



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





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





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





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





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





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





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





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





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





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





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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''sshleifer/tiny-gpt2'''

         def _check_summary_is_not_empty(_lowerCAmelCase			):
               self.assertTrue(hasattr(_lowerCAmelCase       ,     '''sequential'''			)			)
               self.assertTrue(hasattr(_lowerCAmelCase       ,     '''cumulative'''			)			)
               self.assertTrue(hasattr(_lowerCAmelCase       ,     '''current'''			)			)
               self.assertTrue(hasattr(_lowerCAmelCase       ,     '''total'''			)			)

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




from __future__ import annotations
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	list				)->list:
      if len(_SCREAMING_SNAKE_CASE				) == 0:
            return []
      _lowerCAmelCase			,   _lowerCAmelCase 			=  min(_SCREAMING_SNAKE_CASE				), max(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  int(max_value - min_value				) + 1
      _lowerCAmelCase 			=  [[] for _ in range(_SCREAMING_SNAKE_CASE				)]

      for i in my_list:
            buckets[int(i - min_value				)].append(_SCREAMING_SNAKE_CASE				)

      return [v for bucket in buckets for v in sorted(_SCREAMING_SNAKE_CASE				)]


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
    assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]