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 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						List[Any]		,							A_      :						Any						)     ->			Dict:

       """simple docstring"""






       for model_result in results.values():
             for batch_size, sequence_length in zip(model_result['bs']		,							model_result['ss']						):
                   lowerCamelCase_	  =					model_result['''result'''][batch_size][sequence_length]
                   self.assertIsNotNone(A_						)

 def        a__				(       self      :						List[Any]						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					'''sshleifer/tiny-gpt2'''
       lowerCamelCase_	  =					TensorFlowBenchmarkArguments(
           models=[MODEL_ID]		,							training=A_		,							inference=A_		,							sequence_lengths=[8]		,							batch_sizes=[1]		,							eager_mode=A_		,							multi_process=A_		,							)
       lowerCamelCase_	  =					TensorFlowBenchmark(A_						)
       lowerCamelCase_	  =					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      :						List[str]						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					'''sgugger/tiny-distilbert-classification'''
       lowerCamelCase_	  =					TensorFlowBenchmarkArguments(
           models=[MODEL_ID]		,							training=A_		,							inference=A_		,							sequence_lengths=[8]		,							batch_sizes=[1]		,							multi_process=A_		,							only_pretrain_model=A_		,							)
       lowerCamelCase_	  =					TensorFlowBenchmark(A_						)
       lowerCamelCase_	  =					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						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					'''sshleifer/tiny-gpt2'''
       lowerCamelCase_	  =					TensorFlowBenchmarkArguments(
           models=[MODEL_ID]		,							training=A_		,							inference=A_		,							sequence_lengths=[8]		,							batch_sizes=[1]		,							multi_process=A_		,							)
       lowerCamelCase_	  =					TensorFlowBenchmark(A_						)
       lowerCamelCase_	  =					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						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					'''sshleifer/tiny-gpt2'''
       lowerCamelCase_	  =					AutoConfig.from_pretrained(A_						)
       lowerCamelCase_	  =					TensorFlowBenchmarkArguments(
           models=[MODEL_ID]		,							training=A_		,							inference=A_		,							sequence_lengths=[8]		,							batch_sizes=[1]		,							eager_mode=A_		,							multi_process=A_		,							)
       lowerCamelCase_	  =					TensorFlowBenchmark(A_		,							[config]						)
       lowerCamelCase_	  =					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						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					'''sshleifer/tiny-gpt2'''
       lowerCamelCase_	  =					AutoConfig.from_pretrained(A_						)
       lowerCamelCase_	  =					TensorFlowBenchmarkArguments(
           models=[MODEL_ID]		,							training=A_		,							inference=A_		,							sequence_lengths=[8]		,							batch_sizes=[1]		,							multi_process=A_		,							)
       lowerCamelCase_	  =					TensorFlowBenchmark(A_		,							[config]						)
       lowerCamelCase_	  =					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						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					'''sshleifer/tiny-gpt2'''
       lowerCamelCase_	  =					TensorFlowBenchmarkArguments(
           models=[MODEL_ID]		,							training=A_		,							inference=A_		,							sequence_lengths=[8]		,							batch_sizes=[1]		,							multi_process=A_		,							)
       lowerCamelCase_	  =					TensorFlowBenchmark(A_						)
       lowerCamelCase_	  =					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      :						Optional[int]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					'''sshleifer/tiny-gpt2'''
       lowerCamelCase_	  =					AutoConfig.from_pretrained(A_						)
       lowerCamelCase_	  =					TensorFlowBenchmarkArguments(
           models=[MODEL_ID]		,							training=A_		,							inference=A_		,							sequence_lengths=[8]		,							batch_sizes=[1]		,							multi_process=A_		,							)
       lowerCamelCase_	  =					TensorFlowBenchmark(A_		,							[config]						)
       lowerCamelCase_	  =					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      :						str						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					'''patrickvonplaten/t5-tiny-random'''
       lowerCamelCase_	  =					AutoConfig.from_pretrained(A_						)
       lowerCamelCase_	  =					TensorFlowBenchmarkArguments(
           models=[MODEL_ID]		,							training=A_		,							inference=A_		,							sequence_lengths=[8]		,							batch_sizes=[1]		,							multi_process=A_		,							)
       lowerCamelCase_	  =					TensorFlowBenchmark(A_		,							configs=[config]						)
       lowerCamelCase_	  =					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        a__				(       self      :						int						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					'''sshleifer/tiny-gpt2'''
       lowerCamelCase_	  =					TensorFlowBenchmarkArguments(
           models=[MODEL_ID]		,							training=A_		,							inference=A_		,							sequence_lengths=[8]		,							batch_sizes=[1]		,							use_xla=A_		,							multi_process=A_		,							)
       lowerCamelCase_	  =					TensorFlowBenchmark(A_						)
       lowerCamelCase_	  =					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      :						Tuple						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					'''sshleifer/tiny-gpt2'''
       with tempfile.TemporaryDirectory() as tmp_dir:
             lowerCamelCase_	  =					TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]		,							inference=A_		,							save_to_csv=A_		,							sequence_lengths=[8]		,							batch_sizes=[1]		,							inference_time_csv_file=os.path.join(A_		,							'inf_time.csv'						)		,							inference_memory_csv_file=os.path.join(A_		,							'inf_mem.csv'						)		,							env_info_csv_file=os.path.join(A_		,							'env.csv'						)		,							multi_process=A_		,							)
             lowerCamelCase_	  =					TensorFlowBenchmark(A_						)
             benchmark.run()
             self.assertTrue(Path(os.path.join(A_		,							'inf_time.csv'						)						).exists()						)
             self.assertTrue(Path(os.path.join(A_		,							'inf_mem.csv'						)						).exists()						)
             self.assertTrue(Path(os.path.join(A_		,							'env.csv'						)						).exists()						)





