


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 ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              for model_result in results.values():
                     for batch_size, sequence_length in zip(model_result['''bs'''] ,					model_result['''ss''']						):
                            UpperCamelCase    :	Union[str, Any]   	=		model_result['''result'''][batch_size][sequence_length]
                            self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		'''sshleifer/tiny-gpt2'''
              UpperCamelCase    :	int   	=		PyTorchBenchmarkArguments(
                  models=[MODEL_ID] ,					training=__SCREAMING_SNAKE_CASE ,					inference=__SCREAMING_SNAKE_CASE ,					sequence_lengths=[8] ,					batch_sizes=[1] ,					multi_process=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	Optional[int]   	=		PyTorchBenchmark(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[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						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		'''sgugger/tiny-distilbert-classification'''
              UpperCamelCase    :	Optional[Any]   	=		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 ,					)
              UpperCamelCase    :	List[Any]   	=		PyTorchBenchmark(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[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						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		'''sshleifer/tiny-gpt2'''
              UpperCamelCase    :	List[Any]   	=		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 ,					)
              UpperCamelCase    :	List[Any]   	=		PyTorchBenchmark(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		'''sshleifer/tiny-gpt2'''
              UpperCamelCase    :	str   	=		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 ,					)
              UpperCamelCase    :	List[str]   	=		PyTorchBenchmark(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	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  _lowercase   (     self						):




              """simple docstring"""




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




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		'''sshleifer/tiny-gpt2'''
              UpperCamelCase    :	List[str]   	=		PyTorchBenchmarkArguments(
                  models=[MODEL_ID] ,					training=__SCREAMING_SNAKE_CASE ,					inference=__SCREAMING_SNAKE_CASE ,					sequence_lengths=[8] ,					batch_sizes=[1] ,					multi_process=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	str   	=		PyTorchBenchmark(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		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						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		'''sshleifer/tiny-gpt2'''
              UpperCamelCase    :	List[Any]   	=		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 ,					)
              UpperCamelCase    :	Optional[Any]   	=		PyTorchBenchmark(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[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						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		'''sshleifer/tiny-gpt2'''
              UpperCamelCase    :	str   	=		AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		PyTorchBenchmarkArguments(
                  models=[MODEL_ID] ,					training=__SCREAMING_SNAKE_CASE ,					inference=__SCREAMING_SNAKE_CASE ,					sequence_lengths=[8] ,					batch_sizes=[1] ,					multi_process=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	int   	=		PyTorchBenchmark(__SCREAMING_SNAKE_CASE ,					configs=[config]						)
              UpperCamelCase    :	Tuple   	=		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						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		'''sshleifer/tinier_bart'''
              UpperCamelCase    :	List[Any]   	=		AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[Any]   	=		PyTorchBenchmarkArguments(
                  models=[MODEL_ID] ,					training=__SCREAMING_SNAKE_CASE ,					inference=__SCREAMING_SNAKE_CASE ,					sequence_lengths=[8] ,					batch_sizes=[1] ,					multi_process=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	List[Any]   	=		PyTorchBenchmark(__SCREAMING_SNAKE_CASE ,					configs=[config]						)
              UpperCamelCase    :	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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		'''sshleifer/tiny-gpt2'''
              UpperCamelCase    :	Any   	=		AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		PyTorchBenchmarkArguments(
                  models=[MODEL_ID] ,					training=__SCREAMING_SNAKE_CASE ,					inference=__SCREAMING_SNAKE_CASE ,					sequence_lengths=[8] ,					batch_sizes=[1] ,					multi_process=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	Tuple   	=		PyTorchBenchmark(__SCREAMING_SNAKE_CASE ,					configs=[config]						)
              UpperCamelCase    :	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						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		'''sshleifer/tinier_bart'''
              UpperCamelCase    :	Any   	=		AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[Any]   	=		PyTorchBenchmarkArguments(
                  models=[MODEL_ID] ,					training=__SCREAMING_SNAKE_CASE ,					inference=__SCREAMING_SNAKE_CASE ,					sequence_lengths=[8] ,					batch_sizes=[1] ,					multi_process=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	Optional[Any]   	=		PyTorchBenchmark(__SCREAMING_SNAKE_CASE ,					configs=[config]						)
              UpperCamelCase    :	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						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		'''sshleifer/tiny-gpt2'''
              with tempfile.TemporaryDirectory() as tmp_dir:
                     UpperCamelCase    :	Union[str, Any]   	=		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 ,					)
                     UpperCamelCase    :	Tuple   	=		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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		'''sshleifer/tiny-gpt2'''

              def _check_summary_is_not_empty(__SCREAMING_SNAKE_CASE						):
                     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:
                     UpperCamelCase    :	Any   	=		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 ,					)
                     UpperCamelCase    :	List[Any]   	=		PyTorchBenchmark(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	str   	=		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()						)





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase : Optional[int] 			=				500000

__UpperCAmelCase			,    __UpperCAmelCase : Any 			=				os.path.split(__file__)
__UpperCAmelCase : int 			=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		dataset.map(**SCREAMING_SNAKE_CASE_       )


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		dataset.filter(**SCREAMING_SNAKE_CASE_       )


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCamelCase    :	Dict   	=		datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
              UpperCamelCase    :	List[str]   	=		generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE_							,			'''dataset.arrow'''       )							,			SCREAMING_SNAKE_CASE_							,			num_examples=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Union[str, Any]   	=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''							,			use_fast=SCREAMING_SNAKE_CASE_       )

              def tokenize(SCREAMING_SNAKE_CASE_      :		Dict       ):
                     return tokenizer(examples['''text''']       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''numpy'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''pandas'''       ):
                     UpperCamelCase    :	int   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''torch'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Dict   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''tensorflow'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			function=SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Dict   	=		filter(SCREAMING_SNAKE_CASE_       )

              # Activate later when tokenizer support batched inputs
              # with dataset.formatted_as(type='numpy'):
              #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

       with open(SCREAMING_SNAKE_CASE_							,			'''wb'''       ) as f:
              f.write(json.dumps(SCREAMING_SNAKE_CASE_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()

