


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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase__  :Dict             =							logging.get_logger(__name__)

lowerCAmelCase__  :Tuple             =							{
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class 		__a				(    __UpperCamelCase      ):
		_a     :  Union[str, Any]           =     """gptj"""
		_a     :  int           =     {
		    """max_position_embeddings""": """n_positions""",
		    """hidden_size""": """n_embd""",
		    """num_attention_heads""": """n_head""",
		    """num_hidden_layers""": """n_layer""",
		}
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=50400      ,   _SCREAMING_SNAKE_CASE=2048      ,   _SCREAMING_SNAKE_CASE=4096      ,   _SCREAMING_SNAKE_CASE=28      ,   _SCREAMING_SNAKE_CASE=16      ,   _SCREAMING_SNAKE_CASE=64      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE="gelu_new"      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=1e-5      ,   _SCREAMING_SNAKE_CASE=0.02      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=50256      ,   _SCREAMING_SNAKE_CASE=50256      ,   _SCREAMING_SNAKE_CASE=False      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      vocab_size
									_UpperCAmelCase					=      n_positions
									_UpperCAmelCase					=      n_embd
									_UpperCAmelCase					=      n_layer
									_UpperCAmelCase					=      n_head
									_UpperCAmelCase					=      n_inner
									_UpperCAmelCase					=      rotary_dim
									_UpperCAmelCase					=      activation_function
									_UpperCAmelCase					=      resid_pdrop
									_UpperCAmelCase					=      embd_pdrop
									_UpperCAmelCase					=      attn_pdrop
									_UpperCAmelCase					=      layer_norm_epsilon
									_UpperCAmelCase					=      initializer_range
									_UpperCAmelCase					=      use_cache

									_UpperCAmelCase					=      bos_token_id
									_UpperCAmelCase					=      eos_token_id

									super().__init__(
									    bos_token_id=_SCREAMING_SNAKE_CASE      ,   eos_token_id=_SCREAMING_SNAKE_CASE      ,   tie_word_embeddings=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)



class 		__a				(    __UpperCamelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = "default"      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False      ,   )			->	int:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE      ,   task=_SCREAMING_SNAKE_CASE      ,   patching_specs=_SCREAMING_SNAKE_CASE      ,   use_past=_SCREAMING_SNAKE_CASE						)
									if not getattr(self._config      ,   'pad_token_id'      ,   _SCREAMING_SNAKE_CASE						):
																# TODO: how to do that better?
																_UpperCAmelCase					=      0
		@property
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}						)
									if self.use_past:
																self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE      ,   direction='inputs'						)
																_UpperCAmelCase					=      {0: """batch""", 1: """past_sequence + sequence"""}
									else:
																_UpperCAmelCase					=      {0: """batch""", 1: """sequence"""}

									return common_inputs
		@property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									return self._config.n_layer
		@property
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									return self._config.n_head
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = None      ,   )			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      super(_SCREAMING_SNAKE_CASE      ,   self						).generate_dummy_inputs(
									    _SCREAMING_SNAKE_CASE      ,   batch_size=_SCREAMING_SNAKE_CASE      ,   seq_length=_SCREAMING_SNAKE_CASE      ,   is_pair=_SCREAMING_SNAKE_CASE      ,   framework=_SCREAMING_SNAKE_CASE						)

									# We need to order the input in the way they appears in the forward()
									_UpperCAmelCase					=      OrderedDict({'input_ids': common_inputs['input_ids']}						)

									# Need to add the past_keys
									if self.use_past:
																if not is_torch_available():
																							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
																else:
																							import torch

																							_UpperCAmelCase					=      common_inputs["""input_ids"""].shape
																							# Not using the same length for past_key_values
																							_UpperCAmelCase					=      seqlen + 2
																							_UpperCAmelCase					=      (
																							    batch,
																							    self.num_attention_heads,
																							    past_key_values_length,
																							    self._config.hidden_size // self.num_attention_heads,
																							)
																							_UpperCAmelCase					=      [
																							    (torch.zeros(_SCREAMING_SNAKE_CASE						), torch.zeros(_SCREAMING_SNAKE_CASE						)) for _ in range(self.num_layers						)
																							]

									_UpperCAmelCase					=      common_inputs["""attention_mask"""]
									if self.use_past:
																_UpperCAmelCase					=      ordered_inputs["""attention_mask"""].dtype
																_UpperCAmelCase					=      torch.cat(
																    [ordered_inputs['attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   dtype=_SCREAMING_SNAKE_CASE						)]      ,   dim=1						)

									return ordered_inputs






		@property
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									return 13









import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
		lowerCAmelCase__  :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''')
		lowerCAmelCase__  :Optional[int]             =							parser.parse_args()

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

		lowerCAmelCase__  :Any             =							model.state_dict()
		lowerCAmelCase__  :Dict             =							{}

		for w in ["word_embeddings", "position_embeddings"]:
				lowerCAmelCase__  :List[Any]             =							state_dict[f'''{prefix}.embeddings.{w}.weight''']
		for w in ["weight", "bias"]:
				lowerCAmelCase__  :Union[str, Any]             =							state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

		lowerCAmelCase__  :str             =							0
		for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
				for w in ["weight", "bias"]:
						lowerCAmelCase__  :Any             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
						]
						lowerCAmelCase__  :List[str]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
						]

						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
						]
						lowerCAmelCase__  :int             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
						]

						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
						]
				std_idx += 1

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