from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

UpperCAmelCase_								=							logging.get_logger(__name__)





@add_end_docstrings(__lowerCAmelCase			)
class      lowerCAmelCase						(					__lowerCAmelCase			):



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            super().__init__(*lowerCAmelCase_				,      **lowerCAmelCase_       )
            self.check_model_type(lowerCAmelCase_       )



     def 			a__  (					self				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      **lowerCAmelCase__       ):
            _A,			_A=	{}, {}
            if padding is not None:
                   _A=	padding
            if truncation is not None:
                   _A=	truncation
            if top_k is not None:
                   _A=	top_k
            return preprocess_params, {}, postprocess_params



     def __call__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None				,      **lowerCAmelCase__       ):
            if isinstance(lowerCAmelCase_				,      (Image.Image, str)       ) and isinstance(lowerCAmelCase_				,      lowerCAmelCase_       ):
                   _A=	{'image': image, 'question': question}
            else:
                   _A=	image
            _A=	super().__call__(lowerCAmelCase_				,      **lowerCAmelCase_       )
            return results



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=False				,      lowerCAmelCase__=False       ):
            _A=	load_image(inputs['image']       )
            _A=	self.tokenizer(
                inputs['question']				,      return_tensors=self.framework				,      padding=lowerCAmelCase_				,      truncation=lowerCAmelCase_       )
            _A=	self.image_processor(images=lowerCAmelCase_				,      return_tensors=self.framework       )
            model_inputs.update(lowerCAmelCase_       )
            return model_inputs



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	self.model(**lowerCAmelCase_       )
            return model_outputs





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=5       ):
            if top_k > self.model.config.num_labels:
                   _A=	self.model.config.num_labels

            if self.framework == "pt":
                   _A=	model_outputs.logits.sigmoid()[0]
                   _A,			_A=	probs.topk(lowerCAmelCase_       )
            else:
                   raise ValueError(f"Unsupported framework: {self.framework}"       )

            _A=	scores.tolist()
            _A=	ids.tolist()
            return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_				,      lowerCAmelCase_       )]
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
      UpperCAmelCase_								=							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''')
      UpperCAmelCase_								=							parser.parse_args()

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

      UpperCAmelCase_								=							model.state_dict()
      UpperCAmelCase_								=							{}

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

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

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

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

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