



"""simple docstring"""

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):

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       Union[str, Any]				):





     for model_result in results.values():
         for batch_size, sequence_length in zip(model_result['''bs''']  ,     model_result['''ss''']				):
             snake_case_ :     Optional[Any]				=	model_result['result'][batch_size][sequence_length]
             self.assertIsNotNone(lowercase_				)

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Optional[Any]				=	'sshleifer/tiny-gpt2'
     snake_case_ :     Dict				=	TensorFlowBenchmarkArguments(
         models=[MODEL_ID]  ,     training=lowercase_  ,     inference=lowercase_  ,     sequence_lengths=[8]  ,     batch_sizes=[1]  ,     eager_mode=lowercase_  ,     multi_process=lowercase_  ,     )
     snake_case_ :     Tuple				=	TensorFlowBenchmark(lowercase_				)
     snake_case_ :     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		:       str				):
     snake_case_ :     Dict				=	'sgugger/tiny-distilbert-classification'
     snake_case_ :     int				=	TensorFlowBenchmarkArguments(
         models=[MODEL_ID]  ,     training=lowercase_  ,     inference=lowercase_  ,     sequence_lengths=[8]  ,     batch_sizes=[1]  ,     multi_process=lowercase_  ,     only_pretrain_model=lowercase_  ,     )
     snake_case_ :     int				=	TensorFlowBenchmark(lowercase_				)
     snake_case_ :     Tuple				=	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		:       Tuple				):
     snake_case_ :     str				=	'sshleifer/tiny-gpt2'
     snake_case_ :     Any				=	TensorFlowBenchmarkArguments(
         models=[MODEL_ID]  ,     training=lowercase_  ,     inference=lowercase_  ,     sequence_lengths=[8]  ,     batch_sizes=[1]  ,     multi_process=lowercase_  ,     )
     snake_case_ :     List[str]				=	TensorFlowBenchmark(lowercase_				)
     snake_case_ :     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 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     List[str]				=	'sshleifer/tiny-gpt2'
     snake_case_ :     str				=	AutoConfig.from_pretrained(lowercase_				)
     snake_case_ :     List[str]				=	TensorFlowBenchmarkArguments(
         models=[MODEL_ID]  ,     training=lowercase_  ,     inference=lowercase_  ,     sequence_lengths=[8]  ,     batch_sizes=[1]  ,     eager_mode=lowercase_  ,     multi_process=lowercase_  ,     )
     snake_case_ :     int				=	TensorFlowBenchmark(lowercase_  ,     [config]				)
     snake_case_ :     Optional[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		:       Optional[Any]				):
     snake_case_ :     List[str]				=	'sshleifer/tiny-gpt2'
     snake_case_ :     int				=	AutoConfig.from_pretrained(lowercase_				)
     snake_case_ :     Any				=	TensorFlowBenchmarkArguments(
         models=[MODEL_ID]  ,     training=lowercase_  ,     inference=lowercase_  ,     sequence_lengths=[8]  ,     batch_sizes=[1]  ,     multi_process=lowercase_  ,     )
     snake_case_ :     Tuple				=	TensorFlowBenchmark(lowercase_  ,     [config]				)
     snake_case_ :     Tuple				=	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		:       Tuple				):
     snake_case_ :     Optional[int]				=	'sshleifer/tiny-gpt2'
