"""simple docstring"""





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def  lowercase_ ( _lowerCamelCase:						Optional[int]    , _lowerCamelCase:						Optional[int]    , _lowerCamelCase:						Optional[int]   )			->   Dict:



							'''simple docstring'''

							__lowerCamelCase       :		List[Any]		 =  ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

							__lowerCamelCase       :		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(A__   ):
														os.makedirs(A__   )

							__lowerCamelCase       :		Optional[int]		 =  model.state_dict()

							def to_tf_var_name(_lowerCamelCase:						str   ):
														for patt, repl in iter(A__   ):
																					__lowerCamelCase       :		Optional[int]		 =  name.replace(A__    , A__   )
														return F"""bert/{name}"""

							def create_tf_var(_lowerCamelCase:						Optional[int]    , _lowerCamelCase:						List[str]    , _lowerCamelCase:						Optional[int]   ):
														__lowerCamelCase       :		int		 =  tf.dtypes.as_dtype(tensor.dtype   )
														__lowerCamelCase       :		List[Any]		 =  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:
																					__lowerCamelCase       :		Tuple		 =  to_tf_var_name(A__   )
																					__lowerCamelCase       :		Optional[int]		 =  state_dict[var_name].numpy()
																					if any(x in var_name for x in tensors_to_transpose   ):
																												__lowerCamelCase       :		List[str]		 =  torch_tensor.T
																					__lowerCamelCase       :		List[str]		 =  create_tf_var(tensor=A__    , name=A__    , session=A__   )
																					tf.keras.backend.set_value(A__    , A__   )
																					__lowerCamelCase       :		Optional[Any]		 =  session.run(A__   )
																					print(F"""Successfully created {tf_name}: {np.allclose(A__    , A__   )}"""   )

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


def  lowercase_ ( _lowerCamelCase:						int=None   )			->   List[Any]:



							'''simple docstring'''

							__lowerCamelCase       :		Optional[Any]		 =  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"   )
							__lowerCamelCase       :		str		 =  parser.parse_args(A__   )

							__lowerCamelCase       :		Optional[Any]		 =  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"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = '''T5Config'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig



