





import argparse
import os
import shutil

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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple  )   ->    int:
  _lowercase						    =			args.pruning_method
  _lowercase						    =			args.threshold

  _lowercase						    =			args.model_name_or_path.rstrip('/'  )
  _lowercase						    =			args.target_model_path

  print(F"""Load fine-pruned model from {model_name_or_path}"""  )
  _lowercase						    =			torch.load(os.path.join(snake_case__      ,				'pytorch_model.bin'  )  )
  _lowercase						    =			{}

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

  if target_model_path is None:
    _lowercase						    =			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__":
      snake_case								=							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""",
      )

      snake_case								=							parser.parse_args()

      main(args)
"""simple docstring"""




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowerCAmelCase     :				int    				=    logging.get_logger(__name__)  # pylint: disable=invalid-name

class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  Optional[int]	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=768  ):
							super().__init__(snake_case__  )
							lowerCAmelCase__   = proj_size

							lowerCAmelCase__   = CLIPVisionModel(snake_case__  )
							lowerCAmelCase__   = PaintByExampleMapper(snake_case__  )
							lowerCAmelCase__   = nn.LayerNorm(config.hidden_size  )
							lowerCAmelCase__   = nn.Linear(config.hidden_size	,    self.proj_size  )

							# uncondition for scaling
							lowerCAmelCase__   = nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  Optional[int]	,    snake_case__    :  Optional[Any]=False  ):
							lowerCAmelCase__   = self.model(pixel_values=snake_case__  )
							lowerCAmelCase__   = clip_output.pooler_output
							lowerCAmelCase__   = self.mapper(latent_states[:, None]  )
							lowerCAmelCase__   = self.final_layer_norm(snake_case__  )
							lowerCAmelCase__   = self.proj_out(snake_case__  )
							if return_uncond_vector:
									return latent_states, self.uncond_vector

							return latent_states

class    a_   (  nn.Module      ):




					def __init__(						self    :  int	,    snake_case__    :  Dict  ):
							super().__init__()
							lowerCAmelCase__   = (config.num_hidden_layers + 1) // 5
							lowerCAmelCase__   = config.hidden_size
							lowerCAmelCase__   = 1
							lowerCAmelCase__   = nn.ModuleList(
							    [
							        BasicTransformerBlock(snake_case__	,    snake_case__	,    snake_case__	,    activation_fn="""gelu"""	,    attention_bias=snake_case__  )
							        for _ in range(snake_case__  )
							    ]  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Optional[int]  ):
							for block in self.blocks:
									lowerCAmelCase__   = block(snake_case__  )

							return hidden_states