     snake_case_ :     Tuple				=	TensorFlowBenchmarkArguments(
         models=[MODEL_ID]  ,     training=lowercase_  ,     inference=lowercase_  ,     sequence_lengths=[8]  ,     batch_sizes=[1]  ,     multi_process=lowercase_  ,     )
     snake_case_ :     Optional[int]				=	TensorFlowBenchmark(lowercase_				)
     snake_case_ :     str				=	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		:       Any				):
     snake_case_ :     Tuple				=	'sshleifer/tiny-gpt2'
     snake_case_ :     List[str]				=	AutoConfig.from_pretrained(lowercase_				)
     snake_case_ :     int				=	TensorFlowBenchmarkArguments(
         models=[MODEL_ID]  ,     training=lowercase_  ,     inference=lowercase_  ,     sequence_lengths=[8]  ,     batch_sizes=[1]  ,     multi_process=lowercase_  ,     )
     snake_case_ :     List[Any]				=	TensorFlowBenchmark(lowercase_  ,     [config]				)
     snake_case_ :     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		:       Union[str, Any]				):
     snake_case_ :     Tuple				=	'patrickvonplaten/t5-tiny-random'
     snake_case_ :     List[str]				=	AutoConfig.from_pretrained(lowercase_				)
     snake_case_ :     List[str]				=	TensorFlowBenchmarkArguments(
         models=[MODEL_ID]  ,     training=lowercase_  ,     inference=lowercase_  ,     sequence_lengths=[8]  ,     batch_sizes=[1]  ,     multi_process=lowercase_  ,     )
     snake_case_ :     int				=	TensorFlowBenchmark(lowercase_  ,     configs=[config]				)
     snake_case_ :     Optional[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(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''				)				) == 0  ,     '''Cannot do xla on CPU.'''				)
 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     int				=	'sshleifer/tiny-gpt2'
     snake_case_ :     Union[str, Any]				=	TensorFlowBenchmarkArguments(
         models=[MODEL_ID]  ,     training=lowercase_  ,     inference=lowercase_  ,     sequence_lengths=[8]  ,     batch_sizes=[1]  ,     use_xla=lowercase_  ,     multi_process=lowercase_  ,     )
     snake_case_ :     Union[str, Any]				=	TensorFlowBenchmark(lowercase_				)
     snake_case_ :     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		:       List[str]				):
     snake_case_ :     List[Any]				=	'sshleifer/tiny-gpt2'
     with tempfile.TemporaryDirectory() as tmp_dir:
         snake_case_ :     List[Any]				=	TensorFlowBenchmarkArguments(
             models=[MODEL_ID]  ,     inference=lowercase_  ,     save_to_csv=lowercase_  ,     sequence_lengths=[8]  ,     batch_sizes=[1]  ,     inference_time_csv_file=os.path.join(lowercase_  ,     '''inf_time.csv'''				)  ,     inference_memory_csv_file=os.path.join(lowercase_  ,     '''inf_mem.csv'''				)  ,     env_info_csv_file=os.path.join(lowercase_  ,     '''env.csv'''				)  ,     multi_process=lowercase_  ,     )
         snake_case_ :     List[str]				=	TensorFlowBenchmark(lowercase_				)
         benchmark.run()
         self.assertTrue(Path(os.path.join(lowercase_  ,     '''inf_time.csv'''				)				).exists()				)
         self.assertTrue(Path(os.path.join(lowercase_  ,     '''inf_mem.csv'''				)				).exists()				)
         self.assertTrue(Path(os.path.join(lowercase_  ,     '''env.csv'''				)				).exists()				)






