





'''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    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Optional[Any]:
     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 							_snake_case   (	self		)    ->							int:
     _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 							_snake_case   (	self		)    ->							Optional[Any]:
     _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 							_snake_case   (	self		)    ->							Any:
     _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 							_snake_case   (	self		)    ->							Tuple:
     _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 							_snake_case   (	self		)    ->							Tuple:
     _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 							_snake_case   (	self		)    ->							List[str]:
     _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 							_snake_case   (	self		)    ->							str:
     _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 							_snake_case   (	self		)    ->							Union[str, Any]:
     _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 							_snake_case   (	self		)    ->							Union[str, Any]:
     _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 							_snake_case   (	self		)    ->							List[str]:
     _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 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      "sshleifer/tiny-gpt2"

     def _check_summary_is_not_empty(_lowerCAmelCase		):
         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()		)









'''simple docstring'''


from ...configuration_utils import PretrainedConfig

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"bert-generation"




 def __init__(	self			,     _lowerCAmelCase=50358			,     _lowerCAmelCase=1024			,     _lowerCAmelCase=24			,     _lowerCAmelCase=16			,     _lowerCAmelCase=4096			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=512			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-12			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1			,     _lowerCAmelCase="absolute"			,     _lowerCAmelCase=True			,     **_lowerCAmelCase			,     )    ->							Tuple:
     super().__init__(pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     **_lowerCAmelCase		)

     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      hidden_size
     _lowerCAmelCase				=      num_hidden_layers
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      intermediate_size
     _lowerCAmelCase				=      hidden_dropout_prob
     _lowerCAmelCase				=      attention_probs_dropout_prob
     _lowerCAmelCase				=      max_position_embeddings
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      position_embedding_type
     _lowerCAmelCase				=      use_cache


