
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowerCamelCase__    :		int          =   logging.get_logger(__name__)

def 		UpperCamelCase	(      lowercase_=None , lowercase_=None   )		->	Tuple:


  '''simple docstring'''






  return field(default_factory=lambda: default , metadata=lowercase_   )



@dataclass
class       _snake_case   :
 __lowerCAmelCase  :     List[str]        =			list_field(
     default=[]  ,						metadata={
         'help': (
             'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
             ' of all available models'
         )
     }  ,						)

 __lowerCAmelCase  :     List[int]        =			list_field(
     default=[8]  ,						metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}							)

 __lowerCAmelCase  :     List[int]        =			list_field(
     default=[8, 32, 128, 512]  ,						metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}  ,						)

 __lowerCAmelCase  :     bool        =			field(
     default=UpperCAmelCase_  ,						metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}  ,						)
 __lowerCAmelCase  :     bool        =			field(
     default=UpperCAmelCase_  ,						metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}  ,						)
 __lowerCAmelCase  :     bool        =			field(
     default=UpperCAmelCase_  ,						metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}							)
 __lowerCAmelCase  :     bool        =			field(default=UpperCAmelCase_  ,						metadata={'help': 'Use FP16 to accelerate inference.'}							)
 __lowerCAmelCase  :     bool        =			field(default=UpperCAmelCase_  ,						metadata={'help': 'Benchmark training of model'}							)
 __lowerCAmelCase  :     bool        =			field(default=UpperCAmelCase_  ,						metadata={'help': 'Verbose memory tracing'}							)
 __lowerCAmelCase  :     bool        =			field(
     default=UpperCAmelCase_  ,						metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}  ,						)
 __lowerCAmelCase  :     bool        =			field(
     default=UpperCAmelCase_  ,						metadata={
         'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
     }  ,						)
 __lowerCAmelCase  :     bool        =			field(default=UpperCAmelCase_  ,						metadata={'help': 'Trace memory line by line'}							)
 __lowerCAmelCase  :     bool        =			field(default=UpperCAmelCase_  ,						metadata={'help': 'Save result to a CSV file'}							)
 __lowerCAmelCase  :     bool        =			field(default=UpperCAmelCase_  ,						metadata={'help': 'Save all print statements in a log file'}							)
 __lowerCAmelCase  :     bool        =			field(default=UpperCAmelCase_  ,						metadata={'help': 'Whether to print environment information'}							)
 __lowerCAmelCase  :     bool        =			field(
     default=UpperCAmelCase_  ,						metadata={
         'help': (
             'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
             ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
             ' for debugging / testing and on TPU.'
         )
     }  ,						)
 __lowerCAmelCase  :     str        =			field(
     default=f'''inference_time_{round(time()							)}.csv'''  ,						metadata={'help': 'CSV filename used if saving time results to csv.'}  ,						)
 __lowerCAmelCase  :     str        =			field(
     default=f'''inference_memory_{round(time()							)}.csv'''  ,						metadata={'help': 'CSV filename used if saving memory results to csv.'}  ,						)
 __lowerCAmelCase  :     str        =			field(
     default=f'''train_time_{round(time()							)}.csv'''  ,						metadata={'help': 'CSV filename used if saving time results to csv for training.'}  ,						)
 __lowerCAmelCase  :     str        =			field(
     default=f'''train_memory_{round(time()							)}.csv'''  ,						metadata={'help': 'CSV filename used if saving memory results to csv for training.'}  ,						)
 __lowerCAmelCase  :     str        =			field(
     default=f'''env_info_{round(time()							)}.csv'''  ,						metadata={'help': 'CSV filename used if saving environment information.'}  ,						)
 __lowerCAmelCase  :     str        =			field(
     default=f'''log_{round(time()							)}.csv'''  ,						metadata={'help': 'Log filename used if print statements are saved in log.'}  ,						)
 __lowerCAmelCase  :     int        =			field(default=3  ,						metadata={'help': 'Times an experiment will be run.'}							)
 __lowerCAmelCase  :     bool        =			field(
     default=UpperCAmelCase_  ,						metadata={
         'help': (
             'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
             ' model weights.'
         )
     }  ,						)


 def      lowercase__     (					self):


