






'''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 				lowerCAmelCase       (				unittest.TestCase ):







   """simple docstring"""







   def  __magic_name__     (			self , _A      )     -> Union[str, Any]:


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



   def  __magic_name__     (			self      )     -> Tuple:
    __a     :						Dict          =    'sshleifer/tiny-gpt2'
    __a     :						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 , )
    __a     :						Union[str, Any]          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE      )
    __a     :						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  __magic_name__     (			self      )     -> Any:
    __a     :						Optional[int]          =    'sgugger/tiny-distilbert-classification'
    __a     :						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 , )
    __a     :						str          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE      )
    __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  __magic_name__     (			self      )     -> List[str]:
    __a     :						int          =    'sshleifer/tiny-gpt2'
    __a     :						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 , )
    __a     :						Optional[int]          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE      )
    __a     :						Dict          =    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  __magic_name__     (			self      )     -> Tuple:
    __a     :						Tuple          =    'sshleifer/tiny-gpt2'
    __a     :						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 , )
    __a     :						int          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE      )
    __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  __magic_name__     (			self      )     -> Union[str, Any]:
    __a     :						Tuple          =    'sshleifer/tiny-gpt2'
    __a     :						List[str]          =    AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE      )
    # set architectures equal to `None`
    __a     :						Tuple          =    None
    __a     :						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 , )
    __a     :						List[str]          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE , configs=[config]      )
    __a     :						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  __magic_name__     (			self      )     -> Optional[int]:
    __a     :						List[str]          =    'sshleifer/tiny-gpt2'
    __a     :						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 , )
    __a     :						Dict          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE      )
    __a     :						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  __magic_name__     (			self      )     -> List[Any]:
    __a     :						int          =    'sshleifer/tiny-gpt2'
    __a     :						int          =    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 , )
    __a     :						Union[str, Any]          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE      )
    __a     :						Dict          =    benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result      )
    self.check_results_dict_not_empty(results.memory_train_result      )



   def  __magic_name__     (			self      )     -> str:
    __a     :						List[str]          =    'sshleifer/tiny-gpt2'
    __a     :						Union[str, Any]          =    AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE      )
    __a     :						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 , )
    __a     :						Any          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE , 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  __magic_name__     (			self      )     -> List[str]:
    __a     :						str          =    'sshleifer/tinier_bart'
    __a     :						Optional[Any]          =    AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE      )
    __a     :						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 , )
    __a     :						str          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE , configs=[config]      )
    __a     :						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  __magic_name__     (			self      )     -> Any:
    __a     :						Any          =    'sshleifer/tiny-gpt2'
    __a     :						Tuple          =    AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE      )
    __a     :						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 , )
    __a     :						List[str]          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE , configs=[config]      )
    __a     :						int          =    benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result      )
    self.check_results_dict_not_empty(results.memory_train_result      )



   def  __magic_name__     (			self      )     -> Any:
    __a     :						List[Any]          =    'sshleifer/tinier_bart'
    __a     :						Optional[Any]          =    AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE      )
    __a     :						Dict          =    PyTorchBenchmarkArguments(
        models=[MODEL_ID] , training=_SCREAMING_SNAKE_CASE , inference=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_SCREAMING_SNAKE_CASE , )
    __a     :						Optional[int]          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE , configs=[config]      )
    __a     :						Dict          =    benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result      )
    self.check_results_dict_not_empty(results.memory_train_result      )



   def  __magic_name__     (			self      )     -> Tuple:
    __a     :						Union[str, Any]          =    'sshleifer/tiny-gpt2'
    with tempfile.TemporaryDirectory() as tmp_dir:
     __a     :						int          =    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 , )
     __a     :						Dict          =    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  __magic_name__     (			self      )     -> List[str]:
    __a     :						int          =    'sshleifer/tiny-gpt2'

    def _check_summary_is_not_empty(_A      ):
     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:
     __a     :						List[str]          =    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 , )
     __a     :						str          =    PyTorchBenchmark(_SCREAMING_SNAKE_CASE      )
     __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(_SCREAMING_SNAKE_CASE , 'log.txt'      )      ).exists()      )









"""simple docstring"""

from __future__ import annotations
def  __snake_case							(	__A      :						list[int]     )     ->       list[int]:






				'''simple docstring'''
				if len(__A     ) == 0:
								return array

				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     min(__A     ), max(__A     )

				# Compute the variables
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     _max - _min + 1
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     [0] * holes_range, [0] * holes_range

				# Make the sorting.
				for i in array:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     i - _min
								SCREAMING_SNAKE_CASE						:							Dict							=     i
								holes_repeat[index] += 1

				# Makes the array back by replacing the numbers.
				SCREAMING_SNAKE_CASE						:							int							=     0
				for i in range(__A     ):
								while holes_repeat[i] > 0:
												SCREAMING_SNAKE_CASE						:							int							=     holes[i]
												index += 1
												holes_repeat[i] -= 1

    # Returns the sorted array.
				return array


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							A_						:    Optional[Any]				=      input('Enter numbers separated by comma:\n')
							A_						:    Union[str, Any]				=      [int(x) for x in user_input.split(',')]
							print(pigeon_sort(unsorted))





