




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		str:

    """simple docstring"""


    __UpperCamelCase     						=  args.serialization_dir
    __UpperCamelCase     						=  args.pruning_method
    __UpperCamelCase     						=  args.threshold

    __UpperCamelCase     						=  torch.load(os.path.join(lowercase_    ,   '''pytorch_model.bin'''					)    ,   map_location='''cpu'''					)

    __UpperCamelCase     						=  0  # Number of remaining (not pruned) params in the encoder
    __UpperCamelCase     						=  0  # Number of params in the encoder

    print('''name'''.ljust(60    ,   ''' '''					)    ,   '''Remaining Weights %'''    ,   '''Remaining Weight'''					)
    for name, param in st.items():
        if "encoder" not in name:
            continue

        if "mask_scores" in name:
            if pruning_method == "topK":
                __UpperCamelCase     						=  TopKBinarizer.apply(lowercase_    ,   lowercase_					).sum().item()
            elif pruning_method == "sigmoied_threshold":
                __UpperCamelCase     						=  ThresholdBinarizer.apply(lowercase_    ,   lowercase_    ,   lowercase_					).sum().item()
            elif pruning_method == "l0":
                __UpperCamelCase						,					__UpperCamelCase     						=  -0.1, 1.1
                __UpperCamelCase     						=  torch.sigmoid(lowercase_					)
                __UpperCamelCase     						=  s * (r - l) + l
                __UpperCamelCase     						=  s_bar.clamp(min=0.0    ,   max=1.0					)
                __UpperCamelCase     						=  (mask > 0.0).sum().item()
            else:
                raise ValueError('''Unknown pruning method'''					)
            remaining_count += mask_ones
            print(name.ljust(60    ,   ''' '''					)    ,   str(round(1_00 * mask_ones / param.numel()    ,   3					)					).ljust(20    ,   ''' '''					)    ,   str(lowercase_					)					)
        else:
            encoder_count += param.numel()
            if "bias" in name or "LayerNorm" in name:
                remaining_count += param.numel()

    print(''''''					)
    print('''Remaining Weights (global) %: '''    ,   1_00 * remaining_count / encoder_count					)


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

 parser.add_argument(
     "--pruning_method",
     choices=["l0", "topK", "sigmoied_threshold"],
     type=str,
     required=True,
     help=(
         "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
         " pruning)"
     ),
 )
 parser.add_argument(
     "--threshold",
     type=float,
     required=False,
     help=(
         "For `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(
     "--serialization_dir",
     type=str,
     required=True,
     help="Folder containing the model that was previously fine-pruned",
 )

 a_      						=   parser.parse_args()

 main(args)











"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase:   Dict       			=							logging.get_logger(__name__)

UpperCAmelCase:   Optional[Any]       			=							{
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}





class    UpperCamelCase     (			snake_case    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Dict		    =   "opt"
	SCREAMING_SNAKE_CASE_				:    List[str]		    =   ["past_key_values"]






	def __init__(			self							,UpperCAmelCase_=5_02_72							,UpperCAmelCase_=7_68							,UpperCAmelCase_=12							,UpperCAmelCase_=30_72							,UpperCAmelCase_=20_48							,UpperCAmelCase_=True							,UpperCAmelCase_=False							,UpperCAmelCase_=None							,UpperCAmelCase_=0.1							,UpperCAmelCase_=0.0							,UpperCAmelCase_=12							,UpperCAmelCase_="relu"							,UpperCAmelCase_=0.0							,UpperCAmelCase_=0.02							,UpperCAmelCase_=True							,UpperCAmelCase_=1							,UpperCAmelCase_=2							,UpperCAmelCase_=2							,UpperCAmelCase_=True							,UpperCAmelCase_=True							,**UpperCAmelCase_							,):
			super().__init__(
			    pad_token_id=UpperCAmelCase_							,bos_token_id=UpperCAmelCase_							,eos_token_id=UpperCAmelCase_							,**UpperCAmelCase_							,)
			_lowercase							:      Optional[Any]			=					vocab_size
			_lowercase							:      Tuple			=					max_position_embeddings
			_lowercase							:      Union[str, Any]			=					num_attention_heads
			_lowercase							:      List[str]			=					word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
			_lowercase							:      List[str]			=					ffn_dim
			_lowercase							:      List[str]			=					hidden_size
			_lowercase							:      List[Any]			=					num_hidden_layers
			_lowercase							:      List[Any]			=					dropout
			_lowercase							:      str			=					attention_dropout
			_lowercase							:      List[Any]			=					activation_function
			_lowercase							:      int			=					init_std
			_lowercase							:      List[Any]			=					layerdrop
			_lowercase							:      Tuple			=					use_cache
			_lowercase							:      Any			=					do_layer_norm_before
			# We keep these variables at `True` for backward compatibility.
			_lowercase							:      int			=					enable_bias
			_lowercase							:      str			=					layer_norm_elementwise_affine

			# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
			# with checkpoints that have been fine-tuned before transformers v4.20.1
			# see https://github.com/facebookresearch/metaseq/pull/164
			_lowercase							:      Optional[Any]			=					_remove_final_layer_norm



