

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  ):
							"""simple docstring"""







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


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       Tuple   =						'''sshleifer/tiny-gpt2'''
												_a      :       Dict   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			)
												_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			)       ->       str:
												_a      :       str   =						'''sgugger/tiny-distilbert-classification'''
												_a      :       str   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				only_pretrain_model=_a			,				)
												_a      :       Any   =						PyTorchBenchmark(_a			)
												_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			)       ->       int:
												_a      :       Optional[Any]   =						'''sshleifer/tiny-gpt2'''
												_a      :       Optional[int]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				torchscript=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			)
												_a      :       Any   =						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			)       ->       int:
												_a      :       Optional[int]   =						'''sshleifer/tiny-gpt2'''
												_a      :       Any   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				fpaa=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			)
												_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 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       Union[str, Any]   =						'''sshleifer/tiny-gpt2'''
												_a      :       Dict   =						AutoConfig.from_pretrained(_a			)
												# set architectures equal to `None`
												_a      :       List[Any]   =						None
												_a      :       Tuple   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       List[Any]   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       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			)       ->       Dict:
												_a      :       Tuple   =						'''sshleifer/tiny-gpt2'''
												_a      :       int   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       Dict   =						PyTorchBenchmark(_a			)
												_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			)       ->       Optional[Any]:
												_a      :       Any   =						'''sshleifer/tiny-gpt2'''
												_a      :       Tuple   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				fpaa=_a			,				multi_process=_a			,				)
												_a      :       Tuple   =						PyTorchBenchmark(_a			)
												_a      :       List[str]   =						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:
												_a      :       int   =						'''sshleifer/tiny-gpt2'''
												_a      :       List[str]   =						AutoConfig.from_pretrained(_a			)
												_a      :       List[str]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       Tuple   =						PyTorchBenchmark(_a			,				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			)       ->       Union[str, Any]:
												_a      :       Union[str, Any]   =						'''sshleifer/tinier_bart'''
												_a      :       Optional[int]   =						AutoConfig.from_pretrained(_a			)
												_a      :       Optional[int]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       List[str]   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_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 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[int]   =						'''sshleifer/tiny-gpt2'''
												_a      :       int   =						AutoConfig.from_pretrained(_a			)
												_a      :       Union[str, Any]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       Tuple   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       str   =						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[Any]:
												_a      :       Dict   =						'''sshleifer/tinier_bart'''
												_a      :       Dict   =						AutoConfig.from_pretrained(_a			)
												_a      :       List[Any]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			,				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]:
												_a      :       List[str]   =						'''sshleifer/tiny-gpt2'''
												with tempfile.TemporaryDirectory() as tmp_dir:
																	_a      :       Tuple   =						PyTorchBenchmarkArguments(
																	    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				save_to_csv=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				inference_time_csv_file=os.path.join(_a			,				'''inf_time.csv'''			)			,				train_memory_csv_file=os.path.join(_a			,				'''train_mem.csv'''			)			,				inference_memory_csv_file=os.path.join(_a			,				'''inf_mem.csv'''			)			,				train_time_csv_file=os.path.join(_a			,				'''train_time.csv'''			)			,				env_info_csv_file=os.path.join(_a			,				'''env.csv'''			)			,				multi_process=_a			,				)
																	_a      :       List[str]   =						PyTorchBenchmark(_a			)
																	benchmark.run()
																	self.assertTrue(Path(os.path.join(_a			,				'''inf_time.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''train_time.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''inf_mem.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''train_mem.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''env.csv'''			)			).exists()			)




							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Optional[int]   =						'''sshleifer/tiny-gpt2'''

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

												with tempfile.TemporaryDirectory() as tmp_dir:
																	_a      :       str   =						PyTorchBenchmarkArguments(
																	    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				log_filename=os.path.join(_a			,				'''log.txt'''			)			,				log_print=_a			,				trace_memory_line_by_line=_a			,				multi_process=_a			,				)
																	_a      :       List[str]   =						PyTorchBenchmark(_a			)
																	_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(_a			,				'''log.txt'''			)			).exists()			)







'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_			:  int										=   logging.get_logger(__name__)

UpperCAmelCase_			:  List[Any]										=   {
    'facebook/deit-base-distilled-patch16-224': (
        'https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Union[str, Any]     				=    '''deit'''


 def __init__(   self   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu"   ,   __lowercase=0.0   ,   __lowercase=0.0   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=224   ,   __lowercase=16   ,   __lowercase=3   ,   __lowercase=True   ,   __lowercase=16   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(**__lowercase    )

  __A	:		Union[str, Any]	  =      hidden_size
  __A	:		int	  =      num_hidden_layers
  __A	:		str	  =      num_attention_heads
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		List[str]	  =      hidden_act
  __A	:		str	  =      hidden_dropout_prob
  __A	:		str	  =      attention_probs_dropout_prob
  __A	:		int	  =      initializer_range
  __A	:		Tuple	  =      layer_norm_eps
  __A	:		List[Any]	  =      image_size
  __A	:		Dict	  =      patch_size
  __A	:		Optional[Any]	  =      num_channels
  __A	:		List[Any]	  =      qkv_bias
  __A	:		str	  =      encoder_stride


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    version.parse('''1.11'''					)


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return OrderedDict(
      [
          ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
      ]    )







 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return 1E-4






