
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM






def  _snake_case			(						snake_case__				:       Optional[Any]       ):

	if "model" in orig_key:
		A  				=				orig_key.replace('model.'		, ''       )
	if "norm1" in orig_key:
		A  				=				orig_key.replace('norm1'		, 'attention.output.LayerNorm'       )
	if "norm2" in orig_key:
		A  				=				orig_key.replace('norm2'		, 'output.LayerNorm'       )
	if "norm" in orig_key:
		A  				=				orig_key.replace('norm'		, 'LayerNorm'       )
	if "transformer" in orig_key:
		A  				=				orig_key.split('.'       )[0].split('_'       )[-1]
		A  				=				orig_key.replace(F'transformer_{layer_num}'		, F'encoder.layer.{layer_num}'       )
	if "mha.attn" in orig_key:
		A  				=				orig_key.replace('mha.attn'		, 'attention.self'       )
	if "mha" in orig_key:
		A  				=				orig_key.replace('mha'		, 'attention'       )
	if "W_q" in orig_key:
		A  				=				orig_key.replace('W_q'		, 'self.query'       )
	if "W_k" in orig_key:
		A  				=				orig_key.replace('W_k'		, 'self.key'       )
	if "W_v" in orig_key:
		A  				=				orig_key.replace('W_v'		, 'self.value'       )
	if "ff.0" in orig_key:
		A  				=				orig_key.replace('ff.0'		, 'intermediate.dense'       )
	if "ff.2" in orig_key:
		A  				=				orig_key.replace('ff.2'		, 'output.dense'       )
	if "ff" in orig_key:
		A  				=				orig_key.replace('ff'		, 'output.dense'       )
	if "mlm_class" in orig_key:
		A  				=				orig_key.replace('mlm.mlm_class'		, 'cls.predictions.decoder'       )
	if "mlm" in orig_key:
		A  				=				orig_key.replace('mlm'		, 'cls.predictions.transform'       )
	if "backbone.backbone.encoders" in orig_key:
		A  				=				orig_key.replace('backbone.backbone.encoders'		, 'encoder.layer'       )
	if "cls" not in orig_key:
		A  				=				'mra.' + orig_key

	return orig_key






def  _snake_case			(						snake_case__				:       str		, snake_case__				:       int       ):

	for key in orig_state_dict.copy().keys():
		A  				=				orig_state_dict.pop(lowercase_       )

		if ("pooler" in key) or ("sen_class" in key):
			continue
		else:
			A  				=				val

	A  				=				orig_state_dict['cls.predictions.decoder.bias']
	A  				=				torch.arange(lowercase_       ).expand((1, -1)       ) + 2

	return orig_state_dict






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       List[Any]		, snake_case__				:       Any       ):
	A  				=				torch.load(lowercase_		, map_location='cpu'       )['model_state_dict']
	A  				=				MraConfig.from_json_file(lowercase_       )
	A  				=				MraForMaskedLM(lowercase_       )

	A  				=				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__":
		_lowercase											=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The json file for Mra model config.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		_lowercase											=			parser.parse_args()
		convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

"""simple docstring"""


from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase											=			logging.get_logger(__name__)

_lowercase											=			{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      int =      '''prophetnet'''
						_lowerCamelCase:      Optional[Any] =      ['''past_key_values''']
						_lowerCamelCase:      Dict =      {
						    '''num_attention_heads''': '''num_encoder_attention_heads''',
						}


						def __init__(	self       : Tuple					,A_       : Optional[float] = 0.1					,A_       : Optional[Union[str, Callable]] = "gelu"					,A_       : Optional[int] = 3_0522					,A_       : Optional[int] = 1024					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[float] = 0.1					,A_       : Optional[float] = 0.1					,A_       : Optional[int] = 512					,A_       : Optional[float] = 0.02					,A_       : Optional[bool] = True					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 2					,A_       : Optional[int] = 32					,A_       : Optional[int] = 128					,A_       : Optional[bool] = False					,A_       : Optional[float] = 0.0					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 1					,A_       : Optional[int] = 2					,**A_       : Tuple					,)       ->				Any:
							A  				=				vocab_size
							A  				=				hidden_size
							A  				=				encoder_ffn_dim
							A  				=				num_encoder_layers
							A  				=				num_encoder_attention_heads
							A  				=				decoder_ffn_dim
							A  				=				num_decoder_layers
							A  				=				num_decoder_attention_heads
							A  				=				max_position_embeddings
							A  				=				init_std  # Normal(0, this parameter)
							A  				=				activation_function

							# parameters for prophetnet
							A  				=				ngram
							A  				=				num_buckets
							A  				=				relative_max_distance
							A  				=				disable_ngram_loss
							A  				=				eps

							# 3 Types of Dropout
							A  				=				attention_dropout
							A  				=				activation_dropout
							A  				=				dropout

							A  				=				use_cache

							super().__init__(
							    pad_token_id=A_					,bos_token_id=A_					,eos_token_id=A_					,is_encoder_decoder=A_					,add_cross_attention=A_					,decoder_start_token_id=A_					,**A_					,)


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							return self.num_encoder_layers + self.num_decoder_layers




						@num_hidden_layers.setter
						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : Any )       ->				int:
							raise NotImplementedError(
							    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
							    ' `num_decoder_layers`.' )