
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


_snake_case      :							List[str]     	=    logging.get_logger(__name__)

_snake_case      :							List[Any]     	=    {
    "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   UpperCamelCase_      (      UpperCAmelCase__		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[int]				   =					'''gptj'''
						UpperCamelCase      :  Optional[int]				   =					{
						    '''max_position_embeddings''': '''n_positions''',
						    '''hidden_size''': '''n_embd''',
						    '''num_attention_heads''': '''n_head''',
						    '''num_hidden_layers''': '''n_layer''',
						}





						def __init__(			self							:str		,    lowerCAmelCase__							:List[Any]=50400		,    lowerCAmelCase__							:str=2048		,    lowerCAmelCase__							:int=4096		,    lowerCAmelCase__							:Union[str, Any]=28		,    lowerCAmelCase__							:str=16		,    lowerCAmelCase__							:Dict=64		,    lowerCAmelCase__							:List[Any]=None		,    lowerCAmelCase__							:Tuple="gelu_new"		,    lowerCAmelCase__							:Dict=0.0		,    lowerCAmelCase__							:str=0.0		,    lowerCAmelCase__							:Optional[Any]=0.0		,    lowerCAmelCase__							:Optional[Any]=1E-5		,    lowerCAmelCase__							:Dict=0.02		,    lowerCAmelCase__							:Any=True		,    lowerCAmelCase__							:Optional[Any]=50256		,    lowerCAmelCase__							:Tuple=50256		,    lowerCAmelCase__							:List[Any]=False		,    **lowerCAmelCase__							:List[str]		,    )  ->List[str]:
										lowercase         =      vocab_size
										lowercase         =      n_positions
										lowercase         =      n_embd
										lowercase         =      n_layer
										lowercase         =      n_head
										lowercase         =      n_inner
										lowercase         =      rotary_dim
										lowercase         =      activation_function
										lowercase         =      resid_pdrop
										lowercase         =      embd_pdrop
										lowercase         =      attn_pdrop
										lowercase         =      layer_norm_epsilon
										lowercase         =      initializer_range
										lowercase         =      use_cache

										lowercase         =      bos_token_id
										lowercase         =      eos_token_id

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





class   UpperCamelCase_      (      UpperCAmelCase__		):







						'''simple docstring'''



						def __init__(			self							:Tuple		,    lowerCAmelCase__							:PretrainedConfig		,    lowerCAmelCase__							:str = "default"		,    lowerCAmelCase__							:List[PatchingSpec] = None		,    lowerCAmelCase__							:bool = False		,    )  ->str:
										super().__init__(__lowercase		,    task=__lowercase		,    patching_specs=__lowercase		,    use_past=__lowercase     )
										if not getattr(self._config		,    "pad_token_id"		,    __lowercase     ):
														# TODO: how to do that better?
														lowercase         =      0





						@property
						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Optional[Any]:
										lowercase         =      OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
										if self.use_past:
														self.fill_with_past_key_values_(__lowercase		,    direction="inputs"     )
														lowercase         =      {0: "batch", 1: "past_sequence + sequence"}
										else:
														lowercase         =      {0: "batch", 1: "sequence"}

										return common_inputs





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Tuple:
										return self._config.n_layer





						@property
						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->str:
										return self._config.n_head





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:PreTrainedTokenizer		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:bool = False		,    lowerCAmelCase__							:Optional[TensorType] = None		,    )  ->Any:
										lowercase         =      super(__lowercase		,    self     ).generate_dummy_inputs(
										    __lowercase		,    batch_size=__lowercase		,    seq_length=__lowercase		,    is_pair=__lowercase		,    framework=__lowercase     )

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

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

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

										return ordered_inputs





						@property
						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Union[str, Any]:
										return 13






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	MobileBertConfig.from_json_file(_A				)
	print(f"Building PyTorch model from configuration: {config}"				)
	snake_case_						       =	MobileBertForPreTraining(_A				)
	# Load weights from tf checkpoint
	snake_case_						       =	load_tf_weights_in_mobilebert(_A	,      _A	,      _A				)
	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"				)
	torch.save(model.state_dict()	,      _A				)


if __name__ == "__main__":
		lowercase__							:	Dict											=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--mobilebert_config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained MobileBERT model. \n"
		        "This specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
