
import argparse
import os
import shutil

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







def     A__   (  lowerCamelCase  )					->		Any:
				UpperCamelCase_:							Dict		=  args.pruning_method
				UpperCamelCase_:							List[str]		=  args.threshold

				UpperCamelCase_:							Union[str, Any]		=  args.model_name_or_path.rstrip("""/"""  )
				UpperCamelCase_:							List[str]		=  args.target_model_path

				print(F'''Load fine-pruned model from {model_name_or_path}'''  )
				UpperCamelCase_:							str		=  torch.load(os.path.join(lowerCamelCase       ,      """pytorch_model.bin"""  )  )
				UpperCamelCase_:							Dict		=  {}

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

				if target_model_path is None:
								UpperCamelCase_:							int		=  os.path.join(
								    os.path.dirname(lowerCamelCase  )       ,      F'''bertarized_{os.path.basename(lowerCamelCase  )}'''  )

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

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


if __name__ == "__main__":
							lowerCamelCase_  :	Optional[int] 				=					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""",
							)

							lowerCamelCase_  :	Any 				=					parser.parse_args()

							main(args)








import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCamelCase_  :	Any 				=					logging.get_logger(__name__)


lowerCamelCase_  :	Optional[int] 				=					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase_  :	str 				=					{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_  :	Union[str, Any] 				=					{
    """RUCAIBox/mvp""": 10_24,
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  List[Any]    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  int    =	PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  Any    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  Any    =	["""input_ids""", """attention_mask"""]
			__UpperCamelCase :  int    =	MvpTokenizer

			def __init__(    self	:    Union[str, Any]  ,		snake_case_	:    Any=None  ,		snake_case_	:    Any=None  ,		snake_case_	:    Union[str, Any]=None  ,		snake_case_	:    Tuple="replace"  ,		snake_case_	:    Dict="<s>"  ,		snake_case_	:    Dict="</s>"  ,		snake_case_	:    Tuple="</s>"  ,		snake_case_	:    int="<s>"  ,		snake_case_	:    Tuple="<unk>"  ,		snake_case_	:    Optional[int]="<pad>"  ,		snake_case_	:    Any="<mask>"  ,		snake_case_	:    Union[str, Any]=False  ,		snake_case_	:    Optional[int]=True  ,		**snake_case_	:    Union[str, Any]  ,		):
							super().__init__(
							    snake_case_  ,		snake_case_  ,		tokenizer_file=snake_case_  ,		errors=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		unk_token=snake_case_  ,		pad_token=snake_case_  ,		mask_token=snake_case_  ,		add_prefix_space=snake_case_  ,		trim_offsets=snake_case_  ,		**snake_case_  ,		)

							UpperCamelCase_:							Any		=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
							if pre_tok_state.get("""add_prefix_space"""  ,		snake_case_ ) != add_prefix_space:
											UpperCamelCase_:							str		=  getattr(snake_case_  ,		pre_tok_state.pop("""type""" ) )
											UpperCamelCase_:							int		=  add_prefix_space
											UpperCamelCase_:							int		=  pre_tok_class(**snake_case_ )

							UpperCamelCase_:							int		=  add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							UpperCamelCase_:							Tuple		=  """post_processor"""
							UpperCamelCase_:							Optional[int]		=  getattr(self.backend_tokenizer  ,		snake_case_  ,		snake_case_ )
							if tokenizer_component_instance:
											UpperCamelCase_:							Optional[int]		=  json.loads(tokenizer_component_instance.__getstate__() )

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
															UpperCamelCase_:							Union[str, Any]		=  tuple(state["""sep"""] )
											if "cls" in state:
															UpperCamelCase_:							List[Any]		=  tuple(state["""cls"""] )

											UpperCamelCase_:							List[str]		=  False

											if state.get("""add_prefix_space"""  ,		snake_case_ ) != add_prefix_space:
															UpperCamelCase_:							str		=  add_prefix_space
															UpperCamelCase_:							Union[str, Any]		=  True

											if state.get("""trim_offsets"""  ,		snake_case_ ) != trim_offsets:
															UpperCamelCase_:							Optional[int]		=  trim_offsets
															UpperCamelCase_:							Any		=  True

											if changes_to_apply:
															UpperCamelCase_:							Optional[int]		=  getattr(snake_case_  ,		state.pop("""type""" ) )
															UpperCamelCase_:							Union[str, Any]		=  component_class(**snake_case_ )
															setattr(self.backend_tokenizer  ,		snake_case_  ,		snake_case_ )

			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							if self._mask_token is None:
											if self.verbose:
															logger.error("""Using mask_token, but it is not set yet.""" )
											return None
							return str(self._mask_token )

			@mask_token.setter
			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    Dict ):
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_ ) if isinstance(snake_case_  ,		snake_case_ ) else value
							UpperCamelCase_:							Optional[int]		=  value

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		*snake_case_	:    Dict  ,		**snake_case_	:    Optional[int] ):
							UpperCamelCase_:							Optional[int]		=  kwargs.get("""is_split_into_words"""  ,		snake_case_ )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    """to use it with pretokenized inputs.""" )

							return super()._batch_encode_plus(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		*snake_case_	:    Dict  ,		**snake_case_	:    Tuple ):
							UpperCamelCase_:							Any		=  kwargs.get("""is_split_into_words"""  ,		snake_case_ )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    """to use it with pretokenized inputs.""" )

							return super()._encode_plus(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    str  ,		snake_case_	:    Optional[str] = None ):
							UpperCamelCase_:							Optional[int]		=  self._tokenizer.model.save(snake_case_  ,		name=snake_case_ )
							return tuple(snake_case_ )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Any=None ):
							UpperCamelCase_:							str		=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




			def  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None ):
							UpperCamelCase_:							Tuple		=  [self.sep_token_id]
							UpperCamelCase_:							List[str]		=  [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