   '''simple docstring'''




   warnings.warn(
       f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
       """ are deprecated in general and it is advised to use external Benchmarking libraries """
       """ to benchmark Transformer models."""					,  SCREAMING_SNAKE_CASE_					,  )


 def      lowercase__     (					self):


   '''simple docstring'''




   return json.dumps(dataclasses.asdict(self)					,  indent=2)


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   if len(self.models) <= 0:
     raise ValueError(
         """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
         """ bert-base-cased` or `args.models = ['bert-base-cased'].""")
   return self.models



 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   if not self.multi_process:
     return False
   elif self.is_tpu:
     logger.info("""Multiprocessing is currently not possible on TPU.""")
     return False
   else:
     return True








import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Dict:


  '''simple docstring'''






  lowercase__  :   Dict    		=      ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

  lowercase__  :   Union[str, Any]    		=      (
      ("""layer.""", """layer_"""),
      ("""word_embeddings.weight""", """word_embeddings"""),
      ("""position_embeddings.weight""", """position_embeddings"""),
      ("""token_type_embeddings.weight""", """token_type_embeddings"""),
      (""".""", """/"""),
      ("""LayerNorm/weight""", """LayerNorm/gamma"""),
      ("""LayerNorm/bias""", """LayerNorm/beta"""),
      ("""weight""", """kernel"""),
  )

  if not os.path.isdir(lowercase_   ):
    os.makedirs(lowercase_   )

  lowercase__  :   Optional[Any]    		=      model.state_dict()

  def to_tf_var_name(lowercase_   ):
    for patt, repl in iter(lowercase_   ):
      lowercase__  :   str    		=      name.replace(lowercase_ , lowercase_   )
    return F'bert/{name}'

  def create_tf_var(lowercase_ , lowercase_ , lowercase_   ):
    lowercase__  :   List[str]    		=      tf.dtypes.as_dtype(tensor.dtype   )
    lowercase__  :   Tuple    		=      tf.get_variable(dtype=lowercase_ , shape=tensor.shape , name=lowercase_ , initializer=tf.zeros_initializer()   )
    session.run(tf.variables_initializer([tf_var]   )   )
    session.run(lowercase_   )
    return tf_var

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      lowercase__  :   Any    		=      to_tf_var_name(lowercase_   )
      lowercase__  :   Dict    		=      state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose   ):
        lowercase__  :   Optional[int]    		=      torch_tensor.T
      lowercase__  :   Any    		=      create_tf_var(tensor=lowercase_ , name=lowercase_ , session=lowercase_   )
      tf.keras.backend.set_value(lowercase_ , lowercase_   )
      lowercase__  :   Optional[int]    		=      session.run(lowercase_   )
      print(F'Successfully created {tf_name}: {np.allclose(lowercase_ , lowercase_   )}'   )

    lowercase__  :   List[Any]    		=      tf.train.Saver(tf.trainable_variables()   )
    saver.save(lowercase_ , os.path.join(lowercase_ , model_name.replace("""-""" , """_"""   ) + """.ckpt"""   )   )

def 		UpperCamelCase	(      lowercase_=None   )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   List[str]    		=      argparse.ArgumentParser()
  parser.add_argument("""--model_name""" , type=lowercase_ , required=lowercase_ , help="""model name e.g. bert-base-uncased"""   )
  parser.add_argument(
      """--cache_dir""" , type=lowercase_ , default=lowercase_ , required=lowercase_ , help="""Directory containing pytorch model"""   )
  parser.add_argument("""--pytorch_model_path""" , type=lowercase_ , required=lowercase_ , help="""/path/to/<pytorch-model-name>.bin"""   )
  parser.add_argument("""--tf_cache_dir""" , type=lowercase_ , required=lowercase_ , help="""Directory in which to save tensorflow model"""   )
  lowercase__  :   List[str]    		=      parser.parse_args(lowercase_   )

  lowercase__  :   Any    		=      BertModel.from_pretrained(
      pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path   ) , cache_dir=args.cache_dir , )

  convert_pytorch_checkpoint_to_tf(model=lowercase_ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name   )


if __name__ == "__main__":
     main()






