




'''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   ):







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  Tuple )      ->							Any:


														"""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        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							List[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        __lowerCAmelCase      (  self	:  str )      ->							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        __lowerCAmelCase      (  self	:  Optional[int] )      ->							List[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        __lowerCAmelCase      (  self	:  Tuple )      ->							Optional[int]:


														"""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        __lowerCAmelCase      (  self	:  Optional[int] )      ->							int:


														"""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        __lowerCAmelCase      (  self	:  Optional[int] )      ->							List[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_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	:  Tuple )      ->							List[str]:


														"""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        __lowerCAmelCase      (  self	:  int )      ->							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_inference_result )
														self.check_results_dict_not_empty(results.memory_inference_result )







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


														"""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        __lowerCAmelCase      (  self	:  List[str] )      ->							List[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_train_result )
														self.check_results_dict_not_empty(results.memory_train_result )







							def        __lowerCAmelCase      (  self	:  Dict )      ->							List[str]:


														"""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        __lowerCAmelCase      (  self	:  Dict )      ->							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        __lowerCAmelCase      (  self	:  str )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    '''sshleifer/tiny-gpt2'''

														def _check_summary_is_not_empty(lowerCAmelCase_	:  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() )







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case				:				List[str]       							=		{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				List[Any]       							=		['XLNetTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				int       							=		['XLNetTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Optional[int]       							=		[
				    'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'XLNetForMultipleChoice',
				    'XLNetForQuestionAnswering',
				    'XLNetForQuestionAnsweringSimple',
				    'XLNetForSequenceClassification',
				    'XLNetForTokenClassification',
				    'XLNetLMHeadModel',
				    'XLNetModel',
				    'XLNetPreTrainedModel',
				    'load_tf_weights_in_xlnet',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				str       							=		[
				    'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFXLNetForMultipleChoice',
				    'TFXLNetForQuestionAnsweringSimple',
				    'TFXLNetForSequenceClassification',
				    'TFXLNetForTokenClassification',
				    'TFXLNetLMHeadModel',
				    'TFXLNetMainLayer',
				    'TFXLNetModel',
				    'TFXLNetPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet import XLNetTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet_fast import XLNetTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlnet import (
								    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLNetForMultipleChoice,
								    XLNetForQuestionAnswering,
								    XLNetForQuestionAnsweringSimple,
								    XLNetForSequenceClassification,
								    XLNetForTokenClassification,
								    XLNetLMHeadModel,
								    XLNetModel,
								    XLNetPreTrainedModel,
								    load_tf_weights_in_xlnet,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlnet import (
								    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLNetForMultipleChoice,
								    TFXLNetForQuestionAnsweringSimple,
								    TFXLNetForSequenceClassification,
								    TFXLNetForTokenClassification,
								    TFXLNetLMHeadModel,
								    TFXLNetMainLayer,
								    TFXLNetModel,
								    TFXLNetPreTrainedModel,
								)

else:
				import sys

				_snake_case				:				Union[str, Any]       							=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

