






'''simple docstring'''



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 __UpperCamelCase      :       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""")
 __UpperCamelCase      :       Tuple      			=      parser.parse_args()

 if args.model_type == "bert":
  __UpperCamelCase      :       Optional[int]      			=      BertForMaskedLM.from_pretrained(args.model_name)
  __UpperCamelCase      :       Tuple      			=      """bert"""
 else:
  raise ValueError("""args.model_type should be \"bert\".""")

 __UpperCamelCase      :       Dict      			=      model.state_dict()
 __UpperCamelCase      :       int      			=      {}

 for w in ["word_embeddings", "position_embeddings"]:
  __UpperCamelCase      :       Dict      			=      state_dict[f"""{prefix}.embeddings.{w}.weight"""]
 for w in ["weight", "bias"]:
  __UpperCamelCase      :       Optional[int]      			=      state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

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

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

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

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







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase      :       Tuple      			=      {
    """configuration_blip_2""": [
        """BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Blip2Config""",
        """Blip2QFormerConfig""",
        """Blip2VisionConfig""",
    ],
    """processing_blip_2""": ["""Blip2Processor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[int]      			=      [
     """BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Blip2Model""",
     """Blip2QFormerModel""",
     """Blip2PreTrainedModel""",
     """Blip2ForConditionalGeneration""",
     """Blip2VisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_blip_a import (
     BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlipaConfig,
     BlipaQFormerConfig,
     BlipaVisionConfig,
 )
 from .processing_blip_a import BlipaProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blip_a import (
      BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlipaForConditionalGeneration,
      BlipaModel,
      BlipaPreTrainedModel,
      BlipaQFormerModel,
      BlipaVisionModel,
  )

else:
 import sys

 __UpperCamelCase      :       Dict      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)