



"""simple docstring"""







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 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]	):
        for model_result in results.values():
          for batch_size, sequence_length in zip(model_result['bs']   ,      model_result['ss']	):
            lowerCAmelCase_       :    Dict								=			model_result['result'][batch_size][sequence_length]
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    List[str]								=			'sshleifer/tiny-gpt2'
        lowerCAmelCase_       :    int								=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]   ,      training=SCREAMING_SNAKE_CASE_   ,      inference=SCREAMING_SNAKE_CASE_   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    str								=			PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result	)
        self.check_results_dict_not_empty(results.memory_inference_result	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        lowerCAmelCase_       :    int								=			'sgugger/tiny-distilbert-classification'
        lowerCAmelCase_       :    List[Any]								=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]   ,      training=SCREAMING_SNAKE_CASE_   ,      inference=SCREAMING_SNAKE_CASE_   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=SCREAMING_SNAKE_CASE_   ,      only_pretrain_model=SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    str								=			PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Optional[int]								=			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result	)
        self.check_results_dict_not_empty(results.memory_inference_result	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        lowerCAmelCase_       :    Tuple								=			'sshleifer/tiny-gpt2'
        lowerCAmelCase_       :    str								=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]   ,      training=SCREAMING_SNAKE_CASE_   ,      inference=SCREAMING_SNAKE_CASE_   ,      torchscript=SCREAMING_SNAKE_CASE_   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    Tuple								=			PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Dict								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        lowerCAmelCase_       :    Dict								=			'sshleifer/tiny-gpt2'
        lowerCAmelCase_       :    Tuple								=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]   ,      training=SCREAMING_SNAKE_CASE_   ,      inference=SCREAMING_SNAKE_CASE_   ,      fpaa=SCREAMING_SNAKE_CASE_   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    str								=			PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result	)
        self.check_results_dict_not_empty(results.memory_inference_result	)



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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        lowerCAmelCase_       :    Dict								=			'sshleifer/tiny-gpt2'
        lowerCAmelCase_       :    Tuple								=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]   ,      training=SCREAMING_SNAKE_CASE_   ,      inference=SCREAMING_SNAKE_CASE_   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    int								=			PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    List[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 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Union[str, Any]								=			'sshleifer/tiny-gpt2'
        lowerCAmelCase_       :    Optional[Any]								=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]   ,      training=SCREAMING_SNAKE_CASE_   ,      inference=SCREAMING_SNAKE_CASE_   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      fpaa=SCREAMING_SNAKE_CASE_   ,      multi_process=SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    Tuple								=			PyTorchBenchmark(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    str								=			benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result	)
        self.check_results_dict_not_empty(results.memory_train_result	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    List[Any]								=			'sshleifer/tiny-gpt2'
        lowerCAmelCase_       :    Union[str, Any]								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]   ,      training=SCREAMING_SNAKE_CASE_   ,      inference=SCREAMING_SNAKE_CASE_   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    str								=			PyTorchBenchmark(SCREAMING_SNAKE_CASE_   ,      configs=[config]	)
        lowerCAmelCase_       :    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 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    str								=			'sshleifer/tinier_bart'
        lowerCAmelCase_       :    Optional[int]								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Optional[Any]								=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]   ,      training=SCREAMING_SNAKE_CASE_   ,      inference=SCREAMING_SNAKE_CASE_   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    int								=			PyTorchBenchmark(SCREAMING_SNAKE_CASE_   ,      configs=[config]	)
        lowerCAmelCase_       :    List[Any]								=			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result	)
        self.check_results_dict_not_empty(results.memory_inference_result	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    Union[str, Any]								=			'sshleifer/tiny-gpt2'
        lowerCAmelCase_       :    Any								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]   ,      training=SCREAMING_SNAKE_CASE_   ,      inference=SCREAMING_SNAKE_CASE_   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    int								=			PyTorchBenchmark(SCREAMING_SNAKE_CASE_   ,      configs=[config]	)
        lowerCAmelCase_       :    Dict								=			benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result	)
        self.check_results_dict_not_empty(results.memory_train_result	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        lowerCAmelCase_       :    str								=			'sshleifer/tinier_bart'
        lowerCAmelCase_       :    Optional[Any]								=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Any								=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]   ,      training=SCREAMING_SNAKE_CASE_   ,      inference=SCREAMING_SNAKE_CASE_   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=SCREAMING_SNAKE_CASE_   ,      )
        lowerCAmelCase_       :    Union[str, Any]								=			PyTorchBenchmark(SCREAMING_SNAKE_CASE_   ,      configs=[config]	)
        lowerCAmelCase_       :    int								=			benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result	)
        self.check_results_dict_not_empty(results.memory_train_result	)



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





      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Any								=			'sshleifer/tiny-gpt2'

        def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE_    :				Optional[int]	):
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'sequential'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'cumulative'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'current'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'total'	)	)

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










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__		:    Optional[int]				   =     logging.get_logger(__name__)







class 				UpperCamelCase__       (     lowercase_, lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""maskformer-swin"""

      _SCREAMING_SNAKE_CASE      =					{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }



      def __init__(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2_2_4   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=4   ,      SCREAMING_SNAKE_CASE_    :				int=3   ,      SCREAMING_SNAKE_CASE_    :				Dict=9_6   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=[2, 2, 6, 2]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=[3, 6, 1_2, 2_4]   ,      SCREAMING_SNAKE_CASE_    :				List[str]=7   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=4.0   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				Tuple=0.0   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.0   ,      SCREAMING_SNAKE_CASE_    :				int=0.1   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Dict=False   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.02   ,      SCREAMING_SNAKE_CASE_    :				int=1E-5   ,      SCREAMING_SNAKE_CASE_    :				Dict=None   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=None   ,      **SCREAMING_SNAKE_CASE_    :				str   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Dict								=			image_size
        lowerCAmelCase_       :    Optional[Any]								=			patch_size
        lowerCAmelCase_       :    Optional[int]								=			num_channels
        lowerCAmelCase_       :    List[str]								=			embed_dim
        lowerCAmelCase_       :    Dict								=			depths
        lowerCAmelCase_       :    Optional[Any]								=			len(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			num_heads
        lowerCAmelCase_       :    List[str]								=			window_size
        lowerCAmelCase_       :    Any								=			mlp_ratio
        lowerCAmelCase_       :    Any								=			qkv_bias
        lowerCAmelCase_       :    List[Any]								=			hidden_dropout_prob
        lowerCAmelCase_       :    List[Any]								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Tuple								=			drop_path_rate
        lowerCAmelCase_       :    List[str]								=			hidden_act
        lowerCAmelCase_       :    Any								=			use_absolute_embeddings
        lowerCAmelCase_       :    Optional[Any]								=			layer_norm_eps
        lowerCAmelCase_       :    str								=			initializer_range
        # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        lowerCAmelCase_       :    List[str]								=			int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_	) - 1)	)
        lowerCAmelCase_       :    List[Any]								=			['stem'] + [F"stage{idx}" for idx in range(1   ,      len(SCREAMING_SNAKE_CASE_	) + 1	)]
        lowerCAmelCase_						,lowerCAmelCase_       :    Tuple								=			get_aligned_output_features_output_indices(
            out_features=SCREAMING_SNAKE_CASE_   ,      out_indices=SCREAMING_SNAKE_CASE_   ,      stage_names=self.stage_names	)





