




'''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


_snake_case : Tuple  				=						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      A       (						_a       ):
							lowercase_							     =							'gptj'
							lowercase_							     =							{
							    'max_position_embeddings': 'n_positions',
							    'hidden_size': 'n_embd',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}







							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    Tuple=5_04_00				,     lowerCAmelCase_			:    int=20_48				,     lowerCAmelCase_			:    Optional[int]=40_96				,     lowerCAmelCase_			:    Tuple=28				,     lowerCAmelCase_			:    List[str]=16				,     lowerCAmelCase_			:    Any=64				,     lowerCAmelCase_			:    Union[str, Any]=None				,     lowerCAmelCase_			:    Any="gelu_new"				,     lowerCAmelCase_			:    List[str]=0.0				,     lowerCAmelCase_			:    Optional[int]=0.0				,     lowerCAmelCase_			:    Union[str, Any]=0.0				,     lowerCAmelCase_			:    int=1e-5				,     lowerCAmelCase_			:    Union[str, Any]=0.0_2				,     lowerCAmelCase_			:    str=True				,     lowerCAmelCase_			:    Tuple=5_02_56				,     lowerCAmelCase_			:    Union[str, Any]=5_02_56				,     lowerCAmelCase_			:    List[str]=False				,     **lowerCAmelCase_			:    Dict				,     )     ->				Dict:


														"""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=lowerCAmelCase_				,     eos_token_id=lowerCAmelCase_				,     tie_word_embeddings=lowerCAmelCase_				,     **lowerCAmelCase_   )



class      A       (						_a       ):







							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    PretrainedConfig				,     lowerCAmelCase_			:    str = "default"				,     lowerCAmelCase_			:    List[PatchingSpec] = None				,     lowerCAmelCase_			:    bool = False				,     )     ->				Optional[int]:


														"""simple docstring"""


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







							@property
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


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

														return common_inputs







							@property
							def   __lowerCAmelCase     (    self			:    str   )     ->				int:


														"""simple docstring"""


														return self._config.n_layer







							@property
							def   __lowerCAmelCase     (    self			:    int   )     ->				int:


														"""simple docstring"""


														return self._config.n_head







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    PreTrainedTokenizer				,     lowerCAmelCase_			:    int = -1				,     lowerCAmelCase_			:    int = -1				,     lowerCAmelCase_			:    bool = False				,     lowerCAmelCase_			:    Optional[TensorType] = None				,     )     ->				Mapping[str, Any]:


														"""simple docstring"""


														_a      	=							super(lowerCAmelCase_				,     self   ).generate_dummy_inputs(
														    lowerCAmelCase_				,     batch_size=lowerCAmelCase_				,     seq_length=lowerCAmelCase_				,     is_pair=lowerCAmelCase_				,     framework=lowerCAmelCase_   )

														# 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(lowerCAmelCase_   ), torch.zeros(lowerCAmelCase_   )) 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(lowerCAmelCase_				,     lowerCAmelCase_				,     dtype=lowerCAmelCase_   )]				,     dim=1   )

														return ordered_inputs







							@property
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				int:


														"""simple docstring"""


														return 13








import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def  __magic_name__  (				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[int]  				=     ArgumentParser(
							    description=(
							        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
							    )				)

							# Optional arguments for the launch helper
							parser.add_argument("""--num_cores"""				,       type=lowercase				,       default=1				,       help="""Number of TPU cores to use (1 or 8)."""				)

							# positional
							parser.add_argument(
							    """training_script"""				,       type=lowercase				,       help=(
							        """The full path to the single TPU training """
							        """program/script to be launched in parallel, """
							        """followed by all the arguments for the """
							        """training script"""
							    )				,       )

							# rest from the training program
							parser.add_argument("""training_script_args"""				,       nargs=lowercase				)

							return parser.parse_args()





def  __magic_name__  (				)				-> List[Any]:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     parse_args()

							# Import training_script as a module.
							lowercase_	:   List[Any]  				=     Path(args.training_script				)
							sys.path.append(str(script_fpath.parent.resolve()				)				)
							lowercase_	:   Tuple  				=     script_fpath.stem
							lowercase_	:   List[str]  				=     importlib.import_module(lowercase				)

							# Patch sys.argv
							lowercase_	:   Optional[int]  				=     [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores				)]

							xmp.spawn(mod._mp_fn				,       args=()				,       nprocs=args.num_cores				)


if __name__ == "__main__":
							main()