




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase_     :    Dict      =			logging.get_logger(__name__)

lowercase_     :    Any      =			{
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}






class    __lowerCAmelCase			(	_a			):
			snake_case_				:			List[str]								=					'''plbart'''
			snake_case_				:			Optional[Any]								=					['''past_key_values''']
			snake_case_				:			List[str]								=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







			def __init__(	self   :      int   ,   snake_case__   :      List[Any]=50_005   ,   snake_case__   :      List[str]=1_024   ,   snake_case__   :      str=6   ,   snake_case__   :      Any=3_072   ,   snake_case__   :      List[Any]=12   ,   snake_case__   :      Optional[Any]=6   ,   snake_case__   :      int=3_072   ,   snake_case__   :      List[Any]=12   ,   snake_case__   :      Dict=0.0   ,   snake_case__   :      Optional[Any]=0.0   ,   snake_case__   :      Union[str, Any]=True   ,   snake_case__   :      Optional[Any]=True   ,   snake_case__   :      List[str]="gelu"   ,   snake_case__   :      Union[str, Any]=768   ,   snake_case__   :      Optional[Any]=0.1   ,   snake_case__   :      Optional[Any]=0.1   ,   snake_case__   :      List[str]=0.0   ,   snake_case__   :      Any=0.02   ,   snake_case__   :      Union[str, Any]=0.0   ,   snake_case__   :      Optional[int]=True   ,   snake_case__   :      Dict=1   ,   snake_case__   :      Union[str, Any]=0   ,   snake_case__   :      Optional[int]=2   ,   snake_case__   :      Any=2   ,   **snake_case__   :      Optional[int]   ,   ):




							"""simple docstring"""


							_UpperCAmelCase													=					vocab_size
							_UpperCAmelCase													=					max_position_embeddings
							_UpperCAmelCase													=					d_model
							_UpperCAmelCase													=					encoder_ffn_dim
							_UpperCAmelCase													=					encoder_layers
							_UpperCAmelCase													=					encoder_attention_heads
							_UpperCAmelCase													=					decoder_ffn_dim
							_UpperCAmelCase													=					decoder_layers
							_UpperCAmelCase													=					decoder_attention_heads
							_UpperCAmelCase													=					dropout
							_UpperCAmelCase													=					attention_dropout
							_UpperCAmelCase													=					activation_dropout
							_UpperCAmelCase													=					activation_function
							_UpperCAmelCase													=					init_std
							_UpperCAmelCase													=					encoder_layerdrop
							_UpperCAmelCase													=					decoder_layerdrop
							_UpperCAmelCase													=					classifier_dropout
							_UpperCAmelCase													=					use_cache
							_UpperCAmelCase													=					encoder_layers
							_UpperCAmelCase													=					scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=SCREAMING_SNAKE_CASE__   ,   bos_token_id=SCREAMING_SNAKE_CASE__   ,   eos_token_id=SCREAMING_SNAKE_CASE__   ,   is_encoder_decoder=SCREAMING_SNAKE_CASE__   ,   forced_eos_token_id=SCREAMING_SNAKE_CASE__   ,   **SCREAMING_SNAKE_CASE__   ,   )






class    __lowerCAmelCase			(	_a			):







			@property
			def UpperCamelCase      (	self   :      Dict				):




							"""simple docstring"""


							return OrderedDict(
							    [
							        ("input_ids", {0: "batch", 1: "sequence"}),
							        ("attention_mask", {0: "batch", 1: "sequence"}),
							    ]				)







			@property
			def UpperCamelCase      (	self   :      List[Any]				):




							"""simple docstring"""


							if self.use_past:
											return OrderedDict(
											    [
											        ("last_hidden_state", {0: "batch", 1: "sequence"}),
											        ("past_keys", {0: "batch", 2: "sequence"}),
											        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
											    ]				)
							else:
											return OrderedDict(
											    [
											        ("last_hidden_state", {0: "batch", 1: "sequence"}),
											        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
											    ]				)










import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						if "model" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("model."     ,   ""						)
						if "norm1" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("norm1"     ,   "attention.output.LayerNorm"						)
						if "norm2" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("norm2"     ,   "output.LayerNorm"						)
						if "norm" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("norm"     ,   "LayerNorm"						)
						if "transformer" in orig_key:
												_UpperCAmelCase													=					orig_key.split("."						)[0].split("_"						)[-1]
												_UpperCAmelCase													=					orig_key.replace(f"""transformer_{layer_num}"""     ,   f"""encoder.layer.{layer_num}"""						)
						if "mha.attn" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mha.attn"     ,   "attention.self"						)
						if "mha" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mha"     ,   "attention"						)
						if "W_q" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("W_q"     ,   "self.query"						)
						if "W_k" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("W_k"     ,   "self.key"						)
						if "W_v" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("W_v"     ,   "self.value"						)
						if "ff1" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("ff1"     ,   "intermediate.dense"						)
						if "ff2" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("ff2"     ,   "output.dense"						)
						if "ff" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("ff"     ,   "output.dense"						)
						if "mlm_class" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mlm.mlm_class"     ,   "cls.predictions.decoder"						)
						if "mlm" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mlm"     ,   "cls.predictions.transform"						)
						if "cls" not in orig_key:
												_UpperCAmelCase													=					"nystromformer." + orig_key

						return orig_key


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												_UpperCAmelCase													=					orig_state_dict.pop(snake_case_						)

												if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
																		continue
												else:
																		_UpperCAmelCase													=					val

						_UpperCAmelCase													=					orig_state_dict["cls.predictions.decoder.bias"]
						_UpperCAmelCase													=					(
						    torch.arange(config.max_position_embeddings						).expand((1, -1)						) + 2
						)

						return orig_state_dict


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					torch.load(snake_case_     ,   map_location="cpu"						)["model_state_dict"]
						_UpperCAmelCase													=					NystromformerConfig.from_json_file(snake_case_						)
						_UpperCAmelCase													=					NystromformerForMaskedLM(snake_case_						)

						_UpperCAmelCase													=					convert_checkpoint_helper(snake_case_     ,   snake_case_						)

						model.load_state_dict(snake_case_						)
						model.eval()
						model.save_pretrained(snake_case_						)

						print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
				lowercase_     :    List[str]      =			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Nystromformer pytorch checkpoint.'
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help='The json file for Nystromformer model config.',
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				lowercase_     :    int      =			parser.parse_args()
				convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




