





"""simple docstring"""



import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







def 		__lowercase					(       snake_case_  :  List[str]						)       ->Any:



    '''simple docstring'''







    __A   :						str									=					args.pruning_method
    __A   :						Optional[Any]									=					args.threshold

    __A   :						Optional[int]									=					args.model_name_or_path.rstrip('''/'''						)
    __A   :						Optional[int]									=					args.target_model_path

    print(F"""Load fine-pruned model from {model_name_or_path}"""						)
    __A   :						Union[str, Any]									=					torch.load(os.path.join(snake_case_    ,'''pytorch_model.bin'''						)						)
    __A   :						str									=					{}

    for name, tensor in model.items():
        if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
            __A   :						Any									=					tensor
            print(F"""Copied layer {name}"""						)
        elif "classifier" in name or "qa_output" in name:
            __A   :						int									=					tensor
            print(F"""Copied layer {name}"""						)
        elif "bias" in name:
            __A   :						Any									=					tensor
            print(F"""Copied layer {name}"""						)
        else:
            if pruning_method == "magnitude":
                __A   :						Optional[Any]									=					MagnitudeBinarizer.apply(inputs=snake_case_    ,threshold=snake_case_						)
                __A   :						Any									=					tensor * mask
                print(F"""Pruned layer {name}"""						)
            elif pruning_method == "topK":
                if "mask_scores" in name:
                    continue
                __A   :						Optional[Any]									=					name[:-6]
                __A   :						Tuple									=					model[F"""{prefix_}mask_scores"""]
                __A   :						Any									=					TopKBinarizer.apply(snake_case_    ,snake_case_						)
                __A   :						str									=					tensor * mask
                print(F"""Pruned layer {name}"""						)
            elif pruning_method == "sigmoied_threshold":
                if "mask_scores" in name:
                    continue
                __A   :						Dict									=					name[:-6]
                __A   :						List[str]									=					model[F"""{prefix_}mask_scores"""]
                __A   :						Dict									=					ThresholdBinarizer.apply(snake_case_    ,snake_case_    ,snake_case_						)
                __A   :						Tuple									=					tensor * mask
                print(F"""Pruned layer {name}"""						)
            elif pruning_method == "l0":
                if "mask_scores" in name:
                    continue
                __A   :						Any									=					name[:-6]
                __A   :						str									=					model[F"""{prefix_}mask_scores"""]
                __A			,	__A   :						List[Any]									=					-0.1, 1.1
                __A   :						Union[str, Any]									=					torch.sigmoid(snake_case_						)
                __A   :						Any									=					s * (r - l) + l
                __A   :						int									=					s_bar.clamp(min=0.0    ,max=1.0						)
                __A   :						Optional[int]									=					tensor * mask
                print(F"""Pruned layer {name}"""						)
            else:
                raise ValueError('''Unknown pruning method'''						)

    if target_model_path is None:
        __A   :						Optional[Any]									=					os.path.join(
            os.path.dirname(snake_case_						)    ,F"""bertarized_{os.path.basename(snake_case_						)}"""						)

    if not os.path.isdir(snake_case_						):
        shutil.copytree(snake_case_    ,snake_case_						)
        print(F"""\nCreated folder {target_model_path}"""						)

    torch.save(snake_case_    ,os.path.join(snake_case_    ,'''pytorch_model.bin'''						)						)
    print('''\nPruned model saved! See you later!'''						)


if __name__ == "__main__":
       a_			=      argparse.ArgumentParser()

       parser.add_argument(
           """--pruning_method""",
           choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
           type=str,
           required=True,
           help=(
               """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
               """ sigmoied_threshold = Soft movement pruning)"""
           ),
       )
       parser.add_argument(
           """--threshold""",
           type=float,
           required=False,
           help=(
               """For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
               """For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
               """Not needed for `l0`"""
           ),
       )
       parser.add_argument(
           """--model_name_or_path""",
           type=str,
           required=True,
           help="""Folder containing the model that was previously fine-pruned""",
       )
       parser.add_argument(
           """--target_model_path""",
           default=None,
           type=str,
           required=False,
           help="""Folder containing the model that was previously fine-pruned""",
       )

       a_			=      parser.parse_args()

       main(args)











"""simple docstring"""



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_			=      {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""GPTNeoXTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXForCausalLM""",
           """GPTNeoXForQuestionAnswering""",
           """GPTNeoXForSequenceClassification""",
           """GPTNeoXForTokenClassification""",
           """GPTNeoXLayer""",
           """GPTNeoXModel""",
           """GPTNeoXPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox import (
                  GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXForCausalLM,
                  GPTNeoXForQuestionAnswering,
                  GPTNeoXForSequenceClassification,
                  GPTNeoXForTokenClassification,
                  GPTNeoXLayer,
                  GPTNeoXModel,
                  GPTNeoXPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




