

import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import SwiftFormerForImageClassification, SwiftFormerModel
    from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor
class        _A						:

  def __init__( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=13       ,    _SCREAMING_SNAKE_CASE=3       ,    _SCREAMING_SNAKE_CASE=True       ,    _SCREAMING_SNAKE_CASE=True       ,    _SCREAMING_SNAKE_CASE=0.1       ,    _SCREAMING_SNAKE_CASE=0.1       ,    _SCREAMING_SNAKE_CASE=224       ,    _SCREAMING_SNAKE_CASE=1000       ,    _SCREAMING_SNAKE_CASE=[3, 3, 6, 4]       ,    _SCREAMING_SNAKE_CASE=[48, 56, 112, 220]       ,    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Tuple							=   parent
        SCREAMING_SNAKE_CASE_ :     str							=   batch_size
        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   num_channels
        SCREAMING_SNAKE_CASE_ :     Dict							=   is_training
        SCREAMING_SNAKE_CASE_ :     int							=   use_labels
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   hidden_dropout_prob
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   num_labels
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   image_size
        SCREAMING_SNAKE_CASE_ :     Dict							=   layer_depths
        SCREAMING_SNAKE_CASE_ :     str							=   embed_dims

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     List[Any]							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   None
        if self.use_labels:
              SCREAMING_SNAKE_CASE_ :     str							=   ids_tensor([self.batch_size]       ,    self.num_labels    )

        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   self.get_config()

        return config, pixel_values, labels

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        return SwiftFormerConfig(
            depths=self.layer_depths       ,    embed_dims=self.embed_dims       ,    mlp_ratio=4       ,    downsamples=[True, True, True, True]       ,    hidden_act='gelu'       ,    num_labels=self.num_labels       ,    down_patch_size=3       ,    down_stride=2       ,    down_pad=1       ,    drop_rate=0.0       ,    drop_path_rate=0.0       ,    use_layer_scale=__snake_case       ,    layer_scale_init_value=1e-5       ,    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   SwiftFormerModel(config=__snake_case    )
        model.to(__snake_case    )
        model.eval()
        SCREAMING_SNAKE_CASE_ :     Dict							=   model(__snake_case    )
        self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.embed_dims[-1], 7, 7)    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   self.num_labels
        SCREAMING_SNAKE_CASE_ :     int							=   SwiftFormerForImageClassification(__snake_case    )
        model.to(__snake_case    )
        model.eval()
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   model(__snake_case       ,    labels=__snake_case    )
        self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)    )

        SCREAMING_SNAKE_CASE_ :     Any							=   SwiftFormerForImageClassification(__snake_case    )
        model.to(__snake_case    )
        model.eval()

        SCREAMING_SNAKE_CASE_ :     List[str]							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   model(__snake_case    )
        self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)    )



  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        ((SCREAMING_SNAKE_CASE_)				,  (SCREAMING_SNAKE_CASE_)				,  (SCREAMING_SNAKE_CASE_)) :     Dict							=   self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   {'pixel_values': pixel_values}
        return config, inputs_dict

@require_torch
class        _A						(     lowerCamelCase_      ,   lowerCamelCase_      ,   unittest.TestCase):
  SCREAMING_SNAKE_CASE			:					Any				=     (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE			:					int				=     (
      {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE			:					Optional[int]				=     False
  SCREAMING_SNAKE_CASE			:					Union[str, Any]				=     False
  SCREAMING_SNAKE_CASE			:					Tuple				=     False
  SCREAMING_SNAKE_CASE			:					Optional[int]				=     False
  SCREAMING_SNAKE_CASE			:					Union[str, Any]				=     False

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   SwiftFormerModelTester(self    )
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   ConfigTester(
            self       ,    config_class=__snake_case       ,    has_text_modality=__snake_case       ,    hidden_size=37       ,    num_attention_heads=12       ,    num_hidden_layers=12       ,    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        self.config_tester.run_common_tests()

  @unittest.skip(reason='SwiftFormer does not use inputs_embeds'    )
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_ :     List[Any]							=   model_class(__snake_case    )
              SCREAMING_SNAKE_CASE_ :     Dict							=   model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__snake_case       ,    nn.Linear    )    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_ :     List[Any]							=   model_class(__snake_case    )
              SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   inspect.signature(model.forward    )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_ :     List[str]							=   [*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_ :     Tuple							=   ['pixel_values']
              self.assertListEqual(arg_names[:1]       ,    __snake_case    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     str							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__snake_case    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__snake_case    )

  @slow
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   SwiftFormerModel.from_pretrained(__snake_case    )
              self.assertIsNotNone(__snake_case    )

