





from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


__lowerCamelCase       :     Dict   =     logging.get_logger(__name__)


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset

    from transformers import Trainer



    class 					A__   (       __snake_case			):





          def __init__( self       ,							A_ = 101   ):
           '''simple docstring'''




           UpperCamelCase	:					Dict       	=   length





          def __len__( self   ):
           '''simple docstring'''




           return self.length





          def __getitem__( self       ,							A_   ):
           '''simple docstring'''




           return i



    class 					A__   :





          def __call__( self       ,							A_   ):
           '''simple docstring'''




           return {"input_ids": torch.tensor(A_   ), "labels": torch.tensor(A_   )}



    class 					A__   (       nn.Module			):





          def __init__( self   ):
           '''simple docstring'''




           super().__init__()
           # Add some (unused) params otherwise DDP will complain.
           UpperCamelCase	:					Optional[int]       	=   nn.Linear(120       ,							80   )





          def        __UpperCamelCase( self       ,							A_       ,							A_=None   ):
           '''simple docstring'''




           if labels is not None:
            return torch.tensor(0.0       ,							device=input_ids.device   ), input_ids
           else:
            return input_ids



class 					A__   (       __snake_case			):





      @require_torch_neuroncore
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
       UpperCamelCase	:					Optional[int]       	=   self.get_auto_remove_tmp_dir()
       UpperCamelCase	:					List[str]       	=   F"""--output_dir {output_dir}""".split()
       UpperCamelCase	:					Optional[int]       	=   ["torchrun"] + distributed_args + args
       execute_subprocess_async(A_       ,							env=self.get_env()   )
       # successful return here == success - any errors would have caused an error in the sub-call



class 					A__   (       __snake_case			):





      @require_torch_multi_gpu
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
       UpperCamelCase	:					Optional[Any]       	=   self.get_auto_remove_tmp_dir()
       UpperCamelCase	:					List[str]       	=   F"""--output_dir {output_dir}""".split()
       UpperCamelCase	:					Optional[Any]       	=   ["torchrun"] + distributed_args + args
       execute_subprocess_async(A_       ,							env=self.get_env()   )
       # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
    # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
    #
    # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

    __lowerCamelCase       :     List[str]   =     HfArgumentParser((TrainingArguments,))
    __lowerCamelCase       :     List[str]   =     parser.parse_args_into_dataclasses()[0]

    logger.warning(
        f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
        f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
    )

    # Essentially, what we want to verify in the distributed case is that we get all samples back,
    # in the right order. (this is crucial for prediction for instance)
    for dataset_length in [101, 40, 7]:
        __lowerCamelCase       :     str   =     DummyDataset(dataset_length)



        def 		A_				(     _lowerCAmelCase     )						-> Dict:
         UpperCamelCase	:					Optional[Any]       	=   list(range(len(_lowerCAmelCase     )     )     )
         UpperCamelCase	:					List[Any]       	=   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         if not success and training_args.local_rank == 0:
          logger.warning(
              "Predictions and/or labels do not match expected results:\n  - predictions: "
              F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""     )
         return {"success": success}

        __lowerCamelCase       :     str   =     Trainer(
            model=DummyModel(),
            args=training_args,
            data_collator=DummyDataCollator(),
            eval_dataset=dataset,
            compute_metrics=compute_metrics,
        )
        __lowerCamelCase       :     Any   =     trainer.evaluate()
        logger.info(metrics)
        if metrics["eval_success"] is not True:
            logger.error(metrics)
            exit(1)

        __lowerCamelCase       :     List[Any]   =     trainer.predict(dataset)
        logger.info(p.metrics)
        if p.metrics["test_success"] is not True:
            logger.error(p.metrics)
            exit(1)

        __lowerCamelCase       :     Optional[int]   =     2

        __lowerCamelCase       :     Tuple   =     trainer.evaluate()
        logger.info(metrics)
        if metrics["eval_success"] is not True:
            logger.error(metrics)
            exit(1)

        __lowerCamelCase       :     Tuple   =     trainer.predict(dataset)
        logger.info(p.metrics)
        if p.metrics["test_success"] is not True:
            logger.error(p.metrics)
            exit(1)


        __lowerCamelCase       :     Dict   =     None




"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_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(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

						def create_tf_var(SCREAMING_SNAKE_CASE_		:				np.ndarray ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				tf.Session  ):
												SCREAMING_SNAKE_CASE													=					tf.dtypes.as_dtype(tensor.dtype  )
												SCREAMING_SNAKE_CASE													=					tf.get_variable(dtype=SCREAMING_SNAKE_CASE_ ,			shape=tensor.shape ,			name=SCREAMING_SNAKE_CASE_ ,			initializer=tf.zeros_initializer()  )
												session.run(tf.variables_initializer([tf_var]  )  )
												session.run(SCREAMING_SNAKE_CASE_  )
												return tf_var

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

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


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

						SCREAMING_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=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


