
import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Optional[int]:
				# Initialise PyTorch model
				__a										=    RemBertConfig.from_json_file(a__  )
				print('''Building PyTorch model from configuration: {}'''.format(str(a__  )  )  )
				__a										=    RemBertModel(a__  )

				# Load weights from tf checkpoint
				load_tf_weights_in_rembert(a__						,    a__						,    a__  )

				# Save pytorch-model
				print('''Save PyTorch model to {}'''.format(a__  )  )
				torch.save(model.state_dict()						,    a__  )


if __name__ == "__main__":
					A     :	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(
					    '--rembert_config_file',
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        'The config json file corresponding to the pre-trained RemBERT 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.'
					)
					A     :	Tuple    = parser.parse_args()
					convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate







@dataclass(frozen=__magic_name__	)
class      A__							(     __magic_name__	):
				lowercase			    =  field(default='audio-classification'						,   metadata={'include_in_asdict_even_if_is_default': True}	)
				lowercase			    =  Features({'audio': Audio()}	)
				lowercase			    =  Features({'labels': ClassLabel}	)
				lowercase			    =  "audio"
				lowercase			    =  "labels"






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      Tuple   ):






							'''simple docstring'''







							if self.label_column not in features:
										raise ValueError(f'''Column {self.label_column} is not present in features.'''   )
							if not isinstance(features[self.label_column]    ,     a   ):
										raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''   )
							lowerCAmelCase__      :      Tuple         =		copy.deepcopy(self   )
							lowerCAmelCase__      :      List[Any]         =		self.label_schema.copy()
							lowerCAmelCase__      :      List[Any]         =		features[self.label_column]
							lowerCAmelCase__      :      Optional[int]         =		label_schema
							return task_template






				@property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return {
							    self.audio_column: "audio",
							    self.label_column: "labels",
							}