





"""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 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         for model_result in results.values():
           for batch_size, sequence_length in zip(model_result['bs']    , model_result['ss']      ):
             lowerCAmelCase__			:Union[str, Any]											=							model_result['result'][batch_size][sequence_length]
             self.assertIsNotNone(__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							'sshleifer/tiny-gpt2'
         lowerCAmelCase__			:Dict											=							PyTorchBenchmarkArguments(
             models=[MODEL_ID]    , training=__UpperCAmelCase    , inference=__UpperCAmelCase    , sequence_lengths=[8]    , batch_sizes=[1]    , multi_process=__UpperCAmelCase    , )
         lowerCAmelCase__			:Any											=							PyTorchBenchmark(__UpperCAmelCase      )
         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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							'sgugger/tiny-distilbert-classification'
         lowerCAmelCase__			:List[str]											=							PyTorchBenchmarkArguments(
             models=[MODEL_ID]    , training=__UpperCAmelCase    , inference=__UpperCAmelCase    , sequence_lengths=[8]    , batch_sizes=[1]    , multi_process=__UpperCAmelCase    , only_pretrain_model=__UpperCAmelCase    , )
         lowerCAmelCase__			:Optional[Any]											=							PyTorchBenchmark(__UpperCAmelCase      )
         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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							'sshleifer/tiny-gpt2'
         lowerCAmelCase__			:Optional[Any]											=							PyTorchBenchmarkArguments(
             models=[MODEL_ID]    , training=__UpperCAmelCase    , inference=__UpperCAmelCase    , torchscript=__UpperCAmelCase    , sequence_lengths=[8]    , batch_sizes=[1]    , multi_process=__UpperCAmelCase    , )
         lowerCAmelCase__			:Optional[int]											=							PyTorchBenchmark(__UpperCAmelCase      )
         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      )






