




"""simple docstring"""




import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      str:
			# Initialise PyTorch model
			lowerCamelCase       =      BertConfig.from_json_file(lowerCamelCase_       )
			print(F'Building PyTorch model from configuration: {config}'       )
			lowerCamelCase       =      BertForPreTraining(lowerCamelCase_       )

			# Load weights from tf checkpoint
			load_tf_weights_in_bert(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_       )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'       )
			torch.save(model.state_dict() ,				lowerCamelCase_       )


if __name__ == "__main__":
					lowerCAmelCase							:    str  							=							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(
					    """--bert_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."""
					)
					lowerCAmelCase							:    List[Any]  							=							parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









"""simple docstring"""




from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


lowerCAmelCase							:    List[Any]  							=							logging.get_logger(__name__)


lowerCAmelCase							:    int  							=							TypeVar("""DatasetType""", Dataset, IterableDataset)



def 					a__				(       snake_case__ ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = "first_exhausted" ,				)		->      DatasetType:
			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("""Unable to interleave an empty list of datasets."""       )
			for i, dataset in enumerate(snake_case__       ):
						if not isinstance(snake_case__ ,				(Dataset, IterableDataset)       ):
									if isinstance(snake_case__ ,				(DatasetDict, IterableDatasetDict)       ):
												if not dataset:
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
															    """is an empty dataset dictionary."""       )
												raise ValueError(
												    F'Dataset at position {i} has at least one split: {list(snake_case__       )}\n'
												    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(snake_case__       )       )}\']'       )
									raise ValueError(
									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(snake_case__       ).__name__}.'       )
						if i == 0:
									lowerCamelCase   ,     lowerCamelCase       =      (
									    (Dataset, IterableDataset) if isinstance(snake_case__ ,				snake_case__       ) else (IterableDataset, Dataset)
									)
						elif not isinstance(snake_case__ ,				snake_case__       ):
									raise ValueError(
									    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'       )
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'       )
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    snake_case__ ,				snake_case__ ,				snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				stopping_strategy=snake_case__       )
			else:
						return _interleave_iterable_datasets(
						    snake_case__ ,				snake_case__ ,				snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				stopping_strategy=snake_case__       )



def 					a__				(       snake_case__ ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = 0 ,				)		->      DatasetType:

			if not dsets:
						raise ValueError("""Unable to concatenate an empty list of datasets."""       )
			for i, dataset in enumerate(snake_case__       ):
						if not isinstance(snake_case__ ,				(Dataset, IterableDataset)       ):
									if isinstance(snake_case__ ,				(DatasetDict, IterableDatasetDict)       ):
												if not dataset:
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
															    """is an empty dataset dictionary."""       )
												raise ValueError(
												    F'Dataset at position {i} has at least one split: {list(snake_case__       )}\n'
												    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(snake_case__       )       )}\']'       )
									raise ValueError(
									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(snake_case__       ).__name__}.'       )
						if i == 0:
									lowerCamelCase   ,     lowerCamelCase       =      (
									    (Dataset, IterableDataset) if isinstance(snake_case__ ,				snake_case__       ) else (IterableDataset, Dataset)
									)
						elif not isinstance(snake_case__ ,				snake_case__       ):
									raise ValueError(
									    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'       )
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				axis=snake_case__       )
			else:
						return _concatenate_iterable_datasets(snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				axis=snake_case__       )



