






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def      a_ (      _A      ,   _A      ,   _A  )			->			Dict:







				"""simple docstring"""
				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(_A  ):
								os.makedirs(_A  )

				snake_case__									=     model.state_dict()

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

				def create_tf_var(_A      ,   _A      ,   _A  ):
								snake_case__									=     tf.dtypes.as_dtype(tensor.dtype  )
								snake_case__									=     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:
												snake_case__									=     to_tf_var_name(_A  )
												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=_A      ,   name=_A      ,   session=_A  )
												tf.keras.backend.set_value(_A      ,   _A  )
												snake_case__									=     session.run(_A  )
												print(f'''Successfully created {tf_name}: {np.allclose(_A      ,   _A  )}'''  )

								snake_case__									=     tf.train.Saver(tf.trainable_variables()  )
								saver.save(_A      ,   os.path.join(_A      ,   model_name.replace('-'      ,   '_'  ) + '.ckpt'  )  )
def      a_ (      _A=None  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     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'  )
				snake_case__									=     parser.parse_args(_A  )

				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=_A      ,   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


__UpperCamelCase					:		Any	  =						logging.get_logger(__name__)

__UpperCamelCase					:		int	  =						{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "roformer"
							def __init__(     self:      Tuple       ,	UpperCamelCase:      Optional[Any]=5_00_00       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      Any=7_68       ,	UpperCamelCase:      Dict=12       ,	UpperCamelCase:      List[Any]=12       ,	UpperCamelCase:      List[str]=30_72       ,	UpperCamelCase:      int="gelu"       ,	UpperCamelCase:      str=0.1       ,	UpperCamelCase:      Union[str, Any]=0.1       ,	UpperCamelCase:      Any=15_36       ,	UpperCamelCase:      Dict=2       ,	UpperCamelCase:      Dict=0.02       ,	UpperCamelCase:      List[str]=1e-12       ,	UpperCamelCase:      int=0       ,	UpperCamelCase:      Any=False       ,	UpperCamelCase:      int=True       ,	**UpperCamelCase:      List[Any]       ,	)		->		List[str]:
											super().__init__(pad_token_id=UpperCamelCase       ,	**UpperCamelCase						)

											snake_case__									=     vocab_size
											snake_case__									=     hidden_size if embedding_size is None else embedding_size
											snake_case__									=     hidden_size
											snake_case__									=     num_hidden_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     hidden_act
											snake_case__									=     intermediate_size
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     max_position_embeddings
											snake_case__									=     type_vocab_size
											snake_case__									=     initializer_range
											snake_case__									=     layer_norm_eps
											snake_case__									=     rotary_value
											snake_case__									=     use_cache


class   __SCREAMING_SNAKE_CASE(	a_	):
							@property
							def lowerCAmelCase_ (     self:      Any						)		->		Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
															snake_case__									=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															snake_case__									=     {0: 'batch', 1: 'sequence'}
											snake_case__									=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]						)




