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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

SCREAMING_SNAKE_CASE__		:       List[Any]       						=       {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}

class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_    ):
  lowercase_								=  'swin'

  lowercase_								=  {
      'num_attention_heads': 'num_heads',
      'num_hidden_layers': 'num_layers',
  }




  def __init__(   self  :						str  ,			a_  :						Dict=224  ,			a_  :						Optional[int]=4  ,			a_  :						Optional[Any]=3  ,			a_  :						Optional[int]=96  ,			a_  :						List[str]=[2, 2, 6, 2]  ,			a_  :						Union[str, Any]=[3, 6, 12, 24]  ,			a_  :						int=7  ,			a_  :						List[Any]=4.0  ,			a_  :						Any=True  ,			a_  :						str=0.0  ,			a_  :						str=0.0  ,			a_  :						List[str]=0.1  ,			a_  :						Union[str, Any]="gelu"  ,			a_  :						List[str]=False  ,			a_  :						List[str]=0.02  ,			a_  :						Union[str, Any]=1e-5  ,			a_  :						int=32  ,			a_  :						Optional[Any]=None  ,			a_  :						List[Any]=None  ,			**a_  :						Dict  ,			)->							Any:




       """simple docstring"""
       super().__init__(**a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		image_size
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		patch_size
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		num_channels
       SCREAMING_SNAKE_CASE__					:					Dict      =		embed_dim
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		depths
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		len(a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		num_heads
       SCREAMING_SNAKE_CASE__					:					Any      =		window_size
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		mlp_ratio
       SCREAMING_SNAKE_CASE__					:					Any      =		qkv_bias
       SCREAMING_SNAKE_CASE__					:					Tuple      =		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					:					int      =		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Dict      =		drop_path_rate
       SCREAMING_SNAKE_CASE__					:					Dict      =		hidden_act
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		use_absolute_embeddings
       SCREAMING_SNAKE_CASE__					:					Dict      =		layer_norm_eps
       SCREAMING_SNAKE_CASE__					:					Tuple      =		initializer_range
       SCREAMING_SNAKE_CASE__					:					List[str]      =		encoder_stride
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		int(embed_dim * 2 ** (len(a_					) - 1)					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		['stem'] + [F'''stage{idx}''' for idx in range(1  ,			len(a_					) + 1					)]
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[str]      =		get_aligned_output_features_output_indices(
           out_features=a_  ,			out_indices=a_  ,			stage_names=self.stage_names					)




class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  version.parse('1.11'    )




  @property
  def 						__lowercase(   self  :						Optional[Any]					)->							Mapping[str, Mapping[int, str]]:




       """simple docstring"""
       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]					)




  @property
  def 						__lowercase(   self  :						List[str]					)->							float:




       """simple docstring"""
       return 1e-4








'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






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

						return orig_key






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Any												=  orig_state_dict.pop(UpperCAmelCase_				)

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

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

						return orig_state_dict






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model_state_dict']
						_UpperCamelCase   :						List[str]												=  YosoConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  YosoForMaskedLM(UpperCAmelCase_				)

						_UpperCamelCase   :						Tuple												=  convert_checkpoint_helper(config.max_position_embeddings    ,    UpperCAmelCase_				)

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

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


if __name__ == "__main__":
					snake_case_					:      int    			=    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.'
					)
					snake_case_					:      List[Any]    			=    parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


