



"""simple docstring"""


import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 			_a (					lowerCAmelCase			,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	LongformerTokenizer
			UpperCamelCase__			      =	True
			UpperCamelCase__			      =	LongformerTokenizerFast
			UpperCamelCase__			      =	True



			def  lowercase__						(		self     :    List[str]   )->Optional[int]:
							super().setUp()

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

							_UpperCAmelCase					=    os.path.join(self.tmpdirname				,   VOCAB_FILES_NAMES['''vocab_file''']   )
							_UpperCAmelCase					=    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  lowercase__						(		self     :    Tuple				,   **__UpperCamelCase     :    int   )->Dict:
							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    List[Any]				,   **__UpperCamelCase     :    Tuple   )->str:
							kwargs.update(self.special_tokens_map   )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    Dict   )->Dict:
							_UpperCAmelCase					=    '''lower newer'''
							_UpperCAmelCase					=    '''lower newer'''
							return input_text, output_text



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

							_UpperCAmelCase					=    tokens + [tokenizer.unk_token]
							_UpperCAmelCase					=    [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase   )				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Tuple   )->Optional[Any]:
							_UpperCAmelCase					=    self.get_tokenizer()

							self.assertListEqual(tokenizer.encode('''Hello world!'''				,   add_special_tokens=__UpperCamelCase   )				,   [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]   )
							self.assertListEqual(
							    tokenizer.encode('''Hello world! cécé herlolip 418'''				,   add_special_tokens=__UpperCamelCase   )				,   [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]				,   )



			@slow
			def  lowercase__						(		self     :    Optional[Any]   )->List[Any]:
							_UpperCAmelCase					=    self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''   )

							_UpperCAmelCase					=    tokenizer.encode('''sequence builders'''				,   add_special_tokens=__UpperCamelCase   )
							_UpperCAmelCase					=    tokenizer.encode('''multi-sequence build'''				,   add_special_tokens=__UpperCamelCase   )

							_UpperCAmelCase					=    tokenizer.encode(
							    '''sequence builders'''				,   add_special_tokens=__UpperCamelCase				,   add_prefix_space=__UpperCamelCase   )
							_UpperCAmelCase					=    tokenizer.encode(
							    '''sequence builders'''				,   '''multi-sequence build'''				,   add_special_tokens=__UpperCamelCase				,   add_prefix_space=__UpperCamelCase   )

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

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode



			def  lowercase__						(		self     :    Tuple   )->List[str]:
							_UpperCAmelCase					=    self.get_tokenizer()

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

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

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

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

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

							_UpperCAmelCase					=    '''Encode <mask> sequence'''
							_UpperCAmelCase					=    '''Encode <mask>sequence'''

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

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



			def  lowercase__						(		self     :    str   )->str:
							pass



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

															_UpperCAmelCase					=    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
															_UpperCAmelCase					=    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, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]   )
															self.assertSequenceEqual(tokens_r['''input_ids''']				,   [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 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  lowercase__						(		self     :    List[Any]   )->List[str]:
							for trim_offsets, add_prefix_space in itertools.product([True, False]				,   repeat=2   ):
											_UpperCAmelCase					=    self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname				,   use_fast=__UpperCamelCase				,   add_prefix_space=__UpperCamelCase				,   trim_offsets=__UpperCamelCase   )

											_UpperCAmelCase					=    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()   )
											_UpperCAmelCase					=    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  lowercase__						(		self     :    Any   )->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})'   ):
															_UpperCAmelCase					=    '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
															_UpperCAmelCase					=    F'{text_of_1_token} {text_of_1_token}'

															_UpperCAmelCase					=    self.rust_tokenizer_class.from_pretrained(
															    __UpperCamelCase				,   use_fast=__UpperCamelCase				,   add_prefix_space=__UpperCamelCase				,   trim_offsets=__UpperCamelCase   )
															_UpperCAmelCase					=    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   ))				,   )

															_UpperCAmelCase					=    self.rust_tokenizer_class.from_pretrained(
															    __UpperCamelCase				,   use_fast=__UpperCamelCase				,   add_prefix_space=__UpperCamelCase				,   trim_offsets=__UpperCamelCase   )
															_UpperCAmelCase					=    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   ))				,   )

															_UpperCAmelCase					=    self.rust_tokenizer_class.from_pretrained(
															    __UpperCamelCase				,   use_fast=__UpperCamelCase				,   add_prefix_space=__UpperCamelCase				,   trim_offsets=__UpperCamelCase   )
															_UpperCAmelCase					=    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   ))				,   )

															_UpperCAmelCase					=    self.rust_tokenizer_class.from_pretrained(
															    __UpperCamelCase				,   use_fast=__UpperCamelCase				,   add_prefix_space=__UpperCamelCase				,   trim_offsets=__UpperCamelCase   )
															_UpperCAmelCase					=    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   ))				,   )

															_UpperCAmelCase					=    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)),
															# )

															_UpperCAmelCase					=    self.rust_tokenizer_class.from_pretrained(
															    __UpperCamelCase				,   use_fast=__UpperCamelCase				,   add_prefix_space=__UpperCamelCase				,   trim_offsets=__UpperCamelCase   )
															_UpperCAmelCase					=    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   ))				,   )

															_UpperCAmelCase					=    self.rust_tokenizer_class.from_pretrained(
															    __UpperCamelCase				,   use_fast=__UpperCamelCase				,   add_prefix_space=__UpperCamelCase				,   trim_offsets=__UpperCamelCase   )
															_UpperCAmelCase					=    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   ))				,   )

															_UpperCAmelCase					=    self.rust_tokenizer_class.from_pretrained(
															    __UpperCamelCase				,   use_fast=__UpperCamelCase				,   add_prefix_space=__UpperCamelCase				,   trim_offsets=__UpperCamelCase   )
															_UpperCAmelCase					=    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   ))				,   )








"""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 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    str   )->Tuple:
							for model_result in results.values():
											for batch_size, sequence_length in zip(model_result['''bs''']				,   model_result['''ss''']   ):
															_UpperCAmelCase					=    model_result['''result'''][batch_size][sequence_length]
															self.assertIsNotNone(__UpperCamelCase   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Tuple   )->Optional[Any]:
							_UpperCAmelCase					=    '''sgugger/tiny-distilbert-classification'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   only_pretrain_model=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Tuple   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   torchscript=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    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  lowercase__						(		self     :    Any   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   fpaa=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



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



			def  lowercase__						(		self     :    List[Any]   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    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  lowercase__						(		self     :    List[str]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   fpaa=__UpperCamelCase				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			def  lowercase__						(		self     :    str   )->Dict:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



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



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



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



			def  lowercase__						(		self     :    Optional[Any]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							with tempfile.TemporaryDirectory() as tmp_dir:
											_UpperCAmelCase					=    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				,   )
											_UpperCAmelCase					=    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  lowercase__						(		self     :    Tuple   )->List[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''

							def _check_summary_is_not_empty(__UpperCamelCase     :    str   ):
											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:
											_UpperCAmelCase					=    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				,   )
											_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
											_UpperCAmelCase					=    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()   )



