from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase		:       Tuple									=     logging.get_logger(__name__)

__UpperCamelCase		:       str									=     {
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		"""roberta-prelayernorm"""


						def __init__(       self	:				Tuple    ,		UpperCamelCase__	:				int=5_0265    ,		UpperCamelCase__	:				Optional[int]=768    ,		UpperCamelCase__	:				List[str]=12    ,		UpperCamelCase__	:				Any=12    ,		UpperCamelCase__	:				Any=3072    ,		UpperCamelCase__	:				Union[str, Any]="gelu"    ,		UpperCamelCase__	:				Dict=0.1    ,		UpperCamelCase__	:				str=0.1    ,		UpperCamelCase__	:				List[str]=512    ,		UpperCamelCase__	:				Optional[int]=2    ,		UpperCamelCase__	:				Union[str, Any]=0.02    ,		UpperCamelCase__	:				Optional[int]=1E-12    ,		UpperCamelCase__	:				Dict=1    ,		UpperCamelCase__	:				Any=0    ,		UpperCamelCase__	:				Dict=2    ,		UpperCamelCase__	:				Dict="absolute"    ,		UpperCamelCase__	:				List[str]=True    ,		UpperCamelCase__	:				Tuple=None    ,		**UpperCamelCase__	:				List[Any]    ,		):





												'''simple docstring'''





												super().__init__(pad_token_id=UpperCamelCase__    ,		bos_token_id=UpperCamelCase__    ,		eos_token_id=UpperCamelCase__    ,		**UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							vocab_size
												SCREAMING_SNAKE_CASE							:    str							=							hidden_size
												SCREAMING_SNAKE_CASE							:    int							=							num_hidden_layers
												SCREAMING_SNAKE_CASE							:    List[Any]							=							num_attention_heads
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							hidden_act
												SCREAMING_SNAKE_CASE							:    str							=							intermediate_size
												SCREAMING_SNAKE_CASE							:    List[str]							=							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:    Dict							=							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:    int							=							max_position_embeddings
												SCREAMING_SNAKE_CASE							:    List[str]							=							type_vocab_size
												SCREAMING_SNAKE_CASE							:    Any							=							initializer_range
												SCREAMING_SNAKE_CASE							:    List[str]							=							layer_norm_eps
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							position_embedding_type
												SCREAMING_SNAKE_CASE							:    Tuple							=							use_cache
												SCREAMING_SNAKE_CASE							:    int							=							classifier_dropout






class 					lowercase__						(   UpperCamelCase_):


						@property
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ]						)






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def       A	(    _lowercase						):
						if "model" in orig_key:
												SCREAMING_SNAKE_CASE							:    int							=							orig_key.replace('''model.'''		,  ''''''						)
						if "norm1" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''norm1'''		,  '''attention.output.LayerNorm'''						)
						if "norm2" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''norm2'''		,  '''output.LayerNorm'''						)
						if "norm" in orig_key:
												SCREAMING_SNAKE_CASE							:    Tuple							=							orig_key.replace('''norm'''		,  '''LayerNorm'''						)
						if "transformer" in orig_key:
												SCREAMING_SNAKE_CASE							:    int							=							orig_key.split('''.'''						)[0].split('''_'''						)[-1]
												SCREAMING_SNAKE_CASE							:    List[str]							=							orig_key.replace(f"""transformer_{layer_num}"""		,  f"""encoder.layer.{layer_num}"""						)
						if "mha.attn" in orig_key:
												SCREAMING_SNAKE_CASE							:    Any							=							orig_key.replace('''mha.attn'''		,  '''attention.self'''						)
						if "mha" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''mha'''		,  '''attention'''						)
						if "W_q" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''W_q'''		,  '''self.query'''						)
						if "W_k" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''W_k'''		,  '''self.key'''						)
						if "W_v" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''W_v'''		,  '''self.value'''						)
						if "ff1" in orig_key:
												SCREAMING_SNAKE_CASE							:    Any							=							orig_key.replace('''ff1'''		,  '''intermediate.dense'''						)
						if "ff2" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''ff2'''		,  '''output.dense'''						)
						if "ff" in orig_key:
												SCREAMING_SNAKE_CASE							:    Dict							=							orig_key.replace('''ff'''		,  '''output.dense'''						)
						if "mlm_class" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''mlm.mlm_class'''		,  '''cls.predictions.decoder'''						)
						if "mlm" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''mlm'''		,  '''cls.predictions.transform'''						)
						if "cls" not in orig_key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							'''yoso.''' + orig_key

						return orig_key
def       A	(    _lowercase		,  _lowercase						):
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							orig_state_dict.pop(_lowercase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							val

						SCREAMING_SNAKE_CASE							:    List[str]							=							orig_state_dict['''cls.predictions.decoder.bias''']
						SCREAMING_SNAKE_CASE							:    Dict							=							torch.arange(_lowercase						).expand((1, -1)						) + 2

						return orig_state_dict
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							torch.load(_lowercase		,  map_location='''cpu'''						)['''model_state_dict''']
						SCREAMING_SNAKE_CASE							:    List[Any]							=							YosoConfig.from_json_file(_lowercase						)
						SCREAMING_SNAKE_CASE							:    str							=							YosoForMaskedLM(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							convert_checkpoint_helper(config.max_position_embeddings		,  _lowercase						)

						print(model.load_state_dict(_lowercase						)						)
						model.eval()
						model.save_pretrained(_lowercase						)

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


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





