




"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_lowercase		 =      logging.get_logger(__name__)

_lowercase		 =      {
    """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
}

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     ="mobilevitv2"

				def __init__(				self				:       Any ,SCREAMING_SNAKE_CASE_				:       Any=3 ,SCREAMING_SNAKE_CASE_				:       List[str]=2_5_6 ,SCREAMING_SNAKE_CASE_				:       Optional[Any]=2 ,SCREAMING_SNAKE_CASE_				:       str=2.0 ,SCREAMING_SNAKE_CASE_				:       int="swish" ,SCREAMING_SNAKE_CASE_				:       Tuple=3 ,SCREAMING_SNAKE_CASE_				:       Optional[int]=3_2 ,SCREAMING_SNAKE_CASE_				:       Optional[Any]=0.1 ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=0.02 ,SCREAMING_SNAKE_CASE_				:       Tuple=1E-5 ,SCREAMING_SNAKE_CASE_				:       Tuple=5_1_2 ,SCREAMING_SNAKE_CASE_				:       Dict=[6, 1_2, 1_8] ,SCREAMING_SNAKE_CASE_				:       Optional[Any]=0.1 ,SCREAMING_SNAKE_CASE_				:       str=2_5_5 ,SCREAMING_SNAKE_CASE_				:       Tuple=[2, 4, 3] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=[1_2_8, 1_9_2, 2_5_6] ,SCREAMING_SNAKE_CASE_				:       List[Any]=1.0 ,SCREAMING_SNAKE_CASE_				:       Optional[int]=2 ,SCREAMING_SNAKE_CASE_				:       Optional[int]=0.0 ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=0.0 ,**SCREAMING_SNAKE_CASE_				:       Optional[int] ,):
										super().__init__(**SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Union[str, Any]							     =   num_channels
										snake_case__    :   List[Any]							     =   image_size
										snake_case__    :   Any							     =   patch_size
										snake_case__    :   List[str]							     =   expand_ratio
										snake_case__    :   Tuple							     =   hidden_act
										snake_case__    :   Optional[Any]							     =   conv_kernel_size
										snake_case__    :   int							     =   output_stride
										snake_case__    :   Union[str, Any]							     =   initializer_range
										snake_case__    :   str							     =   layer_norm_eps
										snake_case__    :   Any							     =   n_attn_blocks
										snake_case__    :   List[str]							     =   base_attn_unit_dims
										snake_case__    :   Any							     =   width_multiplier
										snake_case__    :   int							     =   ffn_multiplier
										snake_case__    :   Union[str, Any]							     =   ffn_dropout
										snake_case__    :   Optional[int]							     =   attn_dropout
										snake_case__    :   int							     =   classifier_dropout_prob

										# decode head attributes for semantic segmentation
										snake_case__    :   List[Any]							     =   aspp_out_channels
										snake_case__    :   Dict							     =   atrous_rates
										snake_case__    :   Optional[Any]							     =   aspp_dropout_prob
										snake_case__    :   List[str]							     =   semantic_loss_ignore_index







class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     =version.parse("1.11"		)

				@property
				def  _lowerCamelCase   (				self				:       Optional[int] ):
										return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})] )

				@property
				def  _lowerCamelCase   (				self				:       int ):
										if self.task == "image-classification":
																return OrderedDict([('''logits''', {0: '''batch'''})] )
										else:
																return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )





				@property
				def  _lowerCamelCase   (				self				:       Any ):
										return 1E-4








"""simple docstring"""





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


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

						return orig_key

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												snake_case__    :   int							     =   orig_state_dict.pop(lowerCamelCase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		snake_case__    :   Optional[int]							     =   val

						snake_case__    :   Union[str, Any]							     =   orig_state_dict['''cls.predictions.decoder.bias''']
						snake_case__    :   Any							     =   torch.arange(lowerCamelCase						).expand((1, -1)						) + 2

						return orig_state_dict

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   List[Any]							     =   torch.load(lowerCamelCase ,     map_location='''cpu'''						)['''model_state_dict''']
						snake_case__    :   Optional[Any]							     =   MraConfig.from_json_file(lowerCamelCase						)
						snake_case__    :   List[Any]							     =   MraForMaskedLM(lowerCamelCase						)

						snake_case__    :   List[Any]							     =   convert_checkpoint_helper(config.max_position_embeddings ,     lowerCamelCase						)

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

						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)


