






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 							UpperCamelCase      (	self:  Any      ,	UpperCAmelCase_:  List[Any]     ):







									'''simple docstring'''




									for model_result in results.values():
															for batch_size, sequence_length in zip(model_result["""bs"""]      ,	model_result["""ss"""]     ):
																					_SCREAMING_SNAKE_CASE	      =  model_result["""result"""][batch_size][sequence_length]
																					self.assertIsNotNone(__snake_case     )







			def 							UpperCamelCase      (	self:  List[str]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sshleifer/tiny-gpt2"""
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
									    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	multi_process=__snake_case      ,	)
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case     )
									_SCREAMING_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 							UpperCamelCase      (	self:  List[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sgugger/tiny-distilbert-classification"""
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
									    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	multi_process=__snake_case      ,	only_pretrain_model=__snake_case      ,	)
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case     )
									_SCREAMING_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 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sshleifer/tiny-gpt2"""
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
									    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	torchscript=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	multi_process=__snake_case      ,	)
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case     )
									_SCREAMING_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 							UpperCamelCase      (	self:  int     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sshleifer/tiny-gpt2"""
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
									    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	fpaa=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	multi_process=__snake_case      ,	)
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case     )
									_SCREAMING_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 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sshleifer/tiny-gpt2"""
									_SCREAMING_SNAKE_CASE	      =  AutoConfig.from_pretrained(__snake_case     )
									# set architectures equal to `None`
									_SCREAMING_SNAKE_CASE	      =  None
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
									    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	multi_process=__snake_case      ,	)
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case      ,	configs=[config]     )
									_SCREAMING_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 							UpperCamelCase      (	self:  Dict     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sshleifer/tiny-gpt2"""
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
									    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	multi_process=__snake_case      ,	)
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case     )
									_SCREAMING_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 							UpperCamelCase      (	self:  int     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sshleifer/tiny-gpt2"""
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
									    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	fpaa=__snake_case      ,	multi_process=__snake_case      ,	)
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case     )
									_SCREAMING_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 							UpperCamelCase      (	self:  str     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sshleifer/tiny-gpt2"""
									_SCREAMING_SNAKE_CASE	      =  AutoConfig.from_pretrained(__snake_case     )
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
									    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	multi_process=__snake_case      ,	)
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case      ,	configs=[config]     )
									_SCREAMING_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 							UpperCamelCase      (	self:  List[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sshleifer/tinier_bart"""
									_SCREAMING_SNAKE_CASE	      =  AutoConfig.from_pretrained(__snake_case     )
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
									    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	multi_process=__snake_case      ,	)
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case      ,	configs=[config]     )
									_SCREAMING_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 							UpperCamelCase      (	self:  Tuple     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sshleifer/tiny-gpt2"""
									_SCREAMING_SNAKE_CASE	      =  AutoConfig.from_pretrained(__snake_case     )
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
									    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	multi_process=__snake_case      ,	)
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case      ,	configs=[config]     )
									_SCREAMING_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 							UpperCamelCase      (	self:  Union[str, Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sshleifer/tinier_bart"""
									_SCREAMING_SNAKE_CASE	      =  AutoConfig.from_pretrained(__snake_case     )
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
									    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	multi_process=__snake_case      ,	)
									_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case      ,	configs=[config]     )
									_SCREAMING_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 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




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







			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """sshleifer/tiny-gpt2"""

									def _check_summary_is_not_empty(UpperCAmelCase_:  List[str]     ):
															self.assertTrue(hasattr(__snake_case      ,	"""sequential"""     )     )
															self.assertTrue(hasattr(__snake_case      ,	"""cumulative"""     )     )
															self.assertTrue(hasattr(__snake_case      ,	"""current"""     )     )
															self.assertTrue(hasattr(__snake_case      ,	"""total"""     )     )

									with tempfile.TemporaryDirectory() as tmp_dir:
															_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmarkArguments(
															    models=[MODEL_ID]      ,	training=__snake_case      ,	inference=__snake_case      ,	sequence_lengths=[8]      ,	batch_sizes=[1]      ,	log_filename=os.path.join(__snake_case      ,	"""log.txt"""     )      ,	log_print=__snake_case      ,	trace_memory_line_by_line=__snake_case      ,	multi_process=__snake_case      ,	)
															_SCREAMING_SNAKE_CASE	      =  PyTorchBenchmark(__snake_case     )
															_SCREAMING_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(__snake_case      ,	"""log.txt"""     )     ).exists()     )





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


snake_case							:       Union[str, Any]     			=      logging.get_logger(__name__)

snake_case							:       str     			=      {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    str          =			'''mobilenet_v2'''
					def __init__(      self   :Optional[Any]			,__snake_case   :List[Any]=3			,__snake_case   :Any=2_24			,__snake_case   :Dict=1.0			,__snake_case   :str=8			,__snake_case   :Union[str, Any]=8			,__snake_case   :Optional[Any]=6			,__snake_case   :str=32			,__snake_case   :Tuple=True			,__snake_case   :Union[str, Any]=True			,__snake_case   :Any="relu6"			,__snake_case   :List[str]=True			,__snake_case   :Dict=0.8			,__snake_case   :Optional[int]=0.02			,__snake_case   :Tuple=0.0_01			,__snake_case   :Dict=2_55			,**__snake_case   :Dict			,)					->    Any:
												super().__init__(**__snake_case      )

												if depth_multiplier <= 0:
																			raise ValueError('depth_multiplier must be greater than zero.'      )

												a__             =     num_channels
												a__             =     image_size
												a__             =     depth_multiplier
												a__             =     depth_divisible_by
												a__             =     min_depth
												a__             =     expand_ratio
												a__             =     output_stride
												a__             =     first_layer_is_expansion
												a__             =     finegrained_output
												a__             =     hidden_act
												a__             =     tf_padding
												a__             =     classifier_dropout_prob
												a__             =     initializer_range
												a__             =     layer_norm_eps
												a__             =     semantic_loss_ignore_index







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Tuple          =			version.parse('''1.11'''      )
					@property
					def  lowerCamelCase__(      self   :Any      )					->    Mapping[str, Mapping[int, str]]:
												return OrderedDict([('pixel_values', {0: 'batch'})]      )
					@property
					def  lowerCamelCase__(      self   :List[str]      )					->    Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																			return OrderedDict([('logits', {0: 'batch'})]      )
												else:
																			return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )



					@property
					def  lowerCamelCase__(      self   :Any      )					->    float:
												return 1E-4




