



import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  List[str]:



    """simple docstring"""






    A :       str          =   ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

    A :       Optional[int]          =   (
        ("""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		)

    A :       Any          =   model.state_dict()

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

    def create_tf_var(_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		):
        A :       List[Any]          =   tf.dtypes.as_dtype(tensor.dtype		)
        A :       Optional[int]          =   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:
            A :       Tuple          =   to_tf_var_name(_lowerCAmelCase		)
            A :       str          =   state_dict[var_name].numpy()
            if any(x in var_name for x in tensors_to_transpose		):
                A :       Union[str, Any]          =   torch_tensor.T
            A :       Tuple          =   create_tf_var(tensor=_lowerCAmelCase      ,     name=_lowerCAmelCase      ,     session=_lowerCAmelCase		)
            tf.keras.backend.set_value(_lowerCAmelCase      ,     _lowerCAmelCase		)
            A :       int          =   session.run(_lowerCAmelCase		)
            print(f'''Successfully created {tf_name}: {np.allclose(_lowerCAmelCase      ,     _lowerCAmelCase		)}'''		)

        A :       Tuple          =   tf.train.Saver(tf.trainable_variables()		)
        saver.save(_lowerCAmelCase      ,     os.path.join(_lowerCAmelCase      ,     model_name.replace("""-"""      ,     """_"""		) + """.ckpt"""		)		)




def 	__UpperCamelCase	(  _lowerCAmelCase=None		)       ->  int:



    """simple docstring"""






    A :       Any          =   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"""		)
    A :       Optional[int]          =   parser.parse_args(_lowerCAmelCase		)

    A :       int          =   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()







from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:Any             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      "xmod"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=False,		lowerCamelCase__=2,		lowerCamelCase__=False,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=("en_XX",),		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       int          =   vocab_size
        A :       int          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[str]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Dict          =   intermediate_size
        A :       Optional[int]          =   hidden_dropout_prob
        A :       Union[str, Any]          =   attention_probs_dropout_prob
        A :       int          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Union[str, Any]          =   position_embedding_type
        A :       Any          =   use_cache
        A :       int          =   classifier_dropout
        A :       int          =   pre_norm
        A :       List[str]          =   adapter_reduction_factor
        A :       Any          =   adapter_layer_norm
        A :       Any          =   adapter_reuse_layer_norm
        A :       str          =   ln_before_adapter
        A :       Dict          =   list(lowerCamelCase__		)
        A :       Union[str, Any]          =   default_language






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       Any          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       int          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


