


'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[Any]  					=							["""image_processor""", """tokenizer"""]
      lowerCAmelCase__					:     Optional[int]  					=							"""ViTImageProcessor"""
      lowerCAmelCase__					:     Optional[Any]  					=							("""CLIPTokenizer""", """CLIPTokenizerFast""")



      def __init__(self    :    List[Any]   ,						UpperCamelCase    :    List[str]=None   ,						UpperCamelCase    :    List[Any]=None   ,						**UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                        ''' instead.'''   ,						UpperCamelCase   ,						)
                    lowercase__      			= kwargs.pop('''feature_extractor''' )

             lowercase__      			= image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                    raise ValueError('''You need to specify an `image_processor`.''' )
             if tokenizer is None:
                    raise ValueError('''You need to specify a `tokenizer`.''' )

             super().__init__(UpperCamelCase   ,						UpperCamelCase )



      def __call__(self    :    Optional[Any]   ,						UpperCamelCase    :    Optional[Any]=None   ,						UpperCamelCase    :    Dict=None   ,						UpperCamelCase    :    Union[str, Any]=None   ,						UpperCamelCase    :    List[str]=None   ,						**UpperCamelCase    :    List[Any] ):

             '''simple docstring'''







             if text is None and visual_prompt is None and images is None:
                    raise ValueError('''You have to specify either text, visual prompt or images.''' )

             if text is not None and visual_prompt is not None:
                    raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''' )

             if text is not None:
                    lowercase__      			= self.tokenizer(UpperCamelCase   ,						return_tensors=UpperCamelCase   ,						**UpperCamelCase )

             if visual_prompt is not None:
                    lowercase__      			= self.image_processor(UpperCamelCase   ,						return_tensors=UpperCamelCase   ,						**UpperCamelCase )

             if images is not None:
                    lowercase__      			= self.image_processor(UpperCamelCase   ,						return_tensors=UpperCamelCase   ,						**UpperCamelCase )

             if visual_prompt is not None and images is not None:
                    lowercase__      			= {
                        '''pixel_values''': image_features.pixel_values,
                        '''conditional_pixel_values''': prompt_features.pixel_values,
                    }
                    return encoding
             elif text is not None and images is not None:
                    lowercase__      			= image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             elif visual_prompt is not None:
                    lowercase__      			= {
                        '''conditional_pixel_values''': prompt_features.pixel_values,
                    }
                    return encoding
             else:
                    return BatchEncoding(data=dict(**UpperCamelCase )   ,						tensor_type=UpperCamelCase )



      def       UpperCamelCase__		(self    :    Tuple   ,						*UpperCamelCase    :    List[str]   ,						**UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             return self.tokenizer.batch_decode(*UpperCamelCase   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    List[str]   ,						*UpperCamelCase    :    Any   ,						**UpperCamelCase    :    int ):

             '''simple docstring'''







             return self.tokenizer.decode(*UpperCamelCase   ,						**UpperCamelCase )



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,						UpperCamelCase   ,						)
             return self.image_processor_class






      @property
      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,						UpperCamelCase   ,						)
             return self.image_processor








'''simple docstring'''

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 lowerCamelCase							:   List[str]  						=	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')
 lowerCamelCase							:   str  						=	parser.parse_args()

 if args.model_type == "bert":
  lowerCamelCase							:   List[Any]  						=	BertForMaskedLM.from_pretrained(args.model_name)
  lowerCamelCase							:   Any  						=	'bert'
 else:
  raise ValueError('args.model_type should be "bert".')

 lowerCamelCase							:   int  						=	model.state_dict()
 lowerCamelCase							:   int  						=	{}

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

 lowerCamelCase							:   Tuple  						=	0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  for w in ["weight", "bias"]:
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
   ]
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
   ]
   lowerCamelCase							:   List[Any]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
   ]

   lowerCamelCase							:   Tuple  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
   ]
   lowerCamelCase							:   Optional[int]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
   ]

   lowerCamelCase							:   Optional[Any]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
   ]
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
   ]
   lowerCamelCase							:   Any  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
   ]
  std_idx += 1

 lowerCamelCase							:   Optional[int]  						=	state_dict['cls.predictions.decoder.weight']
 lowerCamelCase							:   str  						=	state_dict['cls.predictions.bias']
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   lowerCamelCase							:   str  						=	state_dict[f"""cls.predictions.transform.dense.{w}"""]
   lowerCamelCase							:   Any  						=	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)




