =			f'{text_of_1_token} {text_of_1_token}'

                   lowerCAmelCase__          =			self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,     use_fast=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       ,     trim_offsets=_UpperCAmelCase       )
                   lowerCAmelCase__          =			tokenizer_r(_UpperCAmelCase       ,     return_offsets_mapping=_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       )
                   self.assertEqual(encoding.offset_mapping[0]       ,     (0, len(_UpperCAmelCase       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,     (len(_UpperCAmelCase       ) + 1, len(_UpperCAmelCase       ) + 1 + len(_UpperCAmelCase       ))       ,     )

                   lowerCAmelCase__          =			self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,     use_fast=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       ,     trim_offsets=_UpperCAmelCase       )
                   lowerCAmelCase__          =			tokenizer_r(_UpperCAmelCase       ,     return_offsets_mapping=_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       )
                   self.assertEqual(encoding.offset_mapping[0]       ,     (0, len(_UpperCAmelCase       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,     (len(_UpperCAmelCase       ) + 1, len(_UpperCAmelCase       ) + 1 + len(_UpperCAmelCase       ))       ,     )

                   lowerCAmelCase__          =			self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,     use_fast=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       ,     trim_offsets=_UpperCAmelCase       )
                   lowerCAmelCase__          =			tokenizer_r(_UpperCAmelCase       ,     return_offsets_mapping=_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       )
                   self.assertEqual(encoding.offset_mapping[0]       ,     (0, len(_UpperCAmelCase       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,     (len(_UpperCAmelCase       ), len(_UpperCAmelCase       ) + 1 + len(_UpperCAmelCase       ))       ,     )

                   lowerCAmelCase__          =			self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,     use_fast=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       ,     trim_offsets=_UpperCAmelCase       )
                   lowerCAmelCase__          =			tokenizer_r(_UpperCAmelCase       ,     return_offsets_mapping=_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       )
                   self.assertEqual(encoding.offset_mapping[0]       ,     (0, len(_UpperCAmelCase       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,     (len(_UpperCAmelCase       ), len(_UpperCAmelCase       ) + 1 + len(_UpperCAmelCase       ))       ,     )

                   lowerCAmelCase__          =			f' {text}'

                   # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                   #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                   # )
                   # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                   # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                   # self.assertEqual(
                   #     encoding.offset_mapping[1],
                   #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                   # )

                   lowerCAmelCase__          =			self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,     use_fast=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       ,     trim_offsets=_UpperCAmelCase       )
                   lowerCAmelCase__          =			tokenizer_r(_UpperCAmelCase       ,     return_offsets_mapping=_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       )
                   self.assertEqual(encoding.offset_mapping[0]       ,     (1, 1 + len(_UpperCAmelCase       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,     (1 + len(_UpperCAmelCase       ) + 1, 1 + len(_UpperCAmelCase       ) + 1 + len(_UpperCAmelCase       ))       ,     )

                   lowerCAmelCase__          =			self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,     use_fast=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       ,     trim_offsets=_UpperCAmelCase       )
                   lowerCAmelCase__          =			tokenizer_r(_UpperCAmelCase       ,     return_offsets_mapping=_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       )
                   self.assertEqual(encoding.offset_mapping[0]       ,     (0, 1 + len(_UpperCAmelCase       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,     (1 + len(_UpperCAmelCase       ), 1 + len(_UpperCAmelCase       ) + 1 + len(_UpperCAmelCase       ))       ,     )

                   lowerCAmelCase__          =			self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase       ,     use_fast=_UpperCAmelCase       ,     add_prefix_space=_UpperCAmelCase       ,     trim_offsets=_UpperCAmelCase       )
                   lowerCAmelCase__          =			tokenizer_r(_UpperCAmelCase       ,     return_offsets_mapping=_UpperCAmelCase       ,     add_special_tokens=_UpperCAmelCase       )
                   self.assertEqual(encoding.offset_mapping[0]       ,     (0, 1 + len(_UpperCAmelCase       ))       )
                   self.assertEqual(
                       encoding.offset_mapping[1]       ,     (1 + len(_UpperCAmelCase       ), 1 + len(_UpperCAmelCase       ) + 1 + len(_UpperCAmelCase       ))       ,     )






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        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	int:
         for model_result in results.values():
              for batch_size, sequence_length in zip(model_result["bs"]       ,     model_result["ss"]       ):
                   lowerCAmelCase__          =			model_result["result"][batch_size][sequence_length]
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )


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


    def        a				(	self					:					int       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sgugger/tiny-distilbert-classification"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     only_pretrain_model=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	int:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     torchscript=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			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        a				(	self					:					Dict       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


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


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


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


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


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


    def        a				(	self					:					Optional[int]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


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


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE__					:					List[Any]       ):
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "sequential"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "cumulative"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "current"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "total"       )       )

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