
"""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   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Dict:
          for model_result in results.values():
               for batch_size, sequence_length in zip(model_result["bs"] ,			model_result["ss"]  ):
                    UpperCAmelCase__							: int 							=						model_result["result"][batch_size][sequence_length]
                    self.assertIsNotNone(__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: Dict 							=						"sshleifer/tiny-gpt2"
          UpperCAmelCase__							: Any 							=						TensorFlowBenchmarkArguments(
              models=[MODEL_ID] ,			training=__UpperCamelCase ,			inference=__UpperCamelCase ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			eager_mode=__UpperCamelCase ,			multi_process=__UpperCamelCase ,			)
          UpperCAmelCase__							: List[str] 							=						TensorFlowBenchmark(__UpperCamelCase  )
          UpperCAmelCase__							: int 							=						benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: List[str] 							=						"sgugger/tiny-distilbert-classification"
          UpperCAmelCase__							: Union[str, Any] 							=						TensorFlowBenchmarkArguments(
              models=[MODEL_ID] ,			training=__UpperCamelCase ,			inference=__UpperCamelCase ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__UpperCamelCase ,			only_pretrain_model=__UpperCamelCase ,			)
          UpperCAmelCase__							: int 							=						TensorFlowBenchmark(__UpperCamelCase  )
          UpperCAmelCase__							: 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  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: List[Any] 							=						"sshleifer/tiny-gpt2"
          UpperCAmelCase__							: int 							=						TensorFlowBenchmarkArguments(
              models=[MODEL_ID] ,			training=__UpperCamelCase ,			inference=__UpperCamelCase ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__UpperCamelCase ,			)
          UpperCAmelCase__							: List[str] 							=						TensorFlowBenchmark(__UpperCamelCase  )
          UpperCAmelCase__							: 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  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: Tuple 							=						"sshleifer/tiny-gpt2"
          UpperCAmelCase__							: Dict 							=						AutoConfig.from_pretrained(__UpperCamelCase  )
          UpperCAmelCase__							: Dict 							=						TensorFlowBenchmarkArguments(
              models=[MODEL_ID] ,			training=__UpperCamelCase ,			inference=__UpperCamelCase ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			eager_mode=__UpperCamelCase ,			multi_process=__UpperCamelCase ,			)
          UpperCAmelCase__							: Optional[Any] 							=						TensorFlowBenchmark(__UpperCamelCase ,			[config]  )
          UpperCAmelCase__							: Dict 							=						benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Union[str, Any] 							=						"sshleifer/tiny-gpt2"
          UpperCAmelCase__							: Dict 							=						AutoConfig.from_pretrained(__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						TensorFlowBenchmarkArguments(
              models=[MODEL_ID] ,			training=__UpperCamelCase ,			inference=__UpperCamelCase ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__UpperCamelCase ,			)
          UpperCAmelCase__							: Tuple 							=						TensorFlowBenchmark(__UpperCamelCase ,			[config]  )
          UpperCAmelCase__							: Any 							=						benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result  )
          self.check_results_dict_not_empty(results.memory_inference_result  )



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: Union[str, Any] 							=						"sshleifer/tiny-gpt2"
          UpperCAmelCase__							: List[Any] 							=						TensorFlowBenchmarkArguments(
              models=[MODEL_ID] ,			training=__UpperCamelCase ,			inference=__UpperCamelCase ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__UpperCamelCase ,			)
          UpperCAmelCase__							: Optional[Any] 							=						TensorFlowBenchmark(__UpperCamelCase  )
          UpperCAmelCase__							: int 							=						benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result  )
          self.check_results_dict_not_empty(results.memory_train_result  )



     def  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: Tuple 							=						"sshleifer/tiny-gpt2"
          UpperCAmelCase__							: str 							=						AutoConfig.from_pretrained(__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						TensorFlowBenchmarkArguments(
              models=[MODEL_ID] ,			training=__UpperCamelCase ,			inference=__UpperCamelCase ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__UpperCamelCase ,			)
          UpperCAmelCase__							: Dict 							=						TensorFlowBenchmark(__UpperCamelCase ,			[config]  )
          UpperCAmelCase__							: str 							=						benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result  )
          self.check_results_dict_not_empty(results.memory_train_result  )