 def        a__				(       self      :						List[str]						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					'''sshleifer/tiny-gpt2'''

       def _check_summary_is_not_empty(A_      :						List[Any]						):
             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:
             lowerCamelCase_	  =					TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]		,							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_		,							eager_mode=A_		,							multi_process=A_		,							)
             lowerCamelCase_	  =					TensorFlowBenchmark(A_						)
             lowerCamelCase_	  =					benchmark.run()
             _check_summary_is_not_empty(result.inference_summary						)
             self.assertTrue(Path(os.path.join(A_		,							'log.txt'						)						).exists()						)









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
 import tensorflow as tf
 from tensorflow.python.eager import context
 from tensorflow.python.framework import ops

 from transformers import GradientAccumulator, create_optimizer







@require_tf
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Optional[int]		,							A_      :						Tuple		,							A_      :						str		,							A_      :						int						)     ->			Any:

       """simple docstring"""






       self.assertEqual(len(A_						)		,							len(A_						)						)
       for a, b in zip(A_		,							A_						):
             self.assertAlmostEqual(A_		,							A_		,							delta=A_						)

 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]						)]						)
       accumulator([tf.constant([-2.0, 1.0]						)]						)
       accumulator([tf.constant([-1.0, 2.0]						)]						)
       with self.assertRaises(A_						):
             accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
       self.assertEqual(accumulator.step		,							3						)
       self.assertEqual(len(accumulator.gradients						)		,							1						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[-2.0, 5.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[0.0, 0.0]		,							tol=1E-2						)





 def        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					None
       ops.enable_eager_execution_internal()
       lowerCamelCase_	  =					tf.config.list_physical_devices('CPU'						)
       if len(A_						) == 1:
             tf.config.set_logical_device_configuration(
                 physical_devices[0]		,							[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       lowerCamelCase_	  =					tf.config.list_logical_devices(device_type='CPU'						)
       lowerCamelCase_	  =					tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
             lowerCamelCase_	  =					GradientAccumulator()
             lowerCamelCase_	  =					tf.Variable([4.0, 3.0]						)
             lowerCamelCase_  ,       lowerCamelCase_	  =					create_optimizer(5E-5		,							10		,							5						)
             lowerCamelCase_	  =					tf.Variable([0.0, 0.0]		,							trainable=A_						)

       def accumulate_on_replica(A_      :						Any						):
             accumulator([gradient]						)

       def apply_on_replica():
             optimizer.apply_gradients(list(zip(accumulator.gradients		,							[variable]						)						)						)

       @tf.function
       def accumulate(A_      :						List[Any]		,							A_      :						Tuple						):
             with strategy.scope():
                   lowerCamelCase_	  =					strategy.experimental_local_results(A_						)
                   local_variables[0].assign(A_						)
                   local_variables[1].assign(A_						)
                   strategy.run(A_		,							args=(gradient_placeholder,)						)

       @tf.function
       def apply_grad():
             with strategy.scope():
                   strategy.run(A_						)

       def _check_local_values(A_      :						List[Any]		,							A_      :						str						):
             lowerCamelCase_	  =					strategy.experimental_local_results(accumulator._gradients[0]						)
             self.assertListAlmostEqual(values[0].value()		,							A_		,							tol=1E-2						)
             self.assertListAlmostEqual(values[1].value()		,							A_		,							tol=1E-2						)

       accumulate([1.0, 2.0]		,							[-1.0, 1.0]						)
       accumulate([3.0, -1.0]		,							[-1.0, -1.0]						)
       accumulate([-2.0, 2.0]		,							[3.0, -2.0]						)
       self.assertEqual(accumulator.step		,							3						)
       _check_local_values([2.0, 3.0]		,							[1.0, -2.0]						)
       apply_grad()
       self.assertListAlmostEqual(variable.value()		,							[4.0, 3.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       _check_local_values([0.0, 0.0]		,							[0.0, 0.0]						)



