
'''simple docstring'''




import copy
import re





class  A				:
     __magic_name__						=		'''hp'''
     __magic_name__						=		{}
     __magic_name__						=		None





     @classmethod
     def 				__lowerCAmelCase							(							cls						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         A       :   Optional[int]					=						prefix
         A       :   List[str]					=						defaults
         cls.build_naming_info()





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         if len(SCREAMING_SNAKE_CASE	) == 0:
             return ""
         A       :   Any					=						None
         if any(char.isdigit() for char in word	):
             raise Exception(F'Parameters should not contain numbers: \'{word}\' contains a number'	)
         if word in info["short_word"]:
             return info["short_word"][word]
         for prefix_len in range(1						,			len(SCREAMING_SNAKE_CASE	) + 1	):
             A       :   Tuple					=						word[:prefix_len]
             if prefix in info["reverse_short_word"]:
                 continue
             else:
                 A       :   str					=						prefix
                 break

         if short_word is None:
             # Paranoid fallback
             def int_to_alphabetic(SCREAMING_SNAKE_CASE	):
                 A       :   Dict					=						''''''
                 while integer != 0:
                     A       :   str					=						chr(ord('''A'''	) + integer % 10	) + s
                     integer //= 10
                 return s

             A       :   Tuple					=						0
             while True:
                 A       :   Optional[int]					=						word + '''#''' + int_to_alphabetic(SCREAMING_SNAKE_CASE	)
                 if sword in info["reverse_short_word"]:
                     continue
                 else:
                     A       :   List[Any]					=						sword
                     break

         A       :   List[Any]					=						short_word
         A       :   str					=						word
         return short_word





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   int					=						param_name.split('''_'''	)

         A       :   int					=						[TrialShortNamer.shortname_for_word(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) for word in words]

         # We try to create a separatorless short name, but if there is a collision we have to fallback
         # to a separated short name
         A       :   Any					=						['''''', '''_''']

         for separator in separators:
             A       :   Union[str, Any]					=						separator.join(SCREAMING_SNAKE_CASE	)
             if shortname not in info["reverse_short_param"]:
                 A       :   Tuple					=						shortname
                 A       :   List[str]					=						param_name
                 return shortname

         return param_name





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Any:






         """simple docstring"""






         A       :   List[str]					=						TrialShortNamer.shortname_for_key(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         A       :   str					=						short_name
         A       :   Optional[int]					=						param_name





     @classmethod
     def 				__lowerCAmelCase							(							cls	) ->				Union[str, Any]:






         """simple docstring"""






         if cls.NAMING_INFO is not None:
             return

         A       :   Any					=						{
             '''short_word''': {},
             '''reverse_short_word''': {},
             '''short_param''': {},
             '''reverse_short_param''': {},
         }

         A       :   List[str]					=						list(cls.DEFAULTS.keys()	)

         for k in field_keys:
             cls.add_new_param_name(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         A       :   Any					=						info





     @classmethod
     def 				__lowerCAmelCase							(							cls						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         cls.build_naming_info()
         assert cls.PREFIX is not None
         A       :   Any					=						[copy.copy(cls.PREFIX	)]

         for k, v in params.items():
             if k not in cls.DEFAULTS:
                 raise Exception(F'You should provide a default value for the param name {k} with value {v}'	)
             if v == cls.DEFAULTS[k]:
                 # The default value is not added to the name
                 continue

             A       :   Any					=						cls.NAMING_INFO['''short_param'''][k]

             if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
                 A       :   Optional[int]					=						1 if v else 0

             A       :   List[str]					=						'''''' if isinstance(SCREAMING_SNAKE_CASE						,			(int, float)	) else '''-'''
             A       :   Optional[int]					=						F'{key}{sep}{v}'
             name.append(SCREAMING_SNAKE_CASE	)

         return "_".join(SCREAMING_SNAKE_CASE	)





     @classmethod
     def 				__lowerCAmelCase							(							cls						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   str					=						repr[len(cls.PREFIX	) + 1 :]
         if repr == "":
             A       :   int					=						[]
         else:
             A       :   Dict					=						repr.split('''_'''	)

         A       :   Tuple					=						{}

         for value in values:
             if "-" in value:
                 A,  A       :   Dict					=						value.split('''-'''	)
             else:
                 A       :   Any					=						re.sub('''[0-9.]'''						,			''''''						,			SCREAMING_SNAKE_CASE	)
                 A       :   int					=						float(re.sub('''[^0-9.]'''						,			''''''						,			SCREAMING_SNAKE_CASE	)	)

             A       :   int					=						cls.NAMING_INFO['''reverse_short_param'''][p_k]

             A       :   Optional[Any]					=						p_v

         for k in cls.DEFAULTS:
             if k not in parameters:
                 A       :   str					=						cls.DEFAULTS[k]

         return parameters






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    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    List[Any]			,  lowercase	:    Dict	):


				'''simple docstring'''


				for model_result in results.values():
							for batch_size, sequence_length in zip(model_result['bs']			,  model_result['ss']	):
										_snake_case		=			model_result['result'][batch_size][sequence_length]
										self.assertIsNotNone(lowercase	)



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			'sgugger/tiny-distilbert-classification'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  only_pretrain_model=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  torchscript=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			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  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  fpaa=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    str	):


				'''simple docstring'''


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



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			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  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  fpaa=lowercase			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tinier_bart'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tinier_bart'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				with tempfile.TemporaryDirectory() as tmp_dir:
							_snake_case		=			PyTorchBenchmarkArguments(
							    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  save_to_csv=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  inference_time_csv_file=os.path.join(lowercase			,  'inf_time.csv'	)			,  train_memory_csv_file=os.path.join(lowercase			,  'train_mem.csv'	)			,  inference_memory_csv_file=os.path.join(lowercase			,  'inf_mem.csv'	)			,  train_time_csv_file=os.path.join(lowercase			,  'train_time.csv'	)			,  env_info_csv_file=os.path.join(lowercase			,  'env.csv'	)			,  multi_process=lowercase			,  )
							_snake_case		=			PyTorchBenchmark(lowercase	)
							benchmark.run()
							self.assertTrue(Path(os.path.join(lowercase			,  'inf_time.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'train_time.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'inf_mem.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'train_mem.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'env.csv'	)	).exists()	)





	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'

				def _check_summary_is_not_empty(lowercase	:    Optional[Any]	):
							self.assertTrue(hasattr(lowercase			,  'sequential'	)	)
							self.assertTrue(hasattr(lowercase			,  'cumulative'	)	)
							self.assertTrue(hasattr(lowercase			,  'current'	)	)
							self.assertTrue(hasattr(lowercase			,  'total'	)	)

				with tempfile.TemporaryDirectory() as tmp_dir:
							_snake_case		=			PyTorchBenchmarkArguments(
							    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  log_filename=os.path.join(lowercase			,  'log.txt'	)			,  log_print=lowercase			,  trace_memory_line_by_line=lowercase			,  multi_process=lowercase			,  )
							_snake_case		=			PyTorchBenchmark(lowercase	)
							_snake_case		=			benchmark.run()
							_check_summary_is_not_empty(result.inference_summary	)
							_check_summary_is_not_empty(result.train_summary	)
							self.assertTrue(Path(os.path.join(lowercase			,  'log.txt'	)	).exists()	)