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Optional[Any] 							=						"patrickvonplaten/t5-tiny-random"
          UpperCAmelCase__							: List[str] 							=						AutoConfig.from_pretrained(__UpperCamelCase  )
          UpperCAmelCase__							: Tuple 							=						TensorFlowBenchmarkArguments(
              models=[MODEL_ID] ,			training=__UpperCamelCase ,			inference=__UpperCamelCase ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=__UpperCamelCase ,			)
          UpperCAmelCase__							: Tuple 							=						TensorFlowBenchmark(__UpperCamelCase ,			configs=[config]  )
          UpperCAmelCase__							: 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  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: Any 							=						"sshleifer/tiny-gpt2"
          UpperCAmelCase__							: List[Any] 							=						TensorFlowBenchmarkArguments(
              models=[MODEL_ID] ,			training=__UpperCamelCase ,			inference=__UpperCamelCase ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			use_xla=__UpperCamelCase ,			multi_process=__UpperCamelCase ,			)
          UpperCAmelCase__							: int 							=						TensorFlowBenchmark(__UpperCamelCase  )
          UpperCAmelCase__							: 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  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Any 							=						"sshleifer/tiny-gpt2"
          with tempfile.TemporaryDirectory() as tmp_dir:
               UpperCAmelCase__							: Optional[Any] 							=						TensorFlowBenchmarkArguments(
                   models=[MODEL_ID] ,			inference=__UpperCamelCase ,			save_to_csv=__UpperCamelCase ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			inference_time_csv_file=os.path.join(__UpperCamelCase ,			"inf_time.csv"  ) ,			inference_memory_csv_file=os.path.join(__UpperCamelCase ,			"inf_mem.csv"  ) ,			env_info_csv_file=os.path.join(__UpperCamelCase ,			"env.csv"  ) ,			multi_process=__UpperCamelCase ,			)
               UpperCAmelCase__							: Optional[Any] 							=						TensorFlowBenchmark(__UpperCamelCase  )
               benchmark.run()
               self.assertTrue(Path(os.path.join(__UpperCamelCase ,			"inf_time.csv"  )  ).exists()  )
               self.assertTrue(Path(os.path.join(__UpperCamelCase ,			"inf_mem.csv"  )  ).exists()  )
               self.assertTrue(Path(os.path.join(__UpperCamelCase ,			"env.csv"  )  ).exists()  )



     def  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: List[Any] 							=						"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:
               UpperCAmelCase__							: Optional[Any] 							=						TensorFlowBenchmarkArguments(
                   models=[MODEL_ID] ,			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 ,			eager_mode=__UpperCamelCase ,			multi_process=__UpperCamelCase ,			)
               UpperCAmelCase__							: str 							=						TensorFlowBenchmark(__UpperCamelCase  )
               UpperCAmelCase__							: int 							=						benchmark.run()
               _check_summary_is_not_empty(result.inference_summary  )
               self.assertTrue(Path(os.path.join(__UpperCamelCase ,			"log.txt"  )  ).exists()  )





"""simple docstring"""




from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 7_68 ,			)->					Union[str, Any]:
          super().__init__()

          UpperCAmelCase__							: str 							=						nn.Parameter(torch.zeros(1 ,			__UpperCamelCase  )  )
          UpperCAmelCase__							: Optional[int] 							=						nn.Parameter(torch.ones(1 ,			__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					Any:
          UpperCAmelCase__							: Dict 							=						nn.Parameter(self.mean.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          UpperCAmelCase__							: Any 							=						nn.Parameter(self.std.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          return self



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						(embeds - self.mean) * 1.0 / self.std
          return embeds



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						(embeds * self.std) + self.mean
          return embeds



