'''simple docstring'''






import os
import string
import sys


__a										=					1 << 8

__a										=					{
    '''tab''': ord('\t'),
    '''newline''': ord('\r'),
    '''esc''': 27,
    '''up''': 65 + ARROW_KEY_FLAG,
    '''down''': 66 + ARROW_KEY_FLAG,
    '''right''': 67 + ARROW_KEY_FLAG,
    '''left''': 68 + ARROW_KEY_FLAG,
    '''mod_int''': 91,
    '''undefined''': sys.maxsize,
    '''interrupt''': 3,
    '''insert''': 50,
    '''delete''': 51,
    '''pg_up''': 53,
    '''pg_down''': 54,
}

__a										=					KEYMAP['''up''']
__a										=					KEYMAP['''left''']

if sys.platform == "win32":
     __a										=					[]
     __a										=					{
         B'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
         B'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
         B'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
         B'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
         B'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
         B'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
         B'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
         B'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
     }

for i in range(10):
     __a										=					ord(str(i))






def    __UpperCAmelCase			(  ):
   if os.name == "nt":
      import msvcrt

      _UpperCAmelCase							:	List[Any]							=   "mbcs"
      # Flush the keyboard buffer
      while msvcrt.kbhit():
         msvcrt.getch()
      if len(__lowercase							) == 0:
         # Read the keystroke
         _UpperCAmelCase							:	List[Any]							=   msvcrt.getch()

         # If it is a prefix char, get second part
         if ch in (b"\x00", b"\xe0"):
            _UpperCAmelCase							:	int							=   ch + msvcrt.getch()
            # Translate actual Win chars to bullet char types
            try:
               _UpperCAmelCase							:	Dict							=   chr(WIN_KEYMAP[cha]							)
               WIN_CH_BUFFER.append(chr(KEYMAP["mod_int"]							)							)
               WIN_CH_BUFFER.append(__lowercase							)
               if ord(__lowercase							) in (
                   KEYMAP["insert"] - 1 << 9,
                   KEYMAP["delete"] - 1 << 9,
                   KEYMAP["pg_up"] - 1 << 9,
                   KEYMAP["pg_down"] - 1 << 9,
               ):
                  WIN_CH_BUFFER.append(chr(126							)							)
               _UpperCAmelCase							:	Optional[Any]							=   chr(KEYMAP["esc"]							)
            except KeyError:
               _UpperCAmelCase							:	Optional[int]							=   cha[1]
         else:
            _UpperCAmelCase							:	Union[str, Any]							=   ch.decode(__lowercase							)
      else:
         _UpperCAmelCase							:	Union[str, Any]							=   WIN_CH_BUFFER.pop(0							)
   elif os.name == "posix":
      import termios
      import tty

      _UpperCAmelCase							:	int							=   sys.stdin.fileno()
      _UpperCAmelCase							:	int							=   termios.tcgetattr(__lowercase							)
      try:
         tty.setraw(__lowercase							)
         _UpperCAmelCase							:	Union[str, Any]							=   sys.stdin.read(1							)
      finally:
         termios.tcsetattr(__lowercase,					termios.TCSADRAIN,					__lowercase							)
   return ch






def    __UpperCAmelCase			(  ):
   _UpperCAmelCase							:	Tuple							=   get_raw_chars()
   if ord(__lowercase							) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
      return char

   elif ord(__lowercase							) == KEYMAP["esc"]:
      _UpperCAmelCase							:	Optional[int]							=   get_raw_chars()
      if ord(__lowercase							) == KEYMAP["mod_int"]:
         _UpperCAmelCase							:	Optional[int]							=   get_raw_chars()
         if ord(__lowercase							) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(__lowercase							) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
            return chr(ord(__lowercase							) + ARROW_KEY_FLAG							)
         else:
            return KEYMAP["undefined"]
      else:
         return get_raw_chars()

   else:
      if char in string.printable:
         return char
      else:
         return KEYMAP["undefined"]

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()	)