






"""simple docstring"""






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


SCREAMING_SNAKE_CASE						:						int					   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:						int					   =       {
    '''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       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					"""gptj"""
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					{
	    """max_position_embeddings""": """n_positions""",
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}


	def __init__(      self  ,					__a=5_0400  ,					__a=2048  ,					__a=4096  ,					__a=28  ,					__a=16  ,					__a=64  ,					__a=None  ,					__a="gelu_new"  ,					__a=0.0  ,					__a=0.0  ,					__a=0.0  ,					__a=1E-5  ,					__a=0.02  ,					__a=True  ,					__a=5_0256  ,					__a=5_0256  ,					__a=False  ,					**__a  ,					):


					"""simple docstring"""

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

					A__							 =       bos_token_id
					A__							 =       eos_token_id

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





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a = "default"  ,					__a = None  ,					__a = False  ,					):


					"""simple docstring"""

					super().__init__(__a  ,					task=__a  ,					patching_specs=__a  ,					use_past=__a						)
					if not getattr(self._config  ,					'pad_token_id'  ,					__a						):
									# TODO: how to do that better?
									A__							 =       0


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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

					return common_inputs


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self._config.n_layer


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self._config.n_head


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = -1  ,					__a = -1  ,					__a = False  ,					__a = None  ,					):


					"""simple docstring"""

					A__							 =       super(__a  ,					self						).generate_dummy_inputs(
					    __a  ,					batch_size=__a  ,					seq_length=__a  ,					is_pair=__a  ,					framework=__a						)

					# We need to order the input in the way they appears in the forward()
					A__							 =       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

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

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

					return ordered_inputs






	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return 13










"""simple docstring"""






from typing import Union

import fire
import torch
from tqdm import tqdm



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = "cpu"       ,lowerCAmelCase__ = None				):
				A__							 =       torch.load(lowerCAmelCase__       ,map_location=lowerCAmelCase__				)
				for k, v in tqdm(state_dict.items()				):
								if not isinstance(lowerCAmelCase__       ,torch.Tensor				):
												raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'				)
								A__							 =       v.half()
				if save_path is None:  # overwrite src_path
								A__							 =       src_path
				torch.save(lowerCAmelCase__       ,lowerCAmelCase__				)


if __name__ == "__main__":
					fire.Fire(convert)


