




'''simple docstring'''







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







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

		return orig_key






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







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

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

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

		return orig_state_dict






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			torch.load(lowercase_		,				map_location="cpu"    )["model_state_dict"]
		_snake_case      			=			MraConfig.from_json_file(lowercase_    )
		_snake_case      			=			MraForMaskedLM(lowercase_    )

		_snake_case      			=			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__":
						_lowerCamelCase				:		int				      =    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.'
						)
						_lowerCamelCase				:		Union[str, Any]				      =    parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











'''simple docstring'''







import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union





class 	lowerCamelCase_				(			enum.Enum				):




					'''simple docstring'''






					lowercase__       =					"""timm"""
					lowercase__       =					"""transformers"""






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







		if stage_names is None:
				raise ValueError("Stage_names must be set for transformers backbones"    )

		if out_features is not None:
				if not isinstance(lowercase_		,				(list,)    ):
						raise ValueError(f'''out_features must be a list {type(lowercase_    )}'''    )
				if any(feat not in stage_names for feat in out_features    ):
						raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''    )

		if out_indices is not None:
				if not isinstance(lowercase_		,				(list, tuple)    ):
						raise ValueError(f'''out_indices must be a list or tuple, got {type(lowercase_    )}'''    )
				if any(idx >= len(lowercase_    ) for idx in out_indices    ):
						raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"    )

		if out_features is not None and out_indices is not None:
				if len(lowercase_    ) != len(lowercase_    ):
						raise ValueError("out_features and out_indices should have the same length if both are set"    )
				if out_features != [stage_names[idx] for idx in out_indices]:
						raise ValueError("out_features and out_indices should correspond to the same stages if both are set"    )






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				):



		'''simple docstring'''







		if out_indices is None and out_features is None:
				_snake_case      			=			[len(lowercase_    ) - 1]
				_snake_case      			=			[stage_names[-1]]
		elif out_indices is None and out_features is not None:
				_snake_case      			=			[stage_names.index(lowercase_    ) for layer in out_features]
		elif out_features is None and out_indices is not None:
				_snake_case      			=			[stage_names[idx] for idx in out_indices]
		return out_features, out_indices






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				):



		'''simple docstring'''







		verify_out_features_out_indices(out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		_snake_case			,       _snake_case      			=			_align_output_features_output_indices(
		    out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		# Verify that the aligned out_features and out_indices are valid
		verify_out_features_out_indices(out_features=lowercase_		,				out_indices=lowercase_		,				stage_names=lowercase_    )
		return output_features, output_indices





class 	lowerCamelCase_				:




					'''simple docstring'''






					lowercase__       =					None






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							if getattr(self,    "_backbone",    __UpperCamelCase					) is None:
									raise ValueError("self._backbone must be set before calling _init_timm_backbone"					)

							# These will diagree with the defaults for the transformers models e.g. for resnet50
							# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
							# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
							_snake_case      			=			[stage["module"] for stage in self._backbone.feature_info.info]
							_snake_case      			=			[stage["num_chs"] for stage in self._backbone.feature_info.info]
							_snake_case      			=			self._backbone.feature_info.out_indices
							_snake_case      			=			self._backbone.feature_info.module_name()

							# We verify the out indices and out features are valid
							verify_out_features_out_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)
							_snake_case			,       _snake_case      			=			out_features, out_indices






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			getattr(__UpperCamelCase,    "stage_names"					)
							_snake_case      			=			getattr(__UpperCamelCase,    "out_features",    __UpperCamelCase					)
							_snake_case      			=			getattr(__UpperCamelCase,    "out_indices",    __UpperCamelCase					)

							_snake_case      			=			stage_names
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=__UpperCamelCase					)
							# Number of channels for each stage. This is set in the transformer backbone model init
							_snake_case      			=			None






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			config

							_snake_case      			=			getattr(__UpperCamelCase,    "use_timm_backbone",    __UpperCamelCase					)
							_snake_case      			=			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

							if self.backbone_type == BackboneType.TIMM:
									self._init_timm_backbone(__UpperCamelCase					)
							elif self.backbone_type == BackboneType.TRANSFORMERS:
									self._init_transformers_backbone(__UpperCamelCase					)
							else:
									raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_features






					@out_features.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_indices






					@out_indices.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# the current backbones will output the number of channels for each stage
							# even if that stage is not in the out_features list.
							return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return [self.out_feature_channels[name] for name in self.out_features]






					def 							__SCREAMING_SNAKE_CASE      (       self,    *__UpperCamelCase,    **__UpperCamelCase					):
							_snake_case      			=			dict(inspect.signature(self.forward					).parameters					)
							_snake_case      			=			{k: v for k, v in kwargs.items() if k in signature}
							return self(*__UpperCamelCase,    **__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase = None,    __UpperCamelCase = None,    __UpperCamelCase = None,    ):
							raise NotImplementedError("This method should be implemented by the derived class."					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			super().to_dict()
							_snake_case      			=			output.pop("_out_features"					)
							_snake_case      			=			output.pop("_out_indices"					)
							return output





class 	lowerCamelCase_				:




					'''simple docstring'''






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_features






					@out_features.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return self._out_indices






					@out_indices.setter
					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case			,       _snake_case      			=			get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,    out_indices=__UpperCamelCase,    stage_names=self.stage_names					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			super().to_dict()
							_snake_case      			=			output.pop("_out_features"					)
							_snake_case      			=			output.pop("_out_indices"					)
							return output





