
from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


__a  			=					logging.get_logger(__name__)

# General docstring
__a  			=					'RegNetConfig'

# Base docstring
__a  			=					'facebook/regnet-y-040'
__a  			=					[1, 1_0_8_8, 7, 7]

# Image classification docstring
__a  			=					'facebook/regnet-y-040'
__a  			=					'tabby, tabby cat'

__a  			=					[
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class  lowercase__(				nn.Module							):



	"""simple docstring"""




	def __init__(       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int = 3       ,							SCREAMING_SNAKE_CASE_  :			int = 1       ,							SCREAMING_SNAKE_CASE_  :			int = 1       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = "relu"       ,							)       ->     int:
					super().__init__()
					lowercase_       =					nn.Convad(
					    UpperCamelCase__       ,							UpperCamelCase__       ,							kernel_size=UpperCamelCase__       ,							stride=UpperCamelCase__       ,							padding=kernel_size // 2       ,							groups=UpperCamelCase__       ,							bias=UpperCamelCase__       ,							)
					lowercase_       =					nn.BatchNormad(UpperCamelCase__       )
					lowercase_       =					ACTaFN[activation] if activation is not None else nn.Identity()




	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Tuple       )       ->     Optional[int]:
					lowercase_       =					self.convolution(UpperCamelCase__       )
					lowercase_       =					self.normalization(UpperCamelCase__       )
					lowercase_       =					self.activation(UpperCamelCase__       )
					return hidden_state

class  lowercase__(				nn.Module							):



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			RegNetConfig       )       ->     Optional[Any]:
					super().__init__()
					lowercase_       =					RegNetConvLayer(
					    config.num_channels       ,							config.embedding_size       ,							kernel_size=3       ,							stride=2       ,							activation=config.hidden_act       )
					lowercase_       =					config.num_channels




	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     Union[str, Any]:
					lowercase_       =					pixel_values.shape[1]
					if num_channels != self.num_channels:
									raise ValueError(
									    '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''       )
					lowercase_       =					self.embedder(UpperCamelCase__       )
					return hidden_state

class  lowercase__(				nn.Module							):



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int = 2       )       ->     Any:
					super().__init__()
					lowercase_       =					nn.Convad(UpperCamelCase__       ,							UpperCamelCase__       ,							kernel_size=1       ,							stride=UpperCamelCase__       ,							bias=UpperCamelCase__       )
					lowercase_       =					nn.BatchNormad(UpperCamelCase__       )




	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Tensor       )       ->     Tensor:
					lowercase_       =					self.convolution(UpperCamelCase__       )
					lowercase_       =					self.normalization(UpperCamelCase__       )
					return hidden_state

class  lowercase__(				nn.Module							):



	"""simple docstring"""




	def __init__(       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     str:
					super().__init__()

					lowercase_       =					nn.AdaptiveAvgPoolad((1, 1)       )
					lowercase_       =					nn.Sequential(
					    nn.Convad(UpperCamelCase__       ,							UpperCamelCase__       ,							kernel_size=1       )       ,							nn.ReLU()       ,							nn.Convad(UpperCamelCase__       ,							UpperCamelCase__       ,							kernel_size=1       )       ,							nn.Sigmoid()       ,							)




	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     Union[str, Any]:
					lowercase_       =					self.pooler(UpperCamelCase__       )
					lowercase_       =					self.attention(UpperCamelCase__       )
					lowercase_       =					hidden_state * attention
					return hidden_state

class  lowercase__(				nn.Module							):



	"""simple docstring"""




	def __init__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			RegNetConfig       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int = 1       )       ->     Any:
					super().__init__()
					lowercase_       =					in_channels != out_channels or stride != 1
					lowercase_       =					max(1       ,							out_channels // config.groups_width       )
					lowercase_       =					(
					    RegNetShortCut(UpperCamelCase__       ,							UpperCamelCase__       ,							stride=UpperCamelCase__       ) if should_apply_shortcut else nn.Identity()
					)
					lowercase_       =					nn.Sequential(
					    RegNetConvLayer(UpperCamelCase__       ,							UpperCamelCase__       ,							kernel_size=1       ,							activation=config.hidden_act       )       ,							RegNetConvLayer(UpperCamelCase__       ,							UpperCamelCase__       ,							stride=UpperCamelCase__       ,							groups=UpperCamelCase__       ,							activation=config.hidden_act       )       ,							RegNetConvLayer(UpperCamelCase__       ,							UpperCamelCase__       ,							kernel_size=1       ,							activation=UpperCamelCase__       )       ,							)
					lowercase_       =					ACTaFN[config.hidden_act]




