





"""simple docstring"""






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def      __A   (      a_    :BertModel ,							a_    :str ,							a_    :str)		->		str:
				__a			:    List[str]      							=	('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

				__a			:    Any      							=	(
				    ('''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_)

				__a			:    List[Any]      							=	model.state_dict()

				def to_tf_var_name(a_    :str):
								for patt, repl in iter(a_):
												__a			:    int      							=	name.replace(a_ ,							a_)
								return F"""bert/{name}"""

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

								__a			:    Tuple      							=	tf.train.Saver(tf.trainable_variables())
								saver.save(a_ ,							os.path.join(a_ ,							model_name.replace('''-''' ,							'''_''') + '''.ckpt'''))

def      __A   (      a_    :int=None)		->		str:
				__a			:    str      							=	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''')
				__a			:    Optional[Any]      							=	parser.parse_args(a_)

				__a			:    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()


import pytest


__A			:					Optional[Any]						=			'__dummy_dataset1__'

__A			:					Optional[int]						=			'\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                "tokens": datasets.Sequence(datasets.Value("string")),\n                "ner_tags": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            "O",\n                            "B-PER",\n                            "I-PER",\n                            "B-ORG",\n                            "I-ORG",\n                            "B-LOC",\n                            "I-LOC",\n                        ]\n                    )\n                ),\n                "langs": datasets.Sequence(datasets.Value("string")),\n                "spans": datasets.Sequence(datasets.Value("string")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, "r", encoding="utf-8") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n'





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 	__a     (      A__			:  Optional[Any]			,  A__			:  List[str]			,  A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   dataset_loading_script_name
    SCREAMING_SNAKE_CASE										=   tmp_path / "datasets" / script_name
    script_dir.mkdir(parents=A__       )
    SCREAMING_SNAKE_CASE										=   script_dir / F"{script_name}.py"
    with open(A__			,  "w"       ) as f:
        f.write(A__       )
    return str(A__       )