
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 					__lowerCAmelCase    (    a__						) ->						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 					__lowerCAmelCase    (    a__		,					a__						) ->						Any:
				for key in orig_state_dict.copy().keys():
								__a    							=					orig_state_dict.pop(a__						)

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

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

				return orig_state_dict


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						List[Any]:
				__a    							=					torch.load(a__		,					map_location='''cpu'''						)['''model_state_dict''']
				__a    							=					MraConfig.from_json_file(a__						)
				__a    							=					MraForMaskedLM(a__						)

				__a    							=					convert_checkpoint_helper(config.max_position_embeddings		,					a__						)

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

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


if __name__ == "__main__":
					A  :			Any     				=				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.'
					)
					A  :			Tuple     				=				parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


A  :			List[str]     				=				logging.get_logger(__name__)







class     __A(				nn.Module    ):




				def __init__(  self       )					->   Union[str, Any]:


								'''simple docstring'''




								super().__init__()
								if version.parse(torch.__version__       ) < version.parse('''1.12.0'''       ):
												raise ImportError(
												    F"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
												    '''PytorchGELUTanh. Please upgrade torch.'''       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return nn.functional.gelu(_snake_case	,  approximate='''tanh'''       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi       ) * (input + 0.04_4715 * torch.pow(_snake_case	,  3.0       ))       ))







class     __A(				nn.Module    ):




				def __init__(  self	,  _snake_case = False       )					->   Tuple:


								'''simple docstring'''




								super().__init__()
								if use_gelu_python:
												__a    							=					self._gelu_python
								else:
												__a    							=					nn.functional.gelu




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0       )       ))




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return self.act(_snake_case       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * torch.sigmoid(1.702 * input       )







class     __A(				nn.Module    ):




				def __init__(  self	,  _snake_case	,  _snake_case       )					->   Optional[int]:


								'''simple docstring'''




								if min > max:
												raise ValueError(F"""min should be < max (got min: {min}, max: {max})"""       )

								super().__init__()
								__a    							=					min
								__a    							=					max




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return torch.clip(gelu(_snake_case       )	,  self.min	,  self.max       )







class     __A(				nn.Module    ):




				def __init__(  self       )					->   str:


								'''simple docstring'''




								super().__init__()
								__a    							=					math.sqrt(2 / math.pi       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(_snake_case	,  3       ))       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return nn.functional.silu(_snake_case       )







class     __A(				nn.Module    ):




				def __init__(  self       )					->   List[Any]:


								'''simple docstring'''




								super().__init__()
								if version.parse(torch.__version__       ) < version.parse('''1.9.0'''       ):
												__a    							=					self._mish_python
								else:
												__a    							=					nn.functional.mish




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * torch.tanh(nn.functional.softplus(_snake_case       )       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return self.act(_snake_case       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case=0.70_7107	,  _snake_case=0.28_2095       )					->   int:


								'''simple docstring'''




								__a    							=					(input - mu).div(sigma * math.sqrt(2.0       )       )
								return 0.5 * (1.0 + torch.erf(_snake_case       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Any:


								'''simple docstring'''




								__a    							=					nn.functional.relu(_snake_case       )
								__a    							=					torch.square(_snake_case       )
								return squared







class     __A(				a    ):




				def __getitem__(  self	,  _snake_case       )					->   Dict:


								'''simple docstring'''




								__a    							=					super().__getitem__(_snake_case       )
								__a							,  __a    							=					content if isinstance(_snake_case	,  _snake_case       ) else (content, {})
								return cls(**_snake_case       )



A  :			str     				=				{
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -1_0, 'max': 1_0}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
A  :			Any     				=				ClassInstantier(ACTaCLS)


def 					__lowerCAmelCase    (    a__						) ->						Optional[int]:
				if activation_string in ACTaFN:
								return ACTaFN[activation_string]
				else:
								raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"""						)


# For backwards compatibility with: from activations import gelu_python
A  :			Optional[Any]     				=				get_activation('gelu_python')
A  :			str     				=				get_activation('gelu_new')
A  :			Any     				=				get_activation('gelu')
A  :			int     				=				get_activation('gelu_fast')
A  :			Any     				=				get_activation('quick_gelu')
A  :			int     				=				get_activation('silu')
A  :			List[str]     				=				get_activation('mish')






A  :			Dict     				=				get_activation('linear')