       @unittest.skipIf(torch_device == 'cpu'    , 'Cant do half precision'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							'sshleifer/tiny-gpt2'
         lowerCAmelCase__			:List[str]											=							PyTorchBenchmarkArguments(
             models=[MODEL_ID]    , training=__UpperCAmelCase    , inference=__UpperCAmelCase    , fpaa=__UpperCAmelCase    , sequence_lengths=[8]    , batch_sizes=[1]    , multi_process=__UpperCAmelCase    , )
         lowerCAmelCase__			:Optional[int]											=							PyTorchBenchmark(__UpperCAmelCase      )
         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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							'sshleifer/tiny-gpt2'
         lowerCAmelCase__			:List[Any]											=							AutoConfig.from_pretrained(__UpperCAmelCase      )
         # set architectures equal to `None`
         lowerCAmelCase__			:str											=							None
         lowerCAmelCase__			:str											=							PyTorchBenchmarkArguments(
             models=[MODEL_ID]    , training=__UpperCAmelCase    , inference=__UpperCAmelCase    , sequence_lengths=[8]    , batch_sizes=[1]    , multi_process=__UpperCAmelCase    , )
         lowerCAmelCase__			:Any											=							PyTorchBenchmark(__UpperCAmelCase    , configs=[config]      )
         lowerCAmelCase__			:Union[str, Any]											=							benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result      )
         self.check_results_dict_not_empty(results.memory_inference_result      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							'sshleifer/tiny-gpt2'
         lowerCAmelCase__			:Optional[Any]											=							PyTorchBenchmarkArguments(
             models=[MODEL_ID]    , training=__UpperCAmelCase    , inference=__UpperCAmelCase    , sequence_lengths=[8]    , batch_sizes=[1]    , multi_process=__UpperCAmelCase    , )
         lowerCAmelCase__			:Optional[int]											=							PyTorchBenchmark(__UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							'sshleifer/tiny-gpt2'
         lowerCAmelCase__			:Optional[int]											=							PyTorchBenchmarkArguments(
             models=[MODEL_ID]    , training=__UpperCAmelCase    , inference=__UpperCAmelCase    , sequence_lengths=[8]    , batch_sizes=[1]    , fpaa=__UpperCAmelCase    , multi_process=__UpperCAmelCase    , )
         lowerCAmelCase__			:Optional[int]											=							PyTorchBenchmark(__UpperCAmelCase      )
         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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							'sshleifer/tiny-gpt2'
         lowerCAmelCase__			:Tuple											=							AutoConfig.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:List[str]											=							PyTorchBenchmarkArguments(
             models=[MODEL_ID]    , training=__UpperCAmelCase    , inference=__UpperCAmelCase    , sequence_lengths=[8]    , batch_sizes=[1]    , multi_process=__UpperCAmelCase    , )
         lowerCAmelCase__			:List[Any]											=							PyTorchBenchmark(__UpperCAmelCase    , 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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							'sshleifer/tinier_bart'
         lowerCAmelCase__			:Dict											=							AutoConfig.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:Tuple											=							PyTorchBenchmarkArguments(
             models=[MODEL_ID]    , training=__UpperCAmelCase    , inference=__UpperCAmelCase    , sequence_lengths=[8]    , batch_sizes=[1]    , multi_process=__UpperCAmelCase    , )
         lowerCAmelCase__			:Tuple											=							PyTorchBenchmark(__UpperCAmelCase    , 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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							'sshleifer/tiny-gpt2'
         lowerCAmelCase__			:str											=							AutoConfig.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:Any											=							PyTorchBenchmarkArguments(
             models=[MODEL_ID]    , training=__UpperCAmelCase    , inference=__UpperCAmelCase    , sequence_lengths=[8]    , batch_sizes=[1]    , multi_process=__UpperCAmelCase    , )
         lowerCAmelCase__			:List[str]											=							PyTorchBenchmark(__UpperCAmelCase    , configs=[config]      )
         lowerCAmelCase__			:Any											=							benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result      )
         self.check_results_dict_not_empty(results.memory_train_result      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							'sshleifer/tinier_bart'
         lowerCAmelCase__			:Dict											=							AutoConfig.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							PyTorchBenchmarkArguments(
             models=[MODEL_ID]    , training=__UpperCAmelCase    , inference=__UpperCAmelCase    , sequence_lengths=[8]    , batch_sizes=[1]    , multi_process=__UpperCAmelCase    , )
         lowerCAmelCase__			:Optional[Any]											=							PyTorchBenchmark(__UpperCAmelCase    , 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      snake_case						(    self      ):



         '''simple docstring'''

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							'sshleifer/tiny-gpt2'

         def _check_summary_is_not_empty(__UpperCAmelCase      ):
           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:
           lowerCAmelCase__			:Optional[int]											=							PyTorchBenchmarkArguments(
               models=[MODEL_ID]    , training=__UpperCAmelCase    , 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    , multi_process=__UpperCAmelCase    , )
           lowerCAmelCase__			:List[Any]											=							PyTorchBenchmark(__UpperCAmelCase      )
           lowerCAmelCase__			:Dict											=							benchmark.run()
           _check_summary_is_not_empty(result.inference_summary      )
           _check_summary_is_not_empty(result.train_summary      )
           self.assertTrue(Path(os.path.join(__UpperCAmelCase    , 'log.txt'      )      ).exists()      )







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__A											=		{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""ReformerTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""ReformerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ReformerAttention""",
      """ReformerForMaskedLM""",
      """ReformerForQuestionAnswering""",
      """ReformerForSequenceClassification""",
      """ReformerLayer""",
      """ReformerModel""",
      """ReformerModelWithLMHead""",
      """ReformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer import ReformerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer_fast import ReformerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_reformer import (
        REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ReformerAttention,
        ReformerForMaskedLM,
        ReformerForQuestionAnswering,
        ReformerForSequenceClassification,
        ReformerLayer,
        ReformerModel,
        ReformerModelWithLMHead,
        ReformerPreTrainedModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