	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Dict       )       ->     str:
					lowercase_       =					hidden_state
					lowercase_       =					self.layer(UpperCamelCase__       )
					lowercase_       =					self.shortcut(UpperCamelCase__       )
					hidden_state += residual
					lowercase_       =					self.activation(UpperCamelCase__       )
					return hidden_state

class  lowercase__(				nn.Module							):



	"""simple docstring"""




	def __init__(       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			RegNetConfig       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int = 1       )       ->     str:
					super().__init__()
					lowercase_       =					in_channels != out_channels or stride != 1
					lowercase_       =					max(1       ,							out_channels // config.groups_width       )
					lowercase_       =					(
					    RegNetShortCut(UpperCamelCase__       ,							UpperCamelCase__       ,							stride=UpperCamelCase__       ) if should_apply_shortcut else nn.Identity()
					)
					lowercase_       =					nn.Sequential(
					    RegNetConvLayer(UpperCamelCase__       ,							UpperCamelCase__       ,							kernel_size=1       ,							activation=config.hidden_act       )       ,							RegNetConvLayer(UpperCamelCase__       ,							UpperCamelCase__       ,							stride=UpperCamelCase__       ,							groups=UpperCamelCase__       ,							activation=config.hidden_act       )       ,							RegNetSELayer(UpperCamelCase__       ,							reduced_channels=int(round(in_channels / 4       )       )       )       ,							RegNetConvLayer(UpperCamelCase__       ,							UpperCamelCase__       ,							kernel_size=1       ,							activation=UpperCamelCase__       )       ,							)
					lowercase_       =					ACTaFN[config.hidden_act]




	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     List[str]:
					lowercase_       =					hidden_state
					lowercase_       =					self.layer(UpperCamelCase__       )
					lowercase_       =					self.shortcut(UpperCamelCase__       )
					hidden_state += residual
					lowercase_       =					self.activation(UpperCamelCase__       )
					return hidden_state

class  lowercase__(				nn.Module							):



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			RegNetConfig       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int = 2       ,							SCREAMING_SNAKE_CASE_  :			int = 2       ,							)       ->     List[Any]:
					super().__init__()

					lowercase_       =					RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer

					lowercase_       =					nn.Sequential(
					    # downsampling is done in the first layer with stride of 2
					    layer(
					        UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							stride=UpperCamelCase__       ,							)       ,							*[layer(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ) for _ in range(depth - 1       )]       ,							)




	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Union[str, Any]:
					lowercase_       =					self.layers(UpperCamelCase__       )
					return hidden_state

class  lowercase__(				nn.Module							):



	"""simple docstring"""




	def __init__(       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			RegNetConfig       )       ->     Optional[int]:
					super().__init__()
					lowercase_       =					nn.ModuleList([]       )
					# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
					self.stages.append(
					    RegNetStage(
					        UpperCamelCase__       ,							config.embedding_size       ,							config.hidden_sizes[0]       ,							stride=2 if config.downsample_in_first_stage else 1       ,							depth=config.depths[0]       ,							)       )
					lowercase_       =					zip(config.hidden_sizes       ,							config.hidden_sizes[1:]       )
					for (in_channels, out_channels), depth in zip(UpperCamelCase__       ,							config.depths[1:]       ):
									self.stages.append(RegNetStage(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							depth=UpperCamelCase__       )       )




