'''simple docstring'''

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      UpperCAmelCase    (		a__   ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE   						=       ["image_processor", "tokenizer"]
       SCREAMING_SNAKE_CASE   						=       "AutoImageProcessor"
       SCREAMING_SNAKE_CASE   						=       "AutoTokenizer"

       def __init__(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase					)    ->    Any:
        super().__init__(__a      ,  __a					)
        lowercase__							:       Union[str, Any]										=   self.image_processor

       def __call__(		self      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  **__lowerCAmelCase					)    ->    Any:

        if text is None and images is None:
         raise ValueError('''You have to specify either text or images. Both cannot be none.'''					)

        if text is not None:
         lowercase__							:       Optional[int]										=   self.tokenizer(__a      ,  return_tensors=__a      ,  **__a					)

        if images is not None:
         lowercase__							:       List[Any]										=   self.image_processor(__a      ,  return_tensors=__a      ,  **__a					)

        if text is not None and images is not None:
         lowercase__							:       Tuple										=   image_features.pixel_values
         return encoding
        elif text is not None:
         return encoding
        else:
         return BatchEncoding(data=dict(**__a					)      ,  tensor_type=__a					)

       def _lowerCAmelCase(		self      ,  *__lowerCAmelCase      ,  **__lowerCAmelCase					)    ->    Any:
        return self.tokenizer.batch_decode(*__a      ,  **__a					)

       def _lowerCAmelCase(		self      ,  *__lowerCAmelCase      ,  **__lowerCAmelCase					)    ->    Any:
        return self.tokenizer.decode(*__a      ,  **__a					)


       @property
       def _lowerCAmelCase(		self					)    ->    int:
        return ["input_ids", "attention_mask", "pixel_values"]



"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser(
				    description=(
				        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
				        """ Distillation"""
				    )
				)
				parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
				parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
				parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
				parser.add_argument("""--vocab_transform""", action="""store_true""")
				_a								=						parser.parse_args()

				if args.model_type == "bert":
								_a								=						BertForMaskedLM.from_pretrained(args.model_name)
								_a								=						"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a								=						model.state_dict()
				_a								=						{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a								=						state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a								=						state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

				_a								=						0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a								=						state_dict["""cls.predictions.decoder.weight"""]
				_a								=						state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a								=						state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a								=						state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

				print(F"""N layers selected for distillation: {std_idx}""")
				print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

				print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
				torch.save(compressed_sd, args.dump_checkpoint)