 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Optional[Any]				=	'sshleifer/tiny-gpt2'

     def _check_summary_is_not_empty(lowercase_		:       Union[str, Any]				):
         self.assertTrue(hasattr(lowercase_  ,     '''sequential'''				)				)
         self.assertTrue(hasattr(lowercase_  ,     '''cumulative'''				)				)
         self.assertTrue(hasattr(lowercase_  ,     '''current'''				)				)
         self.assertTrue(hasattr(lowercase_  ,     '''total'''				)				)

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






import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')





def    __lowercase     (						lowerCamelCase					:     Any	):
 UpperCamelCase_					:						Union[str, Any] =					test_file.split(os.path.sep	)
 if components[0:2] != ["tests", "models"]:
  raise ValueError(
      '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
      F"{test_file} instead."	)
 UpperCamelCase_					:						str =					components[-1]
 if not test_fn.endswith('py'	):
  raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead."	)
 if not test_fn.startswith('test_modeling_'	):
  raise ValueError(
      F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."	)

 UpperCamelCase_					:						Union[str, Any] =					components[:-1] + [test_fn.replace('.py'  ,  ''	)]
 UpperCamelCase_					:						List[Any] =					'.'.join(lowerCamelCase	)

 return test_module_path





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						List[Any] =					get_module_path(lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					importlib.import_module(lowerCamelCase	)

 return test_module





def    __lowercase     (						lowerCamelCase					:     List[str]	):
 UpperCamelCase_					:						int =					[]
 UpperCamelCase_					:						Tuple =					get_test_module(lowerCamelCase	)
 for attr in dir(lowerCamelCase	):
  if attr.endswith('ModelTester'	):
   tester_classes.append(getattr(lowerCamelCase  ,  lowerCamelCase	)	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						List[str] =					[]
 UpperCamelCase_					:						Union[str, Any] =					get_test_module(lowerCamelCase	)
 for attr in dir(lowerCamelCase	):
  UpperCamelCase_					:						Dict =					getattr(lowerCamelCase  ,  lowerCamelCase	)
  # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
  # `all_model_classes` is not empty (which also excludes other special classes).
  UpperCamelCase_					:						Optional[int] =					getattr(lowerCamelCase  ,  'all_model_classes'  ,  []	)
  if len(lowerCamelCase	) > 0:
   test_classes.append(lowerCamelCase	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     Dict	):
 UpperCamelCase_					:						int =					get_test_classes(lowerCamelCase	)
 UpperCamelCase_					:						List[Any] =					set()
 for test_class in test_classes:
  model_classes.update(test_class.all_model_classes	)

 # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     Tuple	):
 UpperCamelCase_					:						int =					test_class()
 if hasattr(lowerCamelCase  ,  'setUp'	):
  test.setUp()

 UpperCamelCase_					:						List[Any] =					None
 if hasattr(lowerCamelCase  ,  'model_tester'	):
  # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
  if test.model_tester is not None:
   UpperCamelCase_					:						Optional[Any] =					test.model_tester.__class__

 return model_tester





def    __lowercase     (						lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Dict	):
 UpperCamelCase_					:						Optional[Any] =					get_test_classes(lowerCamelCase	)

 UpperCamelCase_					:						Tuple =					[]
 for test_class in test_classes:
  if model_class in test_class.all_model_classes:
   target_test_classes.append(lowerCamelCase	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     Tuple	):
 UpperCamelCase_					:						List[Any] =					get_test_classes_for_model(lowerCamelCase  ,  lowerCamelCase	)

 UpperCamelCase_					:						int =					[]
 for test_class in test_classes:
  UpperCamelCase_					:						Tuple =					get_model_tester_from_test_class(lowerCamelCase	)
  if tester_class is not None:
   tester_classes.append(lowerCamelCase	)

    # sort with class names
 return sorted(lowerCamelCase  ,  key=lambda lowerCamelCase	: x.__name__	)





def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Tuple =					get_test_classes(lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					{test_class: get_model_tester_from_test_class(lowerCamelCase	) for test_class in test_classes}
 return test_tester_mapping





def    __lowercase     (						lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[str] =					get_model_classes(lowerCamelCase	)
 UpperCamelCase_					:						int =					{
     model_class: get_test_classes_for_model(lowerCamelCase  ,  lowerCamelCase	) for model_class in model_classes
 }
 return model_test_mapping





def    __lowercase     (						lowerCamelCase					:     Tuple	):
 UpperCamelCase_					:						Tuple =					get_model_classes(lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					{
     model_class: get_tester_classes_for_model(lowerCamelCase  ,  lowerCamelCase	) for model_class in model_classes
 }
 return model_to_tester_mapping






def    __lowercase     (						lowerCamelCase					:     Any	):
 if isinstance(lowerCamelCase  ,  lowerCamelCase	):
  return o
 elif isinstance(lowerCamelCase  ,  lowerCamelCase	):
  return o.__name__
 elif isinstance(lowerCamelCase  ,  (list, tuple)	):
  return [to_json(lowerCamelCase	) for x in o]
 elif isinstance(lowerCamelCase  ,  lowerCamelCase	):
  return {to_json(lowerCamelCase	): to_json(lowerCamelCase	) for k, v in o.items()}
 else:
  return o



