

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







							def 							__lowercase							(     self			,				_a			)       ->       str:
												for model_result in results.values():
																	for batch_size, sequence_length in zip(model_result['''bs''']			,				model_result['''ss''']			):
																						_a      :       Optional[Any]   =						model_result['''result'''][batch_size][sequence_length]
																						self.assertIsNotNone(_a			)


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       Tuple   =						'''sshleifer/tiny-gpt2'''
												_a      :       Dict   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			)
												_a      :       str   =						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			)       ->       str:
												_a      :       str   =						'''sgugger/tiny-distilbert-classification'''
												_a      :       str   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				only_pretrain_model=_a			,				)
												_a      :       Any   =						PyTorchBenchmark(_a			)
												_a      :       Dict   =						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			)       ->       int:
												_a      :       Optional[Any]   =						'''sshleifer/tiny-gpt2'''
												_a      :       Optional[int]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				torchscript=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			)
												_a      :       Any   =						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			)       ->       int:
												_a      :       Optional[int]   =						'''sshleifer/tiny-gpt2'''
												_a      :       Any   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				fpaa=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			)
												_a      :       Any   =						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]:
												_a      :       Union[str, Any]   =						'''sshleifer/tiny-gpt2'''
												_a      :       Dict   =						AutoConfig.from_pretrained(_a			)
												# set architectures equal to `None`
												_a      :       List[Any]   =						None
												_a      :       Tuple   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       List[Any]   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       int   =						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			)       ->       Dict:
												_a      :       Tuple   =						'''sshleifer/tiny-gpt2'''
												_a      :       int   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       Dict   =						PyTorchBenchmark(_a			)
												_a      :       str   =						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			)       ->       Optional[Any]:
												_a      :       Any   =						'''sshleifer/tiny-gpt2'''
												_a      :       Tuple   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				fpaa=_a			,				multi_process=_a			,				)
												_a      :       Tuple   =						PyTorchBenchmark(_a			)
												_a      :       List[str]   =						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			)       ->       Any:
												_a      :       int   =						'''sshleifer/tiny-gpt2'''
												_a      :       List[str]   =						AutoConfig.from_pretrained(_a			)
												_a      :       List[str]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       Tuple   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       List[str]   =						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			)       ->       Union[str, Any]:
												_a      :       Union[str, Any]   =						'''sshleifer/tinier_bart'''
												_a      :       Optional[int]   =						AutoConfig.from_pretrained(_a			)
												_a      :       Optional[int]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       List[str]   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       Any   =						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			)       ->       int:
												_a      :       Optional[int]   =						'''sshleifer/tiny-gpt2'''
												_a      :       int   =						AutoConfig.from_pretrained(_a			)
												_a      :       Union[str, Any]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       Tuple   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       str   =						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			)       ->       List[Any]:
												_a      :       Dict   =						'''sshleifer/tinier_bart'''
												_a      :       Dict   =						AutoConfig.from_pretrained(_a			)
												_a      :       List[Any]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       Any   =						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			)       ->       List[str]:
												_a      :       List[str]   =						'''sshleifer/tiny-gpt2'''
												with tempfile.TemporaryDirectory() as tmp_dir:
																	_a      :       Tuple   =						PyTorchBenchmarkArguments(
																	    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				save_to_csv=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				inference_time_csv_file=os.path.join(_a			,				'''inf_time.csv'''			)			,				train_memory_csv_file=os.path.join(_a			,				'''train_mem.csv'''			)			,				inference_memory_csv_file=os.path.join(_a			,				'''inf_mem.csv'''			)			,				train_time_csv_file=os.path.join(_a			,				'''train_time.csv'''			)			,				env_info_csv_file=os.path.join(_a			,				'''env.csv'''			)			,				multi_process=_a			,				)
																	_a      :       List[str]   =						PyTorchBenchmark(_a			)
																	benchmark.run()
																	self.assertTrue(Path(os.path.join(_a			,				'''inf_time.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''train_time.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''inf_mem.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''train_mem.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''env.csv'''			)			).exists()			)




							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Optional[int]   =						'''sshleifer/tiny-gpt2'''

												def _check_summary_is_not_empty(_a			):
																	self.assertTrue(hasattr(_a			,				'''sequential'''			)			)
																	self.assertTrue(hasattr(_a			,				'''cumulative'''			)			)
																	self.assertTrue(hasattr(_a			,				'''current'''			)			)
																	self.assertTrue(hasattr(_a			,				'''total'''			)			)

												with tempfile.TemporaryDirectory() as tmp_dir:
																	_a      :       str   =						PyTorchBenchmarkArguments(
																	    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				log_filename=os.path.join(_a			,				'''log.txt'''			)			,				log_print=_a			,				trace_memory_line_by_line=_a			,				multi_process=_a			,				)
																	_a      :       List[str]   =						PyTorchBenchmark(_a			)
																	_a      :       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(_a			,				'''log.txt'''			)			).exists()			)





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase     :							Tuple       						=     {
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase     :							int       						=     [
          'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LukeForEntityClassification',
          'LukeForEntityPairClassification',
          'LukeForEntitySpanClassification',
          'LukeForMultipleChoice',
          'LukeForQuestionAnswering',
          'LukeForSequenceClassification',
          'LukeForTokenClassification',
          'LukeForMaskedLM',
          'LukeModel',
          'LukePreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
      from .tokenization_luke import LukeTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_luke import (
                LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
                LukeForEntityClassification,
                LukeForEntityPairClassification,
                LukeForEntitySpanClassification,
                LukeForMaskedLM,
                LukeForMultipleChoice,
                LukeForQuestionAnswering,
                LukeForSequenceClassification,
                LukeForTokenClassification,
                LukeModel,
                LukePreTrainedModel,
            )

else:
      import sys

      lowercase     :							Dict       						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




