
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

    snake_case__    =					(
        ("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(__lowerCAmelCase				):
        os.makedirs(__lowerCAmelCase				)

    snake_case__    =					model.state_dict()

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

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

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

        snake_case__    =					tf.train.Saver(tf.trainable_variables()				)
        saver.save(__lowerCAmelCase     ,		os.path.join(__lowerCAmelCase     ,		model_name.replace("-"     ,		"_"				) + ".ckpt"				)				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase=None				):
    snake_case__    =					argparse.ArgumentParser()
    parser.add_argument("--model_name"     ,		type=__lowerCAmelCase     ,		required=__lowerCAmelCase     ,		help="model name e.g. bert-base-uncased"				)
    parser.add_argument(
        "--cache_dir"     ,		type=__lowerCAmelCase     ,		default=__lowerCAmelCase     ,		required=__lowerCAmelCase     ,		help="Directory containing pytorch model"				)
    parser.add_argument("--pytorch_model_path"     ,		type=__lowerCAmelCase     ,		required=__lowerCAmelCase     ,		help="/path/to/<pytorch-model-name>.bin"				)
    parser.add_argument("--tf_cache_dir"     ,		type=__lowerCAmelCase     ,		required=__lowerCAmelCase     ,		help="Directory in which to save tensorflow model"				)
    snake_case__    =					parser.parse_args(__lowerCAmelCase				)

    snake_case__    =					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=__lowerCAmelCase     ,		ckpt_dir=args.tf_cache_dir     ,		model_name=args.model_name				)


if __name__ == "__main__":
     main()





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


__magic_name__  							=	'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
     import torch

     print('''Torch version:''', torch.__version__)
     print('''Cuda available:''', torch.cuda.is_available())
     print('''Cuda version:''', torch.version.cuda)
     print('''CuDNN version:''', torch.backends.cudnn.version())
     print('''Number of GPUs available:''', torch.cuda.device_count())
     print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
     print('''Torch version:''', None)

try:
     import deepspeed

     print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
     print('''DeepSpeed version:''', None)

try:
     import tensorflow as tf

     print('''TensorFlow version:''', tf.__version__)
     print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
     print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
     print('''TensorFlow version:''', None)



