






import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)







class  _lowercase   (   nn.Module						):
      def __init__(     self   ):
       super().__init__()
       snake_case__  :    Tuple							       =nn.Linear(3				,			4   )
       snake_case__  :    str							       =nn.BatchNormad(4   )
       snake_case__  :    Optional[Any]							       =nn.Linear(4				,			5   )



      def 						lowercase__					(     self				,			a   ):
       return self.lineara(self.batchnorm(self.lineara(a   )   )   )







class  _lowercase   (   unittest.TestCase						):
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =ModelForTest()
       with TemporaryDirectory() as tmp_dir:
        offload_state_dict(a				,			model.state_dict()   )
        snake_case__  :    Dict							       =os.path.join(a				,			"""index.json"""   )
        self.assertTrue(os.path.isfile(a   )   )
        # TODO: add tests on what is inside the index

        for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
         snake_case__  :    Union[str, Any]							       =os.path.join(a				,			F"{key}.dat"   )
         self.assertTrue(os.path.isfile(a   )   )
         # TODO: add tests on the fact weights are properly loaded
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =[torch.floataa, torch.floataa, torch.bfloataa]

       for dtype in dtypes:
        snake_case__  :    Optional[Any]							       =torch.randn(2				,			3				,			dtype=a   )
        with TemporaryDirectory() as tmp_dir:
         snake_case__  :    Dict							       =offload_weight(a				,			"""weight"""				,			a				,			{}   )
         snake_case__  :    List[Any]							       =os.path.join(a				,			"""weight.dat"""   )
         self.assertTrue(os.path.isfile(a   )   )
         self.assertDictEqual(a				,			{"""weight""": {"""shape""": [2, 3], """dtype""": str(a   ).split("""."""   )[1]}}   )

         snake_case__  :    int							       =load_offloaded_weight(a				,			index["""weight"""]   )
         self.assertTrue(torch.equal(a				,			a   )   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =ModelForTest()
       snake_case__  :    Any							       =model.state_dict()
       snake_case__  :    str							       ={k: v for k, v in state_dict.items() if """linear2""" not in k}
       snake_case__  :    Optional[int]							       ={k: v for k, v in state_dict.items() if """linear2""" in k}

       with TemporaryDirectory() as tmp_dir:
        offload_state_dict(a				,			a   )
        snake_case__  :    str							       =OffloadedWeightsLoader(state_dict=a				,			save_folder=a   )

        # Every key is there with the right value
        self.assertEqual(sorted(a   )				,			sorted(state_dict.keys()   )   )
        for key, param in state_dict.items():
         self.assertTrue(torch.allclose(a				,			weight_map[key]   )   )

       snake_case__  :    int							       ={k: v for k, v in state_dict.items() if """weight""" in k}
       snake_case__  :    int							       ={k: v for k, v in state_dict.items() if """weight""" not in k}

       with TemporaryDirectory() as tmp_dir:
        offload_state_dict(a				,			a   )
        snake_case__  :    Optional[Any]							       =OffloadedWeightsLoader(state_dict=a				,			save_folder=a   )

        # Every key is there with the right value
        self.assertEqual(sorted(a   )				,			sorted(state_dict.keys()   )   )
        for key, param in state_dict.items():
         self.assertTrue(torch.allclose(a				,			weight_map[key]   )   )

       with TemporaryDirectory() as tmp_dir:
        offload_state_dict(a				,			a   )
        # Duplicates are removed
        snake_case__  :    Union[str, Any]							       =OffloadedWeightsLoader(state_dict=a				,			save_folder=a   )

        # Every key is there with the right value
        self.assertEqual(sorted(a   )				,			sorted(state_dict.keys()   )   )
        for key, param in state_dict.items():
         self.assertTrue(torch.allclose(a				,			weight_map[key]   )   )



      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       ={"""a.1""": 0, """a.10""": 1, """a.2""": 2}
       snake_case__  :    Any							       =extract_submodules_state_dict(a				,			["""a.1""", """a.2"""]   )
       self.assertDictEqual(a				,			{"""a.1""": 0, """a.2""": 2}   )

       snake_case__  :    List[str]							       ={"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
       snake_case__  :    Union[str, Any]							       =extract_submodules_state_dict(a				,			["""a.1""", """a.2"""]   )
       self.assertDictEqual(a				,			{"""a.1.a""": 0, """a.2.a""": 2}   )













import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()







def 			A__							(      _a  :					int		,  _a  :					Any		,  _a  :					Union[str, Any]		,  _a  :					Tuple	):





 '''simple docstring'''







 snake_case__  :    Any							       =BigBirdConfig.from_json_file(_a	)
 print(f"Building PyTorch model from configuration: {config}"	)

 if is_trivia_qa:
  snake_case__  :    str							       =BigBirdForQuestionAnswering(_a	)
 else:
  snake_case__  :    Optional[int]							       =BigBirdForPreTraining(_a	)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_a		,  _a		,  is_trivia_qa=_a	)

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"	)
 model.save_pretrained(_a	)


if __name__ == "__main__":
       __lowerCamelCase	:		Union[str, Any]										=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--big_bird_config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained BERT model. \n"""
               """This specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
       )
       __lowerCamelCase	:		Tuple										=   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





