






"""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        UpperCamelCase			(	unittest.TestCase      ):


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


 def _lowercase	(     self		:						List[str]							)					->       Tuple:
   _a  :       Dict             =	"""sshleifer/tiny-gpt2"""
   _a  :       Any             =	PyTorchBenchmarkArguments(
       models=[MODEL_ID]							,						training=UpperCAmelCase__							,						inference=UpperCAmelCase__							,						sequence_lengths=[8]							,						batch_sizes=[1]							,						multi_process=UpperCAmelCase__							,						)
   _a  :       Tuple             =	PyTorchBenchmark(UpperCAmelCase__							)
   _a  :       Optional[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		:						str							)					->       List[Any]:
   _a  :       Optional[int]             =	"""sgugger/tiny-distilbert-classification"""
   _a  :       List[str]             =	PyTorchBenchmarkArguments(
       models=[MODEL_ID]							,						training=UpperCAmelCase__							,						inference=UpperCAmelCase__							,						sequence_lengths=[8]							,						batch_sizes=[1]							,						multi_process=UpperCAmelCase__							,						only_pretrain_model=UpperCAmelCase__							,						)
   _a  :       Optional[int]             =	PyTorchBenchmark(UpperCAmelCase__							)
   _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		:						List[Any]							)					->       Any:
   _a  :       List[str]             =	"""sshleifer/tiny-gpt2"""
   _a  :       Dict             =	PyTorchBenchmarkArguments(
       models=[MODEL_ID]							,						training=UpperCAmelCase__							,						inference=UpperCAmelCase__							,						torchscript=UpperCAmelCase__							,						sequence_lengths=[8]							,						batch_sizes=[1]							,						multi_process=UpperCAmelCase__							,						)
   _a  :       Any             =	PyTorchBenchmark(UpperCAmelCase__							)
   _a  :       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		:						Optional[int]							)					->       Any:
   _a  :       int             =	"""sshleifer/tiny-gpt2"""
   _a  :       Optional[Any]             =	PyTorchBenchmarkArguments(
       models=[MODEL_ID]							,						training=UpperCAmelCase__							,						inference=UpperCAmelCase__							,						fpaa=UpperCAmelCase__							,						sequence_lengths=[8]							,						batch_sizes=[1]							,						multi_process=UpperCAmelCase__							,						)
   _a  :       Optional[int]             =	PyTorchBenchmark(UpperCAmelCase__							)
   _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		:						Tuple							)					->       List[str]:
   _a  :       Optional[Any]             =	"""sshleifer/tiny-gpt2"""
   _a  :       List[Any]             =	AutoConfig.from_pretrained(UpperCAmelCase__							)
   # set architectures equal to `None`
   _a  :       int             =	None
   _a  :       str             =	PyTorchBenchmarkArguments(
       models=[MODEL_ID]							,						training=UpperCAmelCase__							,						inference=UpperCAmelCase__							,						sequence_lengths=[8]							,						batch_sizes=[1]							,						multi_process=UpperCAmelCase__							,						)
   _a  :       Optional[Any]             =	PyTorchBenchmark(UpperCAmelCase__							,						configs=[config]							)
   _a  :       Optional[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		:						Union[str, Any]							)					->       Any:
   _a  :       Optional[int]             =	"""sshleifer/tiny-gpt2"""
   _a  :       Optional[Any]             =	PyTorchBenchmarkArguments(
       models=[MODEL_ID]							,						training=UpperCAmelCase__							,						inference=UpperCAmelCase__							,						sequence_lengths=[8]							,						batch_sizes=[1]							,						multi_process=UpperCAmelCase__							,						)
   _a  :       Any             =	PyTorchBenchmark(UpperCAmelCase__							)
   _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		:						Any							)					->       Optional[Any]:
   _a  :       Dict             =	"""sshleifer/tiny-gpt2"""
   _a  :       Optional[int]             =	PyTorchBenchmarkArguments(
       models=[MODEL_ID]							,						training=UpperCAmelCase__							,						inference=UpperCAmelCase__							,						sequence_lengths=[8]							,						batch_sizes=[1]							,						fpaa=UpperCAmelCase__							,						multi_process=UpperCAmelCase__							,						)
   _a  :       Union[str, Any]             =	PyTorchBenchmark(UpperCAmelCase__							)
   _a  :       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		:						Any							)					->       str:
   _a  :       List[Any]             =	"""sshleifer/tiny-gpt2"""
   _a  :       Union[str, Any]             =	AutoConfig.from_pretrained(UpperCAmelCase__							)
   _a  :       Dict             =	PyTorchBenchmarkArguments(
       models=[MODEL_ID]							,						training=UpperCAmelCase__							,						inference=UpperCAmelCase__							,						sequence_lengths=[8]							,						batch_sizes=[1]							,						multi_process=UpperCAmelCase__							,						)
   _a  :       str             =	PyTorchBenchmark(UpperCAmelCase__							,						configs=[config]							)
   _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		:						Optional[Any]							)					->       str:
   _a  :       Any             =	"""sshleifer/tinier_bart"""
   _a  :       str             =	AutoConfig.from_pretrained(UpperCAmelCase__							)
   _a  :       Optional[int]             =	PyTorchBenchmarkArguments(
       models=[MODEL_ID]							,						training=UpperCAmelCase__							,						inference=UpperCAmelCase__							,						sequence_lengths=[8]							,						batch_sizes=[1]							,						multi_process=UpperCAmelCase__							,						)
   _a  :       Optional[Any]             =	PyTorchBenchmark(UpperCAmelCase__							,						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		:						str							)					->       Optional[Any]:
   _a  :       Any             =	"""sshleifer/tiny-gpt2"""
   _a  :       Union[str, Any]             =	AutoConfig.from_pretrained(UpperCAmelCase__							)
   _a  :       str             =	PyTorchBenchmarkArguments(
       models=[MODEL_ID]							,						training=UpperCAmelCase__							,						inference=UpperCAmelCase__							,						sequence_lengths=[8]							,						batch_sizes=[1]							,						multi_process=UpperCAmelCase__							,						)
   _a  :       Dict             =	PyTorchBenchmark(UpperCAmelCase__							,						configs=[config]							)
   _a  :       Optional[int]             =	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		:						Dict							)					->       Any:
   _a  :       Optional[Any]             =	"""sshleifer/tinier_bart"""
   _a  :       str             =	AutoConfig.from_pretrained(UpperCAmelCase__							)
   _a  :       List[str]             =	PyTorchBenchmarkArguments(
       models=[MODEL_ID]							,						training=UpperCAmelCase__							,						inference=UpperCAmelCase__							,						sequence_lengths=[8]							,						batch_sizes=[1]							,						multi_process=UpperCAmelCase__							,						)
   _a  :       int             =	PyTorchBenchmark(UpperCAmelCase__							,						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]							)					->       List[str]:
   _a  :       Optional[Any]             =	"""sshleifer/tiny-gpt2"""
   with tempfile.TemporaryDirectory() as tmp_dir:
     _a  :       Tuple             =	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__							,						)
     _a  :       Optional[int]             =	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		:						List[str]							)					->       Any:
   _a  :       str             =	"""sshleifer/tiny-gpt2"""

   def _check_summary_is_not_empty(UpperCAmelCase__		:						List[Any]							):
     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:
     _a  :       Optional[Any]             =	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__							,						)
     _a  :       Any             =	PyTorchBenchmark(UpperCAmelCase__							)
     _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(UpperCAmelCase__							,						"""log.txt"""							)							).exists()							)









"""simple docstring"""




def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   ):



  '''simple docstring'''
  _a  :       Optional[Any]             =	[redshift, radiation_density, matter_density, dark_energy]
  if any(p < 0 for p in parameters				):
    raise ValueError("""All input parameters must be positive"""				)

  if any(p > 1 for p in parameters[1:4]				):
    raise ValueError("""Relative densities cannot be greater than one"""				)
  else:
    _a  :       Tuple             =	1 - (matter_density + radiation_density + dark_energy)

    _a  :       int             =	(
        radiation_density * (redshift + 1) ** 4
        + matter_density * (redshift + 1) ** 3
        + curvature * (redshift + 1) ** 2
        + dark_energy
    )

    _a  :       List[str]             =	hubble_constant * e_a ** (1 / 2)
    return hubble


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo LCDM approximation
 _snake_case          =   0.3

 print(
     hubble_parameter(
         hubble_constant=68.3,
         radiation_density=1e-4,
         matter_density=matter_density,
         dark_energy=1 - matter_density,
         redshift=0,
     )
 )

