



'''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 __A     (    unittest.TestCase		):



 '''simple docstring'''







 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      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(A			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _a           =    benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result			)
      self.check_results_dict_not_empty(results.memory_inference_result			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    '''sgugger/tiny-distilbert-classification'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  only_pretrain_model=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _a           =    benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result			)
      self.check_results_dict_not_empty(results.memory_inference_result			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  torchscript=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _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    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  fpaa=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _a           =    benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result			)
      self.check_results_dict_not_empty(results.memory_inference_result			)



 def    a__    (self			)       ->	int:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    AutoConfig.from_pretrained(A			)
      # set architectures equal to `None`
      _a           =    None
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A					,  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    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _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    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  fpaa=A					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A			)
      _a           =    benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result			)
      self.check_results_dict_not_empty(results.memory_train_result			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    AutoConfig.from_pretrained(A			)
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A					,  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    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    '''sshleifer/tinier_bart'''
      _a           =    AutoConfig.from_pretrained(A			)
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A					,  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    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      _a           =    AutoConfig.from_pretrained(A			)
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A					,  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    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    '''sshleifer/tinier_bart'''
      _a           =    AutoConfig.from_pretrained(A			)
      _a           =    PyTorchBenchmarkArguments(
          models=[MODEL_ID]					,  training=A					,  inference=A					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=A					,  )
      _a           =    PyTorchBenchmark(A					,  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    a__    (self			)       ->	int:


      """simple docstring"""



      _a           =    '''sshleifer/tiny-gpt2'''
      with tempfile.TemporaryDirectory() as tmp_dir:
           _a           =    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           =    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    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    '''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           =    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           =    PyTorchBenchmark(A			)
           _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(A					,  '''log.txt'''			)			).exists()			)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__     :     str		    =       {
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				SCREAMING_SNAKE_CASE__     :     Any		    =       [
				    "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "AltCLIPPreTrainedModel",
				    "AltCLIPModel",
				    "AltCLIPTextModel",
				    "AltCLIPVisionModel",
				]


if TYPE_CHECKING:
				from .configuration_altclip import (
				    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AltCLIPConfig,
				    AltCLIPTextConfig,
				    AltCLIPVisionConfig,
				)
				from .processing_altclip import AltCLIPProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_altclip import (
								    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AltCLIPModel,
								    AltCLIPPreTrainedModel,
								    AltCLIPTextModel,
								    AltCLIPVisionModel,
								)


else:
				import sys

				SCREAMING_SNAKE_CASE__     :     Tuple		    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
