






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


UpperCamelCase        =				logging.get_logger(__name__)

UpperCamelCase        =				{
    "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        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				_snake_case				:   int        =			"""longformer"""







				def __init__( self     :List[str]  ,     lowerCamelCase__     :Union[List[int], int] = 5_12  ,     lowerCamelCase__     :int = 2  ,     lowerCamelCase__     :int = 1  ,     lowerCamelCase__     :int = 0  ,     lowerCamelCase__     :int = 2  ,     lowerCamelCase__     :int = 3_05_22  ,     lowerCamelCase__     :int = 7_68  ,     lowerCamelCase__     :int = 12  ,     lowerCamelCase__     :int = 12  ,     lowerCamelCase__     :int = 30_72  ,     lowerCamelCase__     :str = "gelu"  ,     lowerCamelCase__     :float = 0.1  ,     lowerCamelCase__     :float = 0.1  ,     lowerCamelCase__     :int = 5_12  ,     lowerCamelCase__     :int = 2  ,     lowerCamelCase__     :float = 0.02  ,     lowerCamelCase__     :float = 1e-12  ,     lowerCamelCase__     :bool = False  ,     **lowerCamelCase__     :Tuple  ,     ):
					super().__init__(pad_token_id=lowerCamelCase__  ,     **lowerCamelCase__     )

					UpperCamelCase__ :List[Any]						=      attention_window
					UpperCamelCase__ :Union[str, Any]						=      sep_token_id
					UpperCamelCase__ :Tuple						=      bos_token_id
					UpperCamelCase__ :Optional[Any]						=      eos_token_id
					UpperCamelCase__ :Tuple						=      vocab_size
					UpperCamelCase__ :str						=      hidden_size
					UpperCamelCase__ :str						=      num_hidden_layers
					UpperCamelCase__ :Dict						=      num_attention_heads
					UpperCamelCase__ :Any						=      hidden_act
					UpperCamelCase__ :str						=      intermediate_size
					UpperCamelCase__ :str						=      hidden_dropout_prob
					UpperCamelCase__ :Any						=      attention_probs_dropout_prob
					UpperCamelCase__ :List[str]						=      max_position_embeddings
					UpperCamelCase__ :Any						=      type_vocab_size
					UpperCamelCase__ :str						=      initializer_range
					UpperCamelCase__ :Optional[Any]						=      layer_norm_eps
					UpperCamelCase__ :Optional[Any]						=      onnx_export

class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				def __init__( self     :Optional[Any]  ,     lowerCamelCase__     :"PretrainedConfig"  ,     lowerCamelCase__     :str = "default"  ,     lowerCamelCase__     :"List[PatchingSpec]" = None     ):
					super().__init__(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )
					UpperCamelCase__ :Any						=      True







				@property
				def 				__a   ( self     :Optional[Any]     ):
					if self.task == "multiple-choice":
						UpperCamelCase__ :List[Any]						=      {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
						UpperCamelCase__ :List[str]						=      {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""global_attention_mask""", dynamic_axis),
					    ]     )







				@property
				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :int						=      super().outputs
					if self.task == "default":
						UpperCamelCase__ :Tuple						=      {0: """batch"""}
					return outputs







				@property
				def 				__a   ( self     :List[Any]     ):
					return 1e-4







				@property
				def 				__a   ( self     :Tuple     ):
					# needs to be >= 14 to support tril operator
					return max(super().default_onnx_opset  ,     14     )







				def 				__a   ( self     :List[Any]  ,     lowerCamelCase__     :"PreTrainedTokenizerBase"  ,     lowerCamelCase__     :int = -1  ,     lowerCamelCase__     :int = -1  ,     lowerCamelCase__     :bool = False  ,     lowerCamelCase__     :Optional[TensorType] = None  ,     ):
					UpperCamelCase__ :Any						=      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
					UpperCamelCase__ :Union[str, Any]						=      torch.zeros_like(inputs["""input_ids"""]     )
					# make every second token global
					UpperCamelCase__ :Union[str, Any]						=      1

					return inputs







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_UpperCamelCase			:	List[str]								=HfArgumentParser(InitializationArguments)
_UpperCamelCase			:	Dict								=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_UpperCamelCase			:	List[Any]								=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCamelCase			:	str								={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
_UpperCamelCase			:	int								=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCamelCase			:	List[str]								=AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)



