



import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			A__       =    ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

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

			A__       =    model.state_dict()

			def to_tf_var_name(__UpperCamelCase					):
						for patt, repl in iter(__UpperCamelCase					):
									A__       =    name.replace(__UpperCamelCase ,	__UpperCamelCase					)
						return f'''bert/{name}'''

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

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

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


def A						(       __UpperCamelCase=None					)						->      Optional[Any]:
			A__       =    argparse.ArgumentParser()
			parser.add_argument('--model_name' ,	type=__UpperCamelCase ,	required=__UpperCamelCase ,	help='model name e.g. bert-base-uncased'					)
			parser.add_argument(
			    '--cache_dir' ,	type=__UpperCamelCase ,	default=__UpperCamelCase ,	required=__UpperCamelCase ,	help='Directory containing pytorch model'					)
			parser.add_argument('--pytorch_model_path' ,	type=__UpperCamelCase ,	required=__UpperCamelCase ,	help='/path/to/<pytorch-model-name>.bin'					)
			parser.add_argument('--tf_cache_dir' ,	type=__UpperCamelCase ,	required=__UpperCamelCase ,	help='Directory in which to save tensorflow model'					)
			A__       =    parser.parse_args(__UpperCamelCase					)

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


if __name__ == "__main__":
							main()









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[str]			       =    "roberta"



					def __init__(     self					:       List[str]				,					_snake_case					:       Union[str, Any]=5_02_65				,					_snake_case					:       List[Any]=7_68				,					_snake_case					:       List[str]=12				,					_snake_case					:       List[str]=12				,					_snake_case					:       Any=30_72				,					_snake_case					:       Union[str, Any]="gelu"				,					_snake_case					:       int=0.1				,					_snake_case					:       Union[str, Any]=0.1				,					_snake_case					:       Tuple=5_12				,					_snake_case					:       Union[str, Any]=2				,					_snake_case					:       Any=0.02				,					_snake_case					:       Any=1E-12				,					_snake_case					:       List[Any]=1				,					_snake_case					:       int=0				,					_snake_case					:       Any=2				,					_snake_case					:       Optional[Any]="absolute"				,					_snake_case					:       int=True				,					_snake_case					:       Any=None				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


								super().__init__(pad_token_id=_snake_case				,					bos_token_id=_snake_case				,					eos_token_id=_snake_case				,					**_snake_case					)

								A__       =    vocab_size
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    hidden_act
								A__       =    intermediate_size
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    max_position_embeddings
								A__       =    type_vocab_size
								A__       =    initializer_range
								A__       =    layer_norm_eps
								A__       =    position_embedding_type
								A__       =    use_cache
								A__       =    classifier_dropout







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@property
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								if self.task == "multiple-choice":
											A__       =    {0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
											A__       =    {0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ]					)