  @unittest.skip(reason='SwiftFormer does not output attentions'    )
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        def check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):
              SCREAMING_SNAKE_CASE_ :     List[Any]							=   model_class(__snake_case    )
              model.to(__snake_case    )
              model.eval()

              with torch.no_grad():
                    SCREAMING_SNAKE_CASE_ :     Any							=   model(**self._prepare_for_class(__snake_case       ,    __snake_case    )    )

              SCREAMING_SNAKE_CASE_ :     Optional[int]							=   outputs.hidden_states

              SCREAMING_SNAKE_CASE_ :     List[str]							=   8
              self.assertEqual(len(__snake_case    )       ,    __snake_case    )  # TODO

              # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
              # with the width and height being successively divided by 2, after every 2 blocks
              for i in range(len(__snake_case    )    ):
                    self.assertEqual(
                        hidden_states[i].shape       ,    torch.Size(
                            [
                                self.model_tester.batch_size,
                                self.model_tester.embed_dims[i // 2],
                                (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                (self.model_tester.image_size // 4) // 2 ** (i // 2),
                            ]    )       ,    )

        SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_ :     str							=   True
              check_hidden_states_output(__snake_case       ,    __snake_case       ,    __snake_case    )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE_ :     str							=   True

              check_hidden_states_output(__snake_case       ,    __snake_case       ,    __snake_case    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        def _config_zero_init(_SCREAMING_SNAKE_CASE    ):
              SCREAMING_SNAKE_CASE_ :     Any							=   copy.deepcopy(__snake_case    )
              for key in configs_no_init.__dict__.keys():
                    if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                          setattr(__snake_case       ,    __snake_case       ,    1e-10    )
                    if isinstance(getattr(__snake_case       ,    __snake_case       ,    __snake_case    )       ,    __snake_case    ):
                          SCREAMING_SNAKE_CASE_ :     Tuple							=   _config_zero_init(getattr(__snake_case       ,    __snake_case    )    )
                          setattr(__snake_case       ,    __snake_case       ,    __snake_case    )
              return configs_no_init

        SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

        SCREAMING_SNAKE_CASE_ :     int							=   _config_zero_init(__snake_case    )
        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_ :     List[str]							=   model_class(config=__snake_case    )
              for name, param in model.named_parameters():
                    if param.requires_grad:
                          self.assertIn(
                              ((param.data.mean() * 1e9) / 1e9).round().item()       ,    [0.0, 1.0]       ,    msg=f"Parameter {name} of model {model_class} seems not properly initialized"       ,    )



  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

def       A_     (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     str							=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
      return image



@require_torch
@require_vision
class        _A						(     unittest.TestCase):

  @cached_property
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'    ) if is_vision_available() else None



  @slow
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Dict							=   SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'    ).to(__snake_case    )

        SCREAMING_SNAKE_CASE_ :     int							=   self.default_image_processor
        SCREAMING_SNAKE_CASE_ :     str							=   prepare_img()
        SCREAMING_SNAKE_CASE_ :     int							=   image_processor(images=__snake_case       ,    return_tensors='pt'    ).to(__snake_case    )

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_ :     Dict							=   model(**__snake_case    )

        # verify the logits
        SCREAMING_SNAKE_CASE_ :     str							=   torch.Size((1, 1000)    )
        self.assertEqual(outputs.logits.shape       ,    __snake_case    )

        SCREAMING_SNAKE_CASE_ :     str							=   torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]]    ).to(__snake_case    )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,    __snake_case       ,    atol=1e-4    )    )










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        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[Any]      )					->    Union[str, Any]:
												for model_result in results.values():
																			for batch_size, sequence_length in zip(model_result['bs']			,model_result['ss']      ):
																										a__             =     model_result['result'][batch_size][sequence_length]
																										self.assertIsNotNone(__snake_case      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :List[Any]      )					->    Union[str, Any]:
												a__             =     'sgugger/tiny-distilbert-classification'
												a__             =     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			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :Optional[Any]      )					->    Optional[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,torchscript=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :int      )					->    str:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,fpaa=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :Optional[int]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												# set architectures equal to `None`
												a__             =     None
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     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   :Dict      )					->    int:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :int      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,fpaa=__snake_case			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :str      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     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   :List[Any]      )					->    Any:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     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   :Tuple      )					->    Dict:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     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   :Union[str, Any]      )					->    Optional[int]:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     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   :Optional[int]      )					->    List[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     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			,)
																			a__             =     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  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'

												def _check_summary_is_not_empty(__snake_case   :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:
																			a__             =     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			,)
																			a__             =     PyTorchBenchmark(__snake_case      )
																			a__             =     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()      )




