



'''simple docstring'''


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Dict:






   """simple docstring"""
   UpperCamelCase					=  ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

   UpperCamelCase					=  (
       ('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(A__    ):
      os.makedirs(A__    )

   UpperCamelCase					=  model.state_dict()

   def to_tf_var_name(A__    ):
      for patt, repl in iter(A__    ):
         UpperCamelCase					=  name.replace(A__    ,						A__    )
      return F"""bert/{name}"""

   def create_tf_var(A__    ,						A__    ,						A__    ):
      UpperCamelCase					=  tf.dtypes.as_dtype(tensor.dtype    )
      UpperCamelCase					=  tf.get_variable(dtype=A__    ,						shape=tensor.shape    ,						name=A__    ,						initializer=tf.zeros_initializer()    )
      session.run(tf.variables_initializer([tf_var]    )    )
      session.run(A__    )
      return tf_var

   tf.reset_default_graph()
   with tf.Session() as session:
      for var_name in state_dict:
         UpperCamelCase					=  to_tf_var_name(A__    )
         UpperCamelCase					=  state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose    ):
            UpperCamelCase					=  torch_tensor.T
         UpperCamelCase					=  create_tf_var(tensor=A__    ,						name=A__    ,						session=A__    )
         tf.keras.backend.set_value(A__    ,						A__    )
         UpperCamelCase					=  session.run(A__    )
         print(F"""Successfully created {tf_name}: {np.allclose(A__    ,						A__    )}"""    )

      UpperCamelCase					=  tf.train.Saver(tf.trainable_variables()    )
      saver.save(A__    ,						os.path.join(A__    ,						model_name.replace('-'    ,						'_'    ) + '.ckpt'    )    )







def 				__lowerCamelCase				(   A__=None    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  argparse.ArgumentParser()
   parser.add_argument('--model_name'    ,						type=A__    ,						required=A__    ,						help='model name e.g. bert-base-uncased'    )
   parser.add_argument(
       '--cache_dir'    ,						type=A__    ,						default=A__    ,						required=A__    ,						help='Directory containing pytorch model'    )
   parser.add_argument('--pytorch_model_path'    ,						type=A__    ,						required=A__    ,						help='/path/to/<pytorch-model-name>.bin'    )
   parser.add_argument('--tf_cache_dir'    ,						type=A__    ,						required=A__    ,						help='Directory in which to save tensorflow model'    )
   UpperCamelCase					=  parser.parse_args(A__    )

   UpperCamelCase					=  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=A__    ,						ckpt_dir=args.tf_cache_dir    ,						model_name=args.model_name    )


if __name__ == "__main__":
   main()






'''simple docstring'''


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase	:       List[str]      	=	5_0000
_lowerCamelCase	:       Optional[int]      	=	5000

_lowerCamelCase	,_lowerCamelCase	:       int      	=	os.path.split(__file__)
_lowerCamelCase	:       str      	=	os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   for i in range(A__    ):
      UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   for i in range(0    ,						len(A__    )    ,						A__    ):
      UpperCamelCase					=  dataset[i : i + batch_size]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[Any]:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(A__    ):
         UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(0    ,						A__    ,						A__    ):
         UpperCamelCase					=  dataset[i : i + batch_size]







def 				__lowerCamelCase				(   )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  {'num examples': SPEED_TEST_N_EXAMPLES}
   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
       (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]

   UpperCamelCase					=  [
       (read, {'length': SMALL_TEST}),
       (read, {'length': SPEED_TEST_N_EXAMPLES}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
       (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
       (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
       (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print('generating dataset'    )
      UpperCamelCase					=  datasets.Features(
          {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
      UpperCamelCase					=  generate_example_dataset(
          os.path.join(A__    ,						'dataset.arrow'    )    ,						A__    ,						num_examples=A__    ,						seq_shapes={'list': (100,)}    ,						)
      print('first set of iterations'    )
      for func, kwargs in functions:
         print(func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(A__    ,						**A__    )

      print('shuffling dataset'    )
      UpperCamelCase					=  dataset.shuffle()
      print('Second set of iterations (after shuffling'    )
      for func, kwargs in functions_shuffled:
         print('shuffled '    ,						func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(
             A__    ,						**A__    )

   with open(A__    ,						'wb'    ) as f:
      f.write(json.dumps(A__    ).encode('utf-8'    )    )


if __name__ == "__main__":  # useful to run the profiler
   benchmark_iterating()