	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			Tensor       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							SCREAMING_SNAKE_CASE_  :			bool = True       )       ->     BaseModelOutputWithNoAttention:
					lowercase_       =					() if output_hidden_states else None

					for stage_module in self.stages:
									if output_hidden_states:
													lowercase_       =					hidden_states + (hidden_state,)

									lowercase_       =					stage_module(UpperCamelCase__       )

					if output_hidden_states:
									lowercase_       =					hidden_states + (hidden_state,)

					if not return_dict:
									return tuple(v for v in [hidden_state, hidden_states] if v is not None       )

					return BaseModelOutputWithNoAttention(last_hidden_state=UpperCamelCase__       ,							hidden_states=UpperCamelCase__       )

class  lowercase__(				_A							):



	"""simple docstring"""




	a			:Optional[int]         =	RegNetConfig
	a			:Union[str, Any]         =	'regnet'
	a			:Any         =	'pixel_values'
	a			:Optional[int]         =	True
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			List[str]       )       ->     Union[str, Any]:






					if isinstance(UpperCamelCase__       ,							nn.Convad       ):
									nn.init.kaiming_normal_(module.weight       ,							mode='''fan_out'''       ,							nonlinearity='''relu'''       )
					elif isinstance(UpperCamelCase__       ,							(nn.BatchNormad, nn.GroupNorm)       ):
									nn.init.constant_(module.weight       ,							1       )
									nn.init.constant_(module.bias       ,							0       )




	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Dict=False       )       ->     Optional[int]:






					if isinstance(UpperCamelCase__       ,							UpperCamelCase__       ):
									lowercase_       =					value

__a  			=					R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

__a  			=					R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.' ,						_A ,						)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class  lowercase__(				_A							):



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			List[str]       )       ->     Dict:
					super().__init__(UpperCamelCase__       )
					lowercase_       =					config
					lowercase_       =					RegNetEmbeddings(UpperCamelCase__       )
					lowercase_       =					RegNetEncoder(UpperCamelCase__       )
					lowercase_       =					nn.AdaptiveAvgPoolad((1, 1)       )
					# Initialize weights and apply final processing
					self.post_init()




	@add_start_docstrings_to_model_forward(UpperCamelCase__       )
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC       ,							output_type=UpperCamelCase__       ,							config_class=_CONFIG_FOR_DOC       ,							modality='''vision'''       ,							expected_output=_EXPECTED_OUTPUT_SHAPE       ,							)
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Tensor       ,							SCREAMING_SNAKE_CASE_  :			Optional[bool] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[bool] = None       )       ->     BaseModelOutputWithPoolingAndNoAttention:
					lowercase_       =					(
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					lowercase_       =					return_dict if return_dict is not None else self.config.use_return_dict

					lowercase_       =					self.embedder(UpperCamelCase__       )

					lowercase_       =					self.encoder(
					    UpperCamelCase__       ,							output_hidden_states=UpperCamelCase__       ,							return_dict=UpperCamelCase__       )

					lowercase_       =					encoder_outputs[0]

					lowercase_       =					self.pooler(UpperCamelCase__       )

					if not return_dict:
									return (last_hidden_state, pooled_output) + encoder_outputs[1:]

					return BaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=UpperCamelCase__       ,							pooler_output=UpperCamelCase__       ,							hidden_states=encoder_outputs.hidden_states       ,							)

@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    ' ,						_A ,						)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class  lowercase__(				_A							):



	"""simple docstring"""




	def __init__(       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Dict       )       ->     Union[str, Any]:
					super().__init__(UpperCamelCase__       )
					lowercase_       =					config.num_labels
					lowercase_       =					RegNetModel(UpperCamelCase__       )
					# classification head
					lowercase_       =					nn.Sequential(
					    nn.Flatten()       ,							nn.Linear(config.hidden_sizes[-1]       ,							config.num_labels       ) if config.num_labels > 0 else nn.Identity()       ,							)
					# initialize weights and apply final processing
					self.post_init()




