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 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   str)						->	str:


   """simple docstring"""







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







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    """sshleifer/tiny-gpt2"""
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=A_							, inference=A_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=A_							, )
   _UpperCamelCase           =    PyTorchBenchmark(A_)
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    """sgugger/tiny-distilbert-classification"""
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=A_							, inference=A_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=A_							, only_pretrain_model=A_							, )
   _UpperCamelCase           =    PyTorchBenchmark(A_)
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







 def 		__UpperCAmelCase				(		self				:   int)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    """sshleifer/tiny-gpt2"""
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=A_							, inference=A_							, torchscript=A_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=A_							, )
   _UpperCamelCase           =    PyTorchBenchmark(A_)
   _UpperCamelCase           =    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 		__UpperCAmelCase				(		self				:   Any)						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    """sshleifer/tiny-gpt2"""
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=A_							, inference=A_							, fpaa=A_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=A_							, )
   _UpperCamelCase           =    PyTorchBenchmark(A_)
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







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


   """simple docstring"""







   _UpperCamelCase           =    """sshleifer/tiny-gpt2"""
   _UpperCamelCase           =    AutoConfig.from_pretrained(A_)
   # set architectures equal to `None`
   _UpperCamelCase           =    None
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=A_							, inference=A_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=A_							, )
   _UpperCamelCase           =    PyTorchBenchmark(A_							, configs=[config])
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







 def 		__UpperCAmelCase				(		self				:   List[str])						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    """sshleifer/tiny-gpt2"""
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=A_							, inference=A_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=A_							, )
   _UpperCamelCase           =    PyTorchBenchmark(A_)
   _UpperCamelCase           =    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 		__UpperCAmelCase				(		self				:   List[Any])						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    """sshleifer/tiny-gpt2"""
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=A_							, inference=A_							, sequence_lengths=[8]							, batch_sizes=[1]							, fpaa=A_							, multi_process=A_							, )
   _UpperCamelCase           =    PyTorchBenchmark(A_)
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result)
   self.check_results_dict_not_empty(results.memory_train_result)







 def 		__UpperCAmelCase				(		self				:   int)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    """sshleifer/tiny-gpt2"""
   _UpperCamelCase           =    AutoConfig.from_pretrained(A_)
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=A_							, inference=A_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=A_							, )
   _UpperCamelCase           =    PyTorchBenchmark(A_							, configs=[config])
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







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


   """simple docstring"""







   _UpperCamelCase           =    """sshleifer/tinier_bart"""
   _UpperCamelCase           =    AutoConfig.from_pretrained(A_)
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=A_							, inference=A_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=A_							, )
   _UpperCamelCase           =    PyTorchBenchmark(A_							, configs=[config])
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result)
   self.check_results_dict_not_empty(results.memory_inference_result)







 def 		__UpperCAmelCase				(		self				:   Any)						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    """sshleifer/tiny-gpt2"""
   _UpperCamelCase           =    AutoConfig.from_pretrained(A_)
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=A_							, inference=A_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=A_							, )
   _UpperCamelCase           =    PyTorchBenchmark(A_							, configs=[config])
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result)
   self.check_results_dict_not_empty(results.memory_train_result)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    """sshleifer/tinier_bart"""
   _UpperCamelCase           =    AutoConfig.from_pretrained(A_)
   _UpperCamelCase           =    PyTorchBenchmarkArguments(
       models=[MODEL_ID]							, training=A_							, inference=A_							, sequence_lengths=[8]							, batch_sizes=[1]							, multi_process=A_							, )
   _UpperCamelCase           =    PyTorchBenchmark(A_							, configs=[config])
   _UpperCamelCase           =    benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result)
   self.check_results_dict_not_empty(results.memory_train_result)







 def 		__UpperCAmelCase				(		self				:   str)						->	Optional[int]:


   """simple docstring"""







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







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    """sshleifer/tiny-gpt2"""

   def _check_summary_is_not_empty(lowercase_				:   int):
     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:
     _UpperCamelCase           =    PyTorchBenchmarkArguments(
         models=[MODEL_ID]							, training=A_							, 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_							, multi_process=A_							, )
     _UpperCamelCase           =    PyTorchBenchmark(A_)
     _UpperCamelCase           =    benchmark.run()
     _check_summary_is_not_empty(result.inference_summary)
     _check_summary_is_not_empty(result.train_summary)
     self.assertTrue(Path(os.path.join(A_							, "log.txt")).exists())








from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''ernie_m'''
     __SCREAMING_SNAKE_CASE     					=    {"dropout": "classifier_dropout", "num_classes": "num_labels"}


     def __init__(  self							,					A_ = 25_00_02							,					A_ = 7_68							,					A_ = 12							,					A_ = 12							,					A_ = 30_72							,					A_ = "gelu"							,					A_ = 0.1							,					A_ = 0.1							,					A_ = 5_14							,					A_ = 0.0_2							,					A_ = 1							,					A_ = 1e-05							,					A_=None							,					A_=False							,					A_=0.0							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					**A_    )
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							List[str]							=     hidden_size
       _UpperCAmelCase		:							Optional[int]							=     num_hidden_layers
       _UpperCAmelCase		:							Tuple							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     intermediate_size
       _UpperCAmelCase		:							List[Any]							=     hidden_act
       _UpperCAmelCase		:							Any							=     hidden_dropout_prob
       _UpperCAmelCase		:							Any							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Tuple							=     max_position_embeddings
       _UpperCAmelCase		:							Dict							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							str							=     classifier_dropout
       _UpperCAmelCase		:							Any							=     is_decoder
       _UpperCAmelCase		:							Union[str, Any]							=     act_dropout





