

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE  :						Tuple         =    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''')
  _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    parser.parse_args()

  if args.model_type == "bert":
    _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    BertForMaskedLM.from_pretrained(args.model_name)
    _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    '''bert'''
  else:
    raise ValueError('''args.model_type should be "bert".''')

  _SCREAMING_SNAKE_CASE  :						List[str]         =    model.state_dict()
  _SCREAMING_SNAKE_CASE  :						Dict         =    {}

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

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

      _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    state_dict[
          F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
      ]
      _SCREAMING_SNAKE_CASE  :						List[str]         =    state_dict[
          F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
      ]

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

  _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    state_dict['''cls.predictions.decoder.weight''']
  _SCREAMING_SNAKE_CASE  :						int         =    state_dict['''cls.predictions.bias''']
  if args.vocab_transform:
    for w in ["weight", "bias"]:
      _SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    state_dict[F"cls.predictions.transform.dense.{w}"]
      _SCREAMING_SNAKE_CASE  :						Union[str, 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)





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


_SCREAMING_SNAKE_CASE  :						Any         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						List[str]         =    {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "longformer"






  def __init__(							self		:	int			,     __lowerCamelCase		:	Union[List[int], int] = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 1			,     __lowerCamelCase		:	int = 0			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 3_0522			,     __lowerCamelCase		:	int = 768			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 3072			,     __lowerCamelCase		:	str = "gelu"			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	int = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	float = 0.02			,     __lowerCamelCase		:	float = 1e-12			,     __lowerCamelCase		:	bool = False			,     **__lowerCamelCase		:	Tuple			,     )	->							List[Any]:
       super().__init__(pad_token_id=__lowerCamelCase			,     **__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					attention_window
       SCREAMING_SNAKE_CASE__					=					sep_token_id
       SCREAMING_SNAKE_CASE__					=					bos_token_id
       SCREAMING_SNAKE_CASE__					=					eos_token_id
       SCREAMING_SNAKE_CASE__					=					vocab_size
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					max_position_embeddings
       SCREAMING_SNAKE_CASE__					=					type_vocab_size
       SCREAMING_SNAKE_CASE__					=					initializer_range
       SCREAMING_SNAKE_CASE__					=					layer_norm_eps
       SCREAMING_SNAKE_CASE__					=					onnx_export






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	Dict			,     __lowerCamelCase		:	"PretrainedConfig"			,     __lowerCamelCase		:	str = "default"			,     __lowerCamelCase		:	"List[PatchingSpec]" = None      )	->							List[str]:
       super().__init__(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					True






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
               ('''global_attention_mask''', dynamic_axis),
           ]      )






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							Mapping[str, Mapping[int, str]]:
       SCREAMING_SNAKE_CASE__					=					super().outputs
       if self.task == "default":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch'''}
       return outputs






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							float:
       return 1e-4






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							int:
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset			,     14      )







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	"PreTrainedTokenizerBase"			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	Optional[TensorType] = None			,     )	->							Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__					=					super().generate_dummy_inputs(
           preprocessor=__lowerCamelCase			,     batch_size=__lowerCamelCase			,     seq_length=__lowerCamelCase			,     is_pair=__lowerCamelCase			,     framework=__lowerCamelCase      )
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       SCREAMING_SNAKE_CASE__					=					torch.zeros_like(inputs['''input_ids''']      )
       # make every second token global
       SCREAMING_SNAKE_CASE__					=					1

       return inputs