	@add_start_docstrings_to_model_forward(UpperCamelCase__       )
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,							output_type=UpperCamelCase__       ,							config_class=_CONFIG_FOR_DOC       ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,							)
	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[torch.FloatTensor] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[torch.LongTensor] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[bool] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[bool] = None       ,							)       ->     ImageClassifierOutputWithNoAttention:
					lowercase_       =					return_dict if return_dict is not None else self.config.use_return_dict

					lowercase_       =					self.regnet(UpperCamelCase__       ,							output_hidden_states=UpperCamelCase__       ,							return_dict=UpperCamelCase__       )

					lowercase_       =					outputs.pooler_output if return_dict else outputs[1]

					lowercase_       =					self.classifier(UpperCamelCase__       )

					lowercase_       =					None

					if labels is not None:
									if self.config.problem_type is None:
													if self.num_labels == 1:
																	lowercase_       =					'''regression'''
													elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																	lowercase_       =					'''single_label_classification'''
													else:
																	lowercase_       =					'''multi_label_classification'''
									if self.config.problem_type == "regression":
													lowercase_       =					MSELoss()
													if self.num_labels == 1:
																	lowercase_       =					loss_fct(logits.squeeze()       ,							labels.squeeze()       )
													else:
																	lowercase_       =					loss_fct(UpperCamelCase__       ,							UpperCamelCase__       )
									elif self.config.problem_type == "single_label_classification":
													lowercase_       =					CrossEntropyLoss()
													lowercase_       =					loss_fct(logits.view(-1       ,							self.num_labels       )       ,							labels.view(-1       )       )
									elif self.config.problem_type == "multi_label_classification":
													lowercase_       =					BCEWithLogitsLoss()
													lowercase_       =					loss_fct(UpperCamelCase__       ,							UpperCamelCase__       )

					if not return_dict:
									lowercase_       =					(logits,) + outputs[2:]
									return (loss,) + output if loss is not None else output

					return ImageClassifierOutputWithNoAttention(loss=UpperCamelCase__       ,							logits=UpperCamelCase__       ,							hidden_states=outputs.hidden_states       )






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf

    from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments




@require_tf
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: int    )			->	str:







    """simple docstring"""






    for model_result in results.values():
      for batch_size, sequence_length in zip(model_result["""bs"""]			,							model_result["""ss"""]    ):
        __magic_name__				     =       model_result["""result"""][batch_size][sequence_length]
        self.assertIsNotNone(UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       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			: Optional[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sgugger/tiny-distilbert-classification"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							only_pretrain_model=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       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			: Any    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       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			: Tuple    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       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			: Any    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       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]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       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			: Optional[Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       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			: Union[str, Any]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       """patrickvonplaten/t5-tiny-random"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							configs=[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""    )    ) == 0			,							"""Cannot do xla on CPU."""    )
  def 							_lowercase							( self			: Tuple    )			->	int:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							use_xla=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       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]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    with tempfile.TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       TensorFlowBenchmarkArguments(
          models=[MODEL_ID]			,							inference=UpperCamelCase__			,							save_to_csv=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							inference_time_csv_file=os.path.join(UpperCamelCase__			,							"""inf_time.csv"""    )			,							inference_memory_csv_file=os.path.join(UpperCamelCase__			,							"""inf_mem.csv"""    )			,							env_info_csv_file=os.path.join(UpperCamelCase__			,							"""env.csv"""    )			,							multi_process=UpperCamelCase__			,							)
      __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
      benchmark.run()
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""inf_time.csv"""    )    ).exists()    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""inf_mem.csv"""    )    ).exists()    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""env.csv"""    )    ).exists()    )





  def 							_lowercase							( self			: int    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""

    def _check_summary_is_not_empty(UpperCamelCase__			: Dict    ):
      self.assertTrue(hasattr(UpperCamelCase__			,							"""sequential"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""cumulative"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""current"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""total"""    )    )

    with tempfile.TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       TensorFlowBenchmarkArguments(
          models=[MODEL_ID]			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							log_filename=os.path.join(UpperCamelCase__			,							"""log.txt"""    )			,							log_print=UpperCamelCase__			,							trace_memory_line_by_line=UpperCamelCase__			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
      __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
      __magic_name__				     =       benchmark.run()
      _check_summary_is_not_empty(result.inference_summary    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""log.txt"""    )    ).exists()    